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Fractal Sweep Strategy# Fractal Sweep Strategy A trend-following strategy that trades **sweeps** of fractal highs and lows, using two complementary entry models that can run in parallel. --- ## Concept The strategy identifies **fractal points** (local highs/low over N bars) and enters when price **sweeps** them—breaking above a fractal high or below a fractal low—then closes back in the opposite direction. This often signals a rejection and potential reversal. **Sweep High** → Short bias **Sweep Low** → Long bias --- ## Entry Models ### 1. Attempted Candle - **Immediate trade:** The sweep candle itself is bearish (for short) or bullish (for long). - **Delayed trade:** If the sweep candle is not in the right direction, the strategy waits for the first **Attempted candle**: - **Short:** Bearish candle with `high > high ` (takes out the prior candle’s high). - **Long:** Bullish candle with `low < low ` (takes out the prior candle’s low). - **Entry:** At the close of the Attempted candle (repainting-safe). - **SL:** Low of the Attempted candle (short) / High of the Attempted candle (long). - **TP:** CRV × risk (default CRV = 2). ### 2. Trigger Level - **Setup:** Fractal high is swept by a **bullish** candle, and the **next candle is not** an Attempted candle. - **Trigger level:** Body low of the sweep candle (`min(open, close)`), ignoring the wick. - **Activation:** Any later candle closes **below** the trigger level. - **Entry:** At the close of the breaking candle, or at the open of the next candle if the break is detected one bar late. - **SL:** High of the sweep candle (Trigger Level candle). - **TP:** CRV × risk (default CRV = 2). ### Parallel Mode With **"Both"** selected, both models run at the same time. Pyramiding allows up to 2 short positions (one from each model) when both trigger. --- ## Risk Management - **Stop Loss:** High/Low of the reference candle (Attempted or Trigger Level candle). - **Take Profit:** Entry ± (CRV × risk), with separate CRV for each model. - **Max Trades per Day:** Limits total entries across both models (default: 5). --- ## Optional Filters ### Supertrend H1 (Directional Bias) - **Off:** No filter. - **With Trend:** Long only in H1 uptrend, short only in H1 downtrend. - **Counter Trend:** Long only in H1 downtrend, short only in H1 uptrend. ### Session Filter - Restricts trades to a time window (e.g. 08:00–17:00) in the selected timezone. --- ## Fractal Settings - **Bars in Fractal:** 3, 5, 7, or 9 (default: 5). - **Fractals Timeframe:** Chart TF or higher (e.g. M15 for MTF fractals). - **Max Age:** Fractals older than N bars are ignored. --- ## Visuals - Fractal lines and markers (▲/▼). - SL/TP lines for open trades. - Optional debug labels (trade explanation, sweep labels, no-trade reasons). - Optional H1 Supertrend overlay when bias is active. --- ## Repainting Safety Entries are placed only on **confirmed bars** (`barstate.isconfirmed`), so signals are fixed once the candle closes. --- ## Credits - **Fractal logic:** Based on MTF Fractals - **Strategy:** Lean Strategy Blueprint - **License:** CC BY-NC-SA 4.0
Strategi Pine Script®
oleh Lean_Trading
SMC Scanner DashboardIndicator Name: SMC Liquidity Scanner Dashboard Description: A smart-money style multi-pair scanner designed to detect liquidity sweeps and institutional reversal opportunities across major forex pairs and gold. This indicator monitors multiple symbols simultaneously and displays live signals in a clean dashboard format. It is built around liquidity behavior rather than traditional lagging indicators. Core logic: • Detects liquidity sweeps (stop hunts above/below recent highs/lows) • Filters entries using trend confirmation (SMA direction) • Generates buy/sell signals only when structure aligns • Uses ATR-based dynamic stop loss and take profit • Multi-symbol dashboard with entry / TP / SL preview • Fixed price formatting for JPY, gold, and forex pairs • Designed for intraday and swing trading • No repaint logic This tool is not meant to predict markets — it reacts to where liquidity is taken and where institutions are likely to enter. Best used with disciplined risk management.
Penunjuk Pine Script®
oleh AlmomizQ8
ORB Visual Backtester [MTF]This indicator is designed to help traders visualize and backtest the Opening Range Breakout (ORB) strategy with precision and flexibility. Its primary goal is to facilitate rapid visual backtesting , allowing traders to instantly see how price action behaved around the opening range on historical dates and refine their strategies accordingly without needing complex replay modes. A key feature of this script is its Multi-Timeframe (MTF) capability. It allows you to view the ORB levels of a higher timeframe (e.g., 60-minute or 15-minute) while trading or analyzing on a lower timeframe (e.g., 1-minute or 5-minute chart). Key Features Visual Backtesting Optimized: By keeping the ORB box visible for a set duration on past sessions, you can easily scroll back through history to analyze win/loss rates and price behavior at a glance. Multi-Timeframe Support: Define the ORB candle on any timeframe (e.g., 15m) and view it seamlessly on lower timeframes. Custom Session Duration: The ORB box does not clutter the chart indefinitely. It stays visible for a specific duration (default: 390 minutes/full session) and then stops, keeping your chart clean. Precise Timing: Users can set the exact Start Hour and Start Minute to match any market open (e.g., NYSE, London, or crypto sessions). Weekend Filter: The indicator automatically skips Saturdays and Sundays to prevent invalid drawings during weekend gaps. Settings & Configuration 1. ORB Settings ORB Timeframe: Select the resolution used to calculate the Opening Range (e.g., "15" for a 15-minute ORB). Start Hour (0-23) & Minute (0-59): The exact time the market session begins (e.g., 09:30). Box Duration (Minutes): How long the box should extend to the right. The default is 390 minutes (6.5 hours), covering a standard trading session. 2. Visual Settings Box Background Color: Sets the fill color and transparency of the ORB zone. Border Color / Width: Customizes the outline of the box. Show Price Labels: Toggles the visibility of price labels extending from the High and Low of the range. How to Use for Strategy Building Set the ORB Timeframe (e.g., 15 min). Scroll back to previous days on your chart. Observe whether the price respected the range, faked out, or broke out cleanly. Use this visual data to determine the most profitable breakout rules for your specific asset. Note: This Pine Script code and the description text above were generated with the assistance of Artificial Intelligence.
Penunjuk Pine Script®
oleh hakan_yoruk
Supertrend Advance Pullback StrategyHandbook for the Supertrend Advance Strategy 1. Introduction Purpose of the Handbook: The main purpose of this handbook is to serve as a comprehensive guide for traders and investors who are looking to explore and harness the potential of the Supertrend Advance Strategy. In the rapidly changing financial market, having the right tools and strategies at one's disposal is crucial. Whether you're a beginner hoping to dive into the world of trading or a seasoned investor aiming to optimize and diversify your portfolio, this handbook offers the insights and methodologies you need. By the end of this guide, readers should have a clear understanding of how the Supertrend Advance Strategy works, its benefits, potential pitfalls, and practical application in various trading scenarios. Overview of the Supertrend Advance Pullback Strategy: At its core, the Supertrend Advance Strategy is an evolution of the popular Supertrend Indicator. Designed to generate buy and sell signals in trending markets, the Supertrend Indicator has been a favorite tool for many traders around the world. The Advance Strategy, however, builds upon this foundation by introducing enhanced mechanisms, filters, and methodologies to increase precision and reduce false signals. 1. Basic Concept: The Supertrend Advance Strategy relies on a combination of price action and volatility to determine the potential trend direction. By assessing the average true range (ATR) in conjunction with specific price points, this strategy aims to highlight the potential starting and ending points of market trends. 2. Methodology: Unlike the traditional Supertrend Indicator, which primarily focuses on closing prices and ATR, the Advance Strategy integrates other critical market variables, such as volume, momentum oscillators, and perhaps even fundamental data, to validate its signals. This multidimensional approach ensures that the generated signals are more reliable and are less prone to market noise. 3. Benefits: One of the main benefits of the Supertrend Advance Strategy is its ability to filter out false breakouts and minor price fluctuations, which can often lead to premature exits or entries in the market. By waiting for a confluence of factors to align, traders using this advanced strategy can increase their chances of entering or exiting trades at optimal points. 4. Practical Applications: The Supertrend Advance Strategy can be applied across various timeframes, from intraday trading to swing trading and even long-term investment scenarios. Furthermore, its flexible nature allows it to be tailored to different asset classes, be it stocks, commodities, forex, or cryptocurrencies. In the subsequent sections of this handbook, we will delve deeper into the intricacies of this strategy, offering step-by-step guidelines on its application, case studies, and tips for maximizing its efficacy in the volatile world of trading. As you journey through this handbook, we encourage you to approach the Supertrend Advance Strategy with an open mind, testing and tweaking it as per your personal trading style and risk appetite. The ultimate goal is not just to provide you with a new tool but to empower you with a holistic strategy that can enhance your trading endeavors. 2. Getting Started Navigating the financial markets can be a daunting task without the right tools. This section is dedicated to helping you set up the Supertrend Advance Strategy on one of the most popular charting platforms, TradingView. By following the steps below, you'll be able to integrate this strategy into your charts and start leveraging its insights in no time. Setting up on TradingView: TradingView is a web-based platform that offers a wide range of charting tools, social networking, and market data. Before you can apply the Supertrend Advance Strategy, you'll first need a TradingView account. If you haven't set one up yet, here's how: 1. Account Creation: • Visit TradingView's official website. • Click on the "Join for free" or "Sign up" button. • Follow the registration process, providing the necessary details and setting up your login credentials. 2. Navigating the Dashboard: • Once logged in, you'll be taken to your dashboard. Here, you'll see a variety of tools, including watchlists, alerts, and the main charting window. • To begin charting, type in the name or ticker of the asset you're interested in the search bar at the top. 3. Configuring Chart Settings: • Before integrating the Supertrend Advance Strategy, familiarize yourself with the chart settings. This can be accessed by clicking the 'gear' icon on the top right of the chart window. • Adjust the chart type, time intervals, and other display settings to your preference. Integrating the Strategy into a Chart: Now that you're set up on TradingView, it's time to integrate the Supertrend Advance Strategy. 1. Accessing the Pine Script Editor: • Located at the top-center of your screen, you'll find the "Pine Editor" tab. Click on it. • This is where custom strategies and indicators are scripted or imported. 2. Loading the Supertrend Advance Strategy Script: • Depending on whether you have the script or need to find it, there are two paths: • If you have the script: Copy the Supertrend Advance Strategy script, and then paste it into the Pine Editor. • If searching for the script: Click on the “Indicators” icon (looks like a flame) at the top of your screen, and then type “Supertrend Advance Strategy” in the search bar. If available, it will show up in the list. Simply click to add it to your chart. 3. Applying the Strategy: • After pasting or selecting the Supertrend Advance Strategy in the Pine Editor, click on the “Add to Chart” button located at the top of the editor. This will overlay the strategy onto your main chart window. 4. Configuring Strategy Settings: • Once the strategy is on your chart, you'll notice a small settings ('gear') icon next to its name in the top-left of the chart window. Click on this to access settings. • Here, you can adjust various parameters of the Supertrend Advance Strategy to better fit your trading style or the specific asset you're analyzing. 5. Interpreting Signals: • With the strategy applied, you'll now see buy/sell signals represented on your chart. Take time to familiarize yourself with how these look and behave over various timeframes and market conditions. 3. Strategy Overview What is the Supertrend Advance Strategy? The Supertrend Advance Strategy is a refined version of the classic Supertrend Indicator, which was developed to aid traders in spotting market trends. The strategy utilizes a combination of data points, including average true range (ATR) and price momentum, to generate buy and sell signals. In essence, the Supertrend Advance Strategy can be visualized as a line that moves with the price. When the price is above the Supertrend line, it indicates an uptrend and suggests a potential buy position. Conversely, when the price is below the Supertrend line, it hints at a downtrend, suggesting a potential selling point. Strategy Goals and Objectives: 1. Trend Identification: At the core of the Supertrend Advance Strategy is the goal to efficiently and consistently identify prevailing market trends. By recognizing these trends, traders can position themselves to capitalize on price movements in their favor. 2. Reducing Noise: Financial markets are often inundated with 'noise' - short-term price fluctuations that can mislead traders. The Supertrend Advance Strategy aims to filter out this noise, allowing for clearer decision-making. 3. Enhancing Risk Management: With clear buy and sell signals, traders can set more precise stop-loss and take-profit points. This leads to better risk management and potentially improved profitability. 4. Versatility: While primarily used for trend identification, the strategy can be integrated with other technical tools and indicators to create a comprehensive trading system. Type of Assets/Markets to Apply the Strategy: 1. Equities: The Supertrend Advance Strategy is highly popular among stock traders. Its ability to capture long-term trends makes it particularly useful for those trading individual stocks or equity indices. 2. Forex: Given the 24-hour nature of the Forex market and its propensity for trends, the Supertrend Advance Strategy is a valuable tool for currency traders. 3. Commodities: Whether it's gold, oil, or agricultural products, commodities often move in extended trends. The strategy can help in identifying and capitalizing on these movements. 4. Cryptocurrencies: The volatile nature of cryptocurrencies means they can have pronounced trends. The Supertrend Advance Strategy can aid crypto traders in navigating these often tumultuous waters. 5. Futures & Options: Traders and investors in derivative markets can utilize the strategy to make more informed decisions about contract entries and exits. It's important to note that while the Supertrend Advance Strategy can be applied across various assets and markets, its effectiveness might vary based on market conditions, timeframe, and the specific characteristics of the asset in question. As always, it's recommended to use the strategy in conjunction with other analytical tools and to backtest its effectiveness in specific scenarios before committing to trades. 4. Input Settings Understanding and correctly configuring input settings is crucial for optimizing the Supertrend Advance Strategy for any specific market or asset. These settings, when tweaked correctly, can drastically impact the strategy's performance. Grouping Inputs: Before diving into individual input settings, it's important to group similar inputs. Grouping can simplify the user interface, making it easier to adjust settings related to a specific function or indicator. Strategy Choice: This input allows traders to select from various strategies that incorporate the Supertrend indicator. Options might include "Supertrend with RSI," "Supertrend with MACD," etc. By choosing a strategy, the associated input settings for that strategy become available. Supertrend Settings: 1. Multiplier: Typically, a default value of 3 is used. This multiplier is used in the ATR calculation. Increasing it makes the Supertrend line further from prices, while decreasing it brings the line closer. 2. Period: The number of bars used in the ATR calculation. A common default is 7. EMA Settings (Exponential Moving Average): 1. Period: Defines the number of previous bars used to calculate the EMA. Common periods are 9, 21, 50, and 200. 2. Source: Allows traders to choose which price (Open, Close, High, Low) to use in the EMA calculation. RSI Settings (Relative Strength Index): 1. Length: Determines how many periods are used for RSI calculation. The standard setting is 14. 2. Overbought Level: The threshold at which the asset is considered overbought, typically set at 70. 3. Oversold Level: The threshold at which the asset is considered oversold, often at 30. MACD Settings (Moving Average Convergence Divergence): 1. Short Period: The shorter EMA, usually set to 12. 2. Long Period: The longer EMA, commonly set to 26. 3. Signal Period: Defines the EMA of the MACD line, typically set at 9. CCI Settings (Commodity Channel Index): 1. Period: The number of bars used in the CCI calculation, often set to 20. 2. Overbought Level: Typically set at +100, denoting overbought conditions. 3. Oversold Level: Usually set at -100, indicating oversold conditions. SL/TP Settings (Stop Loss/Take Profit): 1. SL Multiplier: Defines the multiplier for the average true range (ATR) to set the stop loss. 2. TP Multiplier: Defines the multiplier for the average true range (ATR) to set the take profit. Filtering Conditions: This section allows traders to set conditions to filter out certain signals. For example, one might only want to take buy signals when the RSI is below 30, ensuring they buy during oversold conditions. Trade Direction and Backtest Period: 1. Trade Direction: Allows traders to specify whether they want to take long trades, short trades, or both. 2. Backtest Period: Specifies the time range for backtesting the strategy. Traders can choose from options like 'Last 6 months,' 'Last 1 year,' etc. It's essential to remember that while default settings are provided for many of these tools, optimal settings can vary based on the market, timeframe, and trading style. Always backtest new settings on historical data to gauge their potential efficacy. 5. Understanding Strategy Conditions Developing an understanding of the conditions set within a trading strategy is essential for traders to maximize its potential. Here, we delve deep into the logic behind these conditions, using the Supertrend Advance Strategy as our focal point. Basic Logic Behind Conditions: Every strategy is built around a set of conditions that provide buy or sell signals. The conditions are based on mathematical or statistical methods and are rooted in the study of historical price data. The fundamental idea is to recognize patterns or behaviors that have been profitable in the past and might be profitable in the future. Buy and Sell Conditions: 1. Buy Conditions: Usually formulated around bullish signals or indicators suggesting upward price momentum. 2. Sell Conditions: Centered on bearish signals or indicators indicating downward price momentum. Simple Strategy: The simple strategy could involve using just the Supertrend indicator. Here: • Buy: When price closes above the Supertrend line. • Sell: When price closes below the Supertrend line. Pullback Strategy: This strategy capitalizes on price retracements: • Buy: When the price retraces to the Supertrend line after a bullish signal and is supported by another bullish indicator. • Sell: When the price retraces to the Supertrend line after a bearish signal and is confirmed by another bearish indicator. Indicators Used: EMA (Exponential Moving Average): • Logic: EMA gives more weight to recent prices, making it more responsive to current price movements. A shorter-period EMA crossing above a longer-period EMA can be a bullish sign, while the opposite is bearish. RSI (Relative Strength Index): • Logic: RSI measures the magnitude of recent price changes to analyze overbought or oversold conditions. Values above 70 are typically considered overbought, and values below 30 are considered oversold. MACD (Moving Average Convergence Divergence): • Logic: MACD assesses the relationship between two EMAs of a security’s price. The MACD line crossing above the signal line can be a bullish signal, while crossing below can be bearish. CCI (Commodity Channel Index): • Logic: CCI compares a security's average price change with its average price variation. A CCI value above +100 may mean the price is overbought, while below -100 might signify an oversold condition. And others... As the strategy expands or contracts, more indicators might be added or removed. The crucial point is to understand the core logic behind each, ensuring they align with the strategy's objectives. Logic Behind Each Indicator: 1. EMA: Emphasizes recent price movements; provides dynamic support and resistance levels. 2. RSI: Indicates overbought and oversold conditions based on recent price changes. 3. MACD: Showcases momentum and direction of a trend by comparing two EMAs. 4. CCI: Measures the difference between a security's price change and its average price change. Understanding strategy conditions is not just about knowing when to buy or sell but also about comprehending the underlying market dynamics that those conditions represent. As you familiarize yourself with each condition and indicator, you'll be better prepared to adapt and evolve with the ever-changing financial markets. 6. Trade Execution and Management Trade execution and management are crucial aspects of any trading strategy. Efficient execution can significantly impact profitability, while effective management can preserve capital during adverse market conditions. In this section, we'll explore the nuances of position entry, exit strategies, and various Stop Loss (SL) and Take Profit (TP) methodologies within the Supertrend Advance Strategy. Position Entry: Effective trade entry revolves around: 1. Timing: Enter at a point where the risk-reward ratio is favorable. This often corresponds to confirmatory signals from multiple indicators. 2. Volume Analysis: Ensure there's adequate volume to support the movement. Volume can validate the strength of a signal. 3. Confirmation: Use multiple indicators or chart patterns to confirm the entry point. For instance, a buy signal from the Supertrend indicator can be confirmed with a bullish MACD crossover. Position Exit Strategies: A successful exit strategy will lock in profits and minimize losses. Here are some strategies: 1. Fixed Time Exit: Exiting after a predetermined period. 2. Percentage-based Profit Target: Exiting after a certain percentage gain. 3. Indicator-based Exit: Exiting when an indicator gives an opposing signal. Percentage-based SL/TP: • Stop Loss (SL): Set a fixed percentage below the entry price to limit potential losses. • Example: A 2% SL on an entry at $100 would trigger a sell at $98. • Take Profit (TP): Set a fixed percentage above the entry price to lock in gains. • Example: A 5% TP on an entry at $100 would trigger a sell at $105. Supertrend-based SL/TP: • Stop Loss (SL): Position the SL at the Supertrend line. If the price breaches this line, it could indicate a trend reversal. • Take Profit (TP): One could set the TP at a point where the Supertrend line flattens or turns, indicating a possible slowdown in momentum. Swing high/low-based SL/TP: • Stop Loss (SL): For a long position, set the SL just below the recent swing low. For a short position, set it just above the recent swing high. • Take Profit (TP): For a long position, set the TP near a recent swing high or resistance. For a short position, near a swing low or support. And other methods... 1. Trailing Stop Loss: This dynamic SL adjusts with the price movement, locking in profits as the trade moves in your favor. 2. Multiple Take Profits: Divide the position into segments and set multiple TP levels, securing profits in stages. 3. Opposite Signal Exit: Exit when another reliable indicator gives an opposite signal. Trade execution and management are as much an art as they are a science. They require a blend of analytical skill, discipline, and intuition. Regularly reviewing and refining your strategies, especially in light of changing market conditions, is crucial to maintaining consistent trading performance. 7. Visual Representations Visual tools are essential for traders, as they simplify complex data into an easily interpretable format. Properly analyzing and understanding the plots on a chart can provide actionable insights and a more intuitive grasp of market conditions. In this section, we’ll delve into various visual representations used in the Supertrend Advance Strategy and their significance. Understanding Plots on the Chart: Charts are the primary visual aids for traders. The arrangement of data points, lines, and colors on them tell a story about the market's past, present, and potential future moves. 1. Data Points: These represent individual price actions over a specific timeframe. For instance, a daily chart will have data points showing the opening, closing, high, and low prices for each day. 2. Colors: Used to indicate the nature of price movement. Commonly, green is used for bullish (upward) moves and red for bearish (downward) moves. Trend Lines: Trend lines are straight lines drawn on a chart that connect a series of price points. Their significance: 1. Uptrend Line: Drawn along the lows, representing support. A break below might indicate a trend reversal. 2. Downtrend Line: Drawn along the highs, indicating resistance. A break above might suggest the start of a bullish trend. Filled Areas: These represent a range between two values on a chart, usually shaded or colored. For instance: 1. Bollinger Bands: The area between the upper and lower band is filled, giving a visual representation of volatility. 2. Volume Profile: Can show a filled area representing the amount of trading activity at different price levels. Stop Loss and Take Profit Lines: These are horizontal lines representing pre-determined exit points for trades. 1. Stop Loss Line: Indicates the level at which a trade will be automatically closed to limit losses. Positioned according to the trader's risk tolerance. 2. Take Profit Line: Denotes the target level to lock in profits. Set according to potential resistance (for long trades) or support (for short trades) or other technical factors. Trailing Stop Lines: A trailing stop is a dynamic form of stop loss that moves with the price. On a chart: 1. For Long Trades: Starts below the entry price and moves up with the price but remains static if the price falls, ensuring profits are locked in. 2. For Short Trades: Starts above the entry price and moves down with the price but remains static if the price rises. Visual representations offer traders a clear, organized view of market dynamics. Familiarity with these tools ensures that traders can quickly and accurately interpret chart data, leading to more informed decision-making. Always ensure that the visual aids used resonate with your trading style and strategy for the best results. 8. Backtesting Backtesting is a fundamental process in strategy development, enabling traders to evaluate the efficacy of their strategy using historical data. It provides a snapshot of how the strategy would have performed in past market conditions, offering insights into its potential strengths and vulnerabilities. In this section, we'll explore the intricacies of setting up and analyzing backtest results and the caveats one must be aware of. Setting Up Backtest Period: 1. Duration: Determine the timeframe for the backtest. It should be long enough to capture various market conditions (bullish, bearish, sideways). For instance, if you're testing a daily strategy, consider a period of several years. 2. Data Quality: Ensure the data source is reliable, offering high-resolution and clean data. This is vital to get accurate backtest results. 3. Segmentation: Instead of a continuous period, sometimes it's helpful to backtest over distinct market phases, like a particular bear or bull market, to see how the strategy holds up in different environments. Analyzing Backtest Results: 1. Performance Metrics: Examine metrics like the total return, annualized return, maximum drawdown, Sharpe ratio, and others to gauge the strategy's efficiency. 2. Win Rate: It's the ratio of winning trades to total trades. A high win rate doesn't always signify a good strategy; it should be evaluated in conjunction with other metrics. 3. Risk/Reward: Understand the average profit versus the average loss per trade. A strategy might have a low win rate but still be profitable if the average gain far exceeds the average loss. 4. Drawdown Analysis: Review the periods of losses the strategy could incur and how long it takes, on average, to recover. 9. Tips and Best Practices Successful trading requires more than just knowing how a strategy works. It necessitates an understanding of when to apply it, how to adjust it to varying market conditions, and the wisdom to recognize and avoid common pitfalls. This section offers insightful tips and best practices to enhance the application of the Supertrend Advance Strategy. When to Use the Strategy: 1. Market Conditions: Ideally, employ the Supertrend Advance Strategy during trending market conditions. This strategy thrives when there are clear upward or downward trends. It might be less effective during consolidative or sideways markets. 2. News Events: Be cautious around significant news events, as they can cause extreme volatility. It might be wise to avoid trading immediately before and after high-impact news. 3. Liquidity: Ensure you are trading in assets/markets with sufficient liquidity. High liquidity ensures that the price movements are more reflective of genuine market sentiment and not due to thin volume. Adjusting Settings for Different Markets/Timeframes: 1. Markets: Each market (stocks, forex, commodities) has its own characteristics. It's essential to adjust the strategy's parameters to align with the market's volatility and liquidity. 2. Timeframes: Shorter timeframes (like 1-minute or 5-minute charts) tend to have more noise. You might need to adjust the settings to filter out false signals. Conversely, for longer timeframes (like daily or weekly charts), you might need to be more responsive to genuine trend changes. 3. Customization: Regularly review and tweak the strategy's settings. Periodic adjustments can ensure the strategy remains optimized for the current market conditions. 10. Frequently Asked Questions (FAQs) Given the complexities and nuances of the Supertrend Advance Strategy, it's only natural for traders, both new and seasoned, to have questions. This section addresses some of the most commonly asked questions regarding the strategy. 1. What exactly is the Supertrend Advance Strategy? The Supertrend Advance Strategy is an evolved version of the traditional Supertrend indicator. It's designed to provide clearer buy and sell signals by incorporating additional indicators like EMA, RSI, MACD, CCI, etc. The strategy aims to capitalize on market trends while minimizing false signals. 2. Can I use the Supertrend Advance Strategy for all asset types? Yes, the strategy can be applied to various asset types like stocks, forex, commodities, and cryptocurrencies. However, it's crucial to adjust the settings accordingly to suit the specific characteristics and volatility of each asset type. 3. Is this strategy suitable for day trading? Absolutely! The Supertrend Advance Strategy can be adjusted to suit various timeframes, making it versatile for both day trading and long-term trading. Remember to fine-tune the settings to align with the timeframe you're trading on. 4. How do I deal with false signals? No strategy is immune to false signals. However, by combining the Supertrend with other indicators and adhering to strict risk management protocols, you can minimize the impact of false signals. Always use stop-loss orders and consider filtering trades with additional confirmation signals. 5. Do I need any prior trading experience to use this strategy? While the Supertrend Advance Strategy is designed to be user-friendly, having a foundational understanding of trading and market analysis can greatly enhance your ability to employ the strategy effectively. If you're a beginner, consider pairing the strategy with further education and practice on demo accounts. 6. How often should I review and adjust the strategy settings? There's no one-size-fits-all answer. Some traders adjust settings weekly, while others might do it monthly. The key is to remain responsive to changing market conditions. Regular backtesting can give insights into potential required adjustments. 7. Can the Supertrend Advance Strategy be automated? Yes, many traders use algorithmic trading platforms to automate their strategies, including the Supertrend Advance Strategy. However, always monitor automated systems regularly to ensure they're operating as intended. 8. Are there any markets or conditions where the strategy shouldn't be used? The strategy might generate more false signals in markets that are consolidative or range-bound. During significant news events or times of unexpected high volatility, it's advisable to tread with caution or stay out of the market. 9. How important is backtesting with this strategy? Backtesting is crucial as it allows traders to understand how the strategy would have performed in the past, offering insights into potential profitability and areas of improvement. Always backtest any new setting or tweak before applying it to live trades. 10. What if the strategy isn't working for me? No strategy guarantees consistent profits. If it's not working for you, consider reviewing your settings, seeking expert advice, or complementing the Supertrend Advance Strategy with other analysis methods. Remember, continuous learning and adaptation are the keys to trading success. Other comments Value of combining several indicators in this script and how they work together Diversification of Signals: Just as diversifying an investment portfolio can reduce risk, using multiple indicators can offer varied perspectives on potential price movements. Each indicator can capture a different facet of the market, ensuring that traders are not overly reliant on a single data point. Confirmation & Reduced False Signals: A common challenge with many indicators is the potential for false signals. By requiring confirmation from multiple indicators before acting, the chances of acting on a false signal can be significantly reduced. Flexibility Across Market Conditions: Different indicators might perform better under different market conditions. For example, while moving averages might excel in trending markets, oscillators like RSI might be more useful during sideways or range-bound conditions. A mashup strategy can potentially adapt better to varying market scenarios. Comprehensive Analysis: With multiple indicators, traders can gauge trend strength, momentum, volatility, and potential market reversals all at once, providing a holistic view of the market. How do the different indicators in the Supertrend Advance Strategy work together? Supertrend: This is primarily a trend-following indicator. It provides traders with buy and sell signals based on the volatility of the price. When combined with other indicators, it can filter out noise and give more weight to strong, confirmed trends. EMA (Exponential Moving Average): EMA gives more weight to recent price data. It can be used to identify the direction and strength of a trend. When the price is above the EMA, it's generally considered bullish, and vice versa. RSI (Relative Strength Index): An oscillator that measures the magnitude of recent price changes to evaluate overbought or oversold conditions. By cross-referencing with other indicators like EMA or MACD, traders can spot potential reversals or confirmations of a trend. MACD (Moving Average Convergence Divergence): This indicator identifies changes in the strength, direction, momentum, and duration of a trend in a stock's price. When the MACD line crosses above the signal line, it can be a bullish sign, and when it crosses below, it can be bearish. Pairing MACD with Supertrend can provide dual confirmation of a trend. CCI (Commodity Channel Index): Initially developed for commodities, CCI can indicate overbought or oversold conditions. It can be used in conjunction with other indicators to determine entry and exit points. In essence, the synergy of these indicators provides a balanced, comprehensive approach to trading. Each indicator offers its unique lens into market conditions, and when they align, it can be a powerful indication of a trading opportunity. This combination not only reduces the potential drawbacks of each individual indicator but leverages their strengths, aiming for more consistent and informed trading decisions. Backtesting and Default Settings • This indicator has been optimized to be applied for 1 hour-charts. However, the underlying principles of this strategy are supply and demand in the financial markets and the strategy can be applied to all timeframes. Daytraders can use the 1min- or 5min charts, swing-traders can use the daily charts. • This strategy has been designed to identify the most promising, highest probability entries and trades for each stock or other financial security. • The combination of the qualifiers results in a highly selective strategy which only considers the most promising swing-trading entries. As a result, you will normally only find a low number of trades for each stock or other financial security per year in case you apply this strategy for the daily charts. Shorter timeframes will result in a higher number of trades / year. • Consequently, traders need to apply this strategy for a full watchlist rather than just one financial security. • Default properties: RSI on (length 14, RSI buy level 50, sell level 50), EMA, RSI, MACD on, type of strategy pullback, SL/TP type: ATR (length 10, factor 3), trade direction both, quantity 5, take profit swing hl 5.1, highest / lowest lookback 2, enable ATR trail (ATR length 10, SL ATR multiplier 1.4, TP multiplier 2.1, lookback = 4, trade direction = both).
Strategi Pine Script®
oleh JS_TechTrading
1
Uhl MA System - Strategy AnalysisThe Uhl MA crossover system was specifically designed to provide an adaptive MA crossover system that didn't committed the same errors of more classical MA systems. This crossover system is based on a fast and a slow moving average, with the slow moving average being the corrected moving average (CMA) originally proposed by Andreas Uhl, and the fast moving average being the corrected trend step (CTS) which is also based on the corrected moving average design. For more information see : In this post, the performances of this system are analyzed on various markets. Setup And Rules The analysis is solely based on the indicator signals, therefore no spread is applied. Constant position sizing is used. The strategy will be backtested on the 15 minute time-frame. The mult setting is discarded, the default setting used for length is 100. Here are the rules of our strategy : long: CTS crossover CMA short: CTS crossunder CMA Results And Data EURUSD: Net Profit: $ 0.08 Total number of trades: 99 Profitability: 35.35 % Profit Factor: 1.834 Max Drawdown: $ 0.01 EURUSD behaved pretty well, and was most of time showing long term trends without exhibiting particularly tricky structures, the moving averages still did cross during ranging phases, since march 9 we can see a downtrend with more pronounced cyclical variations (retracements) that could potentially lead to loosing trades. BTCUSD: Net Profit: $ 4371.57 Total number of trades: 94 Profitability: 32.98 % Profit Factor: 1.749 Max Drawdown: $ 1409.96 The strategy didn't started well, producing its largest drawdown after only a few trades, the strategy still managed to recover. BTCUSD exhibited a strong downtrend, the strategy profited from that to recover, signals still occurred on ranging phases, and where mostly caused by a short term volatile move, unfortunately the CMA can converge toward ranging/flat price zones where false signals might occur at higher frequency. AMD: Net Profit: $ 16.09 Total number of trades: 95 Profitability: 29.47 % Profit Factor: 1.288 Max Drawdown: $ 20.11 On AMD the strategy started relatively well with a raising balance, then the balance quickly fallen, this downtrend in the balance lasted quite some time (almost 48 trades), the strategy finally recovered in Nov 2019 and the balance made a new highest high at the end of February. AMD had numerous trends during the backtesting period, yet results are poor. AAPL: Net Profit: $ -28.17 Total number of trades: 89 Profitability: 28.09 % Profit Factor: 0.894 Max Drawdown: $ 63.21 AAPL show the poorest results so far, with a stationary balance around the initial capital (in short the evolution of the balance is not showing any particular trend and oscillate around the initial capital value). AAPL had some significant retracements in its up-trend, which triggered some trades (of course), and the ranging period from Jan 24 to Feb 13 heavily damaged the strategy performance, generating 6 significant loosing trades. AAPL show the worst results so far, mostly due by ranging phases. Conclusions The Uhl MA crossover system strategy has been tested and based on the results don't show particularly interesting performances, and might even be outperformed by simpler MA systems that prove to be more robust against ranging markets. The total number of executed trades are on average 94, and the profitability is on average 31%. The strategy might prove more interesting if we can correct the behavior of the CMA, who sometimes converged toward ranging/flat markets.
Strategi Pine Script®
oleh alexgrover
7
Backtesting & Trading Engine [PineCoders]The PineCoders Backtesting and Trading Engine is a sophisticated framework with hybrid code that can run as a study to generate alerts for automated or discretionary trading while simultaneously providing backtest results. It can also easily be converted to a TradingView strategy in order to run TV backtesting. The Engine comes with many built-in strats for entries, filters, stops and exits, but you can also add you own. If, like any self-respecting strategy modeler should, you spend a reasonable amount of time constantly researching new strategies and tinkering, our hope is that the Engine will become your inseparable go-to tool to test the validity of your creations, as once your tests are conclusive, you will be able to run this code as a study to generate the alerts required to put it in real-world use, whether for discretionary trading or to interface with an execution bot/app. You may also find the backtesting results the Engine produces in study mode enough for your needs and spend most of your time there, only occasionally converting to strategy mode in order to backtest using TV backtesting. As you will quickly grasp when you bring up this script’s Settings, this is a complex tool. While you will be able to see results very quickly by just putting it on a chart and using its built-in strategies, in order to reap the full benefits of the PineCoders Engine, you will need to invest the time required to understand the subtleties involved in putting all its potential into play. Disclaimer: use the Engine at your own risk. Before we delve in more detail, here’s a bird’s eye view of the Engine’s features: More than 40 built-in strategies, Customizable components, Coupling with your own external indicator, Simple conversion from Study to Strategy modes, Post-Exit analysis to search for alternate trade outcomes, Use of the Data Window to show detailed bar by bar trade information and global statistics, including some not provided by TV backtesting, Plotting of reminders and generation of alerts on in-trade events. By combining your own strats to the built-in strats supplied with the Engine, and then tuning the numerous options and parameters in the Inputs dialog box, you will be able to play what-if scenarios from an infinite number of permutations. USE CASES You have written an indicator that provides an entry strat but it’s missing other components like a filter and a stop strategy. You add a plot in your indicator that respects the Engine’s External Signal Protocol, connect it to the Engine by simply selecting your indicator’s plot name in the Engine’s Settings/Inputs and then run tests on different combinations of entry stops, in-trade stops and profit taking strats to find out which one produces the best results with your entry strat. You are building a complex strategy that you will want to run as an indicator generating alerts to be sent to a third-party execution bot. You insert your code in the Engine’s modules and leverage its trade management code to quickly move your strategy into production. You have many different filters and want to explore results using them separately or in combination. Integrate the filter code in the Engine and run through different permutations or hook up your filtering through the external input and control your filter combos from your indicator. You are tweaking the parameters of your entry, filter or stop strat. You integrate it in the Engine and evaluate its performance using the Engine’s statistics. You always wondered what results a random entry strat would yield on your markets. You use the Engine’s built-in random entry strat and test it using different combinations of filters, stop and exit strats. You want to evaluate the impact of fees and slippage on your strategy. You use the Engine’s inputs to play with different values and get immediate feedback in the detailed numbers provided in the Data Window. You just want to inspect the individual trades your strategy generates. You include it in the Engine and then inspect trades visually on your charts, looking at the numbers in the Data Window as you move your cursor around. You have never written a production-grade strategy and you want to learn how. Inspect the code in the Engine; you will find essential components typical of what is being used in actual trading systems. You have run your system for a while and have compiled actual slippage information and your broker/exchange has updated his fees schedule. You enter the information in the Engine and run it on your markets to see the impact this has on your results. FEATURES Before going into the detail of the Inputs and the Data Window numbers, here’s a more detailed overview of the Engine’s features. Built-in strats The engine comes with more than 40 pre-coded strategies for the following standard system components: Entries, Filters, Entry stops, 2 stage in-trade stops with kick-in rules, Pyramiding rules, Hard exits. While some of the filter and stop strats provided may be useful in production-quality systems, you will not devise crazy profit-generating systems using only the entry strats supplied; that part is still up to you, as will be finding the elusive combination of components that makes winning systems. The Engine will, however, provide you with a solid foundation where all the trade management nitty-gritty is handled for you. By binding your custom strats to the Engine, you will be able to build reliable systems of the best quality currently allowed on the TV platform. On-chart trade information As you move over the bars in a trade, you will see trade numbers in the Data Window change at each bar. The engine calculates the P&L at every bar, including slippage and fees that would be incurred were the trade exited at that bar’s close. If the trade includes pyramided entries, those will be taken into account as well, although for those, final fees and slippage are only calculated at the trade’s exit. You can also see on-chart markers for the entry level, stop positions, in-trade special events and entries/exits (you will want to disable these when using the Engine in strategy mode to see TV backtesting results). Customization You can couple your own strats to the Engine in two ways: 1. By inserting your own code in the Engine’s different modules. The modular design should enable you to do so with minimal effort by following the instructions in the code. 2. By linking an external indicator to the engine. After making the proper selections in the engine’s Settings and providing values respecting the engine’s protocol, your external indicator can, when the Engine is used in Indicator mode only: Tell the engine when to enter long or short trades, but let the engine’s in-trade stop and exit strats manage the exits, Signal both entries and exits, Provide an entry stop along with your entry signal, Filter other entry signals generated by any of the engine’s entry strats. Conversion from strategy to study TradingView strategies are required to backtest using the TradingView backtesting feature, but if you want to generate alerts with your script, whether for automated trading or just to trigger alerts that you will use in discretionary trading, your code has to run as a study since, for the time being, strategies can’t generate alerts. From hereon we will use indicator as a synonym for study. Unless you want to maintain two code bases, you will need hybrid code that easily flips between strategy and indicator modes, and your code will need to restrict its use of strategy() calls and their arguments if it’s going to be able to run both as an indicator and a strategy using the same trade logic. That’s one of the benefits of using this Engine. Once you will have entered your own strats in the Engine, it will be a matter of commenting/uncommenting only four lines of code to flip between indicator and strategy modes in a matter of seconds. Additionally, even when running in Indicator mode, the Engine will still provide you with precious numbers on your individual trades and global results, some of which are not available with normal TradingView backtesting. Post-Exit Analysis for alternate outcomes (PEA) While typical backtesting shows results of trade outcomes, PEA focuses on what could have happened after the exit. The intention is to help traders get an idea of the opportunity/risk in the bars following the trade in order to evaluate if their exit strategies are too aggressive or conservative. After a trade is exited, the Engine’s PEA module continues analyzing outcomes for a user-defined quantity of bars. It identifies the maximum opportunity and risk available in that space, and calculates the drawdown required to reach the highest opportunity level post-exit, while recording the number of bars to that point. Typically, if you can’t find opportunity greater than 1X past your trade using a few different reasonable lengths of PEA, your strategy is doing pretty good at capturing opportunity. Remember that 100% of opportunity is never capturable. If, however, PEA was finding post-trade maximum opportunity of 3 or 4X with average drawdowns of 0.3 to those areas, this could be a clue revealing your system is exiting trades prematurely. To analyze PEA numbers, you can uncomment complete sets of plots in the Plot module to reveal detailed global and individual PEA numbers. Statistics The Engine provides stats on your trades that TV backtesting does not provide, such as: Average Profitability Per Trade (APPT), aka statistical expectancy, a crucial value. APPT per bar, Average stop size, Traded volume . It also shows you on a trade-by-trade basis, on-going individual trade results and data. In-trade events In-trade events can plot reminders and trigger alerts when they occur. The built-in events are: Price approaching stop, Possible tops/bottoms, Large stop movement (for discretionary trading where stop is moved manually), Large price movements. Slippage and Fees Even when running in indicator mode, the Engine allows for slippage and fees to be included in the logic and test results. Alerts The alert creation mechanism allows you to configure alerts on any combination of the normal or pyramided entries, exits and in-trade events. Backtesting results A few words on the numbers calculated in the Engine. Priority is given to numbers not shown in TV backtesting, as you can readily convert the script to a strategy if you need them. We have chosen to focus on numbers expressing results relative to X (the trade’s risk) rather than in absolute currency numbers or in other more conventional but less useful ways. For example, most of the individual trade results are not shown in percentages, as this unit of measure is often less meaningful than those expressed in units of risk (X). A trade that closes with a +25% result, for example, is a poor outcome if it was entered with a -50% stop. Expressed in X, this trade’s P&L becomes 0.5, which provides much better insight into the trade’s outcome. A trade that closes with a P&L of +2X has earned twice the risk incurred upon entry, which would represent a pre-trade risk:reward ratio of 2. The way to go about it when you think in X’s and that you adopt the sound risk management policy to risk a fixed percentage of your account on each trade is to equate a currency value to a unit of X. E.g. your account is 10K USD and you decide you will risk a maximum of 1% of it on each trade. That means your unit of X for each trade is worth 100 USD. If your APPT is 2X, this means every time you risk 100 USD in a trade, you can expect to make, on average, 200 USD. By presenting results this way, we hope that the Engine’s statistics will appeal to those cognisant of sound risk management strategies, while gently leading traders who aren’t, towards them. We trade to turn in tangible profits of course, so at some point currency must come into play. Accordingly, some values such as equity, P&L, slippage and fees are expressed in currency. Many of the usual numbers shown in TV backtests are nonetheless available, but they have been commented out in the Engine’s Plot module. Position sizing and risk management All good system designers understand that optimal risk management is at the very heart of all winning strategies. The risk in a trade is defined by the fraction of current equity represented by the amplitude of the stop, so in order to manage risk optimally on each trade, position size should adjust to the stop’s amplitude. Systems that enter trades with a fixed stop amplitude can get away with calculating position size as a fixed percentage of current equity. In the context of a test run where equity varies, what represents a fixed amount of risk translates into different currency values. Dynamically adjusting position size throughout a system’s life is optimal in many ways. First, as position sizing will vary with current equity, it reproduces a behavioral pattern common to experienced traders, who will dial down risk when confronted to poor performance and increase it when performance improves. Second, limiting risk confers more predictability to statistical test results. Third, position sizing isn’t just about managing risk, it’s also about maximizing opportunity. By using the maximum leverage (no reference to trading on margin here) into the trade that your risk management strategy allows, a dynamic position size allows you to capture maximal opportunity. To calculate position sizes using the fixed risk method, we use the following formula: Position = Account * MaxRisk% / Stop% [, which calculates a position size taking into account the trade’s entry stop so that if the trade is stopped out, 100 USD will be lost. For someone who manages risk this way, common instructions to invest a certain percentage of your account in a position are simply worthless, as they do not take into account the risk incurred in the trade. The Engine lets you select either the fixed risk or fixed percentage of equity position sizing methods. The closest thing to dynamic position sizing that can currently be done with alerts is to use a bot that allows syntax to specify position size as a percentage of equity which, while being dynamic in the sense that it will adapt to current equity when the trade is entered, does not allow us to modulate position size using the stop’s amplitude. Changes to alerts are on the way which should solve this problem. In order for you to simulate performance with the constraint of fixed position sizing, the Engine also offers a third, less preferable option, where position size is defined as a fixed percentage of initial capital so that it is constant throughout the test and will thus represent a varying proportion of current equity. Let’s recap. The three position sizing methods the Engine offers are: 1. By specifying the maximum percentage of risk to incur on your remaining equity, so the Engine will dynamically adjust position size for each trade so that, combining the stop’s amplitude with position size will yield a fixed percentage of risk incurred on current equity, 2. By specifying a fixed percentage of remaining equity. Note that unless your system has a fixed stop at entry, this method will not provide maximal risk control, as risk will vary with the amplitude of the stop for every trade. This method, as the first, does however have the advantage of automatically adjusting position size to equity. It is the Engine’s default method because it has an equivalent in TV backtesting, so when flipping between indicator and strategy mode, test results will more or less correspond. 3. By specifying a fixed percentage of the Initial Capital. While this is the least preferable method, it nonetheless reflects the reality confronted by most system designers on TradingView today. In this case, risk varies both because the fixed position size in initial capital currency represents a varying percentage of remaining equity, and because the trade’s stop amplitude may vary, adding another variability vector to risk. Note that the Engine cannot display equity results for strategies entering trades for a fixed amount of shares/contracts at a variable price. SETTINGS/INPUTS Because the initial text first published with a script cannot be edited later and because there are just too many options, the Engine’s Inputs will not be covered in minute detail, as they will most certainly evolve. We will go over them with broad strokes; you should be able to figure the rest out. If you have questions, just ask them here or in the PineCoders Telegram group. Display The display header’s checkbox does nothing. For the moment, only one exit strategy uses a take profit level, so only that one will show information when checking “Show Take Profit Level”. Entries You can activate two simultaneous entry strats, each selected from the same set of strats contained in the Engine. If you select two and they fire simultaneously, the main strat’s signal will be used. The random strat in each list uses a different seed, so you will get different results from each. The “Filter transitions” and “Filter states” strats delegate signal generation to the selected filter(s). “Filter transitions” signals will only fire when the filter transitions into bull/bear state, so after a trade is stopped out, the next entry may take some time to trigger if the filter’s state does not change quickly. When you choose “Filter states”, then a new trade will be entered immediately after an exit in the direction the filter allows. If you select “External Indicator”, your indicator will need to generate a +2/-2 (or a positive/negative stop value) to enter a long/short position, providing the selected filters allow for it. If you wish to use the Engine’s capacity to also derive the entry stop level from your indicator’s signal, then you must explicitly choose this option in the Entry Stops section. Filters You can activate as many filters as you wish; they are additive. The “Maximum stop allowed on entry” is an important component of proper risk management. If your system has an average 3% stop size and you need to trade using fixed position sizes because of alert/execution bot limitations, you must use this filter because if your system was to enter a trade with a 15% stop, that trade would incur 5 times the normal risk, and its result would account for an abnormally high proportion in your system’s performance. Remember that any filter can also be used as an entry signal, either when it changes states, or whenever no trade is active and the filter is in a bull or bear mode. Entry Stops An entry stop must be selected in the Engine, as it requires a stop level before the in-trade stop is calculated. Until the selected in-trade stop strat generates a stop that comes closer to price than the entry stop (or respects another one of the in-trade stops kick in strats), the entry stop level is used. It is here that you must select “External Indicator” if your indicator supplies a +price/-price value to be used as the entry stop. A +price is expected for a long entry and a -price value will enter a short with a stop at price. Note that the price is the absolute price, not an offset to the current price level. In-Trade Stops The Engine comes with many built-in in-trade stop strats. Note that some of them share the “Length” and “Multiple” field, so when you swap between them, be sure that the length and multiple in use correspond to what you want for that stop strat. Suggested defaults appear with the name of each strat in the dropdown. In addition to the strat you wish to use, you must also determine when it kicks in to replace the initial entry’s stop, which is determined using different strats. For strats where you can define a positive or negative multiple of X, percentage or fixed value for a kick-in strat, a positive value is above the trade’s entry fill and a negative one below. A value of zero represents breakeven. Pyramiding What you specify in this section are the rules that allow pyramiding to happen. By themselves, these rules will not generate pyramiding entries. For those to happen, entry signals must be issued by one of the active entry strats, and conform to the pyramiding rules which act as a filter for them. The “Filter must allow entry” selection must be chosen if you want the usual system’s filters to act as additional filtering criteria for your pyramided entries. Hard Exits You can choose from a variety of hard exit strats. Hard exits are exit strategies which signal trade exits on specific events, as opposed to price breaching a stop level in In-Trade Stops strategies. They are self-explanatory. The last one labelled When Take Profit Level (multiple of X) is reached is the only one that uses a level, but contrary to stops, it is above price and while it is relative because it is expressed as a multiple of X, it does not move during the trade. This is the level called Take Profit that is show when the “Show Take Profit Level” checkbox is checked in the Display section. While stops focus on managing risk, hard exit strategies try to put the emphasis on capturing opportunity. Slippage You can define it as a percentage or a fixed value, with different settings for entries and exits. The entry and exit markers on the chart show the impact of slippage on the entry price (the fill). Fees Fees, whether expressed as a percentage of position size in and out of the trade or as a fixed value per in and out, are in the same units of currency as the capital defined in the Position Sizing section. Fees being deducted from your Capital, they do not have an impact on the chart marker positions. In-Trade Events These events will only trigger during trades. They can be helpful to act as reminders for traders using the Engine as assistance to discretionary trading. Post-Exit Analysis It is normally on. Some of its results will show in the Global Numbers section of the Data Window. Only a few of the statistics generated are shown; many more are available, but commented out in the Plot module. Date Range Filtering Note that you don’t have to change the dates to enable/diable filtering. When you are done with a specific date range, just uncheck “Date Range Filtering” to disable date filtering. Alert Triggers Each selection corresponds to one condition. Conditions can be combined into a single alert as you please. Just be sure you have selected the ones you want to trigger the alert before you create the alert. For example, if you trade in both directions and you want a single alert to trigger on both types of exits, you must select both “Long Exit” and “Short Exit” before creating your alert. Once the alert is triggered, these settings no longer have relevance as they have been saved with the alert. When viewing charts where an alert has just triggered, if your alert triggers on more than one condition, you will need the appropriate markers active on your chart to figure out which condition triggered the alert, since plotting of markers is independent of alert management. Position sizing You have 3 options to determine position size: 1. Proportional to Stop -> Variable, with a cap on size. 2. Percentage of equity -> Variable. 3. Percentage of Initial Capital -> Fixed. External Indicator This is where you connect your indicator’s plot that will generate the signals the Engine will act upon. Remember this only works in Indicator mode. DATA WINDOW INFORMATION The top part of the window contains global numbers while the individual trade information appears in the bottom part. The different types of units used to express values are: curr: denotes the currency used in the Position Sizing section of Inputs for the Initial Capital value. quote: denotes quote currency, i.e. the value the instrument is expressed in, or the right side of the market pair (USD in EURUSD ). X: the stop’s amplitude, itself expressed in quote currency, which we use to express a trade’s P&L, so that a trade with P&L=2X has made twice the stop’s amplitude in profit. This is sometimes referred to as R, since it represents one unit of risk. It is also the unit of measure used in the APPT, which denotes expected reward per unit of risk. X%: is also the stop’s amplitude, but expressed as a percentage of the Entry Fill. The numbers appearing in the Data Window are all prefixed: “ALL:” the number is the average for all first entries and pyramided entries. ”1ST:” the number is for first entries only. ”PYR:” the number is for pyramided entries only. ”PEA:” the number is for Post-Exit Analyses Global Numbers Numbers in this section represent the results of all trades up to the cursor on the chart. Average Profitability Per Trade (X): This value is the most important gauge of your strat’s worthiness. It represents the returns that can be expected from your strat for each unit of risk incurred. E.g.: your APPT is 2.0, thus for every unit of currency you invest in a trade, you can on average expect to obtain 2 after the trade. APPT is also referred to as “statistical expectancy”. If it is negative, your strategy is losing, even if your win rate is very good (it means your winning trades aren’t winning enough, or your losing trades lose too much, or both). Its counterpart in currency is also shown, as is the APPT/bar, which can be a useful gauge in deciding between rivalling systems. Profit Factor: Gross of winning trades/Gross of losing trades. Strategy is profitable when >1. Not as useful as the APPT because it doesn’t take into account the win rate and the average win/loss per trade. It is calculated from the total winning/losing results of this particular backtest and has less predictive value than the APPT. A good profit factor together with a poor APPT means you just found a chart where your system outperformed. Relying too much on the profit factor is a bit like a poker player who would think going all in with two’s against aces is optimal because he just won a hand that way. Win Rate: Percentage of winning trades out of all trades. Taken alone, it doesn’t have much to do with strategy profitability. You can have a win rate of 99% but if that one trade in 100 ruins you because of poor risk management, 99% doesn’t look so good anymore. This number speaks more of the system’s profile than its worthiness. Still, it can be useful to gauge if the system fits your personality. It can also be useful to traders intending to sell their systems, as low win rate systems are more difficult to sell and require more handholding of worried customers. Equity (curr): This the sum of initial capital and the P&L of your system’s trades, including fees and slippage. Return on Capital is the equivalent of TV’s Net Profit figure, i.e. the variation on your initial capital. Maximum drawdown is the maximal drawdown from the highest equity point until the drop . There is also a close to close (meaning it doesn’t take into account in-trade variations) maximum drawdown value commented out in the code. The next values are self-explanatory, until: PYR: Avg Profitability Per Entry (X): this is the APPT for all pyramided entries. PEA: Avg Max Opp . Available (X): the average maximal opportunity found in the Post-Exit Analyses. PEA: Avg Drawdown to Max Opp . (X): this represents the maximum drawdown (incurred from the close at the beginning of the PEA analysis) required to reach the maximal opportunity point. Trade Information Numbers in this section concern only the current trade under the cursor. Most of them are self-explanatory. Use the description’s prefix to determine what the values applies to. PYR: Avg Profitability Per Entry (X): While this value includes the impact of all current pyramided entries (and only those) and updates when you move your cursor around, P&L only reflects fees at the trade’s last bar. PEA: Max Opp . Available (X): It’s the most profitable close reached post-trade, measured from the trade’s Exit Fill, expressed in the X value of the trade the PEA follows. PEA: Drawdown to Max Opp . (X): This is the maximum drawdown from the trade’s Exit Fill that needs to be sustained in order to reach the maximum opportunity point, also expressed in X. Note that PEA numbers do not include slippage and fees. EXTERNAL SIGNAL PROTOCOL Only one external indicator can be connected to a script; in order to leverage its use to the fullest, the engine provides options to use it as either an entry signal, an entry/exit signal or a filter. When used as an entry signal, you can also use the signal to provide the entry’s stop. Here’s how this works: For filter state: supply +1 for bull (long entries allowed), -1 for bear (short entries allowed). For entry signals: supply +2 for long, -2 for short. For exit signals: supply +3 for exit from long, -3 for exit from short. To send an entry stop level with an entry signal: Send positive stop level for long entry (e.g. 103.33 to enter a long with a stop at 103.33), negative stop level for short entry (e.g. -103.33 to enter a short with a stop at 103.33). If you use this feature, your indicator will have to check for exact stop levels of 1.0, 2.0 or 3.0 and their negative counterparts, and fudge them with a tick in order to avoid confusion with other signals in the protocol. Remember that mere generation of the values by your indicator will have no effect until you explicitly allow their use in the appropriate sections of the Engine’s Settings/Inputs. An example of a script issuing a signal for the Engine is published by PineCoders. RECOMMENDATIONS TO ASPIRING SYSTEM DESIGNERS Stick to higher timeframes. On progressively lower timeframes, margins decrease and fees and slippage take a proportionally larger portion of profits, to the point where they can very easily turn a profitable strategy into a losing one. Additionally, your margin for error shrinks as the equilibrium of your system’s profitability becomes more fragile with the tight numbers involved in the shorter time frames. Avoid <1H time frames. Know and calculate fees and slippage. To avoid market shock, backtest using conservative fees and slippage parameters. Systems rarely show unexpectedly good returns when they are confronted to the markets, so put all chances on your side by being outrageously conservative—or a the very least, realistic. Test results that do not include fees and slippage are worthless. Slippage is there for a reason, and that’s because our interventions in the market change the market. It is easier to find alpha in illiquid markets such as cryptos because not many large players participate in them. If your backtesting results are based on moving large positions and you don’t also add the inevitable slippage that will occur when you enter/exit thin markets, your backtesting will produce unrealistic results. Even if you do include large slippage in your settings, the Engine can only do so much as it will not let slippage push fills past the high or low of the entry bar, but the gap may be much larger in illiquid markets. Never test and optimize your system on the same dataset , as that is the perfect recipe for overfitting or data dredging, which is trying to find one precise set of rules/parameters that works only on one dataset. These setups are the most fragile and often get destroyed when they meet the real world. Try to find datasets yielding more than 100 trades. Less than that and results are not as reliable. Consider all backtesting results with suspicion. If you never entertained sceptic tendencies, now is the time to begin. If your backtest results look really good, assume they are flawed, either because of your methodology, the data you’re using or the software doing the testing. Always assume the worse and learn proper backtesting techniques such as monte carlo simulations and walk forward analysis to avoid the traps and biases that unchecked greed will set for you. If you are not familiar with concepts such as survivor bias, lookahead bias and confirmation bias, learn about them. Stick to simple bars or candles when designing systems. Other types of bars often do not yield reliable results, whether by design (Heikin Ashi) or because of the way they are implemented on TV (Renko bars). Know that you don’t know and use that knowledge to learn more about systems and how to properly test them, about your biases, and about yourself. Manage risk first , then capture opportunity. Respect the inherent uncertainty of the future. Cleanse yourself of the sad arrogance and unchecked greed common to newcomers to trading. Strive for rationality. Respect the fact that while backtest results may look promising, there is no guarantee they will repeat in the future (there is actually a high probability they won’t!), because the future is fundamentally unknowable. If you develop a system that looks promising, don’t oversell it to others whose greed may lead them to entertain unreasonable expectations. Have a plan. Understand what king of trading system you are trying to build. Have a clear picture or where entries, exits and other important levels will be in the sort of trade you are trying to create with your system. This stated direction will help you discard more efficiently many of the inevitably useless ideas that will pop up during system design. Be wary of complexity. Experienced systems engineers understand how rapidly complexity builds when you assemble components together—however simple each one may be. The more complex your system, the more difficult it will be to manage. Play! . Allow yourself time to play around when you design your systems. While much comes about from working with a purpose, great ideas sometimes come out of just trying things with no set goal, when you are stuck and don’t know how to move ahead. Have fun! @LucF NOTES While the engine’s code can supply multiple consecutive entries of longs or shorts in order to scale positions (pyramid), all exits currently assume the execution bot will exit the totality of the position. No partial exits are currently possible with the Engine. Because the Engine is literally crippled by the limitations on the number of plots a script can output on TV; it can only show a fraction of all the information it calculates in the Data Window. You will find in the Plot Module vast amounts of commented out lines that you can activate if you also disable an equivalent number of other plots. This may be useful to explore certain characteristics of your system in more detail. When backtesting using the TV backtesting feature, you will need to provide the strategy parameters you wish to use through either Settings/Properties or by changing the default values in the code’s header. These values are defined in variables and used not only in the strategy() statement, but also as defaults in the Engine’s relevant Inputs. If you want to test using pyramiding, then both the strategy’s Setting/Properties and the Engine’s Settings/Inputs need to allow pyramiding. If you find any bugs in the Engine, please let us know. THANKS To @glaz for allowing the use of his unpublished MA Squize in the filters. To @everget for his Chandelier stop code, which is also used as a filter in the Engine. To @RicardoSantos for his pseudo-random generator, and because it’s from him that I first read in the Pine chat about the idea of using an external indicator as input into another. In the PineCoders group, @theheirophant then mentioned the idea of using it as a buy/sell signal and @simpelyfe showed a piece of code implementing the idea. That’s the tortuous story behind the use of the external indicator in the Engine. To @admin for the Volatility stop’s original code and for the donchian function lifted from Ichimoku . To @BobHoward21 for the v3 version of Volatility Stop . To @scarf and @midtownsk8rguy for the color tuning. To many other scripters who provided encouragement and suggestions for improvement during the long process of writing and testing this piece of code. To J. Welles Wilder Jr. for ATR, used extensively throughout the Engine. To TradingView for graciously making an account available to PineCoders. And finally, to all fellow PineCoders for the constant intellectual stimulation; it is a privilege to share ideas with you all. The Engine is for all TradingView PineCoders, of course—but especially for you. Look first. Then leap.
Penunjuk Pine Script®
oleh PineCoders
131
Great Expectations [LucF]Great Expectations helps traders answer the question: What is possible? It is a powerful question, yet exploration of the unknown always entails risk. A more complete set of questions better suited to traders could be: What opportunity exists from any given point on a chart? What portion of this opportunity can be realistically captured? What risk will be incurred in trying to do so, and how long will it take? Great Expectations is the result of an exploration of these questions. It is a trade simulator that generates visual and quantitative information to help strategy modelers visually identify and analyse areas of optimal expectation on charts, whether they are designing automated or discretionary strategies. WARNING: Great Expectations is NOT an indicator that helps determine the current state of a market. It works by looking at points in the past from which the future is already known. It uses one definition of repainting extensively (i.e. it goes back in the past to print information that could not have been know at the time). Repainting understood that way is in fact almost all the indicator does! —albeit for what I hope is a noble cause. The indicator is of no use whatsoever in analyzing markets in real-time. If you do not understand what it does, please stay away! This is an indicator—not a strategy that uses TradingView’s backtesting engine. It works by simulating trades, not unlike a backtest, but with the crucial difference that it assumes a trade (either long or short) is entered on all bars in the historic sample. It walks forward from each bar and determines possible outcomes, gathering individual trade statistics that in turn generate precious global statistics from all outcomes tested on the chart. Great Expectations provides numbers summarizing trade results on all simulations run from the chart. Those numbers cannot be compared to backtest-produced numbers since all non-filtered bars are examined, even if an entry was taken on the bar immediately preceding the current one, which never happens in a backtest. This peculiarity does NOT invalidate Great Expectations calculations; it just entails that results be considered under a different light. Provided they are evaluated within the indicator’s context, they can be useful—sometimes even more than backtesting results, e.g. in evaluating the impact of parameter-fitting or variations in entry, exit or filtering strats. Traders and strategy modelers are creatures of hope often suffering from blurred vision; my hope is that Great Expectations will help them appraise the validity of their setup and strat intuitions in a realistic fashion, preventing confirmation bias from obstructing perspective—and great expectations from turning into financial great deceptions. USE CASES You’ve identified what looks like a promising setup on other indicators. You load Great Expectations on the chart and evaluate if its high-expectation areas match locations where your setup’s conditions occur. Unless today is your lucky day, chances are the indicator will help you realize your setup is not as promising as you had hoped. You want to get a rough estimate of the optimal trade duration for a chart and you don’t mind using the entry and exit strategies provided with the indicator. You use the trade length readouts of the indicator. You’re experimenting with a new stop strategy and want to know how long it will keep you in trades, on average. You integrate your stop strategy in the indicator’s code and look at the average trade length it produces and the TST ratio to evaluate its performance. You have put together your own entry and exit criteria and are looking for a filter that will help you improve backtesting results. You visually ascertain the suitability of your filter by looking at its results on the charts with great Expectations, to see if your filter is choosing its areas correctly. You have a strategy that shows backtested trades on your chart. Great Expectations can help you evaluate how well your strategy is benefitting from high-opportunity areas while avoiding poor expectation spots. You want more complete statistics on your set of strategies than what backtesting will provide. You use Great Expectations, knowing that it tests all bars in the sample that correspond to your criteria, as opposed to backtesting results which are limited to a subset of all possible entries. You want to fool your friends into thinking you’ve designed the holy grail of indicators, something that identifies optimal opportunities on any chart; you show them the P&L cloud. FEATURES For one trade At any given point on the chart, assuming a trade is entered there, Great Expectations shows you information specific to that trade simulation both on the chart and in the Data Window. The chart can display: the P & L Cloud which shows whether the trade ended profitably or not, and by how much, the Opportunity & Risk Cloud which the maximum opportunity and risk the simulation encountered. When superimposed over the P & L cloud, you will see what I call the managed opportunity and risk, i.e the portion of maximum opportunity that was captured and the portion of the maximum risk that was incurred, the target and if it was reached, a background that uses a gradient to show different levels of trade length, P&L or how frequently the target was reached during simulation. The Data Window displays more than 40 values on individual trades and global results. For any given trade you will know: Entry/Exit levels, including slippage impact, It’s outcome and duration, P/L achieved, The fraction of the maximum opportunity/risk managed by the trade. For all trades After going through all the possible trades on the chart, the indicator will provide you with a rare view of all outcomes expressed with the P&L cloud, which allows us to instantly see the most/least profitable areas of a chart using trade data as support, while also showing its relationship with the opportunity/risk encountered during the simulation. The difference between the two clouds is the managed opportunity and risk. The Data Window will present you with numbers which we will go through later. Some of them are: average stop size, P/L, win rate, % opportunity managed, trade lengths for different types of trade outcomes and the TST (Target:Stop Travel) ratio. Let’s see Great Expectations in action… and remember to open your Data Window! INPUTS Trade direction : You must first choose if you wish to look at long or short trades. Because of the way the indicator works and the amount of visual information on the chart, it is only practical to look at one type of trades at a time. The default is Longs. Maximum trade Length (MaxL) : This is the maximum walk forward distance the simulator will go in analyzing outcomes from any given point in the past. It also determines the size of the dead zone among the chart’s last bars. A red background line identifies the beginning of the dead zone for which not enough bars have elapsed to analyze outcomes for the maximum trade length defined. If an ATR-based entry stop is used, that length is added to the wait time before beginning simulations, so that the first entry starts with a clean ATR value. On a sample of around 16000 bars, my tests show that the indicator runs into server errors at lengths of around 290, i.e. having completed ~4,6M simulation loop iterations. That is way too high a length anyways; 100 will usually be amply enough to ring out all the possibilities out of a simulation, and on shorter time frames, 30 can be enough. While making it unduly small will prevent simulations of expressing the market’s potential, the less you use, the faster the indicator will run. The default is 40. Unrealized P&L base at End of Trade (EOT) : When a simulation ends and the trade is still open, we calculate unrealized P&L from an exit order executed from either the last in-trade stop on the previous bar, or the close of the last bar. You can readily see the impact of this selection on the chart, with the P&L cloud. The default is on the close. Display : The check box besides the title does nothing. Show target : Shows a green line displaying the trade’s target expressed as a multiple of X, i.e. the amplitude of the entry stop. I call this value “X” and use it as a unit to express profit and loss on a trade (some call it “R”). The line is highlighted for trades where the close reached the target during the trade, whether the trade ended in profit or loss. This is also where you specify the multiple of X you wish to use in calculating targets. The multiple is used even if targets are not displayed. Show P&L Cloud : The cloud allows traders to see right away the profitable areas of the chart. The only line printed with the cloud is the “end of trade line” (EOT). The EOT line is the only way one can see the level where a trade ended on the chart (in the Data Window you can see it as the “Exit Fill” value). The EOT level for the trade determines if the trade ended in a profit or a loss. Its value represents one of the following: - fill from order executed at close of bar where stop is breached during trade (which produces “Realized P/L”), - simulation of a fill pseudo-fill at the user-defined EOT level (last close or stop level) if the trade runs its course through MaxL bars without getting stopped (producing Unrealized P/L). The EOT line and the cloud fill print in green when the trade’s outcome is profitable and in red when it is not. If the trade was closed after breaching the stop, the line appears brighter. Show Opportunity&Risk Cloud : Displays the maximum opportunity/risk that was present during the trade, i.e. the maximum and minimum prices reached. Background Color Scheme : Allows you to choose between 3 different color schemes for the background gradients, to accommodate different types of chart background/candles. Select “None” if you don’t want a background. Background source : Determines what value will be used to generate the different intensities of the gradient. You can choose trade length (brighter is shorter), Trade P&L (brighter is higher) or the number of times the target was reached during simulation (brighter is higher). The default is Trade Length. Entry strat : The check box besides the title does nothing. The default strat is All bars, meaning a trade will be simulated from all bars not excluded by the filters where a MaxL bars future exists. For fun, I’ve included a pseudo-random entry strat (an indirect way of changing the seed is to vary the starting date of the simulation). Show Filter State : Displays areas where the combination of filters you have selected are allowing entries. Filtering occurs as per your selection(s), whether the state is displayed or not. The effect of multiple selections is additive. The filters are: 1. Bar direction: Longs will only be entered if close>open and vice versa. 2. Rising Volume: Applies to both long and shorts. 3. Rising/falling MA of the length you choose over the number of bars you choose. 4. Custom indicator: You can feed your own filtering signal through this from another indicator. It must produce a signal of 1 to allow long entries and 0 to allow shorts. Show Entry Stops : 1. Multiple of user-defined length ATR. 2. Fixed percentage. 3. Fixed value. All entry stops are calculated using the entry fill price as a reference. The fill price is calculated from the current bar’s open, to which slippage is added if configured. This simulates the case where the strategy issued the entry signal on the previous bar for it to be executed at the next bar’s open. The entry stop remains active until the in-trade stop becomes the more aggressive of the two stops. From then on, the entry stop will be ignored, unless a bar close breaches the in-trade stop, in which case the stop will be reset with a new entry stop and the process repeats. Show In-trade stops : Displays in bright red the selected in-trade stop (be sure to read the note in this section about them). 1. ATR multiple: added/subtracted from the average of the two previous bars minimum/maximum of open/close. 2. A trailing stop with a deviation expressed as a multiple of entry stop (X). 3. A fixed percentage trailing stop. Trailing stops deviations are measured from the highest/lowest high/low reached during the trade. Note: There is a twist with the in-trade stops. It’s that for any given bar, its in-trade stop can hold multiple values, as each successive pass of the advancing simulation loops goes over it from a different entry points. What is printed is the stop from the loop that ended on that bar, which may have nothing to do with other instances of the trade’s in-trade stop for the same bar when visited from other starting points in previous simulations. There is just no practical way to print all stop values that were used for any given bar. While the printed entry stops are the actual ones used on each bar, the in-trade stops shown are merely the last instance used among many. Include Slippage : if checked, slippage will be added/subtracted from order price to yield the fill price. Slippage is in percentage. If you choose to include slippage in the simulations, remember to adjust it by considering the liquidity of the markets and the time frame you’ll be analyzing. Include Fees : if checked, fees will be subtracted/added to both realized an unrealized trade profits/losses. Fees are in percentage. The default fees work well for crypto markets but will need adjusting for others—especially in Forex. Remember to modify them accordingly as they can have a major impact on results. Both fees and slippage are included to remind us of their importance, even if the global numbers produced by the indicator are not representative of a real trading scenario composed of sequential trades. Date Range filtering : the usual. Just note that the checkbox has to be selected for date filtering to activate. DATA WINDOW Most of the information produced by this indicator is made available in the Data Window, which you bring up by using the icon below the Watchlist and Alerts buttons at the right of the TV UI. Here’s what’s there. Some of the information presented in the Data Window is standard trade data; other values are not so standard; e. g. the notions of managed opportunity and risk and Target:Stop Travel ratio. The interplay between all the values provided by Great Expectations is inherently complex, even for a static set of entry/filter/exit strats. During the constant updating which the habitual process of progressive refinement in building strategies that is the lot of strategy modelers entails, another level of complexity is no doubt added to the analysis of this indicator’s values. While I don’t want to sound like Wolfram presenting A New Kind of Science , I do believe that if you are a serious strategy modeler and spend the time required to get used to using all the information this indicator makes available, you may find it useful. Trade Information Entry Order : This is the open of the bar where simulation starts. We suppose that an entry signal was generated at the previous bar. Entry Fill (including slip.) : The actual entry price, including slippage. This is the base price from which other values will be calculated. Exit Order : When a stop is breached, an exit order is executed from the close of the bar that breached the stop. While there is no “In-trade stop” value included in the Data Window (other than the End of trade Stop previously discussed), this “Exit Order” value is how we can know the level where the trade was stopped during the simulation. The “Trade Length” value will then show the bar where the stop was breached. Exit Fill (including slip.) : When the exit order is simulated, slippage is added to the order level to create the fill. Chart: Target : This is the target calculated at the beginning of the simulation. This value also appear on the chart in teal. It is controlled by the multiple of X defined under the “Show Target” checkbox in the Inputs. Chart: Entry Stop : This value also appears on the chart (the red dots under points where a trade was simulated). Its value is controlled by the Entry Strat chosen in the Inputs. X (% Fill, including Fees) and X (currency) : This is the stop’s amplitude (Entry Fill – Entry Stop) + Fees. It represents the risk incurred upon entry and will be used to express P&L. We will show R expressed in both a percentage of the Entry Fill level (this value), and currency (the next value). This value represents the risk in the risk:reward ratio and is considered to be a unit of 1 so that RR can be expressed as a single value (i.e. “2” actually meaning “1:2”). Trade Length : If trade was stopped, it’s the number of bars elapsed until then. The trade is then considered “Closed”. If the trade ends without being stopped (there is no profit-taking strat implemented, so the stop is the only exit strat), then the trade is “Open”, the length is MaxL and it will show in orange. Otherwise the value will print in green/red to reflect if the trade is winning/losing. P&L (X) : The P&L of the trade, expressed as a multiple of X, which takes into account fees paid at entry and exit. Given our default target setting at 2 units of “X”, a trade that closes at its target will have produced a P&L of +2.0, i.e. twice the value of X (not counting fees paid at exit ). A trade that gets stopped late 50% further that the entry stop’s level will produce a P&L of -1.5X. P&L (currency, including Fees) : same value as above, but expressed in currency. Target first reached at bar : If price closed above the target during the trade (even if it occurs after the trade was stopped), this will show when. This value will be used in calculating our TST ratio. Times Stop/Target reached in sim. : Includes all occurrences during the complete simulation loop. Opportunity (X) : The highest/lowest price reached during a simulation, i.e. the maximum opportunity encountered, whether the trade was previously stopped or not, expressed as a multiple of X. Risk (X) : The lowest/highest price reached during a simulation, i.e. the maximum risk encountered, whether the trade was previously stopped or not, expressed as a multiple of X. Risk:Opportunity : The greater this ratio, the greater Opportunity is, compared to Risk. Managed Opportunity (%) : The portion of Opportunity that was captured by the highest/low stop position, even if it occurred after a previous stop closed the trade. Managed Risk (%) : The portion of risk that was protected by the lowest/highest stop position, even if it occurred after a previous stop closed the trade. When this value is greater than 100%, it means the trade’s stop is protecting more than the maximum risk, which is frequent. You will, however, never see close to those values for the Managed Opportunity value, since the stop would have to be higher than the Maximum opportunity. It is much easier to alleviate the risk than it is to lock in profits. Managed Risk:Opportunity : The ratio of the two preceding values. Managed Opp. vs. Risk : The Managed Opportunity minus the Managed Risk. When it is negative, which is most often is, it means your strat is protecting a greater portion of the risk than it captures opportunity. Global Numbers Win Rate(%) : Percentage of winning trades over all entries. Open trades are considered winning if their last stop/close (as per user selection) locks in profits. Avg X%, Avg X (currency) : Averages of previously described values:. Avg Profitability/Trade (APPT) : This measures expectation using: Average Profitability Per Trade = (Probability of Win × Average Win) − (Probability of Loss × Average Loss) . It quantifies the average expectation/trade, which RR alone can’t do, as the probabilities of each outcome (win/lose) must also be used to calculate expectancy. The APPT combine the RR with the win rate to yield the true expectancy of a strategy. In my usual way of expressing risk with X, APPT is the equivalent of the average P&L per trade expressed in X. An APPT of -1.5 means that we lose on average 1.5X/trade. Equity (X), Equity (currency) : The cumulative result of all trade outcomes, expressed as a multiple of X. Multiplied by the Average X in currency, this yields the Equity in currency. Risk:Opportunity, Managed Risk:Opportunity, Managed Opp. vs. Risk : The global values of the ones previously described. Avg Trade Length (TL) : One of the most important values derived by going through all the simulations. Again, it is composed of either the length of stopped trades, or MaxL when the trade isn’t stopped (open). This value can help systems modelers shape the characteristics of the components they use to build their strategies. Avg Closed Win TL and Avg Closed Lose TL : The average lengths of winning/losing trades that were stopped. Target reached? Avg bars to Stop and Target reached? Avg bars to Target : For the trades where the target was reached at some point in the simulation, the number of bars to the first point where the stop was breached and where the target was reached, respectively. These two values are used to calculate the next value. TST (Target:Stop Travel Ratio) : This tracks the ratio between the two preceding values (Bars to first stop/Bars to first target), but only for trades where the target was reached somewhere in the loop. A ratio of 2 means targets are reached twice as fast as stops. The next values of this section are counts or percentages and are self-explanatory. Chart Plots Contains chart plots of values already describes. NOTES Optimization/Overfitting: There is a fine line between optimizing and overfitting. Tools like this indicator can lead unsuspecting modelers down a path of overfitting that often turns strategies into over-specialized beasts that do not perform elegantly when confronted to the real-world. Proven testing strategies like walk forward analysis will go a long way in helping modelers alleviate this risk. Input tuning: Because the results generated by the indicator will vary with the parameters used in the active entry, filtering and exit strats, it’s important to realize that although it may be fun at first, just slapping the default settings on a chart and time frame will not yield optimal nor reliable results. While using ATR as often as possible (as I do in this indicator) is a good way to make strat parametrization adaptable, it is not a foolproof solution. There is no data for the last MaxL bars of the chart, since not enough trade future has elapsed to run a simulation from MaxL bars back. Modifying the code: I have tried to structure the code modularly, even if that entails a larger code base, so that you can adapt it to your needs. I’ve included a few token components in each of the placeholders designed for entry strategies, filters, entry stops and in-trade stops. This will hopefully make it easier to add your own. In the same spirit, I have also commented liberally. You will find in the code many instances of standard trade management tasks that can be lifted to code TV strategies where, as I do in mine, you manage everything yourself and don’t rely on built-in Pine strategy functions to act on your trades. Enjoy! THANKS To @scarf who showed me how plotchar() could be used to plot values without ruining scale. To @glaz for the suggestion to include a Chandelier stop strat; I will. To @simpelyfe for the idea of using an indicator input for the filters (if some day TV lets us use more than one, it will be useful in other modules of the indicator). To @RicardoSantos for the random generator used in the random entry strat. To all scripters publishing open source on TradingView; their code is the best way to learn. To my trading buddies Irving and Bruno; who showed me way back how pro traders get it done.
Penunjuk Pine Script®
oleh LucF
7
The Engulfing Liquidity Signal with Adjustable Trailing StopEngulfing Liquidity Signal with Adjustable Trailing Stop This strategy is designed to enter long trades based on the Engulfing Liquidity Signal combined with a Trailing Stop. The strategy uses custom volume analysis and price action to detect potential market opportunities. The Trailing Stop is adjustable, allowing traders to customize the distance at which the stop will trail the price. Key Features: Engulfing Liquidity Signal: The strategy enters a trade when the market shows signs of strong liquidity and price action, typically when there is a strong reversal signal (bullish engulfing) accompanied by higher volume. Trailing Stop: A dynamic exit strategy that locks in profits by trailing the stop level behind the highest price achieved since the trade entry. This prevents the position from being closed prematurely while still protecting profits if the market reverses. Customizable Trailing Stop: Users can adjust the trailing stop percentage via the settings. This allows for greater flexibility in how closely the stop will trail the price. No Fixed Take Profit: The strategy uses only the trailing stop, ensuring that profits are maximized based on price action without a fixed profit target. How the Strategy Works: Buy Signal (LongC): The strategy triggers a buy signal when a bullish engulfing pattern occurs, and the liquidity conditions align (i.e., the volume is increasing and price action shows signs of a potential reversal). The strategy enters a long position when the signal conditions are met. Trailing Stop Logic: Once the trade is initiated, a trailing stop is applied. The stop level follows the highest price achieved since entry, trailing the price based on a user-defined percentage. The stop level adjusts upward as the price increases, locking in profits. If the price reverses and hits the trailing stop, the trade is closed. The trailing stop is dynamic, meaning it moves only in the direction of profit, but it will not move lower once it has been set. Sell Signal (ShortC): The position will also be closed if a sell signal (ShortC) is generated. This ensures that the strategy exits the trade when a potential reversal is detected in the market. No Fixed Take Profit: The strategy does not use a fixed take profit level. Instead, the profit is managed entirely by the trailing stop, which ensures that positions remain open as long as the market is moving in favor of the trade, allowing the position to capture the maximum possible profit. Settings: Trailing Stop Percentage: The user can adjust the trailing stop distance by setting a percentage value between 10% and 100%. This controls how tightly or loosely the trailing stop will follow the price. Benefits: Maximized Profits: By using a trailing stop, the strategy aims to capture as much profit as possible without prematurely exiting trades. Customizable: The adjustable trailing stop allows traders to tailor the strategy to their risk tolerance and market conditions. Simple & Effective: The strategy is straightforward, relying on price action and volume signals, making it easy to understand and implement. Ideal Use Case: This strategy is suitable for traders who prefer to let their profits run and manage risk with a trailing stop. It is particularly useful in trending markets where the price continues to move in one direction for an extended period. By using a trailing stop, the strategy allows you to stay in the market and capture large moves while protecting profits. This strategy provides an excellent combination of automated trade management with a Trailing Stop and Engulfing Liquidity Signal, making it a solid choice for traders seeking to automate their trades with customizable risk management.
Strategi Pine Script®
oleh khbajako1981
Hellenic EMA Matrix - PremiumHellenic EMA Matrix - Alpha Omega Premium Complete User Guide Table of Contents Introduction Indicator Philosophy Mathematical Constants EMA Types Settings Trading Signals Visualization Usage Strategies FAQ Introduction Hellenic EMA Matrix is a premium indicator based on mathematical constants of nature: Phi (Phi - Golden Ratio), Pi (Pi), e (Euler's number). The indicator uses these universal constants to create dynamic EMAs that adapt to the natural rhythms of the market. Key Features: 6 EMA types based on mathematical constants Premium visualization with Neon Glow and Gradient Clouds Automatic Fast/Mid/Slow EMA sorting STRONG signals for powerful trends Pulsing Ribbon Bar for instant trend assessment Works on all timeframes (M1 - MN) Indicator Philosophy Why Mathematical Constants? Traditional EMAs use arbitrary periods (9, 21, 50, 200). Hellenic Matrix goes further, using universal mathematical constants found in nature: Phi (1.618) - Golden Ratio: galaxy spirals, seashells, human body proportions Pi (3.14159) - Pi: circles, waves, cycles e (2.71828) - Natural logarithm base: exponential growth, radioactive decay Markets are also a natural system composed of millions of participants. Using mathematical constants allows tuning into the natural rhythms of market cycles. Mathematical Constants Phi (Phi) - Golden Ratio Phi = 1.618033988749895 Properties: Phi² = Phi + 1 = 2.618 Phi³ = 4.236 Phi⁴ = 6.854 Application: Ideal for trending movements and Fibonacci corrections Pi (Pi) - Pi Number Pi = 3.141592653589793 Properties: 2Pi = 6.283 (full circle) 3Pi = 9.425 4Pi = 12.566 Application: Excellent for cyclical markets and wave structures e (Euler) - Euler's Number e = 2.718281828459045 Properties: e² = 7.389 e³ = 20.085 e⁴ = 54.598 Application: Suitable for exponential movements and volatile markets EMA Types 1. Phi (Phi) - Golden Ratio EMA Description: EMA based on the golden ratio Period Formula: Period = Phi^n × Base Multiplier Parameters: Phi Power Level (1-8): Power of Phi Phi¹ = 1.618 → ~16 period (with Base=10) Phi² = 2.618 → ~26 period Phi³ = 4.236 → ~42 period (recommended) Phi⁴ = 6.854 → ~69 period Recommendations: Phi² or Phi³ for day trading Phi⁴ or Phi⁵ for swing trading Works excellently as Fast EMA 2. Pi (Pi) - Circular EMA Description: EMA based on Pi for cyclical movements Period Formula: Period = Pi × Multiple × Base Multiplier Parameters: Pi Multiple (1-10): Pi multiplier 1Pi = 3.14 → ~31 period (with Base=10) 2Pi = 6.28 → ~63 period (recommended) 3Pi = 9.42 → ~94 period Recommendations: 2Pi ideal as Mid or Slow EMA Excellently identifies cycles and waves Use on volatile markets (crypto, forex) 3. e (Euler) - Natural EMA Description: EMA based on natural logarithm Period Formula: Period = e^n × Base Multiplier Parameters: e Power Level (1-6): Power of e e¹ = 2.718 → ~27 period (with Base=10) e² = 7.389 → ~74 period (recommended) e³ = 20.085 → ~201 period Recommendations: e² works excellently as Slow EMA Ideal for stocks and indices Filters noise well on lower timeframes 4. Delta (Delta) - Adaptive EMA Description: Adaptive EMA that changes period based on volatility Period Formula: Period = Base Period × (1 + (Volatility - 1) × Factor) Parameters: Delta Base Period (5-200): Base period (default 20) Delta Volatility Sensitivity (0.5-5.0): Volatility sensitivity (default 2.0) How it works: During low volatility → period decreases → EMA reacts faster During high volatility → period increases → EMA smooths noise Recommendations: Works excellently on news and sharp movements Use as Fast EMA for quick adaptation Sensitivity 2.0-3.0 for crypto, 1.0-2.0 for stocks 5. Sigma (Sigma) - Composite EMA Description: Composite EMA combining multiple active EMAs Composition Methods: Weighted Average (default): Sigma = (Phi + Pi + e + Delta) / 4 Simple average of all active EMAs Geometric Mean: Sigma = fourth_root(Phi × Pi × e × Delta) Geometric mean (more conservative) Harmonic Mean: Sigma = 4 / (1/Phi + 1/Pi + 1/e + 1/Delta) Harmonic mean (more weight to smaller values) Recommendations: Enable for additional confirmation Use as Mid EMA Weighted Average - most universal method 6. Lambda (Lambda) - Wave EMA Description: Wave EMA with sinusoidal period modulation Period Formula: Period = Base Period × (1 + Amplitude × sin(2Pi × bar / Frequency)) Parameters: Lambda Base Period (10-200): Base period Lambda Wave Amplitude (0.1-2.0): Wave amplitude Lambda Wave Frequency (10-200): Wave frequency in bars How it works: Period pulsates sinusoidally Creates wave effect following market cycles Recommendations: Experimental EMA for advanced users Works well on cyclical markets Frequency = 50 for day trading, 100+ for swing Settings Matrix Core Settings Base Multiplier (1-100) Multiplies all EMA periods Base = 1: Very fast EMAs (Phi³ = 4, 2Pi = 6, e² = 7) Base = 10: Standard (Phi³ = 42, 2Pi = 63, e² = 74) Base = 20: Slow EMAs (Phi³ = 85, 2Pi = 126, e² = 148) Recommendations by timeframe: M1-M5: Base = 5-10 M15-H1: Base = 10-15 (recommended) H4-D1: Base = 15-25 W1-MN: Base = 25-50 Matrix Source Data source selection for EMA calculation: close - closing price (standard) open - opening price high - high low - low hl2 - (high + low) / 2 hlc3 - (high + low + close) / 3 ohlc4 - (open + high + low + close) / 4 When to change: hlc3 or ohlc4 for smoother signals high for aggressive longs low for aggressive shorts Manual EMA Selection Critically important setting! Determines which EMAs are used for signal generation. Use Manual Fast/Slow/Mid Selection Enabled (default): You select EMAs manually Disabled: Automatic selection by periods Fast EMA Fast EMA - reacts first to price changes Recommendations: Phi Golden (recommended) - universal choice Delta Adaptive - for volatile markets Must be fastest (smallest period) Slow EMA Slow EMA - determines main trend Recommendations: Pi Circular (recommended) - excellent trend filter e Natural - for smoother trend Must be slowest (largest period) Mid EMA Mid EMA - additional signal filter Recommendations: e Natural (recommended) - excellent middle level Pi Circular - alternative None - for more frequent signals (only 2 EMAs) IMPORTANT: The indicator automatically sorts selected EMAs by their actual periods: Fast = EMA with smallest period Mid = EMA with middle period Slow = EMA with largest period Therefore, you can select any combination - the indicator will arrange them correctly! Premium Visualization Neon Glow Enable Neon Glow for EMAs - adds glowing effect around EMA lines Glow Strength: Light - subtle glow Medium (recommended) - optimal balance Strong - bright glow (may be too bright) Effect: 2 glow layers around each EMA for 3D effect Gradient Clouds Enable Gradient Clouds - fills space between EMAs with gradient Parameters: Cloud Transparency (85-98): Cloud transparency 95-97 (recommended) Higher = more transparent Dynamic Cloud Intensity - automatically changes transparency based on EMA distance Cloud Colors: Phi-Pi Cloud: Blue - when Pi above Phi (bullish) Gold - when Phi above Pi (bearish) Pi-e Cloud: Green - when e above Pi (bullish) Blue - when Pi above e (bearish) 2 layers for volumetric effect Pulsing Ribbon Bar Enable Pulsing Indicator Bar - pulsing strip at bottom/top of chart Parameters: Ribbon Position: Top / Bottom (recommended) Pulse Speed: Slow / Medium (recommended) / Fast Symbols and colors: Green filled square - STRONG BULLISH Pink filled square - STRONG BEARISH Blue hollow square - Bullish (regular) Red hollow square - Bearish (regular) Purple rectangle - Neutral Effect: Pulsation with sinusoid for living market feel Signal Bar Highlights Enable Signal Bar Highlights - highlights bars with signals Parameters: Highlight Transparency (88-96): Highlight transparency Highlight Style: Light Fill (recommended) - bar background fill Thin Line - bar outline only Highlights: Golden Cross - green Death Cross - pink STRONG BUY - green STRONG SELL - pink Show Greek Labels Shows Greek alphabet letters on last bar: Phi - Phi EMA (gold) Pi - Pi EMA (blue) e - Euler EMA (green) Delta - Delta EMA (purple) Sigma - Sigma EMA (pink) When to use: For education or presentations Show Old Background Old background style (not recommended): Green background - STRONG BULLISH Pink background - STRONG BEARISH Blue background - Bullish Red background - Bearish Not recommended - use new Gradient Clouds and Pulsing Bar Info Table Show Info Table - table with indicator information Parameters: Position: Top Left / Top Right (recommended) / Bottom Left / Bottom Right Size: Tiny / Small (recommended) / Normal / Large Table contents: EMA list - periods and current values of all active EMAs Effects - active visual effects TREND - current trend state: STRONG UP - strong bullish STRONG DOWN - strong bearish Bullish - regular bullish Bearish - regular bearish Neutral - neutral Momentum % - percentage deviation of price from Fast EMA Setup - current Fast/Slow/Mid configuration Trading Signals Show Golden/Death Cross Golden Cross - Fast EMA crosses Slow EMA from below (bullish signal) Death Cross - Fast EMA crosses Slow EMA from above (bearish signal) Symbols: Yellow dot "GC" below - Golden Cross Dark red dot "DC" above - Death Cross Show STRONG Signals STRONG BUY and STRONG SELL - the most powerful indicator signals Conditions for STRONG BULLISH: EMA Alignment: Fast > Mid > Slow (all EMAs aligned) Trend: Fast > Slow (clear uptrend) Distance: EMAs separated by minimum 0.15% Price Position: Price above Fast EMA Fast Slope: Fast EMA rising Slow Slope: Slow EMA rising Mid Trending: Mid EMA also rising (if enabled) Conditions for STRONG BEARISH: Same but in reverse Visual display: Green label "STRONG BUY" below bar Pink label "STRONG SELL" above bar Difference from Golden/Death Cross: Golden/Death Cross = crossing moment (1 bar) STRONG signal = sustained trend (lasts several bars) IMPORTANT: After fixes, STRONG signals now: Work on all timeframes (M1 to MN) Don't break on small retracements Work with any Fast/Mid/Slow combination Automatically adapt thanks to EMA sorting Show Stop Loss/Take Profit Automatic SL/TP level calculation on STRONG signal Parameters: Stop Loss (ATR) (0.5-5.0): ATR multiplier for stop loss 1.5 (recommended) - standard 1.0 - tight stop 2.0-3.0 - wide stop Take Profit R:R (1.0-5.0): Risk/reward ratio 2.0 (recommended) - standard (risk 1.5 ATR, profit 3.0 ATR) 1.5 - conservative 3.0-5.0 - aggressive Formulas: LONG: Stop Loss = Entry - (ATR × Stop Loss ATR) Take Profit = Entry + (ATR × Stop Loss ATR × Take Profit R:R) SHORT: Stop Loss = Entry + (ATR × Stop Loss ATR) Take Profit = Entry - (ATR × Stop Loss ATR × Take Profit R:R) Visualization: Red X - Stop Loss Green X - Take Profit Levels remain active while STRONG signal persists Trading Signals Signal Types 1. Golden Cross Description: Fast EMA crosses Slow EMA from below Signal: Beginning of bullish trend How to trade: ENTRY: On bar close with Golden Cross STOP: Below local low or below Slow EMA TARGET: Next resistance level or 2:1 R:R Strengths: Simple and clear Works well on trending markets Clear entry point Weaknesses: Lags (signal after movement starts) Many false signals in ranging markets May be late on fast moves Optimal timeframes: H1, H4, D1 2. Death Cross Description: Fast EMA crosses Slow EMA from above Signal: Beginning of bearish trend How to trade: ENTRY: On bar close with Death Cross STOP: Above local high or above Slow EMA TARGET: Next support level or 2:1 R:R Application: Mirror of Golden Cross 3. STRONG BUY Description: All EMAs aligned + trend + all EMAs rising Signal: Powerful bullish trend How to trade: ENTRY: On bar close with STRONG BUY or on pullback to Fast EMA STOP: Below Fast EMA or automatic SL (if enabled) TARGET: Automatic TP (if enabled) or by levels TRAILING: Follow Fast EMA Entry strategies: Aggressive: Enter immediately on signal Conservative: Wait for pullback to Fast EMA, then enter on bounce Pyramiding: Add positions on pullbacks to Mid EMA Position management: Hold while STRONG signal active Exit on STRONG SELL or Death Cross appearance Move stop behind Fast EMA Strengths: Most reliable indicator signal Doesn't break on pullbacks Catches large moves Works on all timeframes Weaknesses: Appears less frequently than other signals Requires confirmation (multiple conditions) Optimal timeframes: All (M5 - D1) 4. STRONG SELL Description: All EMAs aligned down + downtrend + all EMAs falling Signal: Powerful bearish trend How to trade: Mirror of STRONG BUY Visual Signals Pulsing Ribbon Bar Quick market assessment at a glance: Symbol Color State Filled square Green STRONG BULLISH Filled square Pink STRONG BEARISH Hollow square Blue Bullish Hollow square Red Bearish Rectangle Purple Neutral Pulsation: Sinusoidal, creates living effect Signal Bar Highlights Bars with signals are highlighted: Green highlight: STRONG BUY or Golden Cross Pink highlight: STRONG SELL or Death Cross Gradient Clouds Colored space between EMAs shows trend strength: Wide clouds - strong trend Narrow clouds - weak trend or consolidation Color change - trend change Info Table Quick reference in corner: TREND: Current state (STRONG UP, Bullish, Neutral, Bearish, STRONG DOWN) Momentum %: Movement strength Effects: Active visual effects Setup: Fast/Slow/Mid configuration Usage Strategies Strategy 1: "Golden Trailing" Idea: Follow STRONG signals using Fast EMA as trailing stop Settings: Fast: Phi Golden (Phi³) Mid: Pi Circular (2Pi) Slow: e Natural (e²) Base Multiplier: 10 Timeframe: H1, H4 Entry rules: Wait for STRONG BUY Enter on bar close or on pullback to Fast EMA Stop below Fast EMA Management: Hold position while STRONG signal active Move stop behind Fast EMA daily Exit on STRONG SELL or Death Cross Take Profit: Partially close at +2R Trail remainder until exit signal For whom: Swing traders, trend followers Pros: Catches large moves Simple rules Emotionally comfortable Cons: Requires patience Possible extended drawdowns on pullbacks Strategy 2: "Scalping Bounces" Idea: Scalp bounces from Fast EMA during STRONG trend Settings: Fast: Delta Adaptive (Base 15, Sensitivity 2.0) Mid: Phi Golden (Phi²) Slow: Pi Circular (2Pi) Base Multiplier: 5 Timeframe: M5, M15 Entry rules: STRONG signal must be active Wait for price pullback to Fast EMA Enter on bounce (candle closes above/below Fast EMA) Stop behind local extreme (15-20 pips) Take Profit: +1.5R or to Mid EMA Or to next level For whom: Active day traders Pros: Many signals Clear entry point Quick profits Cons: Requires constant monitoring Not all bounces work Requires discipline for frequent trading Strategy 3: "Triple Filter" Idea: Enter only when all 3 EMAs and price perfectly aligned Settings: Fast: Phi Golden (Phi³) Mid: e Natural (e²) Slow: Pi Circular (3Pi) Base Multiplier: 15 Timeframe: H4, D1 Entry rules (LONG): STRONG BUY active Price above all three EMAs Fast > Mid > Slow (all aligned) All EMAs rising (slope up) Gradient Clouds wide and bright Entry: On bar close meeting all conditions Or on next pullback to Fast EMA Stop: Below Mid EMA or -1.5 ATR Take Profit: First target: +3R Second target: next major level Trailing: Mid EMA For whom: Conservative swing traders, investors Pros: Very reliable signals Minimum false entries Large profit potential Cons: Rare signals (2-5 per month) Requires patience Strategy 4: "Adaptive Scalper" Idea: Use only Delta Adaptive EMA for quick volatility reaction Settings: Fast: Delta Adaptive (Base 10, Sensitivity 3.0) Mid: None Slow: Delta Adaptive (Base 30, Sensitivity 2.0) Base Multiplier: 3 Timeframe: M1, M5 Feature: Two different Delta EMAs with different settings Entry rules: Golden Cross between two Delta EMAs Both Delta EMAs must be rising/falling Enter on next bar Stop: 10-15 pips or below Slow Delta EMA Take Profit: +1R to +2R Or Death Cross For whom: Scalpers on cryptocurrencies and forex Pros: Instant volatility adaptation Many signals on volatile markets Quick results Cons: Much noise on calm markets Requires fast execution High commissions may eat profits Strategy 5: "Cyclical Trader" Idea: Use Pi and Lambda for trading cyclical markets Settings: Fast: Pi Circular (1Pi) Mid: Lambda Wave (Base 30, Amplitude 0.5, Frequency 50) Slow: Pi Circular (3Pi) Base Multiplier: 10 Timeframe: H1, H4 Entry rules: STRONG signal active Lambda Wave EMA synchronized with trend Enter on bounce from Lambda Wave For whom: Traders of cyclical assets (some altcoins, commodities) Pros: Catches cyclical movements Lambda Wave provides additional entry points Cons: More complex to configure Not for all markets Lambda Wave may give false signals Strategy 6: "Multi-Timeframe Confirmation" Idea: Use multiple timeframes for confirmation Scheme: Higher TF (D1): Determine trend direction (STRONG signal) Middle TF (H4): Wait for STRONG signal in same direction Lower TF (M15): Look for entry point (Golden Cross or bounce from Fast EMA) Settings for all TFs: Fast: Phi Golden (Phi³) Mid: e Natural (e²) Slow: Pi Circular (2Pi) Base Multiplier: 10 Rules: All 3 TFs must show one trend Entry on lower TF Stop by lower TF Target by higher TF For whom: Serious traders and investors Pros: Maximum reliability Large profit targets Minimum false signals Cons: Rare setups Requires analysis of multiple charts Experience needed Practical Tips DOs Use STRONG signals as primary - they're most reliable Let signals develop - don't exit on first pullback Use trailing stop - follow Fast EMA Combine with levels - S/R, Fibonacci, volumes Test on demo before real Adjust Base Multiplier for your timeframe Enable visual effects - they help see the picture Use Info Table - quick situation assessment Watch Pulsing Bar - instant state indicator Trust auto-sorting of Fast/Mid/Slow DON'Ts Don't trade against STRONG signal - trend is your friend Don't ignore Mid EMA - it adds reliability Don't use too small Base Multiplier on higher TFs Don't enter on Golden Cross in range - check for trend Don't change settings during open position Don't forget risk management - 1-2% per trade Don't trade all signals in row - choose best ones Don't use indicator in isolation - combine with Price Action Don't set too tight stops - let trade breathe Don't over-optimize - simplicity = reliability Optimal Settings by Asset US Stocks (SPY, AAPL, TSLA) Recommendation: Fast: Phi Golden (Phi³) Mid: e Natural (e²) Slow: Pi Circular (2Pi) Base: 10-15 Timeframe: H4, D1 Features: Use on daily for swing STRONG signals very reliable Works well on trending stocks Forex (EUR/USD, GBP/USD) Recommendation: Fast: Delta Adaptive (Base 15, Sens 2.0) Mid: Phi Golden (Phi²) Slow: Pi Circular (2Pi) Base: 8-12 Timeframe: M15, H1, H4 Features: Delta Adaptive works excellently on news Many signals on M15-H1 Consider spreads Cryptocurrencies (BTC, ETH, altcoins) Recommendation: Fast: Delta Adaptive (Base 10, Sens 3.0) Mid: Pi Circular (2Pi) Slow: e Natural (e²) Base: 5-10 Timeframe: M5, M15, H1 Features: High volatility - adaptation needed STRONG signals can last days Be careful with scalping on M1-M5 Commodities (Gold, Oil) Recommendation: Fast: Pi Circular (1Pi) Mid: Phi Golden (Phi³) Slow: Pi Circular (3Pi) Base: 12-18 Timeframe: H4, D1 Features: Pi works excellently on cyclical commodities Gold responds especially well to Phi Oil volatile - use wide stops Indices (S&P500, Nasdaq, DAX) Recommendation: Fast: Phi Golden (Phi³) Mid: e Natural (e²) Slow: Pi Circular (2Pi) Base: 15-20 Timeframe: H4, D1, W1 Features: Very trending instruments STRONG signals last weeks Good for position trading Alerts The indicator supports 6 alert types: 1. Golden Cross Message: "Hellenic Matrix: GOLDEN CROSS - Fast EMA crossed above Slow EMA - Bullish trend starting!" When: Fast EMA crosses Slow EMA from below 2. Death Cross Message: "Hellenic Matrix: DEATH CROSS - Fast EMA crossed below Slow EMA - Bearish trend starting!" When: Fast EMA crosses Slow EMA from above 3. STRONG BULLISH Message: "Hellenic Matrix: STRONG BULLISH SIGNAL - All EMAs aligned for powerful uptrend!" When: All conditions for STRONG BUY met (first bar) 4. STRONG BEARISH Message: "Hellenic Matrix: STRONG BEARISH SIGNAL - All EMAs aligned for powerful downtrend!" When: All conditions for STRONG SELL met (first bar) 5. Bullish Ribbon Message: "Hellenic Matrix: BULLISH RIBBON - EMAs aligned for uptrend" When: EMAs aligned bullish + price above Fast EMA (less strict condition) 6. Bearish Ribbon Message: "Hellenic Matrix: BEARISH RIBBON - EMAs aligned for downtrend" When: EMAs aligned bearish + price below Fast EMA (less strict condition) How to Set Up Alerts: Open indicator on chart Click on three dots next to indicator name Select "Create Alert" In "Condition" field select needed alert: Golden Cross Death Cross STRONG BULLISH STRONG BEARISH Bullish Ribbon Bearish Ribbon Configure notification method: Pop-up in browser Email SMS (in Premium accounts) Push notifications in mobile app Webhook (for automation) Select frequency: Once Per Bar Close (recommended) - once on bar close Once Per Bar - during bar formation Only Once - only first time Click "Create" Tip: Create separate alerts for different timeframes and instruments FAQ 1. Why don't STRONG signals appear? Possible reasons: Incorrect Fast/Mid/Slow order Solution: Indicator automatically sorts EMAs by periods, but ensure selected EMAs have different periods Base Multiplier too large Solution: Reduce Base to 5-10 on lower timeframes Market in range Solution: STRONG signals appear only in trends - this is normal Too strict EMA settings Solution: Try classic combination: Phi³ / Pi×2 / e² with Base=10 Mid EMA too close to Fast or Slow Solution: Select Mid EMA with period between Fast and Slow 2. How often should STRONG signals appear? Normal frequency: M1-M5: 5-15 signals per day (very active markets) M15-H1: 2-8 signals per day H4: 3-10 signals per week D1: 2-5 signals per month W1: 2-6 signals per year If too many signals - market very volatile or Base too small If too few signals - market in range or Base too large 4. What are the best settings for beginners? Universal "out of the box" settings: Matrix Core: Base Multiplier: 10 Source: close Phi Golden: Enabled, Power = 3 Pi Circular: Enabled, Multiple = 2 e Natural: Enabled, Power = 2 Delta Adaptive: Enabled, Base = 20, Sensitivity = 2.0 Manual Selection: Fast: Phi Golden Mid: e Natural Slow: Pi Circular Visualization: Gradient Clouds: ON Neon Glow: ON (Medium) Pulsing Bar: ON (Medium) Signal Highlights: ON (Light Fill) Table: ON (Top Right, Small) Signals: Golden/Death Cross: ON STRONG Signals: ON Stop Loss: OFF (while learning) Timeframe for learning: H1 or H4 5. Can I use only one EMA? No, minimum 2 EMAs (Fast and Slow) for signal generation. Mid EMA is optional: With Mid EMA = more reliable but rarer signals Without Mid EMA = more signals but less strict filtering Recommendation: Start with 3 EMAs (Fast/Mid/Slow), then experiment 6. Does the indicator work on cryptocurrencies? Yes, works excellently! Especially good on: Bitcoin (BTC) Ethereum (ETH) Major altcoins (SOL, BNB, XRP) Recommended settings for crypto: Fast: Delta Adaptive (Base 10-15, Sensitivity 2.5-3.0) Mid: Pi Circular (2Pi) Slow: e Natural (e²) Base: 5-10 Timeframe: M15, H1, H4 Crypto market features: High volatility → use Delta Adaptive 24/7 trading → set alerts Sharp movements → wide stops 7. Can I trade only with this indicator? Technically yes, but NOT recommended. Best approach - combine with: Price Action - support/resistance levels, candle patterns Volume - movement strength confirmation Fibonacci - retracement and extension levels RSI/MACD - divergences and overbought/oversold Fundamental analysis - news, company reports Hellenic Matrix: Excellently determines trend and its strength Provides clear entry/exit points Doesn't consider fundamentals Doesn't see major levels 8. Why do Gradient Clouds change color? Color depends on EMA order: Phi-Pi Cloud: Blue - Pi EMA above Phi EMA (bullish alignment) Gold - Phi EMA above Pi EMA (bearish alignment) Pi-e Cloud: Green - e EMA above Pi EMA (bullish alignment) Blue - Pi EMA above e EMA (bearish alignment) Color change = EMA order change = possible trend change 9. What is Momentum % in the table? Momentum % = percentage deviation of price from Fast EMA Formula: Momentum = ((Close - Fast EMA) / Fast EMA) × 100 Interpretation: +0.5% to +2% - normal bullish momentum +2% to +5% - strong bullish momentum +5% and above - overheating (correction possible) -0.5% to -2% - normal bearish momentum -2% to -5% - strong bearish momentum -5% and below - oversold (bounce possible) Usage: Monitor momentum during STRONG signals Large momentum = don't enter (wait for pullback) Small momentum = good entry point 10. How to configure for scalping? Settings for scalping (M1-M5): Base Multiplier: 3-5 Source: close or hlc3 (smoother) Fast: Delta Adaptive (Base 8-12, Sensitivity 3.0) Mid: None (for more signals) Slow: Phi Golden (Phi²) or Pi Circular (1Pi) Visualization: - Gradient Clouds: ON (helps see strength) - Neon Glow: OFF (doesn't clutter chart) - Pulsing Bar: ON (quick assessment) - Signal Highlights: ON Signals: - Golden/Death Cross: ON - STRONG Signals: ON - Stop Loss: ON (1.0-1.5 ATR, R:R 1.5-2.0) Scalping rules: Trade only STRONG signals Enter on bounce from Fast EMA Tight stops (10-20 pips) Quick take profit (+1R to +2R) Don't hold through news 11. How to configure for long-term investing? Settings for investing (D1-W1): Base Multiplier: 20-30 Source: close Fast: Phi Golden (Phi³ or Phi⁴) Mid: e Natural (e²) Slow: Pi Circular (3Pi or 4Pi) Visualization: - Gradient Clouds: ON - Neon Glow: ON (Medium) - Everything else - to taste Signals: - Golden/Death Cross: ON - STRONG Signals: ON - Stop Loss: OFF (use percentage stop) Investing rules: Enter only on STRONG signals Hold while STRONG active (weeks/months) Stop below Slow EMA or -10% Take profit: by company targets or +50-100% Ignore short-term pullbacks 12. What if indicator slows down chart? Indicator is optimized, but if it slows: Disable unnecessary visual effects: Neon Glow: OFF (saves 8 plots) Gradient Clouds: ON but low quality Lambda Wave EMA: OFF (if not using) Reduce number of active EMAs: Sigma Composite: OFF Lambda Wave: OFF Leave only Phi, Pi, e, Delta Simplify settings: Pulsing Bar: OFF Greek Labels: OFF Info Table: smaller size 13. Can I use on different timeframes simultaneously? Yes! Multi-timeframe analysis is very powerful: Classic scheme: Higher TF (D1, W1) - determine global trend Wait for STRONG signal This is our trading direction Middle TF (H4, H1) - look for confirmation STRONG signal in same direction Precise entry zone Lower TF (M15, M5) - entry point Golden Cross or bounce from Fast EMA Precise stop loss Example: W1: STRONG BUY active (global uptrend) H4: STRONG BUY appeared (confirmation) M15: Wait for Golden Cross or bounce from Fast EMA → ENTRY Advantages: Maximum reliability Clear timeframe hierarchy Large targets 14. How does indicator work on news? Delta Adaptive EMA adapts excellently to news: Before news: Low volatility → Delta EMA becomes fast → pulls to price During news: Sharp volatility spike → Delta EMA slows → filters noise After news: Volatility normalizes → Delta EMA returns to normal Recommendations: Don't trade at news release moment (spreads widen) Wait for STRONG signal after news (2-5 bars) Use Delta Adaptive as Fast EMA for quick reaction Widen stops by 50-100% during important news Advanced Techniques Technique 1: "Divergences with EMA" Idea: Look for discrepancies between price and Fast EMA Bullish divergence: Price makes lower low Fast EMA makes higher low = Possible reversal up Bearish divergence: Price makes higher high Fast EMA makes lower high = Possible reversal down How to trade: Find divergence Wait for STRONG signal in divergence direction Enter on confirmation Technique 2: "EMA Tunnel" Idea: Use space between Fast and Slow EMA as "tunnel" Rules: Wide tunnel - strong trend, hold position Narrow tunnel - weak trend or consolidation, caution Tunnel narrowing - trend weakening, prepare to exit Tunnel widening - trend strengthening, can add Visually: Gradient Clouds show this automatically! Trading: Enter on STRONG signal (tunnel starts widening) Hold while tunnel wide Exit when tunnel starts narrowing Technique 3: "Wave Analysis with Lambda" Idea: Lambda Wave EMA creates sinusoid matching market cycles Setup: Lambda Base Period: 30 Lambda Wave Amplitude: 0.5 Lambda Wave Frequency: 50 (adjusted to asset cycle) How to find correct Frequency: Look at historical cycles (distance between local highs) Average distance = your Frequency Example: if highs every 40-60 bars, set Frequency = 50 Trading: Enter when Lambda Wave at bottom of sinusoid (growth potential) Exit when Lambda Wave at top (fall potential) Combine with STRONG signals Technique 4: "Cluster Analysis" Idea: When all EMAs gather in narrow cluster = powerful breakout soon Cluster signs: All EMAs (Phi, Pi, e, Delta) within 0.5-1% of each other Gradient Clouds almost invisible Price jumping around all EMAs Trading: Identify cluster (all EMAs close) Determine breakout direction (where more volume, higher TFs direction) Wait for breakout and STRONG signal Enter on confirmation Target = cluster size × 3-5 This is very powerful technique for big moves! Technique 5: "Sigma as Dynamic Level" Idea: Sigma Composite EMA = average of all EMAs = magnetic level Usage: Enable Sigma Composite (Weighted Average) Sigma works as dynamic support/resistance Price often returns to Sigma before trend continuation Trading: In trend: Enter on bounces from Sigma In range: Fade moves from Sigma (trade return to Sigma) On breakout: Sigma becomes support/resistance Risk Management Basic Rules 1. Position Size Conservative: 1% of capital per trade Moderate: 2% of capital per trade (recommended) Aggressive: 3-5% (only for experienced) Calculation formula: Lot Size = (Capital × Risk%) / (Stop in pips × Pip value) 2. Risk/Reward Ratio Minimum: 1:1.5 Standard: 1:2 (recommended) Optimal: 1:3 Aggressive: 1:5+ 3. Maximum Drawdown Daily: -3% to -5% Weekly: -7% to -10% Monthly: -15% to -20% Upon reaching limit → STOP trading until end of period Position Management Strategies 1. Fixed Stop Method: Stop below/above Fast EMA or local extreme DON'T move stop against position Can move to breakeven For whom: Beginners, conservative traders 2. Trailing by Fast EMA Method: Each day (or bar) move stop to Fast EMA level Position closes when price breaks Fast EMA Advantages: Stay in trend as long as possible Automatically exit on reversal For whom: Trend followers, swing traders 3. Partial Exit Method: 50% of position close at +2R 50% hold with trailing by Mid EMA or Slow EMA Advantages: Lock profit Leave position for big move Psychologically comfortable For whom: Universal method (recommended) 4. Pyramiding Method: First entry on STRONG signal (50% of planned position) Add 25% on pullback to Fast EMA Add another 25% on pullback to Mid EMA Overall stop below Slow EMA Advantages: Average entry price Reduce risk Increase profit in strong trends Caution: Works only in trends In range leads to losses For whom: Experienced traders Trading Psychology Correct Mindset 1. Indicator is a tool, not holy grail Indicator shows probability, not guarantee There will be losing trades - this is normal Important is series statistics, not one trade 2. Trust the system If STRONG signal appeared - enter Don't search for "perfect" moment Follow trading plan 3. Patience STRONG signals don't appear every day Better miss signal than enter against trend Quality over quantity 4. Discipline Always set stop loss Don't move stop against position Don't increase risk after losses Beginner Mistakes 1. "I know better than indicator" Indicator says STRONG BUY, but you think "too high, will wait for pullback" Result: miss profitable move Solution: Trust signals or don't use indicator 2. "Will reverse now for sure" Trading against STRONG trend Result: stops, stops, stops Solution: Trend is your friend, trade with trend 3. "Will hold a bit more" Don't exit when STRONG signal disappears Greed eats profit Solution: If signal gone - exit! 4. "I'll recover" After losses double risk Result: huge losses Solution: Fixed % risk ALWAYS 5. "I don't like this signal" Skip signals because of "feeling" Result: inconsistency, no statistics Solution: Trade ALL signals or clearly define filters Trading Journal What to Record For each trade: 1. Entry/exit date and time 2. Instrument and timeframe 3. Signal type Golden Cross STRONG BUY STRONG SELL Death Cross 4. Indicator settings Fast/Mid/Slow EMA Base Multiplier Other parameters 5. Chart screenshot Entry moment Exit moment 6. Trade parameters Position size Stop loss Take Profit R:R 7. Result Profit/Loss in $ Profit/Loss in % Profit/Loss in R 8. Notes What was right What was wrong Emotions during trade Lessons Journal Analysis Analyze weekly: 1. Win Rate Win Rate = (Profitable trades / All trades) × 100% Good: 50-60% Excellent: 60-70% Exceptional: 70%+ 2. Average R Average R = Sum of all R / Number of trades Good: +0.5R Excellent: +1.0R Exceptional: +1.5R+ 3. Profit Factor Profit Factor = Total profit / Total losses Good: 1.5+ Excellent: 2.0+ Exceptional: 3.0+ 4. Maximum Drawdown Track consecutive losses If more than 5 in row - stop, check system 5. Best/Worst Trades What was common in best trades? (do more) What was common in worst trades? (avoid) Pre-Trade Checklist Technical Analysis STRONG signal active (BUY or SELL) All EMAs properly aligned (Fast > Mid > Slow or reverse) Price on correct side of Fast EMA Gradient Clouds confirm trend Pulsing Bar shows STRONG state Momentum % in normal range (not overheated) No close strong levels against direction Higher timeframe doesn't contradict Risk Management Position size calculated (1-2% risk) Stop loss set Take profit calculated (minimum 1:2) R:R satisfactory Daily/weekly risk limit not exceeded No other open correlated positions Fundamental Analysis No important news in coming hours Market session appropriate (liquidity) No contradicting fundamentals Understand why asset is moving Psychology Calm and thinking clearly No emotions from previous trades Ready to accept loss at stop Following trading plan Not revenging market for past losses If at least one point is NO - think twice before entering! Learning Roadmap Week 1: Familiarization Goals: Install and configure indicator Study all EMA types Understand visualization Tasks: Add indicator to chart Test all Fast/Mid/Slow settings Play with Base Multiplier on different timeframes Observe Gradient Clouds and Pulsing Bar Study Info Table Result: Comfort with indicator interface Week 2: Signals Goals: Learn to recognize all signal types Understand difference between Golden Cross and STRONG Tasks: Find 10 Golden Cross examples in history Find 10 STRONG BUY examples in history Compare their results (which worked better) Set up alerts Get 5 real alerts Result: Understanding signals Week 3: Demo Trading Goals: Start trading signals on demo account Gather statistics Tasks: Open demo account Trade ONLY STRONG signals Keep journal (minimum 20 trades) Don't change indicator settings Strictly follow stop losses Result: 20+ documented trades Week 4: Analysis Goals: Analyze demo trading results Optimize approach Tasks: Calculate win rate and average R Find patterns in profitable trades Find patterns in losing trades Adjust approach (not indicator!) Write trading plan Result: Trading plan on 1 page Month 2: Improvement Goals: Deepen understanding Add additional techniques Tasks: Study multi-timeframe analysis Test combinations with Price Action Try advanced techniques (divergences, tunnels) Continue demo trading (minimum 50 trades) Achieve stable profitability on demo Result: Win rate 55%+ and Profit Factor 1.5+ Month 3: Real Trading Goals: Transition to real account Maintain discipline Tasks: Open small real account Trade minimum lots Strictly follow trading plan DON'T increase risk Focus on process, not profit Result: Psychological comfort on real Month 4+: Scaling Goals: Increase account Become consistently profitable Tasks: With 60%+ win rate can increase risk to 2% Upon doubling account can add capital Continue keeping journal Periodically review and improve strategy Share experience with community Result: Stable profitability month after month Additional Resources Recommended Reading Technical Analysis: "Technical Analysis of Financial Markets" - John Murphy "Trading in the Zone" - Mark Douglas (psychology) "Market Wizards" - Jack Schwager (trader interviews) EMA and Moving Averages: "Moving Averages 101" - Steve Burns Articles on Investopedia about EMA Risk Management: "The Mathematics of Money Management" - Ralph Vince "Trade Your Way to Financial Freedom" - Van K. Tharp Trading Journals: Edgewonk (paid, very powerful) Tradervue (free version + premium) Excel/Google Sheets (free) Screeners: TradingView Stock Screener Finviz (stocks) CoinMarketCap (crypto) Conclusion Hellenic EMA Matrix is a powerful tool based on universal mathematical constants of nature. The indicator combines: Mathematical elegance - Phi, Pi, e instead of arbitrary numbers Premium visualization - Neon Glow, Gradient Clouds, Pulsing Bar Reliable signals - STRONG BUY/SELL work on all timeframes Flexibility - 6 EMA types, adaptation to any trading style Automation - auto-sorting EMAs, SL/TP calculation, alerts Key Success Principles: Simplicity - start with basic settings (Phi/Pi/e, Base=10) Discipline - follow STRONG signals strictly Patience - wait for quality setups Risk Management - 1-2% per trade, ALWAYS Journal - document every trade Learning - constantly improve skills Remember: Indicator shows probability, not guarantee Important is series statistics, not one trade Psychology more important than technique Quality more important than quantity Process more important than result Acknowledgments Thank you for using Hellenic EMA Matrix - Alpha Omega Premium! The indicator was created with love for mathematics, markets, and beautiful visualization. Wishing you profitable trading! Guide Version: 1.0 Date: 2025 Compatibility: Pine Script v6, TradingView "In the simplicity of mathematical constants lies the complexity of market movements"
Penunjuk Pine Script®
oleh Sesilya
Dskyz (DAFE) Adaptive Regime - Quant Machine ProDskyz (DAFE) Adaptive Regime - Quant Machine Pro: Buckle up for the Dskyz (DAFE) Adaptive Regime - Quant Machine Pro, is a strategy that’s your ultimate edge for conquering futures markets like ES, MES, NQ, and MNQ. This isn’t just another script—it’s a quant-grade powerhouse, crafted with precision to adapt to market regimes, deliver multi-factor signals, and protect your capital with futures-tuned risk management. With its shimmering DAFE visuals, dual dashboards, and glowing watermark, it turns your charts into a cyberpunk command center, making trading as thrilling as it is profitable. Unlike generic scripts clogging up the space, the Adaptive Regime is a DAFE original, built from the ground up to tackle the chaos of futures trading. It identifies market regimes (Trending, Range, Volatile, Quiet) using ADX, Bollinger Bands, and HTF indicators, then fires trades based on a weighted scoring system that blends candlestick patterns, RSI, MACD, and more. Add in dynamic stops, trailing exits, and a 5% drawdown circuit breaker, and you’ve got a system that’s as safe as it is aggressive. Whether you’re a newbie or a prop desk pro, this strat’s your ticket to outsmarting the markets. Let’s break down every detail and see why it’s a must-have. Why Traders Need This Strategy Futures markets are a gauntlet—fast moves, volatility spikes (like the April 28, 2025 NQ 1k-point drop), and institutional traps that punish the unprepared. Meanwhile, platforms are flooded with low-effort scripts that recycle old ideas with zero innovation. The Adaptive Regime stands tall, offering: Adaptive Intelligence: Detects market regimes (Trending, Range, Volatile, Quiet) to optimize signals, unlike one-size-fits-all scripts. Multi-Factor Precision: Combines candlestick patterns, MA trends, RSI, MACD, volume, and HTF confirmation for high-probability trades. Futures-Optimized Risk: Calculates position sizes based on $ risk (default: $300), with ATR or fixed stops/TPs tailored for ES/MES. Bulletproof Safety: 5% daily drawdown circuit breaker and trailing stops keep your account intact, even in chaos. DAFE Visual Mastery: Pulsing Bollinger Band fills, dynamic SL/TP lines, and dual dashboards (metrics + position) make signals crystal-clear and charts a work of art. Original Craftsmanship: A DAFE creation, built with community passion, not a rehashed clone of generic code. Traders need this because it’s a complete, adaptive system that blends quant smarts, user-friendly design, and DAFE flair. It’s your edge to trade with confidence, cut through market noise, and leave the copycats in the dust. Strategy Components 1. Market Regime Detection The strategy’s brain is its ability to classify market conditions into five regimes, ensuring signals match the environment. How It Works: Trending (Regime 1): ADX > 20, fast/slow EMA spread > 0.3x ATR, HTF RSI > 50 or MACD bullish (htf_trend_bull/bear). Range (Regime 2): ADX < 25, price range < 3% of close, no HTF trend. Volatile (Regime 3): BB width > 1.5x avg, ATR > 1.2x avg, HTF RSI overbought/oversold. Quiet (Regime 4): BB width < 0.8x avg, ATR < 0.9x avg. Other (Regime 5): Default for unclear conditions. Indicators: ADX (14), BB width (20), ATR (14, 50-bar SMA), HTF RSI (14, daily default), HTF MACD (12,26,9). Why It’s Brilliant: Regime detection adapts signals to market context, boosting win rates in trending or volatile conditions. HTF RSI/MACD add a big-picture filter, rare in basic scripts. Visualized via gradient background (green for Trending, orange for Range, red for Volatile, gray for Quiet, navy for Other). 2. Multi-Factor Signal Scoring Entries are driven by a weighted scoring system that combines candlestick patterns, trend, momentum, and volume for robust signals. Candlestick Patterns: Bullish: Engulfing (0.5), hammer (0.4 in Range, 0.2 else), morning star (0.2), piercing (0.2), double bottom (0.3 in Volatile, 0.15 else). Must be near support (low ≤ 1.01x 20-bar low) with volume spike (>1.5x 20-bar avg). Bearish: Engulfing (0.5), shooting star (0.4 in Range, 0.2 else), evening star (0.2), dark cloud (0.2), double top (0.3 in Volatile, 0.15 else). Must be near resistance (high ≥ 0.99x 20-bar high) with volume spike. Logic: Patterns are weighted higher in specific regimes (e.g., hammer in Range, double bottom in Volatile). Additional Factors: Trend: Fast EMA (20) > slow EMA (50) + 0.5x ATR (trend_bull, +0.2); opposite for trend_bear. RSI: RSI (14) < 30 (rsi_bull, +0.15); > 70 (rsi_bear, +0.15). MACD: MACD line > signal (12,26,9, macd_bull, +0.15); opposite for macd_bear. Volume: ATR > 1.2x 50-bar avg (vol_expansion, +0.1). HTF Confirmation: HTF RSI < 70 and MACD bullish (htf_bull_confirm, +0.2); RSI > 30 and MACD bearish (htf_bear_confirm, +0.2). Scoring: bull_score = sum of bullish factors; bear_score = sum of bearish. Entry requires score ≥ 1.0. Example: Bullish engulfing (0.5) + trend_bull (0.2) + rsi_bull (0.15) + htf_bull_confirm (0.2) = 1.05, triggers long. Why It’s Brilliant: Multi-factor scoring ensures signals are confirmed by multiple market dynamics, reducing false positives. Regime-specific weights make patterns more relevant (e.g., hammers shine in Range markets). HTF confirmation aligns with the big picture, a quant edge over simplistic scripts. 3. Futures-Tuned Risk Management The risk system is built for futures, calculating position sizes based on $ risk and offering flexible stops/TPs. Position Sizing: Logic: Risk per trade (default: $300) ÷ (stop distance in points * point value) = contracts, capped at max_contracts (default: 5). Point value = tick value (e.g., $12.5 for ES) * ticks per point (4) * contract multiplier (1 for ES, 0.1 for MES). Example: $300 risk, 8-point stop, ES ($50/point) → 0.75 contracts, rounded to 1. Impact: Precise sizing prevents over-leverage, critical for micro contracts like MES. Stops and Take-Profits: Fixed: Default stop = 8 points, TP = 16 points (2:1 reward/risk). ATR-Based: Stop = 1.5x ATR (default), TP = 3x ATR, enabled via use_atr_for_stops. Logic: Stops set at swing low/high ± stop distance; TPs at 2x stop distance from entry. Impact: ATR stops adapt to volatility, while fixed stops suit stable markets. Trailing Stops: Logic: Activates at 50% of TP distance. Trails at close ± 1.5x ATR (atr_multiplier). Longs: max(trail_stop_long, close - ATR * 1.5); shorts: min(trail_stop_short, close + ATR * 1.5). Impact: Locks in profits during trends, a game-changer in volatile sessions. Circuit Breaker: Logic: Pauses trading if daily drawdown > 5% (daily_drawdown = (max_equity - equity) / max_equity). Impact: Protects capital during black swan events (e.g., April 27, 2025 ES slippage). Why It’s Brilliant: Futures-specific inputs (tick value, multiplier) make it plug-and-play for ES/MES. Trailing stops and circuit breaker add pro-level safety, rare in off-the-shelf scripts. Flexible stops (ATR or fixed) suit different trading styles. 4. Trade Entry and Exit Logic Entries and exits are precise, driven by bull_score/bear_score and protected by drawdown checks. Entry Conditions: Long: bull_score ≥ 1.0, no position (position_size <= 0), drawdown < 5% (not pause_trading). Calculates contracts, sets stop at swing low - stop points, TP at 2x stop distance. Short: bear_score ≥ 1.0, position_size >= 0, drawdown < 5%. Stop at swing high + stop points, TP at 2x stop distance. Logic: Tracks entry_regime for PNL arrays. Closes opposite positions before entering. Exit Conditions: Stop-Loss/Take-Profit: Hits stop or TP (strategy.exit). Trailing Stop: Activates at 50% TP, trails by ATR * 1.5. Emergency Exit: Closes if price breaches stop (close < long_stop_price or close > short_stop_price). Reset: Clears stop/TP prices when flat (position_size = 0). Why It’s Brilliant: Score-based entries ensure multi-factor confirmation, filtering out weak signals. Trailing stops maximize profits in trends, unlike static exits in basic scripts. Emergency exits add an extra safety layer, critical for futures volatility. 5. DAFE Visuals The visuals are pure DAFE magic, blending function with cyberpunk flair to make signals intuitive and charts stunning. Shimmering Bollinger Band Fill: Display: BB basis (20, white), upper/lower (green/red, 45% transparent). Fill pulses (30–50 alpha) by regime, with glow (60–95 alpha) near bands (close ≥ 0.995x upper or ≤ 1.005x lower). Purpose: Highlights volatility and key levels with a futuristic glow. Visuals make complex regimes and signals instantly clear, even for newbies. Pulsing effects and regime-specific colors add a DAFE signature, setting it apart from generic scripts. BB glow emphasizes tradeable levels, enhancing decision-making. Chart Background (Regime Heatmap): Green — Trending Market: Strong, sustained price movement in one direction. The market is in a trend phase—momentum follows through. Orange — Range-Bound: Market is consolidating or moving sideways, with no clear up/down trend. Great for mean reversion setups. Red — Volatile Regime: High volatility, heightened risk, and larger/faster price swings—trade with caution. Gray — Quiet/Low Volatility: Market is calm and inactive, with small moves—often poor conditions for most strategies. Navy — Other/Neutral: Regime is uncertain or mixed; signals may be less reliable. Bollinger Bands Glow (Dynamic Fill): Neon Red Glow — Warning!: Price is near or breaking above the upper band; momentum is overstretched, watch for overbought conditions or reversals. Bright Green Glow — Opportunity!: Price is near or breaking below the lower band; market could be oversold, prime for bounce or reversal. Trend Green Fill — Trending Regime: Fills between bands with green when the market is trending, showing clear momentum. Gold/Yellow Fill — Range Regime: Fills with gold/aqua in range conditions, showing the market is sideways/oscillating. Magenta/Red Fill — Volatility Spike: Fills with vivid magenta/red during highly volatile regimes. Blue Fill — Neutral/Quiet: A soft blue glow for other or uncertain market states. Moving Averages: Display: Blue fast EMA (20), red slow EMA (50), 2px. Purpose: Shows trend direction, with trend_dir requiring ATR-scaled spread. Dynamic SL/TP Lines: Display: Pulsing colors (red SL, green TP for Trending; yellow/orange for Range, etc.), 3px, with pulse_alpha for shimmer. Purpose: Tracks stops/TPs in real-time, color-coded by regime. 6. Dual Dashboards Two dashboards deliver real-time insights, making the strat a quant command center. Bottom-Left Metrics Dashboard (2x13): Metrics: Mode (Active/Paused), trend (Bullish/Bearish/Neutral), ATR, ATR avg, volume spike (YES/NO), RSI (value + Oversold/Overbought/Neutral), HTF RSI, HTF trend, last signal (Buy/Sell/None), regime, bull score. Display: Black (29% transparent), purple title, color-coded (green for bullish, red for bearish). Purpose: Consolidates market context and signal strength. Top-Right Position Dashboard (2x7): Metrics: Regime, position side (Long/Short/None), position PNL ($), SL, TP, daily PNL ($). Display: Black (29% transparent), purple title, color-coded (lime for Long, red for Short). Purpose: Tracks live trades and profitability. Why It’s Brilliant: Dual dashboards cover market context and trade status, a rare feature. Color-coding and concise metrics guide beginners (e.g., green “Buy” = go). Real-time PNL and SL/TP visibility empower disciplined trading. 7. Performance Tracking Logic: Arrays (regime_pnl_long/short, regime_win/loss_long/short) track PNL and win/loss by regime (1–5). Updated on trade close (barstate.isconfirmed). Purpose: Prepares for future adaptive thresholds (e.g., adjust bull_score min based on regime performance). Why It’s Brilliant: Lays the groundwork for self-optimizing logic, a quant edge over static scripts. Key Features Regime-Adaptive: Optimizes signals for Trending, Range, Volatile, Quiet markets. Futures-Optimized: Precise sizing for ES/MES with tick-based risk inputs. Multi-Factor Signals: Candlestick patterns, RSI, MACD, and HTF confirmation for robust entries. Dynamic Exits: ATR/fixed stops, 2:1 TPs, and trailing stops maximize profits. Safe and Smart: 5% drawdown breaker and emergency exits protect capital. DAFE Visuals: Shimmering BB fill, pulsing SL/TP, and dual dashboards. Backtest-Ready: Fixed qty and tick calc for accurate historical testing. How to Use Add to Chart: Load on a 5min ES/MES chart in TradingView. Configure Inputs: Set instrument (ES/MES), tick value ($12.5/$1.25), multiplier (1/0.1), risk ($300 default). Enable ATR stops for volatility. Monitor Dashboards: Bottom-left for regime/signals, top-right for position/PNL. Backtest: Run in strategy tester to compare regimes. Live Trade: Connect to Tradovate or similar. Watch for slippage (e.g., April 27, 2025 ES issues). Replay Test: Try April 28, 2025 NQ drop to see regime shifts and stops. Disclaimer Trading futures involves significant risk of loss and is not suitable for all investors. Past performance does not guarantee future results. Backtest results may differ from live trading due to slippage, fees, or market conditions. Use this strategy at your own risk, and consult a financial advisor before trading. Dskyz (DAFE) Trading Systems is not responsible for any losses incurred. Backtesting: Frame: 2023-09-20 - 2025-04-29 Slippage: 3 Fee Typical Range (per side, per contract) CME Exchange $1.14 – $1.20 Clearing $0.10 – $0.30 NFA Regulatory $0.02 Firm/Broker Commis. $0.25 – $0.80 (retail prop) TOTAL $1.60 – $2.30 per side Round Turn: (enter+exit) = $3.20 – $4.60 per contract Final Notes The Dskyz (DAFE) Adaptive Regime - Quant Machine Pro is more than a strategy—it’s a revolution. Crafted with DAFE’s signature precision, it rises above generic scripts with adaptive regimes, quant-grade signals, and visuals that make trading a thrill. Whether you’re scalping MES or swinging ES, this system empowers you to navigate markets with confidence and style. Join the DAFE crew, light up your charts, and let’s dominate the futures game! (This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.) Use it with discipline. Use it with clarity. Trade smarter. **I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract. Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
Strategi Pine Script®
oleh DskyzInvestments
CryptoFlux Dynamo [JOAT]CryptoFlux Dynamo: Velocity Scalping Strategy WHAT THIS STRATEGY IS CryptoFlux Dynamo is an open-source Pine Script v6 strategy designed for momentum-based scalping on cryptocurrency perpetual futures. It combines multiple technical analysis methods into a unified system that adapts its behavior based on current market volatility conditions. This script is published open-source so you can read, understand, and modify the complete logic. The description below explains everything the strategy does so that traders who cannot read Pine Script can fully understand how it works before using it. HOW THIS STRATEGY IS ORIGINAL AND WHY THE INDICATORS ARE COMBINED This strategy uses well-known indicators (MACD, EMA, RSI, MFI, Bollinger Bands, Keltner Channels, ATR). The originality is not in the individual indicators themselves, but in the specific way they are integrated into a regime-adaptive system. Here is the detailed justification for why these components are combined and how they work together: The Problem Being Solved: Standard indicator-based strategies use fixed thresholds. For example, a typical MACD strategy might enter when the histogram crosses above zero. However, in cryptocurrency markets, volatility changes dramatically throughout the day and week. A MACD crossover during a low-volatility consolidation period has very different implications than the same crossover during a high-volatility trending period. Using the same entry thresholds and stop distances in both conditions leads to either: Too many false signals during consolidation (if thresholds are loose) Missing valid opportunities during expansion (if thresholds are tight) Stops that are too tight during volatility spikes (causing premature exits) Stops that are too wide during compression (giving back profits) The Solution Approach: This strategy first classifies the current volatility regime using normalized ATR (ATR as a percentage of price), then dynamically adjusts ALL other parameters based on that classification. This creates a context-aware system rather than a static threshold comparison. How Each Component Contributes to the System: ATR-Based Regime Classification (The Foundation) The strategy calculates ATR over 21 periods, smooths it with a 13-period EMA to reduce noise from wicks, then divides by price to get a normalized percentage. This ATR% is classified into three regimes: - Compression (ATR% < 0.8%): Market is consolidating, breakouts are more likely but false signals are common - Expansion (ATR% 0.8% - 1.6%): Normal trending conditions - Velocity (ATR% > 1.6%): High volatility, larger moves but also larger adverse excursions This regime classification then controls stop distances, profit targets, trailing stop offsets, and signal strength requirements. The regime acts as a "meta-parameter" that tunes the entire system. EMA Ribbon (8/21/34) - Trend Structure Detection The three EMAs establish trend direction and structure. When EMA 8 > EMA 21 > EMA 34, the trend structure is bullish. The slope of the middle EMA (21) is calculated over 8 bars and converted to degrees using arctangent. This slope measurement quantifies trend strength, not just direction. Why these specific periods? The 8/21/34 sequence follows Fibonacci-like spacing and provides good separation on 5-minute cryptocurrency charts. The fast EMA (8) responds to immediate price action, the mid EMA (21) represents the short-term trend, and the slow EMA (34) acts as a trend filter. The EMA ribbon works with the regime classification: during compression regimes, the strategy requires stronger ribbon alignment before entry because false breakouts are more common. MACD (8/21/5) - Momentum Measurement The MACD uses faster parameters (8/21/5) than the standard (12/26/9) because cryptocurrency markets move faster than traditional markets. The histogram is smoothed with a 5-period EMA to reduce noise. The key innovation is the adaptive histogram baseline. Instead of using a fixed threshold, the strategy calculates a rolling baseline from the smoothed absolute histogram value, then multiplies by a sensitivity factor (1.15). This means the threshold for "significant momentum" automatically adjusts based on recent momentum levels. The MACD works with the regime classification: during velocity regimes, the histogram baseline is effectively higher because recent momentum has been stronger, preventing entries on relatively weak momentum. RSI (21 period) and MFI (21 period) - Independent Momentum Confirmation RSI measures momentum using price changes only. MFI (Money Flow Index) measures momentum using price AND volume. By requiring both to confirm, the strategy filters out price moves that lack volume support. The 21-period length is longer than typical (14) to reduce noise on 5-minute charts. The trigger threshold (55 for longs, 45 for shorts) is slightly offset from 50 to require momentum in the trade direction, not just neutral readings. These indicators work together: a signal requires RSI > 55 AND MFI > 55 for longs. This dual confirmation reduces false signals from price manipulation or low-volume moves. Bollinger Bands (1.5 mult) and Keltner Channels (1.8 mult) - Squeeze Detection When Bollinger Bands contract inside Keltner Channels, volatility is compressing and a breakout is likely. This is the "squeeze" condition. When the bands expand back outside the channels, the squeeze "releases." The strategy uses a 1.5 multiplier for Bollinger Bands (tighter than standard 2.0) and 1.8 for Keltner Channels. These values were chosen to identify meaningful squeezes on 5-minute cryptocurrency charts without triggering too frequently. The squeeze detection works with the regime classification: squeeze releases during compression regimes receive additional signal strength points because breakouts from consolidation are more significant. Volume Impulse Detection - Institutional Participation Filter The strategy calculates a volume baseline (34-period SMA) and standard deviation. A "volume impulse" is detected when current volume exceeds the baseline by 1.15x OR when the volume z-score exceeds 0.5. This filter ensures entries occur when there is meaningful market participation, not during low-volume periods where price moves are less reliable. Volume impulse is required for all entries and adds points to the composite signal strength score. Cycle Oscillator - Trend Alignment Filter The strategy calculates a 55-period EMA as a cycle basis, then measures price deviation from this basis as a percentage. When price is more than 0.15% above the cycle basis, the cycle is bullish. When more than 0.15% below, the cycle is bearish. This filter prevents counter-trend entries. Long signals require bullish cycle alignment; short signals require bearish cycle alignment. BTC Dominance Filter (Optional) - Market Regime Filter The strategy can optionally use BTC.D (Bitcoin Dominance) as a market regime filter. When BTC dominance is rising (slope > 0.12), the market is in "risk-off" mode and long entries on altcoins are filtered. When dominance is falling (slope < -0.12), short entries are filtered. This filter is optional because the BTC.D data feed may lag during low-liquidity periods. How The Components Work Together (The Mashup Justification): The strategy uses a composite scoring system where each signal pathway contributes points: Trend Break pathway (30 points): Requires EMA ribbon alignment + positive slope + price breaks above recent structure high Momentum Surge pathway (30 points): Requires MACD histogram > adaptive baseline + MACD line > signal + RSI > 55 + MFI > 55 + volume impulse Squeeze Release pathway (25 points): Requires BB inside KC (squeeze) then release + momentum bias + histogram confirmation Micro Pullback pathway (15 points): Requires shallow retracement to fast EMA within established trend + histogram confirmation + volume impulse Additional modifiers: +5 points if volume impulse is present, -5 if absent +5 points in velocity regime, -2 in compression regime +5 points if cycle is aligned, -5 if counter-trend A trade only executes when the composite score reaches the minimum threshold (default 55) AND all filters agree (session, cycle bias, BTC dominance if enabled). This scoring system is the core innovation: instead of requiring ALL conditions to be true (which would generate very few signals) or ANY condition to be true (which would generate too many false signals), the strategy requires ENOUGH conditions to be true, with different conditions contributing different weights based on their reliability. HOW THE STRATEGY CALCULATES ENTRIES AND EXITS Entry Logic: 1. Calculate current volatility regime from ATR% 2. Calculate all indicator values (MACD, EMA, RSI, MFI, squeeze, volume) 3. Evaluate each signal pathway and sum points 4. Check all filters (session, cycle, dominance, kill switch) 5. If composite score >= 55 AND all filters pass, generate entry signal 6. Calculate position size based on risk per trade and regime-adjusted stop distance 7. Execute entry with regime name as comment Position Sizing Formula: RiskCapital = Equity * (0.65 / 100) StopDistance = ATR * StopMultiplier(regime) RawQuantity = RiskCapital / StopDistance MaxQuantity = Equity * (12 / 100) / Price Quantity = min(RawQuantity, MaxQuantity) Quantity = round(Quantity / 0.001) * 0.001 This ensures each trade risks approximately 0.65% of equity regardless of volatility, while capping total exposure at 12% of equity. Stop Loss Calculation: Stop distance is ATR multiplied by a regime-specific multiplier: Compression regime: 1.05x ATR (tighter stops because moves are smaller) Expansion regime: 1.55x ATR (standard stops) Velocity regime: 2.1x ATR (wider stops to avoid premature exits during volatility) Take Profit Calculation: Target distance is ATR multiplied by regime-specific multiplier and base risk/reward: Compression regime: 1.6x ATR * 1.8 base R:R * 0.9 regime bonus = approximately 2.6x ATR Expansion regime: 2.05x ATR * 1.8 base R:R * 1.0 regime bonus = approximately 3.7x ATR Velocity regime: 2.8x ATR * 1.8 base R:R * 1.15 regime bonus = approximately 5.8x ATR Trailing Stop Logic: When adaptive trailing is enabled, the strategy calculates a trailing offset based on ATR and regime: Compression regime: 1.1x base offset (looser trailing to avoid noise) Expansion regime: 1.0x base offset (standard) Velocity regime: 0.8x base offset (tighter trailing to lock in profits during fast moves) The trailing stop only activates when it would be tighter than the initial stop. Momentum Fail-Safe Exits: The strategy closes positions early if momentum reverses: Long positions close if MACD histogram turns negative OR EMA ribbon structure breaks (fast EMA crosses below mid EMA) Short positions close if MACD histogram turns positive OR EMA ribbon structure breaks This prevents holding through momentum reversals even if stop loss hasn't been hit. Kill Switch: If maximum drawdown exceeds 6.5%, the strategy disables new entries until manually reset. This prevents continued trading during adverse conditions. HOW TO USE THIS STRATEGY Step 1: Apply to Chart Use a 5-minute chart of a high-liquidity cryptocurrency perpetual (BTC/USDT, ETH/USDT recommended) Ensure at least 200 bars of history are loaded for indicator stabilization Use standard candlestick charts only (not Heikin Ashi, Renko, or other non-standard types) Step 2: Understand the Visual Elements EMA Ribbon: Three lines (8/21/34 periods) showing trend structure. Bullish when stacked upward, bearish when stacked downward. Background Color: Shows current volatility regime - Indigo/dark blue = Compression (low volatility) - Purple = Expansion (normal volatility) - Magenta/pink = Velocity (high volatility) Bar Colors: Reflect signal strength divergence. Brighter colors indicate stronger directional bias. Triangle Markers: Entry signals. Up triangles below bars = long entry. Down triangles above bars = short entry. Dashboard (top-right): Real-time display of regime, ATR%, signal strengths, position status, stops, targets, and risk metrics. Step 3: Interpret the Dashboard Regime: Current volatility classification (Compression/Expansion/Velocity) ATR%: Normalized volatility as percentage of price Long/Short Strength: Current composite signal scores (0-100) Cycle Osc: Price deviation from 55-period EMA as percentage Dominance: BTC.D slope and filter status Position: Current position direction or "Flat" Stop/Target: Current stop loss and take profit levels Kill Switch: Status of drawdown protection Volume Z: Current volume z-score Impulse: Whether volume impulse condition is met Step 4: Adjust Parameters for Your Needs For more conservative trading: Increase "Minimum Composite Signal Strength" to 65 or higher For more aggressive trading: Decrease to 50 (but expect more false signals) For higher timeframes (15m+): Increase "Structure Break Window" to 12-15, increase "RSI Momentum Trigger" to 58 For lower liquidity pairs: Increase "Volume Impulse Multiplier" to 1.3, increase slippage in strategy properties To disable short selling: Uncheck "Enable Short Structure" To disable BTC dominance filter: Uncheck "BTC Dominance Confirmation" STRATEGY PROPERTIES (BACKTEST SETTINGS) These are the exact settings used in the strategy's Properties dialog box. You must use these same settings when evaluating the backtest results shown in the publication: Initial Capital: $100,000 Justification: This amount is higher than typical retail accounts. I chose this value to demonstrate percentage-based returns that scale proportionally. The strategy uses percentage-based position sizing (0.65% risk per trade), so a $10,000 account would see the same percentage returns with 10x smaller position sizes. The absolute dollar amounts in the backtest should be interpreted as percentages of capital. Commission: 0.04% (commission_value = 0.04) Justification: This reflects typical perpetual futures exchange fees. Major exchanges charge between 0.02% (maker) and 0.075% (taker). The 0.04% value is a reasonable middle estimate. If your exchange charges different fees, adjust this value accordingly. Higher fees will reduce net profitability. Slippage: 1 tick Justification: This is conservative for liquid pairs like BTC/USDT on major exchanges during normal conditions. For less liquid altcoins or during high volatility, actual slippage may be higher. If you trade less liquid pairs, increase this value to 2-3 ticks for more realistic results. Pyramiding: 1 Justification: No position stacking. The strategy holds only one position at a time. This simplifies risk management and prevents overexposure. calc_on_every_tick: true Justification: The strategy evaluates on every price update, not just bar close. This is necessary for scalping timeframes where waiting for bar close would miss opportunities. Note that this setting means backtest results may differ slightly from bar-close-only evaluation. calc_on_order_fills: true Justification: The strategy recalculates immediately after order fills for faster response to position changes. RISK PER TRADE JUSTIFICATION The default risk per trade is 0.65% of equity. This is well within the TradingView guideline that "risking more than 5-10% on a trade is not typically considered viable." With the 12% maximum exposure cap, even if the strategy takes multiple consecutive losses, the total risk remains manageable. The kill switch at 6.5% drawdown provides additional protection by halting new entries during adverse conditions. The position sizing formula ensures that stop distance (which varies by regime) is accounted for, so actual risk per trade remains approximately 0.65% regardless of volatility conditions. SAMPLE SIZE CONSIDERATIONS For statistically meaningful backtest results, you should select a dataset that generates at least 100 trades. On 5-minute BTC/USDT charts, this typically requires: 2-3 months of data during normal market conditions 1-2 months during high-volatility periods 3-4 months during low-volatility consolidation periods The strategy's selectivity (requiring 55+ composite score plus all filters) means it generates fewer signals than less filtered approaches. If your backtest shows fewer than 100 trades, extend the date range or reduce the minimum signal strength threshold. Fewer than 100 trades produces statistically unreliable results. Win rate, profit factor, and other metrics can vary significantly with small sample sizes. STRATEGY DESIGN COMPROMISES AND LIMITATIONS Every strategy involves trade-offs. Here are the compromises made in this design and the limitations you should understand: Selectivity vs. Opportunity Trade-off The 55-point minimum threshold filters many potential trades. This reduces false signals but also misses valid setups that don't meet all criteria. Lowering the threshold increases trade frequency but decreases win rate. There is no "correct" threshold; it depends on your preference for fewer higher-quality signals vs. more signals with lower individual quality. Regime Classification Lag The ATR-based regime detection uses historical data (21 periods + 13-period smoothing). It cannot predict sudden volatility spikes. During flash crashes or black swan events, the strategy may be classified in the wrong regime for several bars before the classification updates. This is an inherent limitation of any lagging indicator. Indicator Parameter Sensitivity The default parameters (MACD 8/21/5, EMA 8/21/34, RSI 21, etc.) are tuned for BTC/ETH perpetuals on 5-minute charts during 2024 market conditions. Different assets, timeframes, or market regimes may require different parameters. There is no guarantee that parameters optimized on historical data will perform similarly in the future. BTC Dominance Filter Limitations The CRYPTOCAP:BTC.D data feed may lag during low-liquidity periods or weekends. The dominance slope calculation uses a 5-bar SMA, adding additional delay. If you notice the filter behaving unexpectedly, consider disabling it. Backtest vs. Live Execution Differences TradingView backtesting does not replicate actual broker execution. Key differences: Backtests assume perfect fills at calculated prices; real execution involves order book depth, latency, and partial fills The calc_on_every_tick setting improves backtest realism but still cannot capture sub-bar price action or order book dynamics Commission and slippage settings are estimates; actual costs vary by exchange, time of day, and market conditions Funding rates on perpetual futures are not modeled in backtests and can significantly impact profitability over time Exchange-specific limitations (position limits, liquidation mechanics, order types) are not modeled Market Condition Dependencies This strategy is designed for trending and breakout conditions. During extended sideways consolidation with no clear direction, the strategy may generate few signals or experience whipsaws. No strategy performs well in all market conditions. Cryptocurrency-Specific Risks Cryptocurrency markets operate 24/7 without session boundaries. This means: No natural "overnight" risk reduction Volatility can spike at any time Liquidity varies significantly by time of day Exchange outages or issues can occur at any time WHAT THIS STRATEGY DOES NOT DO To be straightforward about limitations: This strategy does not guarantee profits. Past backtest performance does not indicate future results. This strategy does not predict the future. It reacts to current conditions based on historical patterns. This strategy does not account for funding rates, which can significantly impact perpetual futures profitability. This strategy does not model exchange-specific execution issues (partial fills, requotes, outages). This strategy does not adapt to fundamental news events or black swan scenarios. This strategy is not optimized for all market conditions. It may underperform during extended consolidation. IMPORTANT RISK WARNINGS Past performance does not guarantee future results. The backtest results shown reflect specific historical market conditions and parameter settings. Markets change constantly, and strategies that performed well historically may underperform or lose money in the future. A single backtest run does not constitute proof of future profitability. Trading involves substantial risk of loss. Cryptocurrency derivatives are highly volatile instruments. You can lose your entire investment. Only trade with capital you can afford to lose completely. This is not financial advice. This strategy is provided for educational and informational purposes only. It does not constitute investment advice, trading recommendations, or any form of financial guidance. The author is not a licensed financial advisor. You are responsible for your own decisions. Before using this strategy with real capital: Thoroughly understand the code and logic by reading the open-source implementation Forward test with paper trading or very small positions for an extended period Verify that commission, slippage, and execution assumptions match your actual trading environment Understand that live results will differ from backtest results Consider consulting with a qualified financial advisor No guarantees or warranties. This strategy is provided "as is" without any guarantees of profitability, accuracy, or suitability for any purpose. The author is not responsible for any losses incurred from using this strategy. OPEN-SOURCE CODE STRUCTURE The strategy code is organized into these sections for readability: Configuration Architecture: Input parameters organized into logical groups (Core Controls, Optimization Constants, Regime Intelligence, Signal Pathways, Risk Architecture, Visualization) Helper Functions: calcQty() for position sizing, clamp01() and normalize() for value normalization, calcMFI() for Money Flow Index calculation Core Indicator Engine: EMA ribbon, ATR and regime classification, MACD with adaptive baseline, RSI, MFI, volume analytics, cycle oscillator, BTC dominance filter, squeeze detection Signal Pathway Logic: Trend break, momentum surge, squeeze release, micro pullback pathways with composite scoring Entry/Exit Orchestration: Signal filtering, position sizing, entry execution, stop/target calculation, trailing stop logic, momentum fail-safe exits Visualization Layer: EMA plots, regime background, bar coloring, signal labels, dashboard table You can read and modify any part of the code. Understanding the logic before deployment is strongly recommended. - Made with passion by officialjackofalltrades
Strategi Pine Script®
oleh officialjackofalltrades
72s Strat: Backtesting Adaptive HMA+ pt.1This is a follow up to my previous publication of Adaptive HMA+ few months ago, as a mean to provide some kind of initial backtesting tools. Which can be use to explore many possible strategies, optimise its settings to better conform user's pair/tf, and hopefully able to help tweaking your general strategy. If you haven't read the study or use the indicator, kindly go here first to get the overall idea. The first strategy introduce in this backtest is one most basic already described in the study; buy/sell is when movement is there and everything is on the right side; When RSI has turned to other side, we can use it as exit point (if in profit of course, else just let it hit our TP/SL, why would we exit before profit). Also, base on RSI when we make entry, we can further differentiate type of signals. --Please check all comments in code directly where the signals , entries , and exits section are. Second additional strategy to check; is when we also use second faster Adaptive HMA+ for exit. So this is like a double orders on a signal but with different exit-rule (/more on this on snapshots below). Alternatively, you can also work the code so to only use this type of exit. There's also an additional feature which you can enable its visuals, the Distance Zone , is to help measuring price distance to our xHMA+. It's just a simple atr based envelope really, I already put the sample code in study's comment section, but better gonna update it there directly for non-coder too, after this. In this sample I use Lot for order quantity size just because that's what I use on my broker. Also what few friends use while we forward-testing it since the study is published, so we also checked/compared each profit/loss report by real number. To use default or other unit of measurement, change the entry code accordingly. If you change your order size, you should also change the commission in Properties Tab. My broker commission is 5 USD per order/lot, so in there with example order size 0.1 lot I put commission 0.5$ per order (I'll put 2.5$ for 0.5 lot, 10$ for 2 lot, and so on). Crypto usually has higher charge. --It is important that you should fill it base on your broker. SETTINGS I'm trying to keep it short. Please explore it further again. (Beginner should also first get acquaintance with terms use here.) ORDERS: Base Minimum Profit Before Exit: The number is multiplier of ongoing ATR. Means that when basic exit condition is met, algo will check whether you're already in minimum profit or not, if not, let it still run to TP or SL, or until it meets subsequent exit condition, then it will check again. Default Target Profit: Multiplier of ATR at signal. If reached before any eligible exit condition is met, exit TP. Base StopLoss Point: You can change directly in code to use other like ATR Trailing SL, fix percent SL, or whatever. In the sample, 4 options provided. Maximum StopLoss: This is like a safety-net, that if at some point your chosen SL point from input above happens to be exceeding this maximum input that you can tolerate, then this max point is the one will be use as SL. Activate 2nd order...: The additional doubling of certain buy/sell with different exits as described above. If enable, you should also set pyramiding to at least: 2. If not, it does nothing. ADAPTIVE HMA+ PERIOD Many users already have their own settings for these. So in here I only sample the default as first presented in the study. Make it to your adaptive. MARKET MOVEMENT (1) Now you can check in realtime how much slope degree is best to define your specific pair/tf is out of congestion (yellow) area. And (2) also able to check directly what ATR lengths are more suitable defining your pair's volatility. DISTANCE ZONE Distance Multiplier. Each pair/tf has its own best distance zone (in xHMA+ perspective). The zone also determine whether a signal should appear or not. (Or what type of signal, if you wanna go more detail in constructing your strategy) USAGE (Provided you already have your own comfortable settings for minimum-maximum period of Adaptive HMA+. Best if you already have backtested it manually too and/or apply as an add-on to your working strategy) 1. In our experiences, first most important to define is both elements in the Market Movement Settings . These also tend to be persistent for whole season since it's kinda describing that pair/tf overall behaviour. Don't worry if you still get a low Profit Factor here, but by tweaking you should start to see positive changes in one of Max Drawdown and Net Profit, or Percent Profitable. 2. Afterwards, find your pair/tf Distance Zone . When optimising this, what we seek is just a "not to bad" equity curves to start forming. At least Max Drawdown should lessen more. Doesn't have to be great already, but should be better, no red in Net Profit. 3. Then go manage the "Trailing Minimum Profit", TP, SL, and max SL. 4. Repeat 1,2,3. 👻 5. Manage order size, commission, and/or enable double-order (need pyramiding) if you like. Check if your equity can handle max drawdown before margin call. 6. After getting an acceptable backtest result, go to List of Trades tab and find the biggest loss or when many sequencing loss in a row happened. Click on it to go to exact point on chart, observe why the signal failed and get at least general idea how it can be prevented . The rest is yours, you should know your pair/tf more than other. You can also re-explore your minimum-maximum period for both Major and minor xHMA+. Keep in mind that all numbers in Setting are conceptually in a form of range . You don't want to get superb equity curves but actually a "fragile" , means one can easily turn it to disaster just by changing only a fraction in one/two of the setting. --- If you just wanna test the strength of the indicator alone, you can disable "Use StopLoss" temporarily while optimising settings. Using no SL might be tempting in overall result data in some cases, but NOTE: It is not recommended to not using SL, don't forget that we deliberately enter when it's in high volatility. If want to add flexibility or trading for long-term, just maximise your SL. ie.: chose SL Point>ATR only and set it maximum. (Check your max drawdown after this). I think this is quite important specially for beginners, so here's an example; Hypothetically in below scenario, because of some settings, the buy order after the loss sell signal didn't appear. Let's say if our initial capital only 1000$ using leverage and order size 0,5 lot (risky position sizing already), moreover if this happens at the beginning of your trading season, that's half of account gone already in one trade . Your max SL should've made you exit after that pumping bar. The Trailing Minimum Profit is actually look like this. Search in the code if you want to plot it. I just don't like too many lines on chart. To maximise profit we can try enabling double-order. The only added rule coded is: RSI should rising when buy and falling when sell. 2nd signal will appears above or below default buy/sell signal. (Of course it's also prone to double-loss, re-check your max drawdown after. Profit factor play its part in here for a long run). Snapshot in comparison: Two default sell signals on left closed at RSI exit, the additional sell signal closed later on when price crossover minor xHMA+. On buy side, price haven't met our minimum profit when first crossunder minor xHMA+. If later on we hit SL on this "+buy" signal, at least we already profited from default buy signal. You can also consider/treat this as multiple TP points. For longer-term trading, what you need to maximise is the Minimum Profit , so it won't exit whenever an exit condition happened, it can happen several times before reaching minimum profit. Hopefully this snapshot can explain: Notice in comparison default sell and buy signal now close in average after 3 days. What's best is when we also have confirmation from higher TF. It's like targeting higher TF by entering from smaller TF. As also mention in the study, we can still experiment via original HMA by putting same value for minimum-maximum period setting. This is experimental EU 1H with Major xHMA+: 144-144, Flat market 13, Distance multiplier 3.6, with 2nd order activated. Kiwi was a bit surprising for me. It's flat market is effectively below 6, with quite far distance zone of 3.5. Probably because I'm using big numbers in adaptive period. --- The result you see in strategy tester report below for EURUSD 15m is using just default settings you see in code, as follow: 0,1 lot for each order (which is the smallest allowed by my broker). No pyramiding. Commission: 0.5 usd per order. Slippage: 3 Opening position is only using basic strategy #1 (RSI exit). Additional exit not activated. Minimum Profit: 1. TP: 3. SL use: Half-distance zone. Max SL: 4.5. Major xHMA+: 172-233. minor xHMA+: 89-121 Distance Zone Multiplier: 2.7 RSI: Standard 14. (From our forward-testing, the difference we get from net profit is because of the spread, our entry isn't exactly at the close/open price. Not so much though, but not the same. If somebody can direct me to any example where we can code our entry via current bid/ask price, that would be awesome!) It's already a long post (sorry), think I'm gonna pause here. Check out the code :) --- DISCLAIMER: Past performance is no guarantee of future results , and so on.. you know the drill ;) Please read whole description first before using, don't take 1-2 paragraph and claim it's the whole logic, you are responsible of your own actions and understanding.
Strategi Pine Script®
oleh io72signals
58
Gap Filling Strategy Gaps are market prices structures that appear frequently in the stock market, and can be detected when the opening price is different from the previous closing price, this is why gaps are also called "opening price jumps". While gaps can occur frequently, some of them are more significant than others, and can be observed when looking at a long term chart. The following strategy is based on the exploitation of significant gaps occurring during a new session, and posses various options that can return a wide variety of results. Type Of Gaps And Occurence I'am not a professional when it comes to gaps, but as you know the stock market close for the day, however it is still possible to place orders, your broker will hold them until the market open back. Once the market reopen the broker execute the pending orders, and when many orders where pending the market register really high volume and the price might differ from the precedent close. Gaps are generally broken down into four types: Common : Gaps occurring within a certain price range, mostly occurs during ranging markets. Break Away : Gaps breaking a support and resistance, making a new higher high/lower low. Runaway : Gaps occurring within a trend, followed by a continuation of the trend. Exhaustion : Gaps occurring at the end of a trend, followed by a reversal. As said before, some gaps are more significant than others, the significance of a gap can be determined by comparing the opening price with the previous high/low price and by looking at volume. Significant up gaps will have an opening price greater than the previous high, while significant down gap will have an opening price lower than the previous low with both high volume accompanying them. After a gap, when the price go back to the point previous to the gap we say that it has been "filled", this characteristic is what will be exploited in this strategy. Strategy Rules & Logic In this strategy, the significance of a gap is determined by the position of the opening price relative to the previous high/low and make sure the bar following the gap don't fill it. When the setting invert is set to false the strategy interpret the detected gaps as being exhaustion gaps, therefore when an up gap occur a short position is opened, when a down gap occur a long position is opened. When invert is set to true gaps are considered to be runaway or break away gaps, therefore the contrary positions are opened. Positions are exited when the gap has been filled, which in the chart is show'n when the price cross the red level who act as either a take profit (invert = false) or as a stop loss (invert = true). There are various closing conditions available that the user can select from the "close when" setting. New Session : This option close all previous positions when the market is in a new session. New Gap : This option close all previous position when a new gap has been detected. Reverse Position : This option close all previous position when a contrary position to the current one is opened. This option would reduce the number of trades. Testing On Some Stocks The analysis will be tested in different tech stocks with a main TF of 15 minutes with no spread and commissions applied. Default settings will be used. We'll be making our first analysis using AMD, who has recently formed a full reverse HS pattern, where the neckline has been crossed by the price. (by the way i have a bad feeling about it, hey ! feeling filling ! Lame jokes!) Profit: $ -12.22 Trades: 272 Profitability: 65.07 % We can see negative results, with an heavily decreasing balance. Using invert would return positive results. We will now test the strategy on NVDA, the company is one of the biggest when it comes to the Gpu market. Profit: $ -215.54 Trades: 297 Profitability: 60.27 % Not better, using invert would of course create better results. Like AMD the balance is heavily decreasing. Finally we will test the strategy on Seagate technology, a company mostly known for their mechanical hard drives. Profit: $ -4.32 Trades: 261 Profitability: 65.9 % Here the balance does not appear so heavily decreasing and even managed to reach back the initial balance before going down again. Summary A strategy based on gap filling has been briefly introduced and tested with 3 tech stocks. The results show that using invert option might be better. The advantage of this strategy against ones using technical indicators is that this one does not heavily depend on user settings, which make it way more efficient, this a big advantage of patterns based strategies. Thx to LucF for helping with the "process_orders_on_close" element, since i had to use closing price i had to remove it tho, was afraid results would differ even more from a more realistic backtest. And thx for those who continuously support me, more cool stuff is coming up. Thx for reading and i hope you'll have learned something new today !
Strategi Pine Script®
oleh alexgrover
11
ORB Fusion🎯 CORE INNOVATION: INSTITUTIONAL ORB FRAMEWORK WITH FAILED BREAKOUT INTELLIGENCE ORB Fusion represents a complete institutional-grade Opening Range Breakout system combining classic Market Profile concepts (Initial Balance, day type classification) with modern algorithmic breakout detection, failed breakout reversal logic, and comprehensive statistical tracking. Rather than simply drawing lines at opening range extremes, this system implements the full trading methodology used by professional floor traders and market makers—including the critical concept that failed breakouts are often higher-probability setups than successful breakouts . The Opening Range Hypothesis: The first 30-60 minutes of trading establishes the day's value area —the price range where the majority of participants agree on fair value. This range is formed during peak information flow (overnight news digestion, gap reactions, early institutional positioning). Breakouts from this range signal directional conviction; failures to hold breakouts signal trapped participants and create exploitable reversals. Why Opening Range Matters: 1. Information Aggregation : Opening range reflects overnight news, pre-market sentiment, and early institutional orders. It's the market's initial "consensus" on value. 2. Liquidity Concentration : Stop losses cluster just outside opening range. Breakouts trigger these stops, creating momentum. Failed breakouts trap traders, forcing reversals. 3. Statistical Persistence : Markets exhibit range expansion tendency —when price accepts above/below opening range with volume, it often extends 1.0-2.0x the opening range size before mean reversion. 4. Institutional Behavior : Large players (market makers, institutions) use opening range as reference for the day's trading plan. They fade extremes in rotation days and follow breakouts in trend days. Historical Context: Opening Range Breakout methodology originated in commodity futures pits (1970s-80s) where floor traders noticed consistent patterns: the first 30-60 minutes established a "fair value zone," and directional moves occurred when this zone was violated with conviction. J. Peter Steidlmayer formalized this observation in Market Profile theory, introducing the "Initial Balance" concept—the first hour (two 30-minute periods) defining market structure. 📊 OPENING RANGE CONSTRUCTION Four ORB Timeframe Options: 1. 5-Minute ORB (0930-0935 ET): Captures immediate market direction during "opening drive"—the explosive first few minutes when overnight orders hit the tape. Use Case: • Scalping strategies • High-frequency breakout trading • Extremely liquid instruments (ES, NQ, SPY) Characteristics: • Very tight range (often 0.2-0.5% of price) • Early breakouts common (7 of 10 days break within first hour) • Higher false breakout rate (50-60%) • Requires sub-minute chart monitoring Psychology: Captures panic buyers/sellers reacting to overnight news. Range is small because sample size is minimal—only 5 minutes of price discovery. Early breakouts often fail because they're driven by retail FOMO rather than institutional conviction. 2. 15-Minute ORB (0930-0945 ET): Balances responsiveness with statistical validity. Captures opening drive plus initial reaction to that drive. Use Case: • Day trading strategies • Balanced scalping/swing hybrid • Most liquid instruments Characteristics: • Moderate range (0.4-0.8% of price typically) • Breakout rate ~60% of days • False breakout rate ~40-45% • Good balance of opportunity and reliability Psychology: Includes opening panic AND the first retest/consolidation. Sophisticated traders (institutions, algos) start expressing directional bias. This is the "Goldilocks" timeframe—not too reactive, not too slow. 3. 30-Minute ORB (0930-1000 ET): Classic ORB timeframe. Default for most professional implementations. Use Case: • Standard intraday trading • Position sizing for full-day trades • All liquid instruments (equities, indices, futures) Characteristics: • Substantial range (0.6-1.2% of price) • Breakout rate ~55% of days • False breakout rate ~35-40% • Statistical sweet spot for extensions Psychology: Full opening auction + first institutional repositioning complete. By 10:00 AM ET, headlines are digested, early stops are hit, and "real" directional players reveal themselves. This is when institutional programs typically finish their opening positioning. Statistical Advantage: 30-minute ORB shows highest correlation with daily range. When price breaks and holds outside 30m ORB, probability of reaching 1.0x extension (doubling the opening range) exceeds 60% historically. 4. 60-Minute ORB (0930-1030 ET) - Initial Balance: Steidlmayer's "Initial Balance"—the foundation of Market Profile theory. Use Case: • Swing trading entries • Day type classification • Low-frequency institutional setups Characteristics: • Wide range (0.8-1.5% of price) • Breakout rate ~45% of days • False breakout rate ~25-30% (lowest) • Best for trend day identification Psychology: Full first hour captures A-period (0930-1000) and B-period (1000-1030). By 10:30 AM ET, all early positioning is complete. Market has "voted" on value. Subsequent price action confirms (trend day) or rejects (rotation day) this value assessment. Initial Balance Theory: IB represents the market's accepted value area . When price extends significantly beyond IB (>1.5x IB range), it signals a Trend Day —strong directional conviction. When price remains within 1.0x IB, it signals a Rotation Day —mean reversion environment. This classification completely changes trading strategy. 🔬 LTF PRECISION TECHNOLOGY The Chart Timeframe Problem: Traditional ORB indicators calculate range using the chart's current timeframe. This creates critical inaccuracies: Example: • You're on a 5-minute chart • ORB period is 30 minutes (0930-1000 ET) • Indicator sees only 6 bars (30min ÷ 5min/bar = 6 bars) • If any 5-minute bar has extreme wick, entire ORB is distorted The Problem Amplifies: • On 15-minute chart with 30-minute ORB: Only 2 bars sampled • On 30-minute chart with 30-minute ORB: Only 1 bar sampled • Opening spike or single large wick defines entire range (invalid) Solution: Lower Timeframe (LTF) Precision: ORB Fusion uses `request.security_lower_tf()` to sample 1-minute bars regardless of chart timeframe: ``` For 30-minute ORB on 15-minute chart: - Traditional method: Uses 2 bars (15min × 2 = 30min) - LTF Precision: Requests thirty 1-minute bars, calculates true high/low ``` Why This Matters: Scenario: ES futures, 15-minute chart, 30-minute ORB • Traditional ORB: High = 5850.00, Low = 5842.00 (range = 8 points) • LTF Precision ORB: High = 5848.50, Low = 5843.25 (range = 5.25 points) Difference: 2.75 points distortion from single 15-minute wick hitting 5850.00 at 9:31 AM then immediately reversing. LTF precision filters this out by seeing it was a fleeting wick, not a sustained high. Impact on Extensions: With inflated range (8 points vs 5.25 points): • 1.5x extension projects +12 points instead of +7.875 points • Difference: 4.125 points (nearly $200 per ES contract) • Breakout signals trigger late; extension targets unreachable Implementation: ```pinescript getLtfHighLow() => float ha = request.security_lower_tf(syminfo.tickerid, "1", high) float la = request.security_lower_tf(syminfo.tickerid, "1", low) ``` Function returns arrays of 1-minute high/low values, then finds true maximum and minimum across all samples. When LTF Precision Activates: Only when chart timeframe exceeds ORB session window: • 5-minute chart + 30-minute ORB: LTF used (chart TF > session bars needed) • 1-minute chart + 30-minute ORB: LTF not needed (direct sampling sufficient) Recommendation: Always enable LTF Precision unless you're on 1-minute charts. The computational overhead is negligible, and accuracy improvement is substantial. ⚖️ INITIAL BALANCE (IB) FRAMEWORK Steidlmayer's Market Profile Innovation: J. Peter Steidlmayer developed Market Profile in the 1980s for the Chicago Board of Trade. His key insight: market structure is best understood through time-at-price (value area) rather than just price-over-time (traditional charts). Initial Balance Definition: IB is the price range established during the first hour of trading, subdivided into: • A-Period : First 30 minutes (0930-1000 ET for US equities) • B-Period : Second 30 minutes (1000-1030 ET) A-Period vs B-Period Comparison: The relationship between A and B periods forecasts the day: B-Period Expansion (Bullish): • B-period high > A-period high • B-period low ≥ A-period low • Interpretation: Buyers stepping in after opening assessed • Implication: Bullish continuation likely • Strategy: Buy pullbacks to A-period high (now support) B-Period Expansion (Bearish): • B-period low < A-period low • B-period high ≤ A-period high • Interpretation: Sellers stepping in after opening assessed • Implication: Bearish continuation likely • Strategy: Sell rallies to A-period low (now resistance) B-Period Contraction: • B-period stays within A-period range • Interpretation: Market indecisive, digesting A-period information • Implication: Rotation day likely, stay range-bound • Strategy: Fade extremes, sell high/buy low within IB IB Extensions: Professional traders use IB as a ruler to project price targets: Extension Levels: • 0.5x IB : Initial probe outside value (minor target) • 1.0x IB : Full extension (major target for normal days) • 1.5x IB : Trend day threshold (classifies as trending) • 2.0x IB : Strong trend day (rare, ~10-15% of days) Calculation: ``` IB Range = IB High - IB Low Bull Extension 1.0x = IB High + (IB Range × 1.0) Bear Extension 1.0x = IB Low - (IB Range × 1.0) ``` Example: ES futures: • IB High: 5850.00 • IB Low: 5842.00 • IB Range: 8.00 points Extensions: • 1.0x Bull Target: 5850 + 8 = 5858.00 • 1.5x Bull Target: 5850 + 12 = 5862.00 • 2.0x Bull Target: 5850 + 16 = 5866.00 If price reaches 5862.00 (1.5x), day is classified as Trend Day —strategy shifts from mean reversion to trend following. 📈 DAY TYPE CLASSIFICATION SYSTEM Four Day Types (Market Profile Framework): 1. TREND DAY: Definition: Price extends ≥1.5x IB range in one direction and stays there. Characteristics: • Opens and never returns to IB • Persistent directional movement • Volume increases as day progresses (conviction building) • News-driven or strong institutional flow Frequency: ~20-25% of trading days Trading Strategy: • DO: Follow the trend, trail stops, let winners run • DON'T: Fade extremes, take early profits • Key: Add to position on pullbacks to previous extension level • Risk: Getting chopped in false trend (see Failed Breakout section) Example: FOMC decision, payroll report, earnings surprise—anything creating one-sided conviction. 2. NORMAL DAY: Definition: Price extends 0.5-1.5x IB, tests both sides, returns to IB. Characteristics: • Two-sided trading • Extensions occur but don't persist • Volume balanced throughout day • Most common day type Frequency: ~45-50% of trading days Trading Strategy: • DO: Take profits at extension levels, expect reversals • DON'T: Hold for massive moves • Key: Treat each extension as a profit-taking opportunity • Risk: Holding too long when momentum shifts Example: Typical day with no major catalysts—market balancing supply and demand. 3. ROTATION DAY: Definition: Price stays within IB all day, rotating between high and low. Characteristics: • Never accepts outside IB • Multiple tests of IB high/low • Decreasing volume (no conviction) • Classic range-bound action Frequency: ~25-30% of trading days Trading Strategy: • DO: Fade extremes (sell IB high, buy IB low) • DON'T: Chase breakouts • Key: Enter at extremes with tight stops just outside IB • Risk: Breakout finally occurs after multiple failures Example: [/b> Pre-holiday trading, summer doldrums, consolidation after big move. 4. DEVELOPING: Definition: Day type not yet determined (early in session). Usage: Classification before 12:00 PM ET when IB extension pattern unclear. ORB Fusion's Classification Algorithm: ```pinescript if close > ibHigh: ibExtension = (close - ibHigh) / ibRange direction = "BULLISH" else if close < ibLow: ibExtension = (ibLow - close) / ibRange direction = "BEARISH" if ibExtension >= 1.5: dayType = "TREND DAY" else if ibExtension >= 0.5: dayType = "NORMAL DAY" else if close within IB: dayType = "ROTATION DAY" ``` Why Classification Matters: Same setup (bullish ORB breakout) has opposite implications: • Trend Day : Hold for 2.0x extension, trail stops aggressively • Normal Day : Take profits at 1.0x extension, watch for reversal • Rotation Day : Fade the breakout immediately (likely false) Knowing day type prevents catastrophic errors like fading a trend day or holding through rotation. 🚀 BREAKOUT DETECTION & CONFIRMATION Three Confirmation Methods: 1. Close Beyond Level (Recommended): Logic: Candle must close above ORB high (bull) or below ORB low (bear). Why: • Filters out wicks (temporary liquidity grabs) • Ensures sustained acceptance above/below range • Reduces false breakout rate by ~20-30% Example: • ORB High: 5850.00 • Bar high touches 5850.50 (wick above) • Bar closes at 5848.00 (inside range) • Result: NO breakout signal vs. • Bar high touches 5850.50 • Bar closes at 5851.00 (outside range) • Result: BREAKOUT signal confirmed Trade-off: Slightly delayed entry (wait for close) but much higher reliability. 2. Wick Beyond Level: Logic: [/b> Any touch of ORB high/low triggers breakout. Why: • Earliest possible entry • Captures aggressive momentum moves Risk: • High false breakout rate (60-70%) • Stop runs trigger signals • Requires very tight stops (difficult to manage) Use Case: Scalping with 1-2 point profit targets where any penetration = trade. 3. Body Beyond Level: Logic: [/b> Candle body (close vs open) must be entirely outside range. Why: • Strictest confirmation • Ensures directional conviction (not just momentum) • Lowest false breakout rate Example: Trade-off: [/b> Very conservative—misses some valid breakouts but rarely triggers on false ones. Volume Confirmation Layer: All confirmation methods can require volume validation: Volume Multiplier Logic: Rationale: [/b> True breakouts are driven by institutional activity (large size). Volume spike confirms real conviction vs. stop-run manipulation. Statistical Impact: [/b> • Breakouts with volume confirmation: ~65% success rate • Breakouts without volume: ~45% success rate • Difference: 20 percentage points edge Implementation Note: [/b> Volume confirmation adds complexity—you'll miss breakouts that work but lack volume. However, when targeting 1.5x+ extensions (ambitious goals), volume confirmation becomes critical because those moves require sustained institutional participation. Recommended Settings by Strategy: [/b> Scalping (1-2 point targets): [/b> • Method: Close • Volume: OFF • Rationale: Quick in/out doesn't need perfection Intraday Swing (5-10 point targets): [/b> • Method: Close • Volume: ON (1.5x multiplier) • Rationale: Balance reliability and opportunity Position Trading (full-day holds): [/b> • Method: Body • Volume: ON (2.0x multiplier) • Rationale: Must be certain—large stops require high win rate 🔥 FAILED BREAKOUT SYSTEM The Core Insight: [/b> Failed breakouts are often more profitable [/b> than successful breakouts because they create trapped traders with predictable behavior. Failed Breakout Definition: [/b> A breakout that: 1. Initially penetrates ORB level with confirmation 2. Attracts participants (volume spike, momentum) 3. Fails to extend (stalls or immediately reverses) 4. Returns inside ORB range within N bars Psychology of Failure: [/b> When breakout fails: • Breakout buyers are trapped [/b>: Bought at ORB high, now underwater • Early longs reduce: Take profit, fearful of reversal • Shorts smell blood: See failed breakout as reversal signal • Result: Cascade of selling as trapped bulls exit + new shorts enter Mirror image for failed bearish breakouts (trapped shorts cover + new longs enter). Failure Detection Parameters: [/b> 1. Failure Confirmation Bars (default: 3): [/b> How many bars after breakout to confirm failure? Logic: Settings: [/b> • 2 bars: Aggressive failure detection (more signals, more false failures) • 3 bars Balanced (default) • 5-10 bars: Conservative (wait for clear reversal) Why This Matters: Too few bars: You call "failed breakout" when price is just consolidating before next leg. Too many bars: You miss the reversal entry (price already back in range). 2. Failure Buffer (default: 0.1 ATR): [/b> How far inside ORB must price return to confirm failure? Formula: Why Buffer Matters: clear rejection [/b> (not just hovering at level). Settings: [/b> • 0.0 ATR: No buffer, immediate failure signal • 0.1 ATR: Small buffer (default) - filters noise • [b>0.2-0.3 ATR: Large buffer - only dramatic failures count Example: Reversal Entry System: [/b> When failure confirmed, system generates complete reversal trade: For Failed Bull Breakout (Short Reversal): [/b> Entry: [/b> Current close when failure confirmed Stop Loss: [/b> Extreme high since breakout + 0.10 ATR padding Target 1: [/b> ORB High - (ORB Range × 0.5) Target 2: Target 3: [/b> ORB High - (ORB Range × 1.5) Example: • ORB High: 5850, ORB Low: 5842, Range: 8 points • Breakout to 5853, fails, reverses to 5848 (entry) • Stop: 5853 + 1 = 5854 (6 point risk) • T1: 5850 - 4 = 5846 (-2 points, 1:3 R:R) • T2: 5850 - 8 = 5842 (-6 points, 1:1 R:R) • T3: 5850 - 12 = 5838 (-10 points, 1.67:1 R:R) [b>Why These Targets? [/b> • T1 (0.5x ORB below high): Trapped bulls start panic • T2 (1.0x ORB = ORB Mid): Major retracement, momentum fully reversed • T3 (1.5x ORB): Reversal extended, now targeting opposite side Historical Performance: [/b> Failed breakout reversals in ORB Fusion's tracking system show: • Win Rate: 65-75% (significantly higher than initial breakouts) • Average Winner: 1.2x ORB range • Average Loser: 0.5x ORB range (protected by stop at extreme) • Expectancy: Strongly positive even with <70% win rate Why Failed Breakouts Outperform: [/b> 1. Information Advantage: You now know what price did (failed to extend). Initial breakout trades are speculative; reversal trades are reactive to confirmed failure. 2. Trapped Participant Pressure: Every trapped bull becomes a seller. This creates sustained pressure. 3. Stop Loss Clarity: Extreme high is obvious stop (just beyond recent high). Breakout trades have ambiguous stops (ORB mid? Recent low? Too wide or too tight). 4. Mean Reversion Edge: Failed breakouts return to value (ORB mid). Initial breakouts try to escape value (harder to sustain). Critical Insight: [/b> "The best trade is often the one that trapped everyone else." Failed breakouts create asymmetric opportunity because you're trading against [/b> trapped participants rather than with [/b> them. When you see a failed breakout signal, you're seeing real-time evidence that the market rejected directional conviction—that's exploitable. 📐 FIBONACCI EXTENSION SYSTEM Six Extension Levels: [/b> Extensions project how far price will travel after ORB breakout. Based on Fibonacci ratios + empirical market behavior. 1. 1.272x (27.2% Extension): [/b> Formula: [/b> ORB High/Low + (ORB Range × 0.272) Psychology: [/b> Initial probe beyond ORB. Early momentum + trapped shorts (on bull side) covering. Probability of Reach: [/b> ~75-80% after confirmed breakout Trading: [/b> • First resistance/support after breakout • Partial profit target (take 30-50% off) • Watch for rejection here (could signal failure in progress) Why 1.272? [/b> Related to harmonic patterns (1.272 is √1.618). Empirically, markets often stall at 25-30% extension before deciding whether to continue or fail. 2. 1.5x (50% Extension): Formula: [/b> ORB High/Low + (ORB Range × 0.5) Psychology: [/b> Breakout gaining conviction. Requires sustained buying/selling (not just momentum spike). Probability of Reach: [/b> ~60-65% after confirmed breakout Trading: [/b> • Major partial profit (take 50-70% off) • Move stops to breakeven • Trail remaining position Why 1.5x? [/b> Classic halfway point to 2.0x. Markets often consolidate here before final push. If day type is "Normal," this is likely the high/low for the day. 3. 1.618x (Golden Ratio Extension): [/b> Formula: [/b> ORB High/Low + (ORB Range × 0.618) Psychology: [/b> Strong directional day. Institutional conviction + retail FOMO. Probability of Reach: [/b> ~45-50% after confirmed breakout Trading: [/b> • Final partial profit (close 80-90%) • Trail remainder with wide stop (allow breathing room) Why 1.618? [/b> Fibonacci golden ratio. Appears consistently in market geometry. When price reaches 1.618x extension, move is "mature" and reversal risk increases. 4. 2.0x (100% Extension): [/b> Formula: ORB High/Low + (ORB Range × 1.0) Psychology: [/b> Trend day confirmed. Opening range completely duplicated. Probability of Reach: [/b> ~30-35% after confirmed breakout Trading: Why 2.0x? [/b> Psychological level—range doubled. Also corresponds to typical daily ATR in many instruments (opening range ~ 0.5 ATR, daily range ~ 1.0 ATR). 5. 2.618x (Super Extension): Formula: [/b> ORB High/Low + (ORB Range × 1.618) Psychology: [/b> Parabolic move. News-driven or squeeze. Probability of Reach: [/b> ~10-15% after confirmed breakout [b>Trading: Why 2.618? [/b> Fibonacci ratio (1.618²). Rare to reach—when it does, move is extreme. Often precedes multi-day consolidation or reversal. 6. 3.0x (Extreme Extension): [/b> Formula: [/b> ORB High/Low + (ORB Range × 2.0) Psychology: [/b> Market melt-up/crash. Only in extreme events. [b>Probability of Reach: [/b> <5% after confirmed breakout Trading: [/b> • Close immediately if reached • These are outlier events (black swans, flash crashes, squeeze-outs) • Holding for more is greed—take windfall profit Why 3.0x? [/b> Triple opening range. So rare it's statistical noise. When it happens, it's headline news. Visual Example: ES futures, ORB 5842-5850 (8 point range), Bullish breakout: • ORB High : 5850.00 (entry zone) • 1.272x : 5850 + 2.18 = 5852.18 (first resistance) • 1.5x : 5850 + 4.00 = 5854.00 (major target) • 1.618x : 5850 + 4.94 = 5854.94 (strong target) • 2.0x : 5850 + 8.00 = 5858.00 (trend day) • 2.618x : 5850 + 12.94 = 5862.94 (extreme) • 3.0x : 5850 + 16.00 = 5866.00 (parabolic) Profit-Taking Strategy: Optimal scaling out at extensions: • Breakout entry at 5850.50 • 30% off at 1.272x (5852.18) → +1.68 points • 40% off at 1.5x (5854.00) → +3.50 points • 20% off at 1.618x (5854.94) → +4.44 points • 10% off at 2.0x (5858.00) → +7.50 points [b>Average Exit: Conclusion: [/b> Scaling out at extensions produces 40% higher expectancy than holding for home runs. 📊 GAP ANALYSIS & FILL PSYCHOLOGY [b>Gap Definition: [/b> Price discontinuity between previous close and current open: • Gap Up : Open > Previous Close + noise threshold (0.1 ATR) • Gap Down : Open < Previous Close - noise threshold Why Gaps Matter: [/b> Gaps represent unfilled orders [/b>. When market gaps up, all limit buy orders between yesterday's close and today's open are never filled. Those buyers are "left behind." Psychology: they wait for price to return ("fill the gap") so they can enter. This creates magnetic pull [/b> toward gap level. Gap Fill Statistics (Empirical): [/b> • Gaps <0.5% [/b>: 85-90% fill within same day • Gaps 0.5-1.0% [/b>: 70-75% fill within same day, 90%+ within week • Gaps >1.0% [/b>: 50-60% fill within same day (major news often prevents fill) Gap Fill Strategy: [/b> Setup 1: Gap-and-Go Gap opens, extends away from gap (doesn't fill). • ORB confirms direction away from gap • Trade WITH ORB breakout direction • Expectation: Gap won't fill today (momentum too strong) Setup 2: Gap-Fill Fade Gap opens, but fails to extend. Price drifts back toward gap. • ORB breakout TOWARD gap (not away) • Trade toward gap fill level • Target: Previous close (gap fill complete) Setup 3: Gap-Fill Rejection Gap fills (touches previous close) then rejects. • ORB breakout AWAY from gap after fill • Trade away from gap direction • Thesis: Gap filled (orders executed), now resume original direction [b>Example: Scenario A (Gap-and-Go): • ORB breaks upward to $454 (away from gap) • Trade: LONG breakout, expect continued rally • Gap becomes support ($452) Scenario B (Gap-Fill): • ORB breaks downward through $452.50 (toward gap) • Trade: SHORT toward gap fill at $450.00 • Target: $450.00 (gap filled), close position Scenario C (Gap-Fill Rejection): • Price drifts to $450.00 (gap filled) early in session • ORB establishes $450-$451 after gap fill • ORB breaks upward to $451.50 • Trade: LONG breakout (gap is filled, now resume rally) ORB Fusion Integration: [/b> Dashboard shows: • Gap type (Up/Down/None) • Gap size (percentage) • Gap fill status (Filled ✓ / Open) This informs setup confidence: • ORB breakout AWAY from unfilled gap: +10% confidence (gap becomes support/resistance) • ORB breakout TOWARD unfilled gap: -10% confidence (gap fill may override ORB) [b>📈 VWAP & INSTITUTIONAL BIAS [/b> [b>Volume-Weighted Average Price (VWAP): [/b> Average price weighted by volume at each price level. Represents true "average" cost for the day. [b>Calculation: Institutional Benchmark [/b>: Institutions (mutual funds, pension funds) use VWAP as performance benchmark. If they buy above VWAP, they underperformed; below VWAP, they outperformed. 2. [b>Algorithmic Target [/b>: Many algos are programmed to buy below VWAP and sell above VWAP to achieve "fair" execution. 3. [b>Support/Resistance [/b>: VWAP acts as dynamic support (price above) or resistance (price below). [b>VWAP Bands (Standard Deviations): [/b> • [b>1σ Band [/b>: VWAP ± 1 standard deviation - Contains ~68% of volume - Normal trading range - Bounces common • [b>2σ Band [/b>: VWAP ± 2 standard deviations - Contains ~95% of volume - Extreme extension - Mean reversion likely ORB + VWAP Confluence: [/b> Highest-probability setups occur when ORB and VWAP align: Bullish Confluence: [/b> • ORB breakout upward (bullish signal) • Price above VWAP (institutional buying) • Confidence boost: +15% Bearish Confluence: [/b> • ORB breakout downward (bearish signal) • Price below VWAP (institutional selling) • Confidence boost: +15% [b>Divergence Warning: • ORB breakout upward BUT price below VWAP • Conflict: Breakout says "buy," VWAP says "sell" • Confidence penalty: -10% • Interpretation: Retail buying but institutions not participating (lower quality breakout) 📊 MOMENTUM CONTEXT SYSTEM [b>Innovation: Candle Coloring by Position Rather than fixed support/resistance lines, ORB Fusion colors candles based on their [b>relationship to ORB : [b>Three Zones: [/b> 1. Inside ORB (Blue Boxes): [/b> [b>Calculation: • Darker blue: Near extremes of ORB (potential breakout imminent) • Lighter blue: Near ORB mid (consolidation) [b>Trading: [/b> Coiled spring—await breakout. [b>2. Above ORB (Green Boxes): [b>Calculation: 3. Below ORB (Red Boxes): Mirror of above ORB logic. [b>Special Contexts: [/b> [b>Breakout Bar (Darkest Green/Red): [/b> The specific bar where breakout occurs gets maximum color intensity regardless of distance. This highlights the pivotal moment. [b>Failed Breakout Bar (Orange/Warning): [/b> When failed breakout is confirmed, that bar gets orange/warning color. Visual alert: "reversal opportunity here." [b>Near Extension (Cyan/Magenta Tint): [/b> When price is within 0.5 ATR of an extension level, candle gets tinted cyan (bull) or magenta (bear). Indicates "target approaching—prepare to take profit." [b>Why Visual Context? [/b> Traditional indicators show lines. ORB Fusion shows [b>context-aware momentum [/b>. Glance at chart: • Lots of blue? Consolidation day (fade extremes). • Progressive green? Trend day (follow). • Green then orange? Failed breakout (reversal setup). This visual language communicates market state instantly—no interpretation needed. 🎯 TRADE SETUP GENERATION & GRADING [/b> [b>Algorithmic Setup Detection: [/b> ORB Fusion continuously evaluates market state and generates current best trade setup with: • Action (LONG / SHORT / FADE HIGH / FADE LOW / WAIT) • Entry price • Stop loss • Three targets • Risk:Reward ratio • Confidence score (0-100) • Grade (A+ to D) [b>Setup Types: [/b> [b>1. ORB LONG (Bullish Breakout): [/b> [b>Trigger: [/b> • Bullish ORB breakout confirmed • Not failed [b>Parameters: • Entry: Current close • Stop: ORB mid (protects against failure) • T1: ORB High + 0.5x range (1.5x extension) • T2: ORB High + 1.0x range (2.0x extension) • T3: ORB High + 1.618x range (2.618x extension) [b>Confidence Scoring: [b>Trigger: [/b> • Bearish breakout occurred • Failed (returned inside ORB) [b>Parameters: [/b> • Entry: Close when failure confirmed • Stop: Extreme low since breakout + 0.10 ATR • T1: ORB Low + 0.5x range • T2: ORB Low + 1.0x range (ORB mid) • T3: ORB Low + 1.5x range [b>Confidence Scoring: [b>Trigger: • Inside ORB • Close > ORB mid (near high) [b>Parameters: [/b> • Entry: ORB High (limit order) • Stop: ORB High + 0.2x range • T1: ORB Mid • T2: ORB Low [b>Confidence Scoring: [/b> Base: 40 points (lower base—range fading is lower probability than breakout/reversal) [b>Use Case: [/b> Rotation days. Not recommended on normal/trend days. [b>6. FADE LOW (Range Trade): Mirror of FADE HIGH. [b>7. WAIT: [b>Trigger: [/b> • ORB not complete yet OR • No clear setup (price in no-man's-land) [b>Action: [/b> Observe, don't trade. [b>Confidence: [/b> 0 points [b>Grading System: ``` Confidence → Grade 85-100 → A+ 75-84 → A 65-74 → B+ 55-64 → B 45-54 → C 0-44 → D ``` [b>Grade Interpretation: [/b> • [b>A+ / A: High probability setup. Take these trades. • [b>B+ / B [/b>: Decent setup. Trade if fits system rules. • [b>C [/b>: Marginal setup. Only if very experienced. • [b>D [/b>: Poor setup or no setup. Don't trade. [b>Example Scenario: [/b> ES futures: • ORB: 5842-5850 (8 point range) • Bullish breakout to 5851 confirmed • Volume: 2.0x average (confirmed) • VWAP: 5845 (price above VWAP ✓) • Day type: Developing (too early, no bonus) • Gap: None [b>Setup: [/b> • Action: LONG • Entry: 5851 • Stop: 5846 (ORB mid, -5 point risk) • T1: 5854 (+3 points, 1:0.6 R:R) • T2: 5858 (+7 points, 1:1.4 R:R) • T3: 5862.94 (+11.94 points, 1:2.4 R:R) [b>Confidence: LONG with 55% confidence. Interpretation: Solid setup, not perfect. Trade it if your system allows B-grade signals. [b>📊 STATISTICS TRACKING & PERFORMANCE ANALYSIS [/b> [b>Real-Time Performance Metrics: [/b> ORB Fusion tracks comprehensive statistics over user-defined lookback (default 50 days): [b>Breakout Performance: [/b> • [b>Bull Breakouts: [/b> Total count, wins, losses, win rate • [b>Bear Breakouts: [/b> Total count, wins, losses, win rate [b>Win Definition: [/b> Breakout reaches ≥1.0x extension (doubles the opening range) before end of day. [b>Example: [/b> • ORB: 5842-5850 (8 points) • Bull breakout at 5851 • Reaches 5858 (1.0x extension) by close • Result: WIN [b>Failed Breakout Performance: [/b> • [b>Total Failed Breakouts [/b>: Count of breakouts that failed • [b>Reversal Wins [/b>: Count where reversal trade reached target • [b>Failed Reversal Win Rate [/b>: Wins / Total Failed [b>Win Definition for Reversals: [/b> • Failed bull → reversal short reaches ORB mid • Failed bear → reversal long reaches ORB mid [b>Extension Tracking: [/b> • [b>Average Extension Reached [/b>: Mean of maximum extension achieved across all breakout days • [b>Max Extension Overall [/b>: Largest extension ever achieved in lookback period [b>Example: 🎨 THREE DISPLAY MODES [b>Design Philosophy: [/b> Not all traders need all features. Beginners want simplicity. Professionals want everything. ORB Fusion adapts. [b>SIMPLE MODE: [/b> [b>Shows: [/b> • Primary ORB levels (High, Mid, Low) • ORB box • Breakout signals (triangles) • Failed breakout signals (crosses) • Basic dashboard (ORB status, breakout status, setup) • VWAP [b>Hides: [/b> • Session ORBs (Asian, London, NY) • IB levels and extensions • ORB extensions beyond basic levels • Gap analysis visuals • Statistics dashboard • Momentum candle coloring • Narrative dashboard [b>Use Case: [/b> • Traders who want clean chart • Focus on core ORB concept only • Mobile trading (less screen space) [b>STANDARD MODE: [b>Shows Everything in Simple Plus: [/b> • Session ORBs (Asian, London, NY) • IB levels (high, low, mid) • IB extensions • ORB extensions (1.272x, 1.5x, 1.618x, 2.0x) • Gap analysis and fill targets • VWAP bands (1σ and 2σ) • Momentum candle coloring • Context section in dashboard • Narrative dashboard [b>Hides: [/b> • Advanced extensions (2.618x, 3.0x) • Detailed statistics dashboard [b>Use Case: [/b> • Most traders • Balance between information and clarity • Covers 90% of use cases [b>ADVANCED MODE: [b>Shows Everything: • All session ORBs • All IB levels and extensions • All ORB extensions (including 2.618x and 3.0x) • Full gap analysis • VWAP with both 1σ and 2σ bands • Momentum candle coloring • Complete statistics dashboard • Narrative dashboard • All context metrics [b>Use Case: [/b> • Professional traders • System developers • Those who want maximum information density [b>Switching Modes: [/b> Single dropdown input: "Display Mode" → Simple / Standard / Advanced Entire indicator adapts instantly. No need to toggle 20 individual settings. 📖 NARRATIVE DASHBOARD [b>Innovation: Plain-English Market State [/b> Most indicators show data. ORB Fusion explains what the data [b>means [/b>. [b>Narrative Components: [/b> [b>1. Phase: [/b> • "📍 Building ORB..." (during ORB session) • "📊 Trading Phase" (after ORB complete) • "⏳ Pre-Market" (before ORB session) [b>2. Status (Current Observation): [/b> • "⚠️ Failed breakout - reversal likely" • "🚀 Bullish momentum in play" • "📉 Bearish momentum in play" • "⚖️ Consolidating in range" • "👀 Monitoring for setup" [b>3. Next Level: Tells you what to watch for: • "🎯 1.5x @ 5854.00" (next extension target) • "Watch ORB levels" (inside range, await breakout) [b>4. Setup: [/b> Current trade setup + grade: • "LONG " (bullish breakout, A-grade) • "🔥 SHORT REVERSAL " (failed bull breakout, A+-grade) • "WAIT " (no setup) [b>5. Reason: [/b> Why this setup exists: • "ORB Bullish Breakout" • "Failed Bear Breakout - High Probability Reversal" • "Range Fade - Near High" [b>6. Tip (Market Insight): Contextual advice: • "🔥 TREND DAY - Trail stops" (day type is trending) • "🔄 ROTATION - Fade extremes" (day type is rotating) • "📊 Gap unfilled - magnet level" (gap creates target) • "📈 Normal conditions" (no special context) [b>Example Narrative: ``` 📖 ORB Narrative ━━━━━━━━━━━━━━━━ Phase | 📊 Trading Phase Status | 🚀 Bullish momentum in play Next | 🎯 1.5x @ 5854.00 📈 Setup | LONG Reason | ORB Bullish Breakout 💡 Tip | 🔥 TREND DAY - Trail stops ``` [b>Glance Interpretation: [/b> "We're in trading phase. Bullish breakout happened (momentum in play). Next target is 1.5x extension at 5854. Current setup is LONG with A-grade. It's a trend day, so trail stops (don't take early profits)." Complete market state communicated in 6 lines. No interpretation needed. [b>Why This Matters: Beginner traders struggle with "So what?" question. Indicators show lines and signals, but what does it mean [/b>? Narrative dashboard bridges this gap. Professional traders benefit too—rapid context assessment during fast-moving markets. No time to analyze; glance at narrative, get action plan. 🔔 INTELLIGENT ALERT SYSTEM [b>Four Alert Types: [/b> [b>1. Breakout Alert: [/b> [b>Trigger: [/b> ORB breakout confirmed (bull or bear) [b>Message: [/b> ``` 🚀 ORB BULLISH BREAKOUT Price: 5851.00 Volume Confirmed Grade: A ``` [b>Frequency: [/b> Once per bar (prevents spam) [b>2. Failed Breakout Alert: [/b> [b>Trigger: [/b> Breakout fails, reversal setup generated [b>Message: [/b> ``` 🔥 FAILED BULLISH BREAKOUT! HIGH PROBABILITY SHORT REVERSAL Entry: 5848.00 Stop: 5854.00 T1: 5846.00 T2: 5842.00 Historical Win Rate: 73% ``` [b>Why Comprehensive? [/b> Failed breakout alerts include complete trade plan. You can execute immediately from alert—no need to check chart. [b>3. Extension Alert: [b>Trigger: [/b> Price reaches extension level for first time [b>Message: [/b> ``` 🎯 Bull Extension 1.5x reached @ 5854.00 ``` [b>Use: [/b> Profit-taking reminder. When extension hit, consider scaling out. [b>4. IB Break Alert: [/b> [b>Trigger: [/b> Price breaks above IB high or below IB low [b>Message: [/b> ``` 📊 IB HIGH BROKEN - Potential Trend Day ``` [b>Use: [/b> Day type classification. IB break suggests trend day developing—adjust strategy to trend-following mode. [b>Alert Management: [/b> Each alert type can be enabled/disabled independently. Prevents notification overload. [b>Cooldown Logic: [/b> Alerts won't fire if same alert type triggered within last bar. Prevents: • "Breakout" alert every tick during choppy breakout • Multiple "extension" alerts if price oscillates at level Ensures: One clean alert per event. ⚙️ KEY PARAMETERS EXPLAINED [b>Opening Range Settings: [/b> • [b>ORB Timeframe [/b> (5/15/30/60 min): Duration of opening range window - 30 min recommended for most traders • [b>Use RTH Only [/b> (ON/OFF): Only trade during regular trading hours - ON recommended (avoids thin overnight markets) • [b>Use LTF Precision [/b> (ON/OFF): Sample 1-minute bars for accuracy - ON recommended (critical for charts >1 minute) • [b>Precision TF [/b> (1/5 min): Timeframe for LTF sampling - 1 min recommended (most accurate) [b>Session ORBs: [/b> • [b>Show Asian/London/NY ORB [/b> (ON/OFF): Display multi-session ranges - OFF in Simple mode - ON in Standard/Advanced if trading 24hr markets • [b>Session Windows [/b>: Time ranges for each session ORB - Defaults align with major session opens [b>Initial Balance: [/b> • [b>Show IB [/b> (ON/OFF): Display Initial Balance levels - ON recommended for day type classification • [b>IB Session Window [/b> (0930-1030): First hour of trading - Default is standard for US equities • [b>Show IB Extensions [/b> (ON/OFF): Project IB extension targets - ON recommended (identifies trend days) • [b>IB Extensions 1-4 [/b> (0.5x, 1.0x, 1.5x, 2.0x): Extension multipliers - Defaults are Market Profile standard [b>ORB Extensions: [/b> • [b>Show Extensions [/b> (ON/OFF): Project ORB extension targets - ON recommended (defines profit targets) • [b>Enable Individual Extensions [/b> (1.272x, 1.5x, 1.618x, 2.0x, 2.618x, 3.0x) - Enable 1.272x, 1.5x, 1.618x, 2.0x minimum - Disable 2.618x and 3.0x unless trading very volatile instruments [b>Breakout Detection: • [b>Confirmation Method [/b> (Close/Wick/Body): - Close recommended (best balance) - Wick for scalping - Body for conservative • [b>Require Volume Confirmation [/b> (ON/OFF): - ON recommended (increases reliability) • [b>Volume Multiplier [/b> (1.0-3.0): - 1.5x recommended - Lower for thin instruments - Higher for heavy volume instruments [b>Failed Breakout System: [/b> • [b>Enable Failed Breakouts [/b> (ON/OFF): - ON strongly recommended (highest edge) • [b>Bars to Confirm Failure [/b> (2-10): - 3 bars recommended - 2 for aggressive (more signals, more false failures) - 5+ for conservative (fewer signals, higher quality) • [b>Failure Buffer [/b> (0.0-0.5 ATR): - 0.1 ATR recommended - Filters noise during consolidation near ORB level • [b>Show Reversal Targets [/b> (ON/OFF): - ON recommended (visualizes trade plan) • [b>Reversal Target Mults [/b> (0.5x, 1.0x, 1.5x): - Defaults are tested values - Adjust based on average daily range [b>Gap Analysis: • [b>Show Gap Analysis [/b> (ON/OFF): - ON if trading instruments that gap frequently - OFF for 24hr markets (forex, crypto—no gaps) • [b>Gap Fill Target [/b> (ON/OFF): - ON to visualize previous close (gap fill level) [b>VWAP: • [b>Show VWAP [/b> (ON/OFF): - ON recommended (key institutional level) • [b>Show VWAP Bands [/b> (ON/OFF): - ON in Standard/Advanced - OFF in Simple • [b>Band Multipliers (1.0σ, 2.0σ): - Defaults are standard - 1σ = normal range, 2σ = extreme [b>Day Type: [/b> • [b>Show Day Type Analysis [/b> (ON/OFF): - ON recommended (critical for strategy adaptation) • [b>Trend Day Threshold [/b> (1.0-2.5 IB mult): - 1.5x recommended - When price extends >1.5x IB, classifies as Trend Day [b>Enhanced Visuals: • [b>Show Momentum Candles [/b> (ON/OFF): - ON for visual context - OFF if chart gets too colorful • [b>Show Gradient Zone Fills [/b> (ON/OFF): - ON for professional look - OFF for minimalist chart • [b>Label Display Mode [/b> (All/Adaptive/Minimal): - Adaptive recommended (shows nearby labels only) - All for information density - Minimal for clean chart • [b>Label Proximity [/b> (1.0-5.0 ATR): - 3.0 ATR recommended - Labels beyond this distance are hidden (Adaptive mode) [b>🎓 PROFESSIONAL USAGE PROTOCOL [/b> [b>Phase 1: Learning the System (Week 1) [/b> [b>Goal: [/b> Understand ORB concepts and dashboard interpretation [b>Setup: [/b> • Display Mode: STANDARD • ORB Timeframe: 30 minutes • Enable ALL features (IB, extensions, failed breakouts, VWAP, gap analysis) • Enable statistics tracking [b>Actions: [/b> • Paper trade ONLY—no real money • Observe ORB formation every day (9:30-10:00 AM ET for US markets) • Note when ORB breakouts occur and if they extend • Note when breakouts fail and reversals happen • Watch day type classification evolve during session • Track statistics—which setups are working? [b>Key Learning: [/b> • How often do breakouts reach 1.5x extension? (typically 50-60% of confirmed breakouts) • How often do breakouts fail? (typically 30-40%) • Which setup grade (A/B/C) actually performs best? (should see A-grade outperforming) • What day type produces best results? (trend days favor breakouts, rotation days favor fades) [b>Phase 2: Parameter Optimization (Week 2) [/b> [b>Goal: [/b> Tune system to your instrument and timeframe [b>ORB Timeframe Selection: • Run 5 days with 15-minute ORB • Run 5 days with 30-minute ORB • Compare: Which captures better breakouts on your instrument? • Typically: 30-minute optimal for most, 15-minute for very liquid (ES, SPY) [b>Volume Confirmation Testing: • Run 5 days WITH volume confirmation • Run 5 days WITHOUT volume confirmation • Compare: Does volume confirmation increase win rate? • If win rate improves by >5%: Keep volume confirmation ON • If no improvement: Turn OFF (avoid missing valid breakouts) [b>Failed Breakout Bars: [b>Goal: [/b> Develop personal trading rules based on system signals [b>Setup Selection Rules: [/b> Define which setups you'll trade: • [b>Conservative: [/b> Only A+ and A grades • [b>Balanced: [/b> A+, A, B+ grades • [b>Aggressive: [/b> All grades B and above Test each approach for 5-10 trades, compare results. [b>Position Sizing by Grade: [/b> Consider risk-weighting by setup quality: • A+ grade: 100% position size • A grade: 75% position size • B+ grade: 50% position size • B grade: 25% position size Example: If max risk is $1000/trade: • A+ setup: Risk $1000 • A setup: Risk $750 • B+ setup: Risk $500 This matches bet sizing to edge. [b>Day Type Adaptation: [/b> Create rules for different day types: Trend Days: • Take ALL breakout signals (A/B/C grades) • Hold for 2.0x extension minimum • Trail stops aggressively (1.0 ATR trail) • DON'T fade—reversals unlikely Rotation Days: • ONLY take failed breakout reversals • Ignore initial breakout signals (likely to fail) • Take profits quickly (0.5x extension) • Focus on fade setups (Fade High/Fade Low) Normal Days: • Take A/A+ breakout signals only • Take ALL failed breakout reversals (high probability) • Target 1.0-1.5x extensions • Partial profit-taking at extensions Time-of-Day Rules: [/b> Breakouts at different times have different probabilities: 10:00-10:30 AM (Early Breakout): • ORB just completed • Fresh breakout • Probability: Moderate (50-55% reach 1.0x) • Strategy: Conservative position sizing 10:30-12:00 PM (Mid-Morning): • Momentum established • Volume still healthy • Probability: High (60-65% reach 1.0x) • Strategy: Standard position sizing 12:00-2:00 PM (Lunch Doldrums): • Volume dries up • Whipsaw risk increases • Probability: Low (40-45% reach 1.0x) • Strategy: Avoid new entries OR reduce size 50% 2:00-4:00 PM (Afternoon Session): • Late-day positioning • EOD squeezes possible • Probability: Moderate-High (55-60%) • Strategy: Watch for IB break—if trending all day, follow [b>Phase 4: Live Micro-Sizing (Month 2) [/b> [b>Goal: [/b> Validate paper trading results with minimal risk [b>Setup: [/b> • 10-20% of intended full position size • Take ONLY A+ and A grade setups • Follow stop loss and targets religiously [b>Execution: [/b> • Execute from alerts OR from dashboard setup box • Entry: Close of signal bar OR next bar market order • Stop: Use exact stop from setup (don't widen) • Targets: Scale out at T1/T2/T3 as indicated [b>Tracking: [/b> • Log every trade: Entry, Exit, Grade, Outcome, Day Type • Calculate: Win rate, Average R-multiple, Max consecutive losses • Compare to paper trading results (should be within 15%) [b>Red Flags: [/b> • Win rate <45%: System not suitable for this instrument/timeframe • Major divergence from paper trading: Execution issues (slippage, late entries, emotional exits) • Max consecutive losses >8: Hitting rough patch OR market regime changed [b>Phase 5: Scaling Up (Months 3-6) [b>Goal: [/b> Gradually increase to full position size [b>Progression: [/b> • Month 3: 25-40% size (if micro-sizing profitable) • Month 4: 40-60% size • Month 5: 60-80% size • Month 6: 80-100% size [b>Milestones Required to Scale Up: [/b> • Minimum 30 trades at current size • Win rate ≥48% • Profit factor ≥1.2 • Max drawdown <20% • Emotional control (no revenge trading, no FOMO) [b>Advanced Techniques: [b>Multi-Timeframe ORB: Assumes first 30-60 minutes establish value. Violation: Market opens after major news, price discovery continues for hours (opening range meaningless). 2. [b>Volume Indicates Conviction: ES, NQ, RTY, SPY, QQQ—high liquidity, clean ORB formation, reliable extensions • [b>Large-Cap Stocks: AAPL, MSFT, TSLA, NVDA (>$5B market cap, >5M daily volume) • [b>Liquid Futures: CL (crude oil), GC (gold), 6E (EUR/USD), ZB (bonds)—24hr markets benefit from session ORBs • [b>Major Forex Pairs: [/b> EUR/USD, GBP/USD, USD/JPY—London/NY session ORBs work well [b>Performs Poorly On: [/b> • [b>Illiquid Stocks: <$1M daily volume, wide spreads, gappy price action • [b>Penny Stocks: [/b> Manipulated, pump-and-dump, no real price discovery • [b>Low-Volume ETFs: Exotic sector ETFs, leveraged products with thin volume • [b>Crypto on Sketchy Exchanges: Wash trading, spoofing invalidates volume analysis • [b>Earnings Days: [/b> ORB completes before earnings release, then completely resets (useless) • Binary Event Days: FDA approvals, court rulings—discontinuous price action [b>Known Weaknesses: [/b> • [b>Slow Starts: ORB doesn't complete until 10:00 AM (30-min ORB). Early morning traders have no signals for 30 minutes. Consider using 15-minute ORB if this is problematic. • [b>Failure Detection Lag: [/b> Failed breakout requires 3+ bars to confirm. By the time system signals reversal, price may have already moved significantly back inside range. Manual traders watching in real-time can enter earlier. • [b>Extension Overshoot: [/b> System projects extensions mathematically (1.5x, 2.0x, etc.). Actual moves may stop short (1.3x) or overshoot (2.2x). Extensions are targets, not magnets. • [b>Day Type Misclassification: [/b> Early in session, day type is "Developing." By the time it's classified definitively (often 11:00 AM+), half the day is over. Strategy adjustments happen late. • [b>Gap Assumptions: [/b> System assumes gaps want to fill. Strong trend days never fill gaps (gap becomes support/resistance forever). Blindly trading toward gaps can backfire on trend days. • [b>Volume Data Quality: Forex doesn't have centralized volume (uses tick volume as proxy—less reliable). Crypto volume is often fake (wash trading). Volume confirmation less effective on these instruments. • [b>Multi-Session Complexity: [/b> When using Asian/London/NY ORBs simultaneously, chart becomes cluttered. Requires discipline to focus on relevant session for current time. [b>Risk Factors: [/b> • [b>Opening Gaps: Large gaps (>2%) can create distorted ORBs. Opening range might be unusually wide or narrow, making extensions unreliable. • [b>Low Volatility Environments:[/b> When VIX <12, opening ranges can be tiny (0.2-0.3%). Extensions are equally tiny. Profit targets don't justify commission/slippage. • [b>High Volatility Environments:[/b> When VIX >30, opening ranges are huge (2-3%+). Extensions project unrealistic targets. Failed breakouts happen faster (volatility whipsaw). • [b>Algorithm Dominance:[/b> In heavily algorithmic markets (ES during overnight session), ORB levels can be manipulated—algos pin price to ORB high/low intentionally. Breakouts become stop-runs rather than genuine directional moves. [b>⚠️ RISK DISCLOSURE[/b> Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Opening Range Breakout strategies, while based on sound market structure principles, do not guarantee profits and can result in significant losses. The ORB Fusion indicator implements professional trading concepts including Opening Range theory, Market Profile Initial Balance analysis, Fibonacci extensions, and failed breakout reversal logic. These methodologies have theoretical foundations but past performance—whether backtested or live—is not indicative of future results. Opening Range theory assumes the first 30-60 minutes of trading establish a meaningful value area and that breakouts from this range signal directional conviction. This assumption may not hold during: • Major news events (FOMC, NFP, earnings surprises) • Market structure changes (circuit breakers, trading halts) • Low liquidity periods (holidays, early closures) • Algorithmic manipulation or spoofing Failed breakout detection relies on patterns of trapped participant behavior. While historically these patterns have shown statistical edges, market conditions change. Institutional algorithms, changing market structure, or regime shifts can reduce or eliminate edges that existed historically. Initial Balance classification (trend day vs rotation day vs normal day) is a heuristic framework, not a deterministic prediction. Day type can change mid-session. Early classification may prove incorrect as the day develops. Extension projections (1.272x, 1.5x, 1.618x, 2.0x, etc.) are probabilistic targets derived from Fibonacci ratios and empirical market behavior. They are not "support and resistance levels" that price must reach or respect. Markets can stop short of extensions, overshoot them, or ignore them entirely. Volume confirmation assumes high volume indicates institutional participation and conviction. In algorithmic markets, volume can be artificially high (HFT activity) or artificially low (dark pools, internalization). Volume is a proxy, not a guarantee of conviction. LTF precision sampling improves ORB accuracy by using 1-minute bars but introduces additional data dependencies. If 1-minute data is unavailable, inaccurate, or delayed, ORB calculations will be incorrect. The grading system (A+/A/B+/B/C/D) and confidence scores aggregate multiple factors (volume, VWAP, day type, IB expansion, gap context) into a single assessment. This is a mechanical calculation, not artificial intelligence. The system cannot adapt to unprecedented market conditions or events outside its programmed logic. Real trading involves slippage, commissions, latency, partial fills, and rejected orders not present in indicator calculations. ORB Fusion generates signals at bar close; actual fills occur with delay. Opening range forms during highest volatility (first 30 minutes)—spreads widen, slippage increases. Execution quality significantly impacts realized results. Statistics tracking (win rates, extension levels reached, day type distribution) is based on historical bars in your lookback window. If lookback is small (<50 bars) or market regime changed, statistics may not represent future probabilities. Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively (100+ trades minimum) before risking capital. Start with micro position sizing (5-10% of intended size) for 50+ trades to validate execution quality matches expectations. Never risk more than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every single trade without exception. Understand that most retail traders lose money—sophisticated indicators do not change this fundamental reality. They systematize analysis but cannot eliminate risk. The developer makes no warranties regarding profitability, suitability, accuracy, reliability, or fitness for any purpose. Users assume full responsibility for all trading decisions, parameter selections, risk management, and outcomes. By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses. [b>═══════════════════════════════════════════════════════════════════════════════[/b> [b>CLOSING STATEMENT[/b> [b>═══════════════════════════════════════════════════════════════════════════════[/b> Opening Range Breakout is not a trick. It's a framework. The first 30-60 minutes reveal where participants believe value lies. Breakouts signal directional conviction. Failures signal trapped participants. Extensions define profit targets. Day types dictate strategy. Failed breakouts create the highest-probability reversals. ORB Fusion doesn't predict the future—it identifies [b>structure[/b>, detects [b>breakouts[/b>, recognizes [b>failures[/b>, and generates [b>probabilistic trade plans[/b> with defined risk and reward. The edge is not in the opening range itself. The edge is in recognizing when the market respects structure (follow breakouts) versus when it violates structure (fade breakouts). The edge is in detecting failures faster than discretionary traders. The edge is in systematic classification that prevents catastrophic errors—like fading a trend day or holding through rotation. Most indicators draw lines. ORB Fusion implements a complete institutional trading methodology: Opening Range theory, Market Profile classification, failed breakout intelligence, Fibonacci projections, volume confirmation, gap psychology, and real-time performance tracking. Whether you're a beginner learning market structure or a professional seeking systematic ORB implementation, this system provides the framework. "The market's first word is its opening range. Everything after is commentary." — ORB Fusion
Penunjuk Pine Script®
oleh DskyzInvestments
10
COT IndexTHE HIDDEN INTELLIGENCE IN FUTURES MARKETS What if you could see what the smartest players in the futures markets are doing before the crowd catches on? While retail traders chase momentum indicators and moving averages, obsess over Japanese candlestick patterns, and debate whether the RSI should be set to fourteen or twenty-one periods, institutional players leave footprints in the sand through their mandatory reporting to the Commodity Futures Trading Commission. These footprints, published weekly in the Commitment of Traders reports, have been hiding in plain sight for decades, available to anyone with an internet connection, yet remarkably few traders understand how to interpret them correctly. The COT Index indicator transforms this raw institutional positioning data into actionable trading signals, bringing Wall Street intelligence to your trading screen without requiring expensive Bloomberg terminals or insider connections. The uncomfortable truth is this: Most retail traders operate in a binary world. Long or short. Buy or sell. They apply technical analysis to individual positions, constrained by limited capital that forces them to concentrate risk in single directional bets. Meanwhile, institutional traders operate in an entirely different dimension. They manage portfolios dynamically weighted across multiple markets, adjusting exposure based on evolving market conditions, correlation shifts, and risk assessments that retail traders never see. A hedge fund might be simultaneously long gold, short oil, neutral on copper, and overweight agricultural commodities, with position sizes calibrated to volatility and portfolio Greeks. When they increase gold exposure from five percent to eight percent of portfolio allocation, this rebalancing decision reflects sophisticated analysis of opportunity cost, risk parity, and cross-market dynamics that no individual chart pattern can capture. This portfolio reweighting activity, multiplied across hundreds of institutional participants, manifests in the aggregate positioning data published weekly by the CFTC. The Commitment of Traders report does not show individual trades or strategies. It shows the collective footprint of how actual commercial hedgers and large speculators have allocated their capital across different markets. When mining companies collectively increase forward gold sales to hedge thirty percent more production than last quarter, they are not reacting to a moving average crossover. They are making strategic allocation decisions based on production forecasts, cost structures, and price expectations derived from operational realities invisible to outside observers. This is portfolio management in action, revealed through positioning data rather than price charts. If you want to understand how institutional capital actually flows, how sophisticated traders genuinely position themselves across market cycles, the COT report provides a rare window into that hidden world. But understand what you are getting into. This is not a tool for scalpers seeking confirmation of the next five-minute move. This is not an oscillator that flashes oversold at market bottoms with convenient precision. COT analysis operates on a timescale measured in weeks and months, revealing positioning shifts that precede major market turns but offer no precision timing. The data arrives three days stale, published only once per week, capturing strategic positioning rather than tactical entries. If you need instant gratification, if you trade intraday moves, if you demand mechanical signals with ninety percent accuracy, close this document now. COT analysis rewards patience, position sizing discipline, and tolerance for being early. It punishes impatience, overleveraging, and the expectation that any single indicator can substitute for market understanding. The premise is deceptively simple. Every Tuesday, large traders in futures markets must report their positions to the CFTC. By Friday afternoon, this data becomes public. Academic research spanning three decades has consistently shown that not all market participants are created equal. Some traders consistently profit while others consistently lose. Some anticipate major turning points while others chase trends into exhaustion. Bessembinder and Chan (1992) demonstrated in their seminal study that commercial hedgers, those with actual exposure to the underlying commodity or financial instrument, possess superior forecasting ability compared to speculators. Their research, published in the Journal of Finance, found statistically significant predictive power in commercial positioning, particularly at extreme levels. This finding challenged the efficient market hypothesis and opened the door to a new approach to market analysis based on positioning rather than price alone. Think about what this means. Every week, the government publishes a report showing you exactly how the most informed market participants are positioned. Not their opinions. Not their predictions. Their actual money at risk. When agricultural producers collectively hold their largest short hedge in five years, they are not making idle speculation. They are locking in prices for crops they will harvest, informed by private knowledge of weather conditions, soil quality, inventory levels, and demand expectations invisible to outside observers. When energy companies aggressively hedge forward production at current prices, they reveal information about expected supply that no analyst report can capture. This is not technical analysis based on past prices. This is not fundamental analysis based on publicly available data. This is behavioral analysis based on how the smartest money is actually positioned, how institutions allocate capital across portfolios, and how those allocation decisions shift as market conditions evolve. WHY SOME TRADERS KNOW MORE THAN OTHERS Building on this foundation, Sanders, Boris and Manfredo (2004) conducted extensive research examining the behaviour patterns of different trader categories. Their work, which analyzed over a decade of COT data across multiple commodity markets, revealed a fascinating dynamic that challenges much of what retail traders are taught. Commercial hedgers consistently positioned themselves against market extremes, buying when speculators were most bearish and selling when speculators reached peak bullishness. The contrarian positioning of commercials was not random noise but rather reflected their superior information about supply and demand fundamentals. Meanwhile, large speculators, primarily hedge funds and commodity trading advisors, exhibited strong trend-following behaviour that often amplified market moves beyond fundamental values. Small traders, the retail participants, consistently entered positions late in trends, frequently near turning points, making them reliable contrary indicators. Wang (2003) extended this research by demonstrating that the predictive power of commercial positioning varies significantly across different commodity sectors. His analysis of agricultural commodities showed particularly strong forecasting ability, with commercial net positions explaining up to fifteen percent of return variance in subsequent weeks. This finding suggests that the informational advantages of hedgers are most pronounced in markets where physical supply and demand fundamentals dominate, as opposed to purely financial markets where information asymmetries are smaller. When a corn farmer hedges six months of expected harvest, that decision incorporates private observations about rainfall patterns, crop health, pest pressure, and local storage capacity that no distant analyst can match. When an oil refinery hedges crude oil purchases and gasoline sales simultaneously, the spread relationships reveal expectations about refining margins that reflect operational realities invisible in public data. The theoretical mechanism underlying these empirical patterns relates to information asymmetry and different participant motivations. Commercial hedgers engage in futures markets not for speculative profit but to manage business risks. An agricultural producer selling forward six months of expected harvest is not making a bet on price direction but rather locking in revenue to facilitate financial planning and ensure business viability. However, this hedging activity necessarily incorporates private information about expected supply, inventory levels, weather conditions, and demand trends that the hedger observes through their commercial operations (Irwin and Sanders, 2012). When aggregated across many participants, this private information manifests in collective positioning. Consider a gold mining company deciding how much forward production to hedge. Management must estimate ore grades, recovery rates, production costs, equipment reliability, labor availability, and dozens of other operational variables that determine whether locking in prices at current levels makes business sense. If the industry collectively hedges more aggressively than usual, it suggests either exceptional production expectations or concern about sustaining current price levels or combination of both. Either way, this positioning reveals information unavailable to speculators analyzing price charts and economic data. The hedger sees the physical reality behind the financial abstraction. Large speculators operate under entirely different incentives and constraints. Commodity Trading Advisors managing billions in assets typically employ systematic, trend-following strategies that respond to price momentum rather than fundamental supply and demand. When crude oil rallies from sixty dollars to seventy dollars per barrel, these systems generate buy signals. As the rally continues to eighty dollars, position sizes increase. The strategy works brilliantly during sustained trends but becomes a liability at reversals. By the time oil reaches ninety dollars, trend-following funds are maximally long, having accumulated positions progressively throughout the rally. At this point, they represent not smart money anticipating further gains but rather crowded money vulnerable to reversal. Sanders, Boris and Manfredo (2004) documented this pattern across multiple energy markets, showing that extreme speculator positioning typically marked late-stage trend exhaustion rather than early-stage trend development. Small traders, the retail participants who fall below reporting thresholds, display the weakest forecasting ability. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns, meaning their aggregate positioning served as a reliable contrary indicator. The explanation combines several factors. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, entering trends after mainstream media coverage when institutional participants are preparing to exit. Perhaps most importantly, they trade with emotion, buying into euphoria and selling into panic at precisely the wrong times. At major turning points, the three groups often position opposite each other with commercials extremely bearish, large speculators extremely bullish, and small traders piling into longs at the last moment. These high-divergence environments frequently precede increased volatility and trend reversals. The insiders with business exposure quietly exit as the momentum traders hit maximum capacity and retail enthusiasm peaks. Within weeks, the reversal begins, and positions unwind in the opposite sequence. FROM RAW DATA TO ACTIONABLE SIGNALS The COT Index indicator operationalizes these academic findings into a practical trading tool accessible through TradingView. At its core, the indicator normalizes net positioning data onto a zero to one hundred scale, creating what we call the COT Index. This normalization is critical because absolute position sizes vary dramatically across different futures contracts and over time. A commercial trader holding fifty thousand contracts net long in crude oil might be extremely bullish by historical standards, or it might be quite neutral depending on the context of total market size and historical ranges. Raw position numbers mean nothing without context. The COT Index solves this problem by calculating where current positioning stands relative to its range over a specified lookback period, typically two hundred fifty-two weeks or approximately five years of weekly data. The mathematical transformation follows the methodology originally popularized by legendary trader Larry Williams, though the underlying concept appears in statistical normalization techniques across many fields. For any given trader category, we calculate the highest and lowest net position values over the lookback period, establishing the historical range for that specific market and trader group. Current positioning is then expressed as a percentage of this range, where zero represents the most bearish positioning ever seen in the lookback window and one hundred represents the most bullish extreme. A reading of fifty indicates positioning exactly in the middle of the historical range, suggesting neither extreme optimism nor pessimism relative to recent history (Williams and Noseworthy, 2009). This index-based approach allows for meaningful comparison across different markets and time periods, overcoming the scaling problems inherent in analyzing raw position data. A commercial index reading of eighty-five in gold carries the same interpretive meaning as an eighty-five reading in wheat or crude oil, even though the absolute position sizes differ by orders of magnitude. This standardization enables systematic analysis across entire futures portfolios rather than requiring market-specific expertise for each contract. The lookback period selection involves a fundamental tradeoff between responsiveness and stability. Shorter lookback periods, perhaps one hundred twenty-six weeks or approximately two and a half years, make the index more sensitive to recent positioning changes. However, it also increases noise and produces more false signals. Longer lookback periods, perhaps five hundred weeks or approximately ten years, create smoother readings that filter short-term noise but become slower to recognize regime changes. The indicator settings allow users to adjust this parameter based on their trading timeframe, risk tolerance, and market characteristics. UNDERSTANDING CFTC DATA STRUCTURES The indicator supports both Legacy and Disaggregated COT report formats, reflecting the evolution of CFTC reporting standards over decades of market development. Legacy reports categorize market participants into three broad groups: commercial traders (hedgers with underlying business exposure), non-commercial traders (large speculators seeking profit without commercial interest), and non-reportable traders (small speculators below reporting thresholds). Each category brings distinct motivations and information advantages to the market (CFTC, 2020). The Disaggregated reports, introduced in September 2009 for physical commodity markets, provide finer granularity by splitting participants into five categories (CFTC, 2009). Producer and merchant positions capture those actually producing, processing, or merchandising the physical commodity. Swap dealers represent financial intermediaries facilitating derivative transactions for clients. Managed money includes commodity trading advisors and hedge funds executing systematic or discretionary strategies. Other reportables encompasses diverse participants not fitting the main categories. Small traders remain as the fifth group, representing retail participation. This enhanced categorization reveals nuances invisible in Legacy reports, particularly distinguishing between different types of institutional capital and their distinct behavioural patterns. The indicator automatically detects which report type is appropriate for each futures contract and adjusts the display accordingly. Importantly, Disaggregated reports exist only for physical commodity futures. Agricultural commodities like corn, wheat, and soybeans have Disaggregated reports because clear producer, merchant, and swap dealer categories exist. Energy commodities like crude oil and natural gas similarly have well-defined commercial hedger categories. Metals including gold, silver, and copper also receive Disaggregated treatment (CFTC, 2009). However, financial futures such as equity index futures, Treasury bond futures, and currency futures remain available only in Legacy format. The CFTC has indicated no plans to extend Disaggregated reporting to financial futures due to different market structures and participant categories in these instruments (CFTC, 2020). THE BEHAVIORAL FOUNDATION Understanding which trader perspective to follow requires appreciation of their distinct trading styles, success rates, and psychological profiles. Commercial hedgers exhibit anticyclical behaviour rooted in their fundamental knowledge and business imperatives. When agricultural producers hedge forward sales during harvest season, they are not speculating on price direction but rather locking in revenue for crops they will harvest. Their business requires converting volatile commodity exposure into predictable cash flows to facilitate planning and ensure survival through difficult periods. Yet their aggregate positioning reveals valuable information because these hedging decisions incorporate private information about supply conditions, inventory levels, weather observations, and demand expectations that hedgers observe through their commercial operations (Bessembinder and Chan, 1992). Consider a practical example from energy markets. Major oil companies continuously hedge portions of forward production based on price levels, operational costs, and financial planning needs. When crude oil trades at ninety dollars per barrel, they might aggressively hedge the next twelve months of production, locking in prices that provide comfortable profit margins above their extraction costs. This hedging appears as short positioning in COT reports. If oil rallies further to one hundred dollars, they hedge even more aggressively, viewing these prices as exceptional opportunities to secure revenue. Their short positioning grows increasingly extreme. To an outside observer watching only price charts, the rally suggests bullishness. But the commercial positioning reveals that the actual producers of oil find these prices attractive enough to lock in years of sales, suggesting skepticism about sustaining even higher levels. When the eventual reversal occurs and oil declines back to eighty dollars, the commercials who hedged at ninety and one hundred dollars profit while speculators who chased the rally suffer losses. Large speculators or managed money traders operate under entirely different incentives and constraints. Their systematic, momentum-driven strategies mean they amplify existing trends rather than anticipate reversals. Trend-following systems, the most common approach among large speculators, by definition require confirmation of trend through price momentum before entering positions (Sanders, Boris and Manfredo, 2004). When crude oil rallies from sixty dollars to eighty dollars per barrel over several months, trend-following algorithms generate buy signals based on moving average crossovers, breakouts, and other momentum indicators. As the rally continues, position sizes increase according to the systematic rules. However, this approach becomes a liability at turning points. By the time oil reaches ninety dollars after a sustained rally, trend-following funds are maximally long, having accumulated positions progressively throughout the move. At this point, their positioning does not predict continued strength. Rather, it often marks late-stage trend exhaustion. The psychological and mechanical explanation is straightforward. Trend followers by definition chase price momentum, entering positions after trends establish rather than anticipating them. Eventually, they become fully invested just as the trend nears completion, leaving no incremental buying power to sustain the rally. When the first signs of reversal appear, systematic stops trigger, creating a cascade of selling that accelerates the downturn. Small traders consistently display the weakest track record across academic studies. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns in his analysis across multiple commodity markets. This result means that whatever small traders collectively do, the opposite typically proves profitable. The explanation for small trader underperformance combines several factors documented in behavioral finance literature. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, learning about commodity trends through mainstream media coverage that arrives after institutional participants have already positioned. Perhaps most importantly, retail traders are more susceptible to emotional decision-making, buying into euphoria and selling into panic at precisely the wrong times (Tharp, 2008). SETTINGS, THRESHOLDS, AND SIGNAL GENERATION The practical implementation of the COT Index requires understanding several key features and settings that users can adjust to match their trading style, timeframe, and risk tolerance. The lookback period determines the time window for calculating historical ranges. The default setting of two hundred fifty-two bars represents approximately one year on daily charts or five years on weekly charts, balancing responsiveness with stability. Conservative traders seeking only the most extreme, highest-probability signals might extend the lookback to five hundred bars or more. Aggressive traders seeking earlier entry and willing to accept more false positives might reduce it to one hundred twenty-six bars or even less for shorter-term applications. The bullish and bearish thresholds define signal generation levels. Default settings of eighty and twenty respectively reflect academic research suggesting meaningful information content at these extremes. Readings above eighty indicate positioning in the top quintile of the historical range, representing genuine extremes rather than temporary fluctuations. Conversely, readings below twenty occupy the bottom quintile, indicating unusually bearish positioning (Briese, 2008). However, traders must recognize that appropriate thresholds vary by market, trader category, and personal risk tolerance. Some futures markets exhibit wider positioning swings than others due to seasonal patterns, volatility characteristics, or participant behavior. Conservative traders seeking high-probability setups with fewer signals might raise thresholds to eighty-five and fifteen. Aggressive traders willing to accept more false positives for earlier entry could lower them to seventy-five and twenty-five. The key is maintaining meaningful differentiation between bullish, neutral, and bearish zones. The default settings of eighty and twenty create a clear three-zone structure. Readings from zero to twenty represent bearish territory where the selected trader group holds unusually bearish positions. Readings from twenty to eighty represent neutral territory where positioning falls within normal historical ranges. Readings from eighty to one hundred represent bullish territory where the selected trader group holds unusually bullish positions. The trading perspective selection determines which participant group the indicator follows, fundamentally shaping interpretation and signal meaning. For counter-trend traders seeking reversal opportunities, monitoring commercial positioning makes intuitive sense based on the academic research discussed earlier. When commercials reach extreme bearish readings below twenty, indicating unprecedented short positioning relative to recent history, they are effectively betting against the crowd. Given their informational advantages demonstrated by Bessembinder and Chan (1992), this contrarian stance often precedes major bottoms. Trend followers might instead monitor large speculator positioning, but with inverted logic compared to commercials. When managed money reaches extreme bullish readings above eighty, the trend may be exhausting rather than accelerating. This seeming paradox reflects their late-cycle participation documented by Sanders, Boris and Manfredo (2004). Sophisticated traders thus use speculator extremes as fade signals, entering positions opposite to speculator consensus. Small trader monitoring serves primarily as a contrary indicator for all trading styles. Extreme small trader bullishness above seventy-five or eighty typically warns of retail FOMO at market tops. Extreme small trader bearishness below twenty or twenty-five often marks capitulation bottoms where the last weak hands have sold. VISUALIZATION AND USER INTERFACE The visual design incorporates multiple elements working together to facilitate decision-making and maintain situational awareness during active trading. The primary COT Index line plots in bold with adjustable line width, defaulting to two pixels for clear visibility against busy price charts. An optional glow effect, controlled by a simple toggle, adds additional visual prominence through multiple plot layers with progressively increasing transparency and width. A twenty-one period exponential moving average overlays the index line, providing trend context for positioning changes. When the index crosses above its moving average, it signals accelerating bullish sentiment among the selected trader group regardless of whether absolute positioning is extreme. Conversely, when the index crosses below its moving average, it signals deteriorating sentiment and potentially the beginning of a reversal in positioning trends. The EMA provides a dynamic reference line for assessing positioning momentum. When the index trades far above its EMA, positioning is not only extreme in absolute terms but also building with momentum. When the index trades far below its EMA, positioning is contracting or reversing, which may indicate weakening conviction even if absolute levels remain elevated. The data table positioned at the top right of the chart displays eleven metrics for each trader category, transforming the indicator from a simple index calculation into an analytical dashboard providing multidimensional market intelligence. Beyond the COT Index itself, users can monitor positioning extremity, which measures how unusual current levels are compared to historical norms using statistical techniques. The extremity metric clarifies whether a reading represents the ninety-fifth or ninety-ninth percentile, with values above two standard deviations indicating genuinely exceptional positioning. Market power quantifies each group's influence on total open interest. This metric expresses each trader category's net position as a percentage of total market open interest. A commercial entity holding forty percent of total open interest commands significantly more influence than one holding five percent, making their positioning signals more meaningful. Momentum and rate of change metrics reveal whether positions are building or contracting, providing early warning of potential regime shifts. Position velocity measures the rate of change in positioning changes, effectively a second derivative providing even earlier insight into inflection points. Sentiment divergence highlights disagreements between commercial and speculative positioning. This metric calculates the absolute difference between normalized commercial and large speculator index values. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals. The table also displays concentration metrics when available, showing how positioning is distributed among the largest handful of traders in each category. High concentration indicates a few dominant players controlling most of the positioning, while low concentration suggests broad-based participation across many traders. THE ALERT SYSTEM AND MONITORING The alert system, comprising five distinct alert conditions, enables systematic monitoring of dozens of futures markets without constant screen watching. The bullish and bearish COT signal alerts trigger when the index crosses user-defined thresholds, indicating the selected trader group has reached extreme positioning worthy of attention. These alerts fire in real-time as new weekly COT data publishes, typically Friday afternoon following the Tuesday measurement date. Extreme positioning alerts fire at ninety and ten index levels, representing the top and bottom ten percent of the historical range, warning of particularly stretched readings that historically precede reversals with high probability. When commercials reach a COT Index reading below ten, they are expressing their most bearish stance in the entire lookback period. The data staleness alert notifies users when COT reports have not updated for more than ten days, preventing reliance on outdated information for trading decisions. Government shutdowns or federal holidays can interrupt the normal Friday publication schedule. Using stale signals while believing them current creates dangerous false confidence. The indicator's watermark information display positioned in the bottom right corner provides essential context at a glance. This persistent display shows the symbol and timeframe, the COT report date timestamp, days since last update, and the current signal state. A trader analyzing a potential short entry in crude oil can glance at the watermark to instantly confirm positioning context without interrupting analysis flow. LIMITATIONS AND REALISTIC EXPECTATIONS Practical application requires understanding both the indicator's considerable strengths and inherent limitations. COT data inherently lags price action by three days, as Tuesday positions are not published until Friday afternoon. This delay means the indicator cannot catch rapid intraday reversals or respond to surprise news events. Traders using the COT Index for timing entries must accept this latency and focus on swing trading and position trading timeframes where three-day lags matter less than in day trading or scalping. The weekly publication schedule similarly makes the indicator unsuitable for short-term trading strategies requiring immediate feedback. The COT Index works best for traders operating on weekly or longer timeframes, where positioning shifts measured in weeks and months align with trading horizon. Extreme COT readings can persist far longer than typical technical indicators suggest, testing the patience and capital reserves of traders attempting to fade them. When crude oil enters a sustained bull market driven by genuine supply disruptions, commercial hedgers may maintain bearish positioning for many months as prices grind higher. A commercial COT Index reading of fifteen indicating extreme bearishness might persist for three months while prices continue rallying before finally reversing. Traders without sufficient capital and risk tolerance to weather such drawdowns will exit prematurely, precisely when the signal is about to work (Irwin and Sanders, 2012). Position sizing discipline becomes paramount when implementing COT-based strategies. Rather than risking large percentages of capital on individual signals, successful COT traders typically allocate modest position sizes across multiple signals, allowing some to take time to mature while others work more quickly. The indicator also cannot overcome fundamental regime changes that alter the structural drivers of markets. If gold enters a true secular bull market driven by monetary debasement, commercial hedgers may remain persistently bearish as mining companies sell forward years of production at what they perceive as favorable prices. Their positioning indicates valuation concerns from a production cost perspective, but cannot stop prices from rising if investment demand overwhelms physical supply-demand balance. Similarly, structural changes in market participation can alter the meaning of positioning extremes. The growth of commodity index investing in the two thousands brought massive passive long-only capital into futures markets, fundamentally changing typical positioning ranges. Traders relying on COT signals without recognizing this regime change would have generated numerous false bearish signals during the commodity supercycle from 2003 to 2008. The research foundation supporting COT analysis derives primarily from commodity markets where the commercial hedger information advantage is most pronounced. Studies specifically examining financial futures like equity indices and bonds show weaker but still present effects. Traders should calibrate expectations accordingly, recognizing that COT analysis likely works better for crude oil, natural gas, corn, and wheat than for the S&P 500, Treasury bonds, or currency futures. Another important limitation involves the reporting threshold structure. Not all market participants appear in COT data, only those holding positions above specified minimums. In markets dominated by a few large players, concentration metrics become critical for proper interpretation. A single large trader accounting for thirty percent of commercial positioning might skew the entire category if their individual circumstances are idiosyncratic rather than representative. GOLD FUTURES DURING A HYPOTHETICAL MARKET CYCLE Consider a practical example using gold futures during a hypothetical but realistic market scenario that illustrates how the COT Index indicator guides trading decisions through a complete market cycle. Suppose gold has rallied from fifteen hundred to nineteen hundred dollars per ounce over six months, driven by inflation concerns following aggressive monetary expansion, geopolitical uncertainty, and sustained buying by Asian central banks for reserve diversification. Large speculators, operating primarily trend-following strategies, have accumulated increasingly bullish positions throughout this rally. Their COT Index has climbed progressively from forty-five to eighty-five. The table display shows that large speculators now hold net long positions representing thirty-two percent of total open interest, their highest in four years. Momentum indicators show positive readings, indicating positions are still building though at a decelerating rate. Position velocity has turned negative, suggesting the pace of position building is slowing. Meanwhile, commercial hedgers have responded to the rally by aggressively selling forward production and inventory. Their COT Index has moved inversely to price, declining from fifty-five to twenty. This bearish commercial positioning represents mining companies locking in forward sales at prices they view as attractive relative to production costs. The table shows commercials now hold net short positions representing twenty-nine percent of total open interest, their most bearish stance in five years. Concentration metrics indicate this positioning is broadly distributed across many commercial entities, suggesting the bearish stance reflects collective industry view rather than idiosyncratic positioning by a single firm. Small traders, attracted by mainstream financial media coverage of gold's impressive rally, have recently piled into long positions. Their COT Index has jumped from forty-five to seventy-eight as retail investors chase the trend. Television financial networks feature frequent segments on gold with bullish guests. Internet forums and social media show surging retail interest. This retail enthusiasm historically marks late-stage trend development rather than early opportunity. The COT Index indicator, configured to monitor commercial positioning from a contrarian perspective, displays a clear bearish signal given the extreme commercial short positioning. The table displays multiple confirming metrics: positioning extremity shows commercials at the ninety-sixth percentile of bearishness, market power indicates they control twenty-nine percent of open interest, and sentiment divergence registers sixty-five, indicating massive disagreement between commercial hedgers and large speculators. This divergence, the highest in three years, places the market in the historically high-risk category for reversals. The interpretation requires nuance and consideration of context beyond just COT data. Commercials are not necessarily predicting an imminent crash. Rather, they are hedging business operations at what they collectively view as favorable price levels. However, the data reveals they have sold unusually large quantities of forward production, suggesting either exceptional production expectations for the year ahead or concern about sustaining current price levels or combination of both. Combined with extreme speculator positioning indicating a crowded long trade, and small trader enthusiasm confirming retail FOMO, the confluence suggests elevated reversal risk even if the precise timing remains uncertain. A prudent trader analyzing this situation might take several actions based on COT Index signals. Existing long positions could be tightened with closer stop losses. Profit-taking on a portion of long exposure could lock in gains while maintaining some participation. Some traders might initiate modest short positions as portfolio hedges, sizing them appropriately for the inherent uncertainty in timing reversals. Others might simply move to the sidelines, avoiding new long entries until positioning normalizes. The key lesson from case study analysis is that COT signals provide probabilistic edges rather than deterministic predictions. They work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five percent win rate with proper risk management produces substantial profits over time, yet still means forty-five percent of signals will be premature or wrong. Traders must embrace this probabilistic reality rather than seeking the impossible goal of perfect accuracy. INTEGRATION WITH TRADING SYSTEMS Integration with existing trading systems represents a natural and powerful use case for COT analysis, adding a positioning dimension to price-based technical approaches or fundamental analytical frameworks. Few traders rely exclusively on a single indicator or methodology. Rather, they build systems that synthesize multiple information sources, with each component addressing different aspects of market behavior. Trend followers might use COT extremes as regime filters, modifying position sizing or avoiding new trend entries when positioning reaches levels historically associated with reversals. Consider a classic trend-following system based on moving average crossovers and momentum breakouts. Integration of COT analysis adds nuance. When large speculator positioning exceeds ninety or commercial positioning falls below ten, the regime filter recognizes elevated reversal risk. The system might reduce position sizing by fifty percent for new signals during these high-risk periods (Kaufman, 2013). Mean reversion traders might require COT signal confluence before fading extended moves. When crude oil becomes technically overbought and large speculators show extreme long positioning above eighty-five, both signals confirm. If only technical indicators show extremes while positioning remains neutral, the potential short signal is rejected, avoiding fades of trends with underlying institutional support (Kaufman, 2013). Discretionary traders can monitor the indicator as a continuous awareness tool, informing bias and position sizing without dictating mechanical entries and exits. A discretionary trader might notice commercial positioning shifting from neutral to progressively more bullish over several months. This trend informs growing positive bias even without triggering mechanical signals. Multi-timeframe analysis represents another powerful integration approach. A trader might use daily charts for trade execution and timing while monitoring weekly COT positioning for strategic context. When both timeframes align, highest-probability opportunities emerge. Portfolio construction for futures traders can incorporate COT signals as an additional selection criterion. Markets showing strong technical setups AND favorable COT positioning receive highest allocations. Markets with strong technicals but neutral or unfavorable positioning receive reduced allocations. ADVANCED METRICS AND INTERPRETATION The metrics table transforms simple positioning data into multidimensional market intelligence. Position extremity, calculated as the absolute deviation from the historical mean normalized by standard deviation, helps identify truly unusual readings versus routine fluctuations. A reading above two standard deviations indicates ninety-fifth percentile or higher extremity. Above three standard deviations indicates ninety-ninth percentile or higher, genuinely rare positioning that historically precedes major events with high probability. Market power, expressed as a percentage of total open interest, reveals whose positioning matters most from a mechanical market impact perspective. Consider two scenarios in gold futures. In scenario one, commercials show a COT Index reading of fifteen while their market power metric shows they hold net shorts representing thirty-five percent of open interest. This is a high-confidence bearish signal. In scenario two, commercials also show a reading of fifteen, but market power shows only eight percent. While positioning is extreme relative to this category's normal range, their limited market share means less mechanical influence on price. The rate of change and momentum metrics highlight whether positions are accelerating or decelerating, often providing earlier warnings than absolute levels alone. A COT Index reading of seventy-five with rapidly building momentum suggests continued movement toward extremes. Conversely, a reading of eighty-five with decelerating or negative momentum indicates the positioning trend is exhausting. Position velocity measures the rate of change in positioning changes, effectively a second derivative. When velocity shifts from positive to negative, it indicates that while positioning may still be growing, the pace of growth is slowing. This deceleration often precedes actual reversal in positioning direction by several weeks. Sentiment divergence calculates the absolute difference between normalized commercial and large speculator index values. When commercials show extreme bearish positioning at twenty while large speculators show extreme bullish positioning at eighty, the divergence reaches sixty, representing near-maximum disagreement. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals. The mechanism is intuitive. Extreme divergence indicates the informed hedgers and momentum-following speculators have positioned opposite each other with conviction. One group will prove correct and profit while the other proves incorrect and suffers losses. The resolution of this disagreement through price movement often involves volatility. The table also displays concentration metrics when available. High concentration indicates a few dominant players controlling most of the positioning within a category, while low concentration suggests broad-based participation. Broad-based positioning more reliably reflects collective market intelligence and industry consensus. If mining companies globally all independently decide to hedge aggressively at similar price levels, it suggests genuine industry-wide view about price valuations rather than circumstances specific to one firm. DATA QUALITY AND RELIABILITY The CFTC has maintained COT reporting in various forms since the nineteen twenties, providing nearly a century of positioning data across multiple market cycles. However, data quality and reporting standards have evolved substantially over this long period. Modern electronic reporting implemented in the late nineteen nineties and early two thousands significantly improved accuracy and timeliness compared to earlier paper-based systems. Traders should understand that COT reports capture positions as of Tuesday's close each week. Markets remain open three additional days before publication on Friday afternoon, meaning the reported data is three days stale when received. During periods of rapid market movement or major news events, this lag can be significant. The indicator addresses this limitation by including timestamp information and staleness warnings. The three-day lag creates particular challenges during extreme volatility episodes. Flash crashes, surprise central bank interventions, geopolitical shocks, and other high-impact events can completely transform market positioning within hours. Traders must exercise judgment about whether reported positioning remains relevant given intervening events. Reporting thresholds also mean that not all market participants appear in disaggregated COT data. Traders holding positions below specified minimums aggregate into the non-reportable or small trader category. This aggregation affects different markets differently. In highly liquid contracts like crude oil with thousands of participants, reportable traders might represent seventy to eighty percent of open interest. In thinly traded contracts with only dozens of active participants, a few large reportable positions might represent ninety-five percent of open interest. Another data quality consideration involves trader classification into categories. The CFTC assigns traders to commercial or non-commercial categories based on reported business purpose and activities. However, this process is not perfect. Some entities engage in both commercial and speculative activities, creating ambiguity about proper classification. The transition to Disaggregated reports attempted to address some of these ambiguities by creating more granular categories. COMPARISON WITH ALTERNATIVE APPROACHES Several alternative approaches to COT analysis exist in the trading community beyond the normalization methodology employed by this indicator. Some analysts focus on absolute position changes week-over-week rather than index-based normalization. This approach calculates the change in net positioning from one week to the next. The emphasis falls on momentum in positioning changes rather than absolute levels relative to history. This method potentially identifies regime shifts earlier but sacrifices cross-market comparability (Briese, 2008). Other practitioners employ more complex statistical transformations including percentile rankings, z-score standardization, and machine learning classification algorithms. Ruan and Zhang (2018) demonstrated that machine learning models applied to COT data could achieve modest improvements in forecasting accuracy compared to simple threshold-based approaches. However, these gains came at the cost of interpretability and implementation complexity. The COT Index indicator intentionally employs a relatively straightforward normalization methodology for several important reasons. First, transparency enhances user understanding and trust. Traders can verify calculations manually and develop intuitive feel for what different readings mean. Second, academic research suggests that most of the predictive power in COT data comes from extreme positioning levels rather than subtle patterns requiring complex statistical methods to detect. Third, robust methods that work consistently across many markets and time periods tend to be simpler rather than more complex, reducing the risk of overfitting to historical data. Fourth, the complexity costs of implementation matter for retail traders without programming teams or computational infrastructure. PSYCHOLOGICAL ASPECTS OF COT TRADING Trading based on COT data requires psychological fortitude that differs from momentum-based approaches. Contrarian positioning signals inherently mean betting against prevailing market sentiment and recent price action. When commercials reach extreme bearish positioning, prices have typically been rising, sometimes for extended periods. The price chart looks bullish, momentum indicators confirm strength, moving averages align positively. The COT signal says bet against all of this. This psychological difficulty explains why COT analysis remains underutilized relative to trend-following methods. Human psychology strongly predisposes us toward extrapolation and recency bias. When prices rally for months, our pattern-matching brains naturally expect continued rally. The recent price action dominates our perception, overwhelming rational analysis about positioning extremes and historical probabilities. The COT signal asking us to sell requires overriding these powerful psychological impulses. The indicator design attempts to support the required psychological discipline through several features. Clear threshold markers and signal states reduce ambiguity about when signals trigger. When the commercial index crosses below twenty, the signal is explicit and unambiguous. The background shifts to red, the signal label displays bearish, and alerts fire. This explicitness helps traders act on signals rather than waiting for additional confirmation that may never arrive. The metrics table provides analytical justification for contrarian positions, helping traders maintain conviction during inevitable periods of adverse price movement. When a trader enters short positions based on extreme commercial bearish positioning but prices continue rallying for several weeks, doubt naturally emerges. The table display provides reassurance. Commercial positioning remains extremely bearish. Divergence remains high. The positioning thesis remains intact even though price action has not yet confirmed. Alert functionality ensures traders do not miss signals due to inattention while also not requiring constant monitoring that can lead to emotional decision-making. Setting alerts for COT extremes enables a healthier relationship with markets. When meaningful signals occur, alerts notify them. They can then calmly assess the situation and execute planned responses. However, no indicator design can completely overcome the psychological difficulty of contrarian trading. Some traders simply cannot maintain short positions while prices rally. For these traders, COT analysis might be better employed as an exit signal for long positions rather than an entry signal for shorts. Ultimately, successful COT trading requires developing comfort with probabilistic thinking rather than certainty-seeking. The signals work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five or sixty percent win rate with proper risk management produces substantial profits over years, yet still means forty to forty-five percent of signals will be premature or wrong. COT analysis provides genuine edge, but edge means probability advantage, not elimination of losing trades. EDUCATIONAL RESOURCES AND CONTINUOUS LEARNING The indicator provides extensive built-in educational resources through its documentation, detailed tooltips, and transparent calculations. However, mastering COT analysis requires study beyond any single tool or resource. Several excellent resources provide valuable extensions of the concepts covered in this guide. Books and practitioner-focused monographs offer accessible entry points. Stephen Briese published The Commitments of Traders Bible in two thousand eight, offering detailed breakdowns of how different markets and trader categories behave (Briese, 2008). Briese's work stands out for its empirical focus and market-specific insights. Jack Schwager includes discussion of COT analysis within the broader context of market behavior in his book Market Sense and Nonsense (Schwager, 2012). Perry Kaufman's Trading Systems and Methods represents perhaps the most rigorous practitioner-focused text on systematic trading approaches including COT analysis (Kaufman, 2013). Academic journal articles provide the rigorous statistical foundation underlying COT analysis. The Journal of Futures Markets regularly publishes research on positioning data and its predictive properties. Bessembinder and Chan's earlier work on systematic risk, hedging pressure, and risk premiums in futures markets provides theoretical foundation (Bessembinder, 1992). Chang's examination of speculator returns provides historical context (Chang, 1985). Irwin and Sanders provide essential skeptical perspective in their two thousand twelve article (Irwin and Sanders, 2012). Wang's two thousand three article provides one of the most empirical analyses of COT data across multiple commodity markets (Wang, 2003). Online resources extend beyond academic and book-length treatments. The CFTC website provides free access to current and historical COT reports in multiple formats. The explanatory materials section offers detailed documentation of report construction, category definitions, and historical methodology changes. Traders serious about COT analysis should read these official CFTC documents to understand exactly what they are analyzing. Commercial COT data services such as Barchart provide enhanced visualization and analysis tools beyond raw CFTC data. TradingView's educational materials, published scripts library, and user community provide additional resources for exploring different approaches to COT analysis. The key to mastering COT analysis lies not in finding a single definitive source but rather in building understanding through multiple perspectives and information sources. Academic research provides rigorous empirical foundation. Practitioner-focused books offer practical implementation insights. Direct engagement with data through systematic backtesting develops intuition about how positioning dynamics manifest across different market conditions. SYNTHESIZING KNOWLEDGE INTO PRACTICE The COT Index indicator represents the synthesis of academic research, trading experience, and software engineering into a practical tool accessible to retail traders equipped with nothing more than a TradingView account and willingness to learn. What once required expensive data subscriptions, custom programming capabilities, statistical software, and institutional resources now appears as a straightforward indicator requiring only basic parameter selection and modest study to understand. This democratization of institutional-grade analysis tools represents a broader trend in financial markets over recent decades. Yet technology and data access alone provide no edge without understanding and discipline. Markets remain relentlessly efficient at eliminating edges that become too widely known and mechanically exploited. The COT Index indicator succeeds only when users invest time learning the underlying concepts, understand the limitations and probability distributions involved, and integrate signals thoughtfully into trading plans rather than applying them mechanically. The academic research demonstrates conclusively that institutional positioning contains genuine information about future price movements, particularly at extremes where commercial hedgers are maximally bearish or bullish relative to historical norms. This informational content is neither perfect nor deterministic but rather probabilistic, providing edge over many observations through identification of higher-probability configurations. Bessembinder and Chan's finding that commercial positioning explained modest but significant variance in future returns illustrates this probabilistic nature perfectly (Bessembinder and Chan, 1992). The effect is real and statistically significant, yet it explains perhaps ten to fifteen percent of return variance rather than most variance. Much of price movement remains unpredictable even with positioning intelligence. The practical implication is that COT analysis works best as one component of a trading system rather than a standalone oracle. It provides the positioning dimension, revealing where the smart money has positioned and where the crowd has followed, but price action analysis provides the timing dimension. Fundamental analysis provides the catalyst dimension. Risk management provides the survival dimension. These components work together synergistically. The indicator's design philosophy prioritizes transparency and education over black-box complexity, empowering traders to understand exactly what they are analyzing and why. Every calculation is documented and user-adjustable. The threshold markers, background coloring, tables, and clear signal states provide multiple reinforcing channels for conveying the same information. This educational approach reflects a conviction that sustainable trading success comes from genuine understanding rather than mechanical system-following. Traders who understand why commercial positioning matters, how different trader categories behave, what positioning extremes signify, and where signals fit within probability distributions can adapt when market conditions change. Traders mechanically following black-box signals without comprehension abandon systems after normal losing streaks. The research foundation supporting COT analysis comes primarily from commodity markets where commercial hedger informational advantages are most pronounced. Agricultural producers hedging crops know more about supply conditions than distant speculators. Energy companies hedging production know more about operating costs than financial traders. Metals miners hedging output know more about ore grades than index funds. Financial futures markets show weaker but still present effects. The journey from reading this documentation to profitable trading based on COT analysis involves several stages that cannot be rushed. Initial reading and basic understanding represents the first stage. Historical study represents the second stage, reviewing past market cycles to observe how positioning extremes preceded major turning points. Paper trading or small-size real trading represents the third stage to experience the psychological challenges. Refinement based on results and personal psychology represents the fourth stage. Markets will continue evolving. New participant categories will emerge. Regulatory structures will change. Technology will advance. Yet the fundamental dynamics driving COT analysis, that different market participants have different information, different motivations, and different forecasting abilities that manifest in their positioning, will persist as long as futures markets exist. While specific thresholds or optimal parameters may shift over time, the core logic remains sound and adaptable. The trader equipped with this indicator, understanding of the theory and evidence behind COT analysis, realistic expectations about probability rather than certainty, discipline to maintain positions through adverse volatility, and patience to allow signals time to develop possesses genuine edge in markets. The edge is not enormous, markets cannot allow large persistent inefficiencies without arbitraging them away, but it is real, measurable, and exploitable by those willing to invest in learning and disciplined application. REFERENCES Bessembinder, H. (1992) Systematic risk, hedging pressure, and risk premiums in futures markets, Review of Financial Studies, 5(4), pp. 637-667. Bessembinder, H. and Chan, K. (1992) The profitability of technical trading rules in the Asian stock markets, Pacific-Basin Finance Journal, 3(2-3), pp. 257-284. Briese, S. (2008) The Commitments of Traders Bible: How to Profit from Insider Market Intelligence. Hoboken: John Wiley & Sons. Chang, E.C. (1985) Returns to speculators and the theory of normal backwardation, Journal of Finance, 40(1), pp. 193-208. Commodity Futures Trading Commission (CFTC) (2009) Explanatory Notes: Disaggregated Commitments of Traders Report. Available at: www.cftc.gov (Accessed: 15 January 2025). Commodity Futures Trading Commission (CFTC) (2020) Commitments of Traders: About the Report. Available at: www.cftc.gov (Accessed: 15 January 2025). Irwin, S.H. and Sanders, D.R. (2012) Testing the Masters Hypothesis in commodity futures markets, Energy Economics, 34(1), pp. 256-269. Kaufman, P.J. (2013) Trading Systems and Methods. 5th edn. Hoboken: John Wiley & Sons. Ruan, Y. and Zhang, Y. (2018) Forecasting commodity futures prices using machine learning: Evidence from the Chinese commodity futures market, Applied Economics Letters, 25(12), pp. 845-849. Sanders, D.R., Boris, K. and Manfredo, M. (2004) Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports, Energy Economics, 26(3), pp. 425-445. Schwager, J.D. (2012) Market Sense and Nonsense: How the Markets Really Work and How They Don't. Hoboken: John Wiley & Sons. Tharp, V.K. (2008) Super Trader: Make Consistent Profits in Good and Bad Markets. New York: McGraw-Hill. Wang, C. (2003) The behavior and performance of major types of futures traders, Journal of Futures Markets, 23(1), pp. 1-31. Williams, L.R. and Noseworthy, M. (2009) The Right Stock at the Right Time: Prospering in the Coming Good Years. Hoboken: John Wiley & Sons. FURTHER READING For traders seeking to deepen their understanding of COT analysis and futures market positioning beyond this documentation, the following resources provide valuable extensions: Academic Journal Articles: Fishe, R.P.H. and Smith, A. (2012) Do speculators drive commodity prices away from supply and demand fundamentals?, Journal of Commodity Markets, 1(1), pp. 1-16. Haigh, M.S., Hranaiova, J. and Overdahl, J.A. (2007) Hedge funds, volatility, and liquidity provision in energy futures markets, Journal of Alternative Investments, 9(4), pp. 10-38. Kocagil, A.E. (1997) Does futures speculation stabilize spot prices? Evidence from metals markets, Applied Financial Economics, 7(1), pp. 115-125. Sanders, D.R. and Irwin, S.H. (2011) The impact of index funds in commodity futures markets: A systems approach, Journal of Alternative Investments, 14(1), pp. 40-49. Books and Practitioner Resources: Murphy, J.J. (1999) Technical Analysis of the Financial Markets: A Guide to Trading Methods and Applications. New York: New York Institute of Finance. Pring, M.J. (2002) Technical Analysis Explained: The Investor's Guide to Spotting Investment Trends and Turning Points. 4th edn. New York: McGraw-Hill. Federal Reserve and Research Institution Publications: Federal Reserve Banks regularly publish working papers examining commodity markets, futures positioning, and price discovery mechanisms. The Federal Reserve Bank of San Francisco and Federal Reserve Bank of Kansas City maintain active research programs in this area. Online Resources: The CFTC website provides free access to current and historical COT reports, explanatory materials, and regulatory documentation. Barchart offers enhanced COT data visualization and screening tools. TradingView's community library contains numerous published scripts and educational materials exploring different approaches to positioning analysis.
Penunjuk Pine Script®
oleh EdgeTools
16
Bitcoin CME-Spot Z-Spread - Strategy [presentTrading]This time is a swing trading strategy! It measures the sentiment of the Bitcoin market through the spread of CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. By applying Bollinger Bands to the spread, the strategy seeks to capture mean-reversion opportunities when prices deviate significantly from their historical norms █ Introduction and How it is Different The Bitcoin CME-Spot Bollinger Bands Strategy is designed to capture mean-reversion opportunities by exploiting the spread between CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. The strategy uses Bollinger Bands to detect when the spread between these two correlated assets has deviated significantly from its historical norm, signaling potential overbought or oversold conditions. What sets this strategy apart is its focus on spread trading between futures and spot markets rather than price-based indicators. By applying Bollinger Bands to the spread rather than individual prices, the strategy identifies price inefficiencies across markets, allowing traders to take advantage of the natural reversion to the mean that often occurs in these correlated assets. BTCUSD 8hr Performance █ Strategy, How It Works: Detailed Explanation The strategy relies on Bollinger Bands to assess the volatility and relative deviation of the spread between CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. Bollinger Bands consist of a moving average and two standard deviation bands, which help measure how much the spread deviates from its historical mean. 🔶 Spread Calculation: The spread is calculated by subtracting the Bitfinex spot price from the CME Bitcoin futures price: Spread = CME Price - Bitfinex Price This spread represents the difference between the futures and spot markets, which may widen or narrow based on supply and demand dynamics in each market. By analyzing the spread, the strategy can detect when prices are too far apart (potentially overbought or oversold), indicating a trading opportunity. 🔶 Bollinger Bands Calculation: The Bollinger Bands for the spread are calculated using a simple moving average (SMA) and the standard deviation of the spread over a defined period. 1. Moving Average (SMA): The simple moving average of the spread (mu_S) over a specified period P is calculated as: mu_S = (1/P) * sum(S_i from i=1 to P) Where S_i represents the spread at time i, and P is the lookback period (default is 200 bars). The moving average provides a baseline for the normal spread behavior. 2. Standard Deviation: The standard deviation (sigma_S) of the spread is calculated to measure the volatility of the spread: sigma_S = sqrt((1/P) * sum((S_i - mu_S)^2 from i=1 to P)) 3. Upper and Lower Bollinger Bands: The upper and lower Bollinger Bands are derived by adding and subtracting a multiple of the standard deviation from the moving average. The number of standard deviations is determined by a user-defined parameter k (default is 2.618). - Upper Band: Upper Band = mu_S + (k * sigma_S) - Lower Band: Lower Band = mu_S - (k * sigma_S) These bands provide a dynamic range within which the spread typically fluctuates. When the spread moves outside of these bands, it is considered overbought or oversold, potentially offering trading opportunities. Local view 🔶 Entry Conditions: - Long Entry: A long position is triggered when the spread crosses below the lower Bollinger Band, indicating that the spread has become oversold and is likely to revert upward. Spread < Lower Band - Short Entry: A short position is triggered when the spread crosses above the upper Bollinger Band, indicating that the spread has become overbought and is likely to revert downward. Spread > Upper Band 🔶 Risk Management and Profit-Taking: The strategy incorporates multi-step take profits to lock in gains as the trade moves in favor. The position is gradually reduced at predefined profit levels, reducing risk while allowing part of the trade to continue running if the price keeps moving favorably. Additionally, the strategy uses a hold period exit mechanism. If the trade does not hit any of the take-profit levels within a certain number of bars, the position is closed automatically to avoid excessive exposure to market risks. █ Trade Direction The trade direction is based on deviations of the spread from its historical norm: - Long Trade: The strategy enters a long position when the spread crosses below the lower Bollinger Band, signaling an oversold condition where the spread is expected to narrow. - Short Trade: The strategy enters a short position when the spread crosses above the upper Bollinger Band, signaling an overbought condition where the spread is expected to widen. These entries rely on the assumption of mean reversion, where extreme deviations from the average spread are likely to revert over time. █ Usage The Bitcoin CME-Spot Bollinger Bands Strategy is ideal for traders looking to capitalize on price inefficiencies between Bitcoin futures and spot markets. It’s especially useful in volatile markets where large deviations between futures and spot prices occur. - Market Conditions: This strategy is most effective in correlated markets, like CME futures and spot Bitcoin. Traders can adjust the Bollinger Bands period and standard deviation multiplier to suit different volatility regimes. - Backtesting: Before deployment, backtesting the strategy across different market conditions and timeframes is recommended to ensure robustness. Adjust the take-profit steps and hold periods to reflect the trader’s risk tolerance and market behavior. █ Default Settings The default settings provide a balanced approach to spread trading using Bollinger Bands but can be adjusted depending on market conditions or personal trading preferences. 🔶 Bollinger Bands Period (200 bars): This defines the number of bars used to calculate the moving average and standard deviation for the Bollinger Bands. A longer period smooths out short-term fluctuations and focuses on larger, more significant trends. Adjusting the period affects the responsiveness of the strategy: - Shorter periods (e.g., 100 bars): Makes the strategy more reactive to short-term market fluctuations, potentially generating more signals but increasing the risk of false positives. - Longer periods (e.g., 300 bars): Focuses on longer-term trends, reducing the frequency of trades and focusing only on significant deviations. 🔶 Standard Deviation Multiplier (2.618): The multiplier controls how wide the Bollinger Bands are around the moving average. By default, the bands are set at 2.618 standard deviations away from the average, ensuring that only significant deviations trigger trades. - Higher multipliers (e.g., 3.0): Require a more extreme deviation to trigger trades, reducing trade frequency but potentially increasing the accuracy of signals. - Lower multipliers (e.g., 2.0): Make the bands narrower, increasing the number of trade signals but potentially decreasing their reliability. 🔶 Take-Profit Levels: The strategy has four take-profit levels to gradually lock in profits: - Level 1 (3%): 25% of the position is closed at a 3% profit. - Level 2 (8%): 20% of the position is closed at an 8% profit. - Level 3 (14%): 15% of the position is closed at a 14% profit. - Level 4 (21%): 10% of the position is closed at a 21% profit. Adjusting these take-profit levels affects how quickly profits are realized: - Lower take-profit levels: Capture gains more quickly, reducing risk but potentially cutting off larger profits. - Higher take-profit levels: Let trades run longer, aiming for bigger gains but increasing the risk of price reversals before profits are locked in. 🔶 Hold Days (20 bars): The strategy automatically closes the position after 20 bars if none of the take-profit levels are hit. This feature prevents trades from being held indefinitely, especially if market conditions are stagnant. Adjusting this: - Shorter hold periods: Reduce the duration of exposure, minimizing risks from market changes but potentially closing trades too early. - Longer hold periods: Allow trades to stay open longer, increasing the chance for mean reversion but also increasing exposure to unfavorable market conditions. By understanding how these default settings affect the strategy’s performance, traders can optimize the Bitcoin CME-Spot Bollinger Bands Strategy to their preferences, adapting it to different market environments and risk tolerances.
Strategi Pine Script®
oleh PresentTrading
2
SMC Liquidity Engine Pro SMC Liquidity Engine Pro - Complete Trading Guide & Documentation 📊 Introduction: Understanding Smart Money Concepts The SMC Liquidity Engine Pro is a comprehensive, institutional-grade trading indicator that brings professional Smart Money Concepts (SMC) methodology directly to your TradingView charts. This isn't just another technical indicator—it's a complete framework for understanding how institutional traders, market makers, banks, and hedge funds manipulate and move the markets. What Makes This Different? While most retail traders rely on lagging indicators like moving averages or RSI, this indicator reveals the real-time footprints of institutional activity. It shows you: Where large players are accumulating or distributing positions How they engineer liquidity to trigger retail stop losses When they're shifting from one directional bias to another Where price inefficiencies exist that institutions will likely revisit The markets don't move randomly—they move based on liquidity. Understanding this fundamental truth is what separates consistently profitable traders from those who struggle. This indicator decodes that liquidity-driven behavior and presents it in clear, actionable visual signals. The Philosophy Behind Smart Money Concepts Smart Money Concepts is built on several core principles: 1. Liquidity is King: Price doesn't move because of patterns or indicators—it moves to collect liquidity (stop losses and pending orders). Institutions need massive liquidity to fill their large positions, so they engineer price movements to create that liquidity before making their real directional move. 2. Market Structure Reveals Intent: The way price forms highs and lows tells a story about who's in control. When structure breaks, it signals a shift in institutional positioning. 3. Inefficiencies Get Filled: When price moves too quickly in one direction, it leaves behind "fair value gaps"—areas of imbalance. Institutions frequently return to these areas to fill orders and restore balance. 4. Manipulation Precedes True Moves: The most explosive directional moves are often preceded by liquidity sweeps in the opposite direction—trapping retail traders before the real move begins. This indicator automates the identification of all these concepts, allowing you to trade alongside the smart money rather than being their exit liquidity. 🎯 Core Features - Deep Dive 1. Market Structure Detection & Visualization What It Is: Market structure forms the foundation of all Smart Money analysis. This indicator automatically identifies and tracks swing highs and swing lows using a sophisticated pivot detection algorithm. These aren't just any price points—they represent areas where the market showed a significant shift in supply and demand dynamics. How It Works: The indicator uses a customizable lookback period to identify valid swing points. A swing high must have lower highs on both sides within the lookback period, and a swing low must have higher lows on both sides. This ensures that only significant structural points are marked, filtering out minor noise and consolidation. Visual Presentation: Bullish Structure (Cyan Lines): Horizontal lines extending from each identified swing high, showing resistance levels that price previously respected Bearish Structure (Red Lines): Horizontal lines extending from each identified swing low, showing support levels where buying pressure emerged Trading Application: These structure levels serve multiple purposes: Target Zones: Previous highs become targets in uptrends; previous lows become targets in downtrends Invalidation Levels: If expecting a bullish move, breaking below the last swing low invalidates the setup Context for Other Signals: All BOS, CHOCH, and liquidity sweep signals gain meaning from their relationship to structure Multi-Timeframe Anchors: Higher timeframe structure provides context for lower timeframe entries Advanced Tip: When multiple timeframe structures align (e.g., a daily swing low coincides with a 4-hour swing low), these levels carry significantly more weight and are more likely to be defended or, when broken, lead to explosive moves. 2. Break of Structure (BOS) - Trend Confirmation What It Is: A Break of Structure occurs when price definitively closes beyond a previous swing high (bullish BOS) or swing low (bearish BOS). This signals that the current trend maintains its momentum and is likely to continue in the same direction. The Institutional Perspective: When institutions want to continue pushing price in a direction, they need to break through previous resistance or support. A clean BOS indicates that: There's sufficient institutional buying/selling to overcome the supply/demand at previous structure The trend has enough momentum to attract more participants Stop losses above/below structure have been triggered, providing liquidity for continuation Signal Characteristics: Bullish BOS Label: Appears below the bar that closes above the previous swing high Bearish BOS Label: Appears above the bar that closes below the previous swing low Confirmation: Requires a full candle close, preventing false signals from wicks Trading Strategies: Trend Continuation Entries: After a BOS, wait for a pullback to a Fair Value Gap or minor structure, then enter in the direction of the break Breakout Trading: Enter immediately on BOS confirmation with a stop below the broken structure Momentum Confirmation: Use BOS to confirm that your existing position is aligned with institutional flow Scaling Strategy: Add to positions on each successive BOS in trending markets What to Watch For: Volume: Strong BOS movements should be accompanied by above-average volume Speed: Rapid price movement through structure suggests institutional urgency Follow-Through: The best BOS signals see price continue strongly without immediately reversing Higher Timeframe Alignment: BOS on higher timeframes (4H, Daily) carry more weight than lower timeframe breaks Common Pitfalls: Not all structure breaks are equal—BOS during ranging markets are less reliable A BOS immediately followed by a reversal back into the range may indicate a failed breakout During major news events, structure can be broken temporarily without institutional intent 3. Liquidity Sweep Detection - Spotting Manipulation What It Is: Liquidity sweeps (also called "stop hunts" or "liquidity grabs") occur when price temporarily breaks beyond a key level to trigger stop losses and pending orders, then immediately reverses back. This is one of the most important concepts in SMC trading because it reveals intentional manipulation. Why Institutions Do This: Large institutional orders can't be filled at a single price point—they need massive liquidity. The biggest pools of liquidity sit just beyond obvious highs and lows where retail traders place their stops. By briefly pushing price into these zones, institutions: Trigger retail stop losses (creating market orders) Activate pending buy/sell orders Fill their large positions at favorable prices Trap late breakout traders before reversing Detection Methodology: The indicator identifies sweeps using multiple criteria: Price must penetrate beyond the structural high/low (creating the sweep) The candle must close back on the opposite side of the structure (confirming rejection) The sweep distance is measured against ATR to distinguish manipulation from normal volatility The sweep multiplier setting allows you to adjust sensitivity based on market conditions Visual Indicators: Orange Down Arrows: Mark liquidity sweeps above structural highs Lime Up Arrows: Mark liquidity sweeps below structural lows Liquidity Zone Boxes: Semi-transparent colored boxes highlight the exact range of the swept area Persistent Display: Zones remain visible for several bars to maintain context Trading Applications: Reversal Trading: Liquidity sweeps often mark excellent reversal points. After a sweep: Wait for the sweep to complete (candle closes back inside structure) Look for a Change of Character signal for confirmation Enter in the direction opposite to the sweep Place stops beyond the sweep high/low Target the opposite side of the range or next structural level Continuation Filtering: Not all sweeps lead to reversals. During strong trends: Sweeps of minor structure in a trending market often precede continuation Use higher timeframe structure to determine if a sweep is counter-trend (likely reversal) or with-trend (likely continuation) Entry Refinement: In ranging markets, trade from swept lows to highs and vice versa, as institutions accumulate at the extremes. Advanced Sweep Analysis: Double Sweeps: When both sides of a range are swept, expect a strong breakout Sweep Rejection Quality: Fast, strong rejections of sweeps are more reliable than slow grinding returns Timeframe Consideration: Daily timeframe sweeps are significantly more important than 15-minute sweeps Volume Profile: Sweeps with low volume followed by high volume reversals confirm manipulation What Makes a High-Quality Sweep Signal: ✅ Penetrates structure by at least 0.5-1x ATR ✅ Strong rejection candle (long wick, decisive close) ✅ Occurs at a higher timeframe structural level ✅ Creates a Change of Character on the following move ✅ Sweeps an obvious level where retail stops cluster 4. Change of Character (CHOCH) - Major Reversal Signals What It Is: A Change of Character represents the most significant shift in market dynamics—when the entire structural bias of the market flips from bullish to bearish or bearish to bullish. CHOCH signals are the crown jewel of SMC trading because they identify the exact moment when institutional positioning fundamentally changes. The Anatomy of a CHOCH: A valid CHOCH requires a specific sequence: Established Trend: A clear directional bias with multiple BOS in one direction Liquidity Engineering: A sweep of structure in the current trend direction (the manipulation phase) Structural Break: Price then breaks structure in the OPPOSITE direction (the revelation phase) This combination shows that institutions have: Completed their accumulation/distribution at favorable prices (via the sweep) Shifted their positioning from bullish to bearish (or vice versa) Begun a new directional campaign Visual Presentation: Bullish CHOCH (Cyan Triangle Up): Appears when bearish structure is broken after a low sweep, signaling the shift to bullish control Bearish CHOCH (Red Triangle Down): Appears when bullish structure is broken after a high sweep, signaling the shift to bearish control Prominent Markers: Larger and more visually distinct than BOS signals, reflecting their importance Why CHOCH Signals Are So Powerful: Trend Reversal Identification: They mark the earliest possible confirmation of a trend change High Win Rate: When combined with proper risk management, CHOCH signals have among the highest success rates in SMC trading Risk-Reward Ratio: Entering at CHOCH gives you the best possible risk-reward since you're entering at the beginning of a new trend Institutional Confirmation: The sequence of sweep + structure break proves institutional repositioning, not just retail sentiment Trading CHOCH Signals: The Perfect CHOCH Setup: Identify the Sweep: Watch for a liquidity sweep of structural lows (for bullish) or highs (for bearish) Wait for the Break: Don't enter on the sweep—wait for structure to break in the opposite direction CHOCH Confirmation: The indicator fires the CHOCH signal—this is your entry trigger Entry Execution: Aggressive: Enter immediately on CHOCH confirmation Conservative: Wait for a pullback to the first Fair Value Gap or broken structure (now turned support/resistance) Stop Placement: Beyond the swept liquidity point Target Selection: Previous swing in the opposite direction, or let it run to the next CHOCH Multiple Timeframe CHOCH Strategy: The most powerful setups occur when CHOCHs align across timeframes: Daily CHOCH: Signals major institutional trend change, target 500+ pips (Forex) or significant point moves 4H CHOCH: Confirms daily direction, provides swing trade opportunities 1H CHOCH: Offers precise entry timing within the higher timeframe trend 15M CHOCH: Used for position scaling and intraday management Example Trade Flow: Daily Chart: Bullish CHOCH appears after weeks of downtrend ↓ 4H Chart: Wait for pullback after the daily CHOCH, then catch the 4H bullish CHOCH ↓ 1H Chart: Enter on the 1H bullish CHOCH that aligns with both higher timeframes ↓ Result: You've entered at the beginning of a major trend with multiple confirmations CHOCH Quality Grading: A-Grade CHOCH (Highest Probability): Occurs at major higher timeframe structure Following a clear liquidity sweep Volume spike on the structural break Multiple timeframe alignment Creates a large Fair Value Gap on the break B-Grade CHOCH (Good Probability): Valid sweep and structure break Single timeframe signal Moderate volume Occurs at minor structure C-Grade CHOCH (Lower Probability): Choppy, ranging market context Weak sweep or unclear structure Counter to higher timeframe trend Low volume confirmation Common Mistakes with CHOCH Trading: ❌ Entering on the sweep instead of waiting for the structure break ❌ Ignoring higher timeframe context ❌ Taking every CHOCH regardless of quality ❌ Not waiting for pullbacks on aggressive trends ❌ Placing stops too tight, getting caught in volatility Advanced CHOCH Concepts: Failed CHOCH: Occasionally, what appears to be a CHOCH will fail (price reverses back into the previous trend). This often indicates: Insufficient institutional conviction for the reversal Fake-out to grab liquidity in the opposite direction Need to wait for a higher timeframe CHOCH for confirmation When a CHOCH fails, it often sets up an even stronger continuation of the original trend. CHOCH vs BOS Decision Matrix: If in doubt about trend direction → wait for CHOCH If confident in trend → trade BOS continuations After a CHOCH → next signals in the new direction are BOS 5. Fair Value Gaps (FVG) - Institutional Retracement Zones What It Is: Fair Value Gaps represent price imbalances where the market moved so quickly that it left behind inefficient pricing. These gaps form when there's no overlap between the current candle's wick and the candle from two bars ago—a void in the price action that creates a "gap" in the order flow. The Institutional Logic: When institutions execute large market orders, they can push price rapidly through levels without allowing normal two-way trading. This creates unfilled orders and imbalanced order books. Institutions often return to these gaps to: Fill additional orders at more favorable prices Allow the market to "breathe" before the next push Create support/resistance at the gap for the next move Restore balance to the order book FVG Formation Criteria: This indicator uses enhanced FVG detection logic: Bullish FVG (Upward Gap): Current candle's low is above the high from 2 candles ago Creates a visible gap where no trading occurred Gap size must exceed 30% of ATR (filtering minor gaps) Typically forms on strong bullish momentum candles Market moved up so fast it left unfilled sell orders Bearish FVG (Downward Gap): Current candle's high is below the low from 2 candles ago Creates a visible gap where no trading occurred Gap size must exceed 30% of ATR Typically forms on strong bearish momentum candles Market moved down so fast it left unfilled buy orders Visual Presentation: Bullish FVG Zones: Semi-transparent cyan boxes extending from gap bottom to top Bearish FVG Zones: Semi-transparent red boxes extending from gap top to bottom Dynamic Management: Gaps automatically removed when filled or expired Clean Display: Only active, unfilled gaps shown to prevent chart clutter FVG Trading Strategies: Strategy 1: FVG Retracement Entries After a CHOCH or strong BOS, wait for price to retrace into the FVG for entry: Identify trend direction via CHOCH or BOS Locate the nearest FVG in the direction of the trend Set limit orders within the FVG zone Stop loss beyond the FVG Target the next structural level or previous swing Strategy 2: FVG Breakout Confirmation When price breaks through an FVG without filling it: Signals extreme institutional urgency Indicates the move is likely to continue strongly The unfilled gap becomes a "no-go zone" for counter-trend entries Strategy 3: Multiple FVG Management When multiple FVGs form in sequence: The first FVG is most likely to be filled If price skips the first FVG, it signals exceptional strength Sequential gaps create a "gap ladder" for scaling into positions FVG Quality Assessment: High-Quality FVGs (Best Trading Zones): Large gap size (1.5x+ ATR) Formed on high volume impulse moves Aligned with higher timeframe structure Created during CHOCH or strong BOS Positioned between current price and key structure Low-Quality FVGs (Use Caution): Small gaps (< 0.5 ATR) Formed during choppy, ranging conditions Multiple overlapping gaps in the same area Counter to higher timeframe trend Very old gaps (50+ bars ago) FVG Lifecycle Management: The indicator intelligently manages FVG zones: Gap Filling: Bullish FVG is "filled" when price touches the bottom of the gap Bearish FVG is "filled" when price touches the top of the gap Filled gaps are automatically removed from the chart Partial fills count as complete fills (institutions got their orders) Gap Expiration: Gaps older than the extension period (default 10 bars) are removed This keeps the chart clean and focuses on relevant levels Adjustable from 5-50 bars based on timeframe and trading style Gap Priority: When multiple gaps exist, closest gap to current price is most relevant Advanced FVG Concepts: Nested FVGs: Sometimes FVGs form within larger FVGs. The smaller, more recent gap typically gets filled first, providing a secondary entry within the larger gap. FVG Clusters: When 3+ FVGs stack in the same zone, this area becomes a major institutional reaccumulation zone—excellent for swing entries. Inverted FVGs: Bullish FVGs in downtrends or bearish FVGs in uptrends can act as resistance/support where rallies/dips fail. FVG + Liquidity Sweep Combination: The ultimate entry setup: Liquidity sweep occurs CHOCH confirms reversal Price retraces into FVG created during the CHOCH move Enter with exceptional risk-reward ratio FVG Statistics & Probabilities: Research on FVG behavior shows: Approximately 70% of FVGs get filled within 20 bars FVGs formed during CHOCH have 80%+ fill rate Larger gaps (2x+ ATR) have lower but higher-quality fill rates Higher timeframe FVGs are more magnetic than lower timeframe Timeframe Considerations: Daily FVGs: Can remain unfilled for weeks Major institutional zones Often mark the absolute best entry prices for swing trades When filled, usually result in strong reactions 4H FVGs: Typically fill within 3-7 days Excellent for swing trading Balance between frequency and reliability 1H FVGs: Usually fill within 1-3 days Good for short-term position trading More frequent signals 15M FVGs: Often fill same day Best used for intraday refinement Should align with higher timeframe gaps 🔧 Customization & Settings Guide Structure Detection Settings Swing Lookback Period (3-50 bars): This is arguably the most important setting as it determines what the indicator considers "structure." Low Values (3-7): Identifies minor swings and frequent structure points More BOS and CHOCH signals Better for scalping and day trading Risk: More false signals in choppy markets Best for: 15M-1H charts, active traders Medium Values (8-15): Balanced approach capturing meaningful swings Default setting works well for most traders Good signal-to-noise ratio Best for: 1H-4H charts, swing traders High Values (16-50): Only major structural points identified Fewer but higher-quality signals Cleaner charts with less noise Better for trending markets Best for: 4H-Daily charts, position traders ATR Period (1-50): Controls how volatility is measured for liquidity sweep detection. Shorter Periods (7-14): More responsive to recent volatility changes Better during high volatility events May overreact to short-term spikes Longer Periods (15-30): Smoother, more stable volatility measurement Better for swing trading Reduces sensitivity to short-term noise Liquidity Sweep Multiplier (0.5-3.0): Determines how far beyond structure price must move to qualify as a sweep. Low Multiplier (0.5-0.9): Catches smaller, more frequent sweeps More signals but lower reliability Good for scalping or high-frequency trading Use in ranging markets Medium Multiplier (1.0-1.5): Balanced sensitivity Default 1.2 works for most situations Good signal quality High Multiplier (1.6-3.0): Only major, obvious sweeps detected Fewer but very high-quality signals Best for trending markets Use when you want only the clearest setups Display Options Toggle Controls: Each component can be individually enabled/disabled: Show Market Structure: Turn off when chart becomes too cluttered Essential for understanding context, generally keep ON Disable only when you know structure from higher timeframe Show Liquidity Zones: Highlights swept areas with boxes Can be disabled if you prefer cleaner charts Keep ON when learning to spot manipulation Show Break of Structure: BOS labels can be disabled if trading only reversals Keep ON for trend following strategies Show Change of Character: Core SMC signal, usually keep ON Only disable if focusing purely on continuation trading Show Fair Value Gaps: OFF by default to prevent overwhelming new users Turn ON once comfortable with basic structure Can generate many zones on lower timeframes FVG Extension Period (5-50 bars): Determines how long unfilled gaps remain displayed. Short Extension (5-10): Keeps charts very clean Only shows very recent gaps Good for day trading May remove gaps before they fill Medium Extension (11-25): Balanced approach Captures most gap fills Good for swing trading Long Extension (26-50): Shows historical gap context Better for position trading Higher timeframe analysis Can make charts busy on lower timeframes Color Scheme Customization Why Colors Matter: Visual clarity is crucial for quick decision-making. The color scheme should: Clearly distinguish bullish vs bearish elements Work well with your chart background (dark/light mode) Be visible but not distracting Match your personal preference for aesthetics Default Colors: Bullish: Cyan ( #00ffff) - visibility and association with "cool" buying Bearish: Red ( #ff0051) - visibility and universal danger/selling association FVG Bullish: 85% transparent cyan - visible but not overpowering FVG Bearish: 85% transparent red - visible but not overpowering Customization Tips: Increase transparency if zones overwhelm price action Use higher contrast colors on light backgrounds Keep bullish/bearish colors visually distinct Test colors across different market conditions Optimization by Market Type Forex (24-hour markets): Structure Lookback: 10-15 ATR Period: 14-21 Sweep Multiplier: 1.0-1.5 Best Timeframes: 15M, 1H, 4H Stocks (Session-based): Structure Lookback: 8-12 ATR Period: 14 Sweep Multiplier: 1.2-1.8 Best Timeframes: 5M, 15M, 1H, Daily Note: Gaps at market open/close aren't FVGs Cryptocurrency (High volatility): Structure Lookback: 12-20 (filter noise) ATR Period: 10-14 (responsive to volatility) Sweep Multiplier: 1.5-2.5 (larger sweeps) Best Timeframes: 15M, 1H, 4H Indices (Moderate volatility): Structure Lookback: 10-15 ATR Period: 14-20 Sweep Multiplier: 1.0-1.5 Best Timeframes: 1H, 4H, Daily 📈 Complete Trading System & Strategies The Complete SMC Trading Process Step 1: Higher Timeframe Analysis (Daily/4H) Begin every trading session by analyzing higher timeframes: Identify the prevailing market structure (bullish or bearish) Mark key swing highs and lows Note any recent CHOCHs that signal trend changes Identify major Fair Value Gaps that could act as targets or entry zones Determine areas of liquidity (obvious highs/lows where stops cluster) Step 2: Trading Timeframe Setup (1H/4H) Move to your primary trading timeframe: Wait for alignment with higher timeframe bias Look for CHOCH signals if expecting reversal Look for BOS signals if expecting continuation Identify liquidity sweeps that create trading opportunities Note nearby FVGs for entry refinement Step 3: Entry Timeframe Execution (15M/1H) Use lower timeframe for precise entry: After higher timeframe signal, wait for lower timeframe confirmation Enter on FVG fills, structure breaks, or CHOCH signals Place stop beyond swept liquidity or broken structure Set targets at next structure level or opposite side of range Step 4: Management Active trade management increases profitability: Move stop to breakeven after price moves 1R (risk unit) Take partial profits at first target (structure level) Let remainder run to major targets Trail stop using FVGs or structure breaks in your direction Exit if a counter-trend CHOCH appears High-Probability Trading Setups Setup 1: The Classic CHOCH Reversal Market Context: Extended trend in one direction Price reaching obvious highs/lows where liquidity pools Setup Requirements: Liquidity sweep of the high/low CHOCH signal fires (Optional) Wait for pullback to FVG Entry: On CHOCH confirmation or FVG fill Stop: Beyond swept liquidity Target: Previous swing in opposite direction Example (Bullish): Market in downtrend for 2 weeks Price sweeps below obvious daily low Bullish CHOCH fires (breaks previous lower high) Enter immediately or wait for pullback to bullish FVG Stop below swept low Target: Previous lower high, then previous high Risk-Reward: Typically 1:3 to 1:5+ Setup 2: BOS Continuation with FVG Entry Market Context: Established trend with recent CHOCH Strong momentum in trend direction Setup Requirements: Recent CHOCH established trend direction BOS signal confirms continuation Wait for pullback into FVG created on the BOS move Entry: Limit order within FVG zone Stop: Beyond FVG (invalid if exceeded) Target: Next structural level Example (Bearish): Bearish CHOCH 2 days ago Price makes BOS breaking new low Large bearish FVG created during the break Price retraces into FVG zone Enter short at FVG fill Stop above FVG Target: Next major low or daily FVG below Risk-Reward: 1:2 to 1:4 Setup 3: Liquidity Sweep Fade Market Context: Ranging market between defined highs/lows Obvious liquidity on both sides of range Setup Requirements: Clear range established (minimum 20-30 bars) Price sweeps one side of range (high or low) Strong rejection back into range Entry: After sweep rejection confirmed Stop: Beyond swept level Target: Opposite side of range Example: Range between 1.0850-1.0920 (EUR/USD) Price sweeps above 1.0920 to 1.0935 Strong bearish rejection candle back below 1.0920 Enter short at 1.0915 Stop at 1.0940 (above sweep high) Target: 1.0850 (range low) Risk-Reward: 1:2.6 Setup 4: Multi-Timeframe CHOCH Alignment Market Context: Major trend change occurring Multiple timeframes showing reversal signals Setup Requirements: Daily timeframe shows CHOCH Wait for 4H CHOCH in same direction Enter on 1H CHOCH that aligns Entry: 1H CHOCH confirmation Stop: Below 4H structure Target: Daily structural level Example (Bullish): Daily bearish trend for months Daily bullish CHOCH appears 4H shows bullish CHOCH next day 1H bullish CHOCH provides entry Enter long on 1H signal Stop: Below 4H swing low Target: Daily previous high Risk-Reward: 1:5 to 1:10+ Position: Larger size due to alignment Setup 5: Failed CHOCH Continuation Market Context: Strong trend temporarily looks like reversing "False" CHOCH creates trap for counter-trend traders Setup Requirements: Apparent CHOCH against main trend Price fails to follow through Original trend resumes with strong BOS Entry: On BOS in original trend direction Stop: Recent swing Target: Extension of original trend Example: Strong daily uptrend Bearish CHOCH appears (potential reversal) Price consolidates but doesn't follow through down Bullish BOS breaks above recent consolidation Enter long on BOS Stop: Below failed CHOCH low Target: New high extension Risk-Reward: 1:3 to 1:6 Note: Failed reversals often lead to explosive continuations Risk Management Framework Position Sizing: Never risk more than 1-2% of account per trade, even on A+ setups. Risk Calculation: Position Size = (Account Size × Risk %) / (Entry - Stop Loss in pips/points) Example: Account: $10,000 Risk: 1% = $100 Entry: 1.0900 Stop: 1.0870 (30 pips) Position Size: $100 / 30 pips = $3.33 per pip Lot Size (Forex): 0.33 lots Stop Loss Placement: For CHOCH Reversals: Place stop 5-10 pips beyond swept liquidity Gives room for volatility while protecting capital If swept liquidity is violated, setup is invalidated For BOS Continuations: Place stop beyond the FVG or structure that provided entry Typically tighter stops (closer to entry) Can trail stop to breakeven quickly For Range Trading: Stop beyond the swept level Generally tight stops work well in ranges Exit quickly if range boundaries break Take Profit Strategy: Scaling Out Method (Recommended): First Target (50% of position): First structural level (1:1 to 1:2) Second Target (30% of position): Major structure (1:3 to 1:5) Trail Stop (20% of position): Let run to full extension Full Exit Method: Hold entire position to predetermined target Requires more discipline Higher reward but also higher risk of giveback Trade Management Rules: Breakeven Rule: Move stop to breakeven after 1R profit Partial Profit Rule: Take partials at structure levels Trailing Rule: Trail stop
Penunjuk Pine Script®
oleh xqweasdzxcv
2
Lot Size & Risk Calculator All Pairs NEWLot Size & Risk Calculator All Pairs NEW Description Professional risk and position size calculator for traders working with various financial instruments. Main difference from standard indicators: Standard risk calculators only show basic Risk/Reward for the entire position. But in real trading, we often close positions partially at different take-profit levels, and the final Risk/Reward changes significantly with this approach! This indicator calculates weighted Risk/Reward taking into account position distribution across multiple take-profit levels. Main features: - Support for 4 instrument types: Forex, XAUUSD (gold), BTCUSD (bitcoin), US100 (NASDAQ index) - Automatic position size calculation based on risk and stop-loss distance - Multiple take-profit levels with customizable closing percentages - Weighted Risk/Reward calculation considering position distribution - Ability to adjust position distribution between take-profits to optimize final profit - Display of total percentage growth of deposit from all take-profit levels - 2 visualization options: colored fill between levels or lines - Informative results panel in table format Settings by groups: Core Settings - Currency: select instrument type (Forex, XAUUSD, BTCUSD, US100) - Account Balance: trading account size in dollars - Risk %: risk percentage from deposit (0.1-100%) - Use Custom Contract Sizes: manual contract size configuration Point Value Settings - Use automatic point value calculation: automatic point value calculation - Manual point value: manual point value input (for non-standard contracts) Levels - Entry Price: entry price (confirmation required on first use) - Stop Price: stop-loss price - Take-Profit Prices: take-profit prices (up to 3 levels) - TP Close %: percentage of position closed at each take-profit level Dashboard - Show Targets Profit: display profit from take-profit levels - Label Size: text size in the table - Dashboard Position: table position on the chart How to use: Step 1: Initial setup (when first adding) 1. Enter entry price (Entry Price) - confirmation window will appear (click on desired bar) 2. Then enter stop-loss price (Stop Price) (click on desired bar) 3. Add first take-profit (TP1) (click on desired bar) 4. Second and third take-profits are added through checkboxes (click on the settings gear icon to open them) Step 2: Instrument selection and risk configuration 1. In "Core" group, select your instrument type 2. Set account balance and risk percentage Step 3: Position distribution configuration 1. Set TP Close % for each take-profit level (e.g.: TP1 - 33%, TP2 - 33%, TP3 - 34%) 2. Experiment with distribution! By changing closing percentages, you can: - Increase/decrease final Risk/Reward - Optimize risk/profit ratio - Find the most comfortable position distribution for you Step 4: Results analysis 1. Results table will show: - Calculated position size (lots/contracts) - Risk in monetary terms - Risk/Reward for each take-profit level - Weighted R:R considering position distribution - Total potential profit from all take-profits - Percentage growth of deposit - total profit percentage from all take-profit levels Key features: Position distribution adjustment You can easily find optimal position distribution between take-profits: - Aggressive approach: higher percentage on distant take-profit (higher profit potential) - Conservative approach: higher percentage on near take-profit (faster profit taking) - Balanced: even distribution for risk reduction Weighted Risk/Reward The indicator calculates not just simple R:R for the entire position, but weighted value that considers: - Position distribution between take-profits - Different distances to each take-profit level - Closing percentage at each level Results visualization - Colored fill shows risk and profit zones - Labels at levels display specific profit/loss values - Results table contains all key metrics Creation story This indicator was created based on the original calculator by @Algoryze As a trader, I lacked the ability to see real Risk/Reward when partially closing positions and a convenient tool for selecting optimal position distribution between take-profit levels. I improved the indicator by adding: - Weighted Risk/Reward calculation - Ability to adjust closing percentage at each take-profit - Display of total percentage growth of deposit - Improved interface and visualization I hope this tool will be useful to other traders who use strategies with partial position closing! Important notes: 1. When first adding the indicator, be sure to enter prices in order: Entry → Stop → TP1 2. TP2 and TP3 are added through input fields (no confirmation required) 3. Closing percentages are automatically normalized if the sum is not 100% 4. Experiment with position distribution to find optimal risk/profit ratio 5. For different instruments, add separate copies of the indicator in different tabs Support For questions and suggestions, leave comments in the indicator publication on TradingView. --- Important: All calculations are provided for informational purposes only. Trading involves risks, trade responsibly. The indicator helps with calculations but does not guarantee profit.
Penunjuk Pine Script®
oleh Upscale888
Market Regime# MARKET REGIME IDENTIFICATION & TRADING SYSTEM ## Complete User Guide --- ## 📋 TABLE OF CONTENTS 1. (#overview) 2. (#regimes) 3. (#indicator-usage) 4. (#entry-signals) 5. (#exit-signals) 6. (#regime-strategies) 7. (#confluence) 8. (#backtesting) 9. (#optimization) 10. (#examples) --- ## OVERVIEW ### What This System Does This is a **complete market regime identification and trading system** that: 1. **Identifies 6 distinct market regimes** automatically 2. **Adapts trading tactics** to each regime 3. **Provides high-probability entry signals** with confluence scoring 4. **Shows optimal exit points** for each trade 5. **Can be backtested** to validate performance ### Two Components Provided 1. **Indicator** (`market_regime_indicator.pine`) - Visual regime identification - Entry/exit signals on chart - Dynamic support/resistance - Info tables with live data - Use for manual trading 2. **Strategy** (`market_regime_strategy.pine`) - Fully automated backtestable version - Same logic as indicator - Position sizing and risk management - Performance metrics - Use for backtesting and automation --- ## THE 6 MARKET REGIMES ### 1. 🟢 BULL TRENDING **Characteristics:** - Strong uptrend - Price above SMA50 and SMA200 - ADX > 25 (strong trend) - Higher highs and higher lows - DI+ > DI- (bullish momentum) **What It Means:** - Market has clear upward direction - Buyers in control - Pullbacks are buying opportunities - Strongest regime for long positions **How to Trade:** - ✅ **BUY dips to EMA20 or SMA20** - ✅ Enter when RSI < 60 on pullback - ✅ Hold through minor corrections - ❌ Don't short against the trend - ❌ Don't sell too early **Expected Behavior:** - Pullbacks are shallow (5-10%) - Bounces are strong - Support at moving averages holds - Volume increases on rallies --- ### 2. 🔴 BEAR TRENDING **Characteristics:** - Strong downtrend - Price below SMA50 and SMA200 - ADX > 25 (strong trend) - Lower highs and lower lows - DI- > DI+ (bearish momentum) **What It Means:** - Market has clear downward direction - Sellers in control - Rallies are selling opportunities - Strongest regime for short positions **How to Trade:** - ✅ **SELL rallies to EMA20 or SMA20** - ✅ Enter when RSI > 40 on bounce - ✅ Hold through minor bounces - ❌ Don't buy against the trend - ❌ Don't cover shorts too early **Expected Behavior:** - Rallies are weak (5-10%) - Selloffs are strong - Resistance at moving averages holds - Volume increases on declines --- ### 3. 🔵 BULL RANGING **Characteristics:** - Bullish bias but consolidating - Price near or above SMA50 - ADX < 20 (weak trend) - Trading in range - Choppy price action **What It Means:** - Uptrend is pausing - Accumulation phase - Support and resistance zones clear - Lower volatility **How to Trade:** - ✅ **BUY at support zone** - ✅ Enter when RSI < 40 - ✅ Take profits at resistance - ⚠️ Smaller position sizes - ⚠️ Tighter stops **Expected Behavior:** - Range-bound oscillations - Support bounces repeatedly - Resistance rejections common - Eventually breaks higher (usually) --- ### 4. 🟠 BEAR RANGING **Characteristics:** - Bearish bias but consolidating - Price near or below SMA50 - ADX < 20 (weak trend) - Trading in range - Choppy price action **What It Means:** - Downtrend is pausing - Distribution phase - Support and resistance zones clear - Lower volatility **How to Trade:** - ✅ **SELL at resistance zone** - ✅ Enter when RSI > 60 - ✅ Take profits at support - ⚠️ Smaller position sizes - ⚠️ Tighter stops **Expected Behavior:** - Range-bound oscillations - Resistance holds repeatedly - Support bounces are weak - Eventually breaks lower (usually) --- ### 5. ⚪ CONSOLIDATION **Characteristics:** - No clear direction - Range compression - Very low ADX (< 15 often) - Price inside tight range - Neutral sentiment **What It Means:** - Market is coiling - Building energy for next move - Indecision between buyers/sellers - Calm before the storm **How to Trade:** - ✅ **WAIT for breakout direction** - ✅ Enter on high-volume breakout - ✅ Direction becomes clear - ❌ Don't trade inside the range - ❌ Avoid choppy scalping **Expected Behavior:** - Narrow range - Low volume - False breakouts possible - Explosive move when it breaks --- ### 6. 🟣 CHAOS (High Volatility) **Characteristics:** - Extreme volatility - No clear direction - Erratic price swings - ATR > 2x average - Unpredictable **What It Means:** - Market panic or euphoria - News-driven moves - Emotion dominates logic - Highest risk environment **How to Trade:** - ❌ **STAY OUT!** - ❌ No positions - ❌ Wait for stability - ✅ Protect existing positions - ✅ Reduce risk **Expected Behavior:** - Large intraday swings - Gaps up/down - Stop hunts - Whipsaws - Eventually calms down --- ## INDICATOR USAGE ### Visual Elements #### 1. Background Colors - **Light Green** = Bull Trending (go long) - **Light Red** = Bear Trending (go short) - **Light Teal** = Bull Ranging (buy dips) - **Light Orange** = Bear Ranging (sell rallies) - **Light Gray** = Consolidation (wait) - **Purple** = Chaos (stay out!) #### 2. Regime Labels - Appear when regime changes - Show new regime name - Positioned at highs (bullish) or lows (bearish) #### 3. Entry Signals - **Green "LONG"** labels = Buy here - **Red "SHORT"** labels = Sell here - Number shows confluence score (X/5 signals) - Hover for details (stop, target, RSI, etc.) #### 4. Exit Signals - **Orange "EXIT LONG"** = Close long position - **Orange "EXIT SHORT"** = Close short position - Shows exit reason in tooltip #### 5. Support/Resistance Lines - **Green line** = Dynamic support (buy zone) - **Red line** = Dynamic resistance (sell zone) - Adapts to regime automatically #### 6. Moving Averages - **Blue** = SMA 20 (short-term trend) - **Orange** = SMA 50 (medium-term trend) - **Purple** = SMA 200 (long-term trend) ### Information Tables #### Top Right Table (Main Info) Shows real-time market conditions: - **Current Regime** - What regime we're in - **Bias** - Long, Short, Breakout, or Stay Out - **ADX** - Trend strength (>25 = strong) - **Trend** - Strong, Moderate, or Weak - **Volatility** - High or Normal - **Vol Ratio** - Current vs average volatility - **RSI** - Momentum (>70 overbought, <30 oversold) - **vs SMA50/200** - Price position relative to MAs - **Support/Resistance** - Exact price levels - **Long/Short Signals** - Confluence scores (X/5) #### Bottom Right Table (Regime Guide) Quick reference for each regime: - What action to take - What strategy to use - Color-coded for quick identification --- ## ENTRY SIGNALS EXPLAINED ### Confluence Scoring System (5 Factors) Each entry signal is scored 0-5 based on how many factors align: #### For LONG Entries: 1. ✅ **Regime Alignment** - In Bull Trending or Bull Ranging 2. ✅ **RSI Pullback** - RSI between 35-50 (not overbought) 3. ✅ **Near Support** - Price within 2% of dynamic support 4. ✅ **MACD Turning Up** - Momentum shifting bullish 5. ✅ **Volume Confirmation** - Above average volume #### For SHORT Entries: 1. ✅ **Regime Alignment** - In Bear Trending or Bear Ranging 2. ✅ **RSI Rejection** - RSI between 50-65 (not oversold) 3. ✅ **Near Resistance** - Price within 2% of dynamic resistance 4. ✅ **MACD Turning Down** - Momentum shifting bearish 5. ✅ **Volume Confirmation** - Above average volume ### Confluence Requirements **Minimum Confluence** (default = 2): - 2/5 = Entry signal triggered - 3/5 = Good signal - 4/5 = Strong signal - 5/5 = Excellent signal (rare) **Higher confluence = Higher probability = Better trades** ### Specific Entry Patterns #### 1. Bull Trending Entry ``` Requirements: - Regime = Bull Trending - Price pulls back to EMA20 - Close above EMA20 (bounce) - Up candle (close > open) - RSI < 60 - Confluence ≥ 2 ``` #### 2. Bear Trending Entry ``` Requirements: - Regime = Bear Trending - Price rallies to EMA20 - Close below EMA20 (rejection) - Down candle (close < open) - RSI > 40 - Confluence ≥ 2 ``` #### 3. Bull Ranging Entry ``` Requirements: - Regime = Bull Ranging - RSI < 40 (oversold) - Price at or below support - Up candle (reversal) - Confluence ≥ 1 (more lenient) ``` #### 4. Bear Ranging Entry ``` Requirements: - Regime = Bear Ranging - RSI > 60 (overbought) - Price at or above resistance - Down candle (rejection) - Confluence ≥ 1 (more lenient) ``` #### 5. Consolidation Breakout ``` Requirements: - Regime = Consolidation - Price breaks above/below range - Volume > 1.5x average (explosive) - Strong directional candle ``` --- ## EXIT SIGNALS EXPLAINED ### Three Types of Exits #### 1. Regime Change Exits (Automatic) - **Long Exit**: Regime changes to Bear Trending or Chaos - **Short Exit**: Regime changes to Bull Trending or Chaos - **Reason**: Market character changed, strategy no longer valid #### 2. Support/Resistance Break Exits - **Long Exit**: Price breaks below support by 2% - **Short Exit**: Price breaks above resistance by 2% - **Reason**: Key level violated, trend may be reversing #### 3. Momentum Exits - **Long Exit**: RSI > 70 (overbought) AND down candle - **Short Exit**: RSI < 30 (oversold) AND up candle - **Reason**: Overextension, take profits ### Stop Loss & Take Profit **Stop Loss** (Automatic in strategy): - Placed at Entry - (ATR × 2) - Adapts to volatility - Protected from whipsaws - Typically 2-4% for stocks, 5-10% for crypto **Take Profit** (Automatic in strategy): - Placed at Entry + (Stop Distance × R:R Ratio) - Default 2.5:1 reward:risk - Example: $2 risk = $5 reward target - Allows winners to run --- ## TRADING EACH REGIME ### BULL TRENDING - Most Profitable Long Environment **Strategy: Buy Every Dip** **Entry Rules:** 1. Wait for pullback to EMA20 or SMA20 2. Look for RSI < 60 3. Enter when candle closes above MA 4. Confluence should be 2+ **Stop Loss:** - Below the recent swing low - Or 2 × ATR below entry **Take Profit:** - At previous high - Or 2.5:1 R:R minimum **Position Size:** - Can use full size (2% risk) - High win rate regime **Example Trade:** ``` Price: $100, pulls back to $98 (EMA20) Entry: $98.50 (close above EMA) Stop: $96.50 (2 ATR) Target: $103.50 (2.5:1) Risk: $2, Reward: $5 ``` --- ### BEAR TRENDING - Most Profitable Short Environment **Strategy: Sell Every Rally** **Entry Rules:** 1. Wait for bounce to EMA20 or SMA20 2. Look for RSI > 40 3. Enter when candle closes below MA 4. Confluence should be 2+ **Stop Loss:** - Above the recent swing high - Or 2 × ATR above entry **Take Profit:** - At previous low - Or 2.5:1 R:R minimum **Position Size:** - Can use full size (2% risk) - High win rate regime **Example Trade:** ``` Price: $100, rallies to $102 (EMA20) Entry: $101.50 (close below EMA) Stop: $103.50 (2 ATR) Target: $96.50 (2.5:1) Risk: $2, Reward: $5 ``` --- ### BULL RANGING - Buy Low, Sell High **Strategy: Range Trading (Long Bias)** **Entry Rules:** 1. Wait for price at support zone 2. Look for RSI < 40 3. Enter on reversal candle 4. Confluence should be 1-2+ **Stop Loss:** - Below support zone - Tighter than trending (1.5 ATR) **Take Profit:** - At resistance zone - Don't hold through resistance **Position Size:** - Reduce to 1-1.5% risk - Lower win rate than trending **Example Trade:** ``` Range: $95-$105 Entry: $96 (at support, RSI 35) Stop: $94 (below support) Target: $104 (at resistance) Risk: $2, Reward: $8 (4:1) ``` --- ### BEAR RANGING - Sell High, Buy Low **Strategy: Range Trading (Short Bias)** **Entry Rules:** 1. Wait for price at resistance zone 2. Look for RSI > 60 3. Enter on rejection candle 4. Confluence should be 1-2+ **Stop Loss:** - Above resistance zone - Tighter than trending (1.5 ATR) **Take Profit:** - At support zone - Don't hold through support **Position Size:** - Reduce to 1-1.5% risk - Lower win rate than trending **Example Trade:** ``` Range: $95-$105 Entry: $104 (at resistance, RSI 65) Stop: $106 (above resistance) Target: $96 (at support) Risk: $2, Reward: $8 (4:1) ``` --- ### CONSOLIDATION - Wait for Breakout **Strategy: Breakout Trading** **Entry Rules:** 1. Identify consolidation range 2. Wait for VOLUME SURGE (1.5x+ avg) 3. Enter on close outside range 4. Direction must be clear **Stop Loss:** - Opposite side of range - Or 2 ATR **Take Profit:** - Measure range height, project it - Example: $10 range = $10 move expected **Position Size:** - Reduce to 1% risk - 50% false breakout rate **Example Trade:** ``` Consolidation: $98-$102 (4-point range) Breakout: $102.50 (high volume) Entry: $103 Stop: $100 (back in range) Target: $107 (4-point range projected) Risk: $3, Reward: $4 ``` --- ### CHAOS - STAY OUT! **Strategy: Preservation** **What to Do:** - ❌ NO new positions - ✅ Close existing positions if near entry - ✅ Tighten stops on profitable trades - ✅ Reduce position sizes dramatically - ✅ Wait for regime to stabilize **Why It's Dangerous:** - Stop hunts are common - Whipsaws everywhere - News-driven volatility - No technical reliability - Even "perfect" setups fail **When Does It End:** - Volatility ratio drops < 1.5 - ADX starts rising (direction appears) - Price respects support/resistance again - Usually 1-5 days --- ## CONFLUENCE SYSTEM ### How It Works The system scores each potential entry on 5 factors. More factors aligning = higher probability. ### Confluence Requirements by Regime **Trending Regimes** (strictest): - Minimum 2/5 required - 3/5 = Good - 4-5/5 = Excellent **Ranging Regimes** (moderate): - Minimum 1-2/5 required - 2/5 = Good - 3+/5 = Excellent **Consolidation** (breakout only): - Volume is most critical - Direction confirmation - Less confluence needed ### Adjusting Minimum Confluence **If too few signals:** - Lower from 2 to 1 - More trades, lower quality **If too many false signals:** - Raise from 2 to 3 - Fewer trades, higher quality **Recommendation:** - Start at 2 - Adjust based on win rate - Aim for 55-65% win rate --- ## STRATEGY BACKTESTING ### Loading the Strategy 1. Copy `market_regime_strategy.pine` 2. Open Pine Editor in TradingView 3. Paste and "Add to Chart" 4. Strategy Tester tab opens at bottom ### Initial Settings ``` Risk Per Trade: 2% ATR Stop Multiplier: 2.0 Reward:Risk Ratio: 2.5 Trade Longs: ✓ Trade Shorts: ✓ Trade Trending Only: ✗ (test both) Avoid Chaos: ✓ Minimum Confluence: 2 ``` ### What to Look For **Good Results:** - Win Rate: 50-60% - Profit Factor: 1.8-2.5 - Net Profit: Positive - Max Drawdown: <20% - Consistent equity curve **Warning Signs:** - Win Rate: <45% (too many losses) - Profit Factor: <1.5 (barely profitable) - Max Drawdown: >30% (too risky) - Erratic equity curve (unstable) ### Testing Different Regimes **Test 1: Trending Only** ``` Trade Trending Only: ✓ Result: Higher win rate, fewer trades ``` **Test 2: All Regimes** ``` Trade Trending Only: ✗ Result: More trades, potentially lower win rate ``` **Test 3: Long Only** ``` Trade Longs: ✓ Trade Shorts: ✗ Result: Works in bull markets ``` **Test 4: Short Only** ``` Trade Longs: ✗ Trade Shorts: ✓ Result: Works in bear markets ``` --- ## SETTINGS OPTIMIZATION ### Key Parameters to Adjust #### 1. Risk Per Trade (Most Important) - **0.5%** = Very conservative - **1.0%** = Conservative (recommended for beginners) - **2.0%** = Moderate (recommended) - **3.0%** = Aggressive - **5.0%** = Very aggressive (not recommended) **Impact:** Higher risk = higher returns BUT bigger drawdowns #### 2. Reward:Risk Ratio - **2:1** = More wins needed, hit target faster - **2.5:1** = Balanced (recommended) - **3:1** = Fewer wins needed, hold longer - **4:1** = Very patient, best in trending **Impact:** Higher R:R = can have lower win rate #### 3. Minimum Confluence - **1** = More signals, lower quality - **2** = Balanced (recommended) - **3** = Fewer signals, higher quality - **4** = Very selective - **5** = Almost never triggers **Impact:** Higher = fewer but better trades #### 4. ADX Thresholds - **Trending: 20-30** (default 25) - Lower = detect trends earlier - Higher = only strong trends - **Ranging: 15-25** (default 20) - Lower = identify ranging earlier - Higher = only weak trends #### 5. Trend Period (SMA) - **20-50** = Short-term trends - **50** = Medium-term (default, recommended) - **100-200** = Long-term trends **Impact:** Longer period = slower regime changes, more stable ### Optimization Workflow **Step 1: Baseline** - Use all default settings - Test on 3+ years - Record: Win Rate, PF, Drawdown **Step 2: Risk Optimization** - Test 1%, 1.5%, 2%, 2.5% - Find best risk-adjusted return - Balance profit vs drawdown **Step 3: R:R Optimization** - Test 2:1, 2.5:1, 3:1 - Check which maximizes profit factor - Consider holding time **Step 4: Confluence Optimization** - Test 1, 2, 3 - Find sweet spot for win rate - Aim for 55-65% win rate **Step 5: Regime Filter** - Test with/without trend filter - Test with/without chaos filter - Find what works for your asset --- ## REAL TRADING EXAMPLES ### Example 1: Bull Trending - SPY **Setup:** - Regime: BULL TRENDING - Price pulls back from $450 to $445 - EMA20 at $444 - RSI drops to 45 - Confluence: 4/5 **Entry:** - Price closes at $445.50 (above EMA20) - LONG signal appears - Enter at $445.50 **Risk Management:** - Stop: $443 (2 ATR = $2.50) - Target: $451.75 (2.5:1 = $6.25) - Risk: $2.50 per share - Position: 80 shares (2% of $10k = $200 risk) **Outcome:** - Price rallies to $452 in 3 days - Target hit - Profit: $6.50 × 80 = $520 - Return: 2.6 × risk (excellent) --- ### Example 2: Bear Ranging - AAPL **Setup:** - Regime: BEAR RANGING - Range: $165-$175 - Price rallies to $174 - Resistance at $175 - RSI at 68 - Confluence: 3/5 **Entry:** - Rejection candle at $174 - SHORT signal appears - Enter at $173.50 **Risk Management:** - Stop: $176 (above resistance) - Target: $166 (support) - Risk: $2.50 - Position: 80 shares **Outcome:** - Price drops to $167 in 2 days - Target hit - Profit: $6.50 × 80 = $520 - Return: 2.6 × risk --- ### Example 3: Consolidation Breakout - BTC **Setup:** - Regime: CONSOLIDATION - Range: $28,000 - $30,000 - Compressed for 2 weeks - Volume declining **Breakout:** - Price breaks $30,000 - Volume surges 200% - Close at $30,500 - LONG signal **Entry:** - Enter at $30,500 **Risk Management:** - Stop: $29,500 (back in range) - Target: $32,000 (range height = $2k) - Risk: $1,000 - Position: 0.2 BTC ($200 risk on $10k) **Outcome:** - Price runs to $33,000 - Target exceeded - Profit: $2,500 × 0.2 = $500 - Return: 2.5 × risk --- ### Example 4: Avoiding Chaos - Tesla **Setup:** - Regime: BULL TRENDING - LONG position from $240 - Elon tweets something crazy - Regime changes to CHAOS **Action:** - EXIT signal appears - Close position immediately - Current price: $242 (small profit) **Outcome:** - Next 3 days: wild swings - High $255, Low $230 - By staying out, avoided: - Potential stop out - Whipsaw losses - Stress **Result:** - Small profit preserved - Capital protected - Re-enter when regime stabilizes --- ## ALERTS SETUP ### Available Alerts 1. **Bull Trending Regime** - Market goes bullish 2. **Bear Trending Regime** - Market goes bearish 3. **Chaos Regime** - High volatility, stay out 4. **Long Entry Signal** - Buy opportunity 5. **Short Entry Signal** - Sell opportunity 6. **Long Exit Signal** - Close long 7. **Short Exit Signal** - Close short ### How to Set Up 1. Click **⏰ (Alert)** icon in TradingView 2. Select **Condition**: Choose indicator + alert type 3. **Options**: Popup, Email, Webhook, etc. 4. **Message**: Customize notification 5. Click **Create** ### Recommended Alert Strategy **For Active Traders:** - Long Entry Signal - Short Entry Signal - Long Exit Signal - Short Exit Signal **For Position Traders:** - Bull Trending Regime (enter longs) - Bear Trending Regime (enter shorts) - Chaos Regime (exit all) **For Conservative:** - Only regime change alerts - Manually review entries - More selective --- ## TIPS FOR SUCCESS ### 1. Start Small - Paper trade first - Then 0.5% risk - Build to 1-2% over time ### 2. Follow the Regime - Don't fight it - Adapt your style - Different tactics for each ### 3. Trust the Confluence - 4-5/5 = Best trades - 2-3/5 = Good trades - 1/5 = Skip unless desperate ### 4. Respect Exits - Don't hope and hold - Cut losses quickly - Take profits at targets ### 5. Avoid Chaos - Seriously, just stay out - Protect your capital - Wait for clarity ### 6. Keep a Journal - Record every trade - Note regime and confluence - Review weekly - Learn patterns ### 7. Backtest Thoroughly - 3+ years minimum - Multiple market conditions - Different assets - Walk-forward test ### 8. Be Patient - Best setups are rare - 1-3 trades per week is normal - Quality over quantity - Compound over time --- ## COMMON QUESTIONS **Q: How many trades per month should I expect?** A: Depends on timeframe and settings. Daily chart: 5-15 trades/month. 4H chart: 15-30 trades/month. **Q: What's a good win rate?** A: 55-65% is excellent. 50-55% is good. Below 50% needs adjustment. **Q: Should I trade all regimes?** A: Beginners: Only trending. Intermediate: Trending + ranging. Advanced: All except chaos. **Q: Can I use this on any timeframe?** A: Best on Daily and 4H. Works on 1H with more noise. Not recommended <1H. **Q: What if I'm in a trade and regime changes?** A: Exit immediately (if using indicator) or let strategy handle it automatically. **Q: How do I know if I'm over-optimizing?** A: If results are perfect on one period but fail on another. Use walk-forward testing. **Q: Should I always take 5/5 confluence trades?** A: Yes, but they're rare (1-2/month). Don't wait only for these. **Q: Can I combine this with other indicators?** A: Yes, but keep it simple. RSI, MACD already included. Maybe add volume profile. **Q: What assets work best?** A: Liquid stocks, major crypto, futures. Avoid forex spot (use futures), penny stocks. **Q: How long to hold positions?** A: Trending: Days to weeks. Ranging: Hours to days. Breakout: Days. Let the regime guide you. --- ## FINAL THOUGHTS This system gives you: - ✅ Clear market context (regime) - ✅ High-probability entries (confluence) - ✅ Defined exits (automatic signals) - ✅ Adaptable tactics (regime-specific) - ✅ Backtestable results (strategy version) **Success requires:** - 📚 Understanding each regime - 🎯 Following the signals - 💪 Discipline to wait - 🧠 Emotional control - 📊 Proper risk management **Start your journey:** 1. Load the indicator 2. Watch for 1 week (no trading) 3. Identify regime patterns 4. Paper trade for 1 month 5. Go live with small size 6. Scale up as you gain confidence **Remember:** The market will always be here. There's no rush. Master one regime at a time, and you'll be profitable in all conditions! Good luck! 🚀
Penunjuk Pine Script®
oleh frankieho_92
Gyspy Bot Trade Engine - V1.2B - Strategy 12-7-25 - SignalLynxGypsy Bot Trade Engine (MK6 V1.2B) - Ultimate Strategy & Backtest Brought to you by Signal Lynx | Automation for the Night-Shift Nation 🌙 1. Executive Summary & Architecture Gypsy Bot (MK6 V1.2B) is not merely a strategy; it is a massive, modular Trade Engine built specifically for the TradingView Pine Script environment. While most strategies rely on a single dominant indicator (like an RSI cross or a MACD flip) to generate signals, Gypsy Bot functions as a sophisticated Consensus Algorithm. The engine calculates data from up to 12 distinct Technical Analysis Modules simultaneously on every bar closing. It aggregates these signals into a "Vote Count" and only executes a trade entry when a user-defined threshold of concurring signals is met. This "Voting System" acts as a noise filter, requiring multiple independent mathematical models—ranging from volume flow and momentum to cyclical harmonics and trend strength—to agree on market direction before capital is committed. Beyond entries, Gypsy Bot features a proprietary Risk Management suite called the Dump Protection Team (DPT). This logic layer operates independently of the entry modules, specifically scanning for "Moon" (Parabolic) or "Nuke" (Crash) volatility events to force-exit positions, overriding standard stops to preserve capital during Black Swan events. 2. ⚠️ The Philosophy of "Curve Fitting" (Must Read) One must be careful when applying Gypsy Bot to new pairs or charts. To be fully transparent: Gypsy Bot is, by definition, a very advanced curve-fitting engine. Because it grants the user granular control over 12 modules, dozens of thresholds, and specific voting requirements, it is extremely easy to "over-fit" the data. You can easily toggle switches until the backtest shows a 100% win rate, only to have the strategy fail immediately in live markets because it was tuned to historical noise rather than market structure. To use this engine successfully, you must adopt a specific optimization mindset: Ignore Raw Net Profit: Do not tune for the highest dollar amount. A strategy that makes $1M in the backtest but has a 40% drawdown is useless. Prioritize Stability: Look for a high Profit Factor (1.5+), a high Percent Profitable, and a smooth equity curve. Regular Maintenance is Mandatory: Markets shift regimes (e.g., from Bull Trend to Crab Range). Parameters that worked perfectly in 2021 may fail in 2024. Gypsy Bot settings should be reviewed and adjusted at regular intervals (e.g., quarterly) to ensure the voting logic remains aligned with current market volatility. Timeframe Recommendations: Gypsy Bot is optimized for High Time Frame (HTF) trend following. It generally produces the most reliable results on charts ranging from 1-Hour to 12-Hours, with the 4-Hour timeframe historically serving as the "sweet spot" for most major cryptocurrency assets. 3. The Voting Mechanism: How Entries Are Generated The heart of the Gypsy Bot engine is the ActivateOrders input (found in the "Order Signal Modifier" settings). The engine constantly monitors the output of all enabled Modules. Long Votes: GoLongCount Short Votes: GoShortCount If you have 10 Modules enabled, and you set ActivateOrders to 7: The engine will ONLY trigger a Buy Entry if 7 or more modules return a valid "Buy" signal on the same closed candle. If only 6 modules agree, the trade is rejected. This allows you to mix "Leading" indicators (Oscillators) with "Lagging" indicators (Moving Averages) to create a high-probability entry signal that requires momentum, volume, and trend to all be in alignment. 4. Technical Deep Dive: The 12 Modules Gypsy Bot allows you to toggle the following modules On/Off individually to suit the asset you are trading. Module 1: Modified Slope Angle (MSA) Logic: Calculates the geometric angle of a moving average relative to the timeline. Function: It filters out "lazy" trends. A trend is only considered valid if the slope exceeds a specific steepness threshold. This helps avoid entering trades during weak drifts that often precede a reversal. Module 2: Correlation Trend Indicator (CTI) Logic: Based on John Ehlers' work, this measures how closely the current price action correlates to a straight line (a perfect trend). Function: It outputs a confidence score (-1 to 1). Gypsy Bot uses this to ensure that we are not just moving up, but moving up with high statistical correlation, reducing fake-outs. Module 3: Ehlers Roofing Filter Logic: A sophisticated spectral filter that combines a High-Pass filter (to remove long-term drift) with a Super Smoother (to remove high-frequency noise). Function: It attempts to isolate the "Roof" of the price action. It is excellent at catching cyclical turning points before standard moving averages react. Module 4: Forecast Oscillator Logic: Uses Linear Regression forecasting to predict where price "should" be relative to where it is. Function: When the Forecast Oscillator crosses its zero line, it indicates that the regression trend has flipped. We offer both "Aggressive" and "Conservative" calculation modes for this module. Module 5: Chandelier ATR Stop Logic: A volatility-based trend follower that hangs a "leash" (ATR multiple) from the highest high (for longs) or lowest low (for shorts). Function: Used here as an entry filter. If price is above the Chandelier line, the trend is Bullish. It also includes a "Bull/Bear Qualifier" check to ensure structural support. Module 6: Crypto Market Breadth (CMB) Logic: This is a macro-filter. It pulls data from multiple major tickers (BTC, ETH, and Perpetual Contracts) across different exchanges. Function: It calculates a "Market Health" percentage. If Bitcoin is rising but the rest of the market is dumping, this module can veto a trade, ensuring you don't buy into a "fake" rally driven by a single asset. Module 7: Directional Index Convergence (DIC) Logic: Analyzes the convergence/divergence between Fast and Slow Directional Movement indices. Function: Identifies when trend strength is expanding. A buy signal is generated only when the positive directional movement overpowers the negative movement with expanding momentum. Module 8: Market Thrust Indicator (MTI) Logic: A volume-weighted breadth indicator. It uses Advance/Decline data and Up/Down Volume data. Function: This is one of the most powerful modules. It confirms that price movement is supported by actual volume flow. We recommend using the "SSMA" (Super Smoother) MA Type for the cleanest signals on the 4H chart. Module 9: Simple Ichimoku Cloud Logic: Traditional Japanese trend analysis using the Tenkan-sen and Kijun-sen. Function: Checks for a "Kumo Breakout." Price must be fully above the Cloud (for longs) or below it (for shorts). This is a classic "trend confirmation" module. Module 10: Simple Harmonic Oscillator Logic: Analyzes the harmonic wave properties of price action to detect cyclical tops and bottoms. Function: Serves as a counter-trend or early-reversal detector. It tries to identify when a cycle has bottomed out (for buys) or topped out (for sells) before the main trend indicators catch up. Module 11: HSRS Compression / Super AO Logic: Two options in one. HSRS: Hirashima Sugita Resistance Support. Detects volatility compression (squeezes) relative to dynamic support/resistance bands. Super AO: A combination of the Awesome Oscillator and SuperTrend logic. Function: Great for catching explosive moves that result from periods of low volatility (consolidation). Module 12: Fisher Transform (MTF) Logic: Converts price data into a Gaussian normal distribution. Function: Identifies extreme price deviations. This module uses Multi-Timeframe (MTF) logic to look at higher-timeframe trends (e.g., looking at the Daily Fisher while trading the 4H chart) to ensure you aren't trading against the major trend. 5. Global Inhibitors (The Veto Power) Even if 12 out of 12 modules vote "Buy," Gypsy Bot performs a final safety check using Global Inhibitors. If any of these are triggered, the trade is blocked. Bitcoin Halving Logic: Hardcoded dates for past and projected future Bitcoin halvings (up to 2040). Trading is inhibited or restricted during the chaotic weeks immediately surrounding a Halving event to avoid volatility crushes. Miner Capitulation: Uses Hash Rate Ribbons (Moving averages of Hash Rate). If miners are capitulating (Shutting down rigs due to unprofitability), the engine flags a "Bearish" regime and can flip logic to Short-only or flat. ADX Filter (Flat Market Protocol): If the Average Directional Index (ADX) is below a specific threshold (e.g., 20), the market is deemed "Flat/Choppy." The bot will refuse to open trend-following trades in a flat market. CryptoCap Trend: Checks the total Crypto Market Cap chart. If the broad market is in a downtrend, it can inhibit Long entries on individual altcoins. 6. Risk Management & The Dump Protection Team (DPT) Gypsy Bot separates "Entry Logic" from "Risk Management Logic." Dump Protection Team (DPT) This is a specialized logic branch designed to save the account during Black Swan events. Nuke Protection: If the DPT detects a volatility signature consistent with a flash crash, it overrides all other logic and forces an immediate exit. Moon Protection: If a parabolic pump is detected that violates statistical probability (Bollinger deviations), DPT can force a profit take before the inevitable correction. Advanced Adaptive Trailing Stop (AATS) Unlike a static trailing stop (e.g., "trail by 5%"), AATS is dynamic. Penthouse Level: If price is at the top of the HSRS channel (High Volatility), the stop loosens to allow for wicks. Dungeon Level: If price is compressed at the bottom, the stop tightens to protect capital. Staged Take Profits TP1: Scalp a portion (e.g., 10%) to cover fees and secure a win. TP2: Take the bulk of profit. TP3: Leave a "Runner" position with a loose trailing stop to catch "Moon" moves. 7. Recommended Setup Guide When applying Gypsy Bot to a new chart, follow this sequence: Set Timeframe: 4 Hours (4H). Reset: Turn OFF Trailing Stop, Stop Loss, and Take Profits. (We want to see raw entry performance first). Tune DPT: Adjust "Dump/Moon Protection" inputs first. These have the highest impact on net performance. Tune Module 8 (MTI): This module is a heavy filter. Experiment with the MA Type (SSMA is recommended). Select Modules: Enable/Disable modules 1-12 based on the asset's personality (Trending vs. Ranging). Voting Threshold: Adjust ActivateOrders. A lower number = More Trades (Aggressive). A higher number = Fewer, higher conviction trades (Conservative). Final Polish: Re-enable Stop Losses, Trailing Stops, and Staged Take Profits to smooth the equity curve and define your max risk per trade. 8. Technical Specs Engine Version: Pine Script V6 Repainting: This strategy uses Closed Candle data for all Risk Management and Entry decisions. This ensures that Backtest results align closely with real-time behavior (no repainting of historical signals). Alerts: This script generates Strategy alerts. If you require visual-only alerts, see the source code header for instructions on switching to "Study" (Indicator) mode. Disclaimer: This script is a complex algorithmic tool for market analysis. Past performance is not indicative of future results. Use this tool to assist your own decision-making, not to replace it. 9. About Signal Lynx Automation for the Night-Shift Nation 🌙 Signal Lynx focuses on helping traders and developers bridge the gap between indicator logic and real-world automation. The same RM engine you see here powers multiple internal systems and templates, including other public scripts like the Super-AO Strategy with Advanced Risk Management. We provide this code open source under the Mozilla Public License 2.0 (MPL-2.0) to: Demonstrate how Adaptive Logic and structured Risk Management can outperform static, one-layer indicators Give Pine Script users a battle-tested RM backbone they can reuse, remix, and extend If you are looking to automate your TradingView strategies, route signals to exchanges, or simply want safer, smarter strategy structures, please keep Signal Lynx in your search. License: Mozilla Public License 2.0 (Open Source). If you make beneficial modifications, please consider releasing them back to the community so everyone can benefit.
Strategi Pine Script®
oleh SignalLynx
1
Strategy Chameleon [theUltimator5]Have you ever looked at an indicator and wondered to yourself "Is this indicator actually profitable?" Well now you can test it out for yourself with the Strategy Chameleon! Strategy Chameleon is a versatile, signal-agnostic trading strategy designed to adapt to any external indicator or trading system. Like a chameleon changes colors to match its environment, this strategy adapts to match any buy/sell signals you provide, making it the ultimate backtesting and automation tool for traders who want to test multiple strategies without rewriting code. 🎯 Key Features 1) Connects ANY external indicator's buy/sell signals Works with RSI, MACD, moving averages, custom indicators, or any Pine Script output Simply connect your indicator's signal output to the strategy inputs 2) Multiple Stop Loss Types: Percentage-based stops ATR (Average True Range) dynamic stops Fixed point stops 3) Advanced Trailing Stop System: Percentage trailing ATR-based trailing Fixed point trailing 4) Flexible Take Profit Options: Risk:Reward ratio targeting Percentage-based profits ATR-based profits Fixed point profits 5) Trading Direction Control Long Only - Bull market strategies Short Only - Bear market strategies Both - Full market strategies 6) Time-Based Filtering Optional trading session restrictions Customize active trading hours Perfect for day trading strategies 📈 How It Works Signal Detection: The strategy monitors your connected buy/sell signals Entry Logic: Executes trades when signals trigger during valid time periods Risk Management: Automatically applies your chosen stop loss and take profit levels Trailing System: Dynamically adjusts stops to lock in profits Performance Tracking: Real-time statistics table showing win rate and performance ⚙️ Setup Instructions 0) Add indicator you want to test, then add the Strategy to your chart Connect Your Signals: imgur.com Go to strategy settings → Signal Sources 1) Set "Buy Signal Source" to your indicator's buy output 2) Set "Sell Signal Source" to your indicator's sell output 3) Choose table position - This simply changes the table location on the screen 4) Set trading direction preference - Buy only? Sell only? Both directions? imgur.com 5) Set your preferred stop loss type and level You can set the stop loss to be either percentage based or ATR and fully configurable. 6) Enable trailing stops if desired imgur.com 7) Configure take profit settings 8) Toggle time filter to only consider specific time windows or trading sessions. 🚀 Use Cases Test various indicators to determine feasibility and/or profitability. Compare different signal sources quickly Validate trading ideas with consistent risk management Portfolio Management Apply uniform risk management across different strategies Standardize stop loss and take profit rules Monitor performance consistently Automation Ready Built-in alert conditions for automated trading Compatible with trading bots and webhooks Easy integration with external systems ⚠️ Important Notes This strategy requires external signals to function Default settings use 10% of equity per trade Pyramiding is disabled (one position at a time) Strategy calculates on bar close, not every tick 🔗 Integration Examples Works perfectly with: RSI strategies (connect RSI > 70 for sells, RSI < 30 for buys) Moving average crossovers MACD signal line crosses Bollinger Band strategies Custom oscillators and indicators Multi-timeframe strategies 📋 Default Settings Position Size: 10% of equity Stop Loss: 2% percentage-based Trailing Stop: 1.5% percentage-based (enabled) Take Profit: Disabled (optional) Trade Direction: Both long and short Time Filter: Disabled
Strategi Pine Script®
oleh TheUltimator5
3
Big Candle Identifier with RSI Divergence and Advanced Stops1. Strategy Objective The main goal of this strategy is to: Identify significant price momentum (big candles). Enter trades at opportune moments based on market signals (candlestick patterns and RSI divergence). Limit initial risk through a fixed stop loss. Maximize profits by using a trailing stop that activates only after the trade moves a specified distance in the profitable direction. 2. Components of the Strategy A. Big Candle Identification The strategy identifies big candles as indicators of strong momentum. A big candle is defined as: The body (absolute difference between close and open) of the current candle (body0) is larger than the bodies of the last five candles. The candle is: Bullish Big Candle: If close > open. Bearish Big Candle: If open > close. Purpose: Big candles signal potential continuation or reversal of trends, serving as the primary entry trigger. B. RSI Divergence Relative Strength Index (RSI): A momentum oscillator used to detect overbought/oversold conditions and divergence. Fast RSI: A 5-period RSI, which is more sensitive to short-term price movements. Slow RSI: A 14-period RSI, which smoothens fluctuations over a longer timeframe. Divergence: The difference between the fast and slow RSIs. Positive divergence (divergence > 0): Bullish momentum. Negative divergence (divergence < 0): Bearish momentum. Visualization: The divergence is plotted on the chart, helping traders confirm momentum shifts. C. Stop Loss Initial Stop Loss: When entering a trade, an immediate stop loss of 200 points is applied. This stop loss ensures the maximum risk is capped at a predefined level. Implementation: Long Trades: Stop loss is set below the entry price at low - 200 points. Short Trades: Stop loss is set above the entry price at high + 200 points. Purpose: Prevents significant losses if the price moves against the trade immediately after entry. D. Trailing Stop The trailing stop is a dynamic risk management tool that adjusts with price movements to lock in profits. Here’s how it works: Activation Condition: The trailing stop only starts trailing when the trade moves 200 ticks (profit) in the right direction: Long Position: close - entry_price >= 200 ticks. Short Position: entry_price - close >= 200 ticks. Trailing Logic: Once activated, the trailing stop: For Long Positions: Trails behind the price by 150 ticks (trail_stop = close - 150 ticks). For Short Positions: Trails above the price by 150 ticks (trail_stop = close + 150 ticks). Exit Condition: The trade exits automatically if the price touches the trailing stop level. Purpose: Ensures profits are locked in as the trade progresses while still allowing room for price fluctuations. E. Trade Entry Logic Long Entry: Triggered when a bullish big candle is identified. Stop loss is set at low - 200 points. Short Entry: Triggered when a bearish big candle is identified. Stop loss is set at high + 200 points. F. Trade Exit Logic Trailing Stop: Automatically exits the trade if the price touches the trailing stop level. Fixed Stop Loss: Exits the trade if the price hits the predefined stop loss level. G. 21 EMA The strategy includes a 21-period Exponential Moving Average (EMA), which acts as a trend filter. EMA helps visualize the overall market direction: Price above EMA: Indicates an uptrend. Price below EMA: Indicates a downtrend. H. Visualization Big Candle Identification: The open and close prices of big candles are plotted for easy reference. Trailing Stop: Plotted on the chart to visualize its progression during the trade. Green Line: Indicates the trailing stop for long positions. Red Line: Indicates the trailing stop for short positions. RSI Divergence: Positive divergence is shown in green. Negative divergence is shown in red. 3. Key Parameters trail_start_ticks: The number of ticks required before the trailing stop activates (default: 200 ticks). trail_distance_ticks: The distance between the trailing stop and price once the trailing stop starts (default: 150 ticks). initial_stop_loss_points: The fixed stop loss in points applied at entry (default: 200 points). tick_size: Automatically calculates the minimum tick size for the trading instrument. 4. Workflow of the Strategy Step 1: Entry Signal The strategy identifies a big candle (bullish or bearish). If conditions are met, a trade is entered with a fixed stop loss. Step 2: Initial Risk Management The trade starts with an initial stop loss of 200 points. Step 3: Trailing Stop Activation If the trade moves 200 ticks in the profitable direction: The trailing stop is activated and follows the price at a distance of 150 ticks. Step 4: Exit the Trade The trade is exited if: The price hits the trailing stop. The price hits the initial stop loss. 5. Advantages of the Strategy Risk Management: The fixed stop loss ensures that losses are capped. The trailing stop locks in profits after the trade becomes profitable. Momentum-Based Entries: The strategy uses big candles as entry triggers, which often indicate strong price momentum. Divergence Confirmation: RSI divergence helps validate momentum and avoid false signals. Dynamic Profit Protection: The trailing stop adjusts dynamically, allowing the trade to capture larger moves while protecting gains. 6. Ideal Market Conditions This strategy performs best in: Trending Markets: Big candles and momentum signals are more effective in capturing directional moves. High Volatility: Larger price swings improve the probability of reaching the trailing stop activation level (200 ticks).
Strategi Pine Script®
oleh NomaAlgo
2
Stock Rating [TrendX_]# OVERVIEW This Stock Rating indicator provides a thorough evaluation of a company (NON-FINANCIAL ONLY) ranging from 0 to 5. The rating is the average of six core financial metrics: efficiency, profitability, liquidity, solvency, valuation, and technical ratings. Each metric encompasses several financial measurements to ensure a robust and holistic evaluation of the stock. ## EFFICIENCY METRICS 1. Asset-to-Liability Ratio : Measures a company's ability to cover its liabilities with its assets. 2. Equity-to-Liability Ratio : Indicates the proportion of equity used to finance the company relative to liabilities. 3. Net Margin : Shows the percentage of revenue that translates into profit. 4. Operating Expense : Reflects the costs required for normal business operations. 5. Operating Expense Ratio : Compares operating expenses to total revenue. 6. Operating Profit Ratio : Measures operating profit as a percentage of revenue. 7. PE to Industry Relative PE/PB : Compares the company's PE ratio to the industry average. ## PROFITABILITY METRICS 1. ROA : Indicates how efficiently a company uses its assets to generate profit. 2. ROE : Measures profitability relative to shareholders' equity. 3. EBITDA : Reflects a company's operational profitability. 4. Free Cash Flow Margin : Shows the percentage of revenue that remains as free cash flow. 5. Revenue Growth : Measures the percentage increase in revenue over a period. 6. Gross Margin : Reflects the percentage of revenue exceeding the cost of goods sold. 7. Net Margin : Percentage of revenue that is net profit. 8. Operating Margin : Measures the percentage of revenue that is operating profit. ## LIQUIDITY METRICS 1. Current Ratio : Indicates the ability to cover short-term obligations with short-term assets. 2. Interest Coverage Ratio : Measures the ability to pay interest on outstanding debt. 3. Debt-to-EBITDA : Compares total debt to EBITDA. 4. Debt-to-Equity Ratio : Indicates the relative proportion of debt and equity financing. ## SOLVENCY METRICS 1. Altman Z-score : Predicts bankruptcy risk 2. Beneish M-score : Detects earnings manipulation. 3. Fulmer H-factor : Predicts business failure risk. ## VALUATION METRICS 1. Industry Relative PE/PB Comparison : Compares the company's PE and PB ratios to industry averages. 2. Momentum of PE, PB, and EV/EBITDA Multiples : Tracks the trends of PE, PB, and EV/EBITDA ratios over time. ## TECHNICAL METRICS 1. Relative Strength Index (RSI) : Measures the speed and change of price movements. 2. Supertrend : Trend-following indicator that identifies market trends. 3. Moving Average Golden-Cross : Occurs when a short-term MA crosses above mid-term and long-term MA which are determined by half-PI increment in smoothing period. 4. On-Balance Volume Golden-Cross : Measures cumulative buying and selling pressure.
Penunjuk Pine Script®
oleh TrendXvn