Daily COC Strategy with SHERLOCK WAVESThis indicator implements a unique trading strategy known as the "Daily COC (Candle Over Candle) Strategy" enhanced with "SHERLOCK WAVES" for pattern recognition. It's designed for traders looking to capitalize on specific candlestick formations with a negative risk-reward ratio, with the aim of achieving a high win rate (over 70%) through numerous trading opportunities, despite each trade having a higher risk relative to the reward.
Key Features:
Pattern Recognition: Identifies a setup based on three consecutive candles - a red candle followed by a shooting star, then an entry candle that does not break below the shooting star's low.
Negative Risk/Reward Trade Selection: Focuses on entries where the potential stop loss is greater than the take profit, banking on a high win rate to offset the individual trade's negative risk-reward ratio.
Visual Signals:
Green Label: Marks potential entry points at the high of the candle before the entry.
Green Dot: Indicates a winning trade closure.
Red Dot: Signals a losing trade closure.
Blue Circle: Warns when the current candle is within 2% of breaking above the previous candle's high, suggesting a potential setup is developing.
Green Circle: Plots the take profit level.
Red Circle: Plots the stop loss level.
Dynamic Statistics: A live updating label showing the number of trades, wins, losses, open trades, current account balance, and win percentage.
Customizable Parameters:
Risk % per Trade: Adjust the percentage of your account balance you're willing to risk on each trade.
Initial Account Balance: Set your starting balance for tracking performance.
Start Date for Strategy: Define when the strategy should start calculating from, allowing for backtesting.
Alerts:
An alert condition is set for when a potential trade setup is developing, helping traders prepare for entries.
Usage Tips:
This strategy is predicated on the idea that a high win rate can compensate for the negative risk-reward ratio of individual trades. It might not suit all market conditions or traders' risk profiles.
Use this strategy in conjunction with other analysis methods to validate trade setups.
Note: Always backtest thoroughly before applying to live markets. Consider this tool as part of a broader trading strategy, not a standalone solution. Monitor your win rate and adjust your risk management accordingly to ensure the strategy remains profitable over time.
This description now correctly explains the purpose behind the negative risk-reward ratio in the context of your trading strategy.
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4 EMA & MACDThe indicator that combines Moving Average and MACD into one is very useful for providing a more complete picture of the market. Here's how it works:
Moving Average (MA): This is a trend indicator that smooths the price to show the dominant trend direction. MA helps traders determine whether the market is in an uptrend, downtrend, or sideways. For example, if the price is above the MA, it might indicate an uptrend, while if the price is below the MA, it might indicate a downtrend.
MACD (Moving Average Convergence Divergence): MACD measures market momentum and can provide entry and exit signals based on the difference between two moving averages (fast MA and slow MA). A buy signal occurs when the MACD crosses above the signal line, and a sell signal occurs when the MACD crosses below the signal line.
Combining both gives traders a more complete view:
MA provides an overview of the larger trend direction.
MACD helps identify moments when momentum supports a position for entering or exiting.
Common usage:
Entry: If the price is above the Moving Average (uptrend) and the MACD shows a buy signal (for example, MACD crossing above the signal line), it can be a signal to buy.
Exit: If the price starts moving below the MA and the MACD shows a sell signal, it can be a signal to sell or exit the position.
There is an indicator called MACD + Moving Average Cross, which combines both elements, providing stronger signals and making it easier to follow the market.
Fibonacci Extension Strt StrategyCore Logic and Steps:
Weekly Trend Identification:
Find the last significant Higher High (HH) and Lower Low (LL) or vice-versa on the Weekly timeframe.
Determine if it's an uptrend (HH followed by LL) or a downtrend (LL followed by HH).
Plot a Fibonacci Extension (or Retracement in reverse order) from the swing point determined to the other significant swing point.
Weekly Retracement Levels:
Display horizontal lines at the 0.236, 0.382, and 0.5 Fibonacci levels from the weekly extension.
Monitor price action on these levels.
Daily Confirmation:
When price hits the Fib levels, examine the Daily chart.
Look for a rejection wick (indicating the pull back is ending) on the identified weekly retracement levels.
Confirm that the price is indeed starting to continue in the direction of the original weekly trend.
Four-Hour Entry:
On the 4H timeframe, plot a new Fib Extension in the opposite direction of the weekly.
If it's an uptrend, the Fib is plotted from last swing low to its swing high. If the weekly trend was bearish the Fib will be plotted from last swing high to the swing low.
Generate an entry when price breaks the high of that candle.
Trade Management:
Entry is on the breakout of the current candle.
Stop Loss: Place the stop loss below the wick of the breakout candle.
Take Profit 1: Close 50% of the position at the 0.5 Fibonacci level. Move the stop loss to breakeven on this position.
Take Profit 2: Close another 25% of the position at the 0.236 Fib level.
Trailing Take Profit: Keep the last 25% open, using a trailing stop loss. (You'll need to define the logic for the trailing stop, e.g., trailing stop using the last high/low)
How to Use in TradingView:
Open a TradingView Chart.
Click on "Pine Editor" at the bottom.
Copy and paste the corrected Pine Script code.
Click "Add to Chart".
The indicator should now be displayed on your chart.
3 Down, 3 Up Strategy█ STRATEGY DESCRIPTION
The "3 Down, 3 Up Strategy" is a mean-reversion strategy designed to capitalize on short-term price reversals. It enters a long position after consecutive bearish closes and exits after consecutive bullish closes. This strategy is NOT optimized and can be used on any timeframes.
█ WHAT ARE CONSECUTIVE DOWN/UP CLOSES?
- Consecutive Down Closes: A sequence of trading bars where each close is lower than the previous close.
- Consecutive Up Closes: A sequence of trading bars where each close is higher than the previous close.
█ SIGNAL GENERATION
1. LONG ENTRY
A Buy Signal is triggered when:
The price closes lower than the previous close for Consecutive Down Closes for Entry (default: 3) consecutive bars.
The signal occurs within the specified time window (between Start Time and End Time).
If enabled, the close price must also be above the 200-period EMA (Exponential Moving Average).
2. EXIT CONDITION
A Sell Signal is generated when the price closes higher than the previous close for Consecutive Up Closes for Exit (default: 3) consecutive bars.
█ ADDITIONAL SETTINGS
Consecutive Down Closes for Entry: Number of consecutive lower closes required to trigger a buy. Default = 3.
Consecutive Up Closes for Exit: Number of consecutive higher closes required to exit. Default = 3.
EMA Filter: Optional 200-period EMA filter to confirm long entries in bullish trends. Default = disabled.
Start Time and End Time: Restrict trading to specific dates (default: 2014-2099).
█ PERFORMANCE OVERVIEW
Designed for volatile markets with frequent short-term reversals.
Performs best when price oscillates between clear support/resistance levels.
The EMA filter improves reliability in trending markets but may reduce trade frequency.
Backtest to optimize consecutive close thresholds and EMA period for specific instruments.
3 Candle AlertThis is a test for integration using a webhook. I am publishing it so I can share it. Ultimately, this is what we want to do:
1. Trade Entry Rules:
Wait until at least the 3rd bar of the day (15 minutes after market open) before entering the first trade.
Order of Priority for Entry:
Look for two consecutive volume bars of the same color (the second bar must have higher volume than the first).
Look for a “price push” beyond the high or low of the day (as determined in the first 15 minutes).
2. Trading Direction:
If the volume bars are RED, I take a Long Position.
If the volume bars are GREEN, I take a Short Position.
ENIGMA Signals with Retests Select higher Time FrameENIGMA Signals with Retests – Script Description
The "ENIGMA Signals with Retests" script is a unique indicator designed for traders who prefer precision trading based on price action retests of key levels derived from higher timeframes. This tool is ideal for those employing multi-timeframe analysis strategies, helping them detect high-probability trade entries when the price interacts with significant support and resistance levels.
What Does This Script Do?
This indicator identifies key levels from a higher timeframe selected by the user (e.g., 4-hour or daily), then tracks price action on lower timeframes to provide actionable buy and sell signals when the price retests these levels. It visually plots the key levels on the chart and triggers alerts for potential trade opportunities when conditions are met.
How It Works
Key Level Detection:
The script uses custom functions to detect recent swing highs and swing lows on the selected higher timeframe (such as 4H or Daily). These levels represent potential areas of support and resistance where price reactions are likely to occur.
Multi-Timeframe Analysis:
The indicator leverages the request.security() function to retrieve price data from the user-defined higher timeframe and plots horizontal lines on the chart for the most recent swing highs and lows.
Retest-Based Signals:
Once the key levels are plotted, the script continuously monitors the price on the lower timeframe:
A Buy Signal is triggered when the price closes below a key high level and then moves back above it, indicating a potential bullish retest.
A Sell Signal is triggered when the price closes above a key low level and then moves back below it, indicating a potential bearish retest.
These retest signals are displayed as green and red arrows on the chart, helping traders identify optimal entry points.
Alerts for Retests:
The script includes built-in alert conditions that notify traders when a valid retest signal occurs. This allows traders to react promptly without constantly monitoring the chart.
How to Use the Script
Select Your Key Timeframe:
From the input settings, choose a higher timeframe that suits your trading style (e.g., 4H for intraday trading or Daily for swing trading).
Adjust Visual Preferences:
Customize the line style (solid, dashed, or dotted) and length of the plotted levels.
Toggle labels for the levels on or off as per your preference.
Trade Execution:
Once a retest signal appears on the lower timeframe, consider entering a trade in the direction of the signal. The buy signal suggests a potential long entry, while the sell signal indicates a potential short entry.
Set Alerts:
Use the alert conditions provided to get notified whenever a valid retest occurs. This helps in reducing screen time and improving trading efficiency.
Underlying Concepts
This script is grounded in the principles of support and resistance, retests, and breakout trading. By focusing on multi-timeframe key levels, it aligns with widely used trading concepts like:
Breakout and Retest: Entering trades after a confirmed breakout and successful retest of a significant level.
Swing Highs and Lows: Recognizing swing points to identify strong price reaction zones.
Multi-Timeframe Confluence: Enhancing trade probability by ensuring that the signals on lower timeframes correspond with key levels from higher timeframes.
Why This Script Is Unique
Unlike many generic trend-following or scalping indicators, "ENIGMA Signals with Retests" offers:
Precision Signals: It only provides signals when specific retest conditions are met, reducing false signals and noise.
Multi-Timeframe Customization: Users can tailor the higher timeframe to their strategy, making it versatile for various trading styles.
Alert Functionality: Alerts are integrated, allowing traders to stay updated without constantly monitoring the charts.
This script is perfect for traders looking for a systematic way to trade retests of key levels across multiple timeframes. Whether you're a scalper, day trader, or swing trader, "ENIGMA Signals with Retests" can help improve your precision and timing in the market.
4H CRT (1AM and 5AM)This TradingView script is designed to assist traders in implementing the "4-Hour Candle Ranges Theory Strategy (CRT)" by identifying key levels and setups based on the 1am and 4am (5am) 4-hour candles. This strategy is particularly effective for trading high-volatility assets such as Gold, EUR/USD, NAS100, US30, and S&P500, with US30 showing a notably high win rate. Here's how the strategy works:
Key Features:
1. Marking 1am and 4am 4-Hour Candle Ranges
- The script highlights the high and low of the 1am 4-hour candle.
- It visually tracks whether the high or low of the 1am candle is taken out by the subsequent 4-hour candle (5am).
2. Entry Setup Rules
- Primary Setup: Wait for the high or low of the 1am candle to be taken out by the 5am candle. Once this sweep occurs, wait for a Market Structure Shift (MSS) on the lower time frame (15min) to confirm your entry.
- Secondary Setup: If the 5am candle fails to take out the high or low of the 1am candle, the setup focuses on the levels formed by the 5am candle.
3. Trade Execution on 15-Minute Timeframe
- The script supports a lower time frame (15min) view to identify MSS and fine-tune entries.
4. Rinse and Repeat
- This process can be applied daily for consistent opportunities across the specified assets.
Advantages:
- Provides clear visual markers for key levels based on the 4-hour candles.
- Automates level plotting, saving traders time and reducing manual errors.
- Integrates well with the 15-minute timeframe for precise entry triggers.
- Optimized for popular trading instruments, especially US30 for a higher probability of success.
This script simplifies the application of CRT by automating the process of identifying and marking critical levels, enabling traders to focus on executing high-probability setups effectively.
Created by Hamid (poraymanfx)
Dabel MS + FVGThis script is designed to assist traders by identifying market structures, imbalances, and potential trade opportunities using Break of Structure (BOS) and Market Structure Shifts (MSS). It visually highlights imbalances in price action, key pivots, and market structure changes, providing actionable information for making trading decisions.
Key features:
Imbalances Detection: Highlights bullish and bearish price gaps (Fair Value Gaps) using colored boxes. Users can choose the line style (solid, dashed, or dotted) for imbalance midlines.
Market Structure Analysis: Tracks pivot highs and lows to identify BOS and MSS in two separate market structures with adjustable pivot strengths.
Customizable Visualization: Allows users to choose line styles, colors, and display options for both imbalances and market structures.
Alerts: Alerts traders when BOS or MSS occur, helping to monitor the market effectively.
Trading Strategy
Imbalance Trading:
Imbalances (gaps) represent areas where supply or demand was left unfilled. These gaps often act as magnet zones where the price revisits to fill.
Bullish Imbalance: Look for buying opportunities when price enters a green imbalance zone.
Bearish Imbalance: Look for selling opportunities when price enters a red imbalance zone.
Use the midline of the imbalance box as a key reference point for potential reversals.
Break of Structure (BOS) and Market Structure Shift (MSS):
BOS: Indicates a continuation of the existing trend. For example:
Bullish BOS: Look for continuation in the uptrend after a high is broken.
Bearish BOS: Look for continuation in the downtrend after a low is broken.
MSS: Suggests a potential reversal in market structure. For example:
Bullish MSS: Indicates a possible shift from a bearish to bullish market.
Bearish MSS: Indicates a potential shift from a bullish to bearish market.
Multiple Market Structures:
This script provide two sets of market structures, allowing traders to compare short-term and long-term trends.
Adjust the pivot strength to suit your trading style (lower for intraday trading, higher for swing or positional trading).
Entry and Exit:
Entry: Look for entries near imbalances or after confirmed BOS/MSS in line with the overall trend.
Exit: Place stop-loss below/above recent pivots and take profit at nearby support/resistance or imbalance zones.
For New Traders
Focus on Basics: Understand what BOS and MSS mean and how they signal trend direction or reversals.
Use Alerts: Rely on the script's alert system to catch important moments without staring at charts all day.
Start Small: Test this strategy on a demo account before using it live. You can understand it more with practice.
Adaptive Momentum Reversion StrategyThe Adaptive Momentum Reversion Strategy: An Empirical Approach to Market Behavior
The Adaptive Momentum Reversion Strategy seeks to capitalize on market price dynamics by combining concepts from momentum and mean reversion theories. This hybrid approach leverages a Rate of Change (ROC) indicator along with Bollinger Bands to identify overbought and oversold conditions, triggering trades based on the crossing of specific thresholds. The strategy aims to detect momentum shifts and exploit price reversions to their mean.
Theoretical Framework
Momentum and Mean Reversion: Momentum trading assumes that assets with a recent history of strong performance will continue in that direction, while mean reversion suggests that assets tend to return to their historical average over time (Fama & French, 1988; Poterba & Summers, 1988). This strategy incorporates elements of both, looking for periods when momentum is either overextended (and likely to revert) or when the asset’s price is temporarily underpriced relative to its historical trend.
Rate of Change (ROC): The ROC is a straightforward momentum indicator that measures the percentage change in price over a specified period (Wilder, 1978). The strategy calculates the ROC over a 2-period window, making it responsive to short-term price changes. By using ROC, the strategy aims to detect price acceleration and deceleration.
Bollinger Bands: Bollinger Bands are used to identify volatility and potential price extremes, often signaling overbought or oversold conditions. The bands consist of a moving average and two standard deviation bounds that adjust dynamically with price volatility (Bollinger, 2002).
The strategy employs two sets of Bollinger Bands: one for short-term volatility (lower band) and another for longer-term trends (upper band), with different lengths and standard deviation multipliers.
Strategy Construction
Indicator Inputs:
ROC Period: The rate of change is computed over a 2-period window, which provides sensitivity to short-term price fluctuations.
Bollinger Bands:
Lower Band: Calculated with a 18-period length and a standard deviation of 1.7.
Upper Band: Calculated with a 21-period length and a standard deviation of 2.1.
Calculations:
ROC Calculation: The ROC is computed by comparing the current close price to the close price from rocPeriod days ago, expressing it as a percentage.
Bollinger Bands: The strategy calculates both upper and lower Bollinger Bands around the ROC, using a simple moving average as the central basis. The lower Bollinger Band is used as a reference for identifying potential long entry points when the ROC crosses above it, while the upper Bollinger Band serves as a reference for exits, when the ROC crosses below it.
Trading Conditions:
Long Entry: A long position is initiated when the ROC crosses above the lower Bollinger Band, signaling a potential shift from a period of low momentum to an increase in price movement.
Exit Condition: A position is closed when the ROC crosses under the upper Bollinger Band, or when the ROC drops below the lower band again, indicating a reversal or weakening of momentum.
Visual Indicators:
ROC Plot: The ROC is plotted as a line to visualize the momentum direction.
Bollinger Bands: The upper and lower bands, along with their basis (simple moving averages), are plotted to delineate the expected range for the ROC.
Background Color: To enhance decision-making, the strategy colors the background when extreme conditions are detected—green for oversold (ROC below the lower band) and red for overbought (ROC above the upper band), indicating potential reversal zones.
Strategy Performance Considerations
The use of Bollinger Bands in this strategy provides an adaptive framework that adjusts to changing market volatility. When volatility increases, the bands widen, allowing for larger price movements, while during quieter periods, the bands contract, reducing trade signals. This adaptiveness is critical in maintaining strategy effectiveness across different market conditions.
The strategy’s pyramiding setting is disabled (pyramiding=0), ensuring that only one position is taken at a time, which is a conservative risk management approach. Additionally, the strategy includes transaction costs and slippage parameters to account for real-world trading conditions.
Empirical Evidence and Relevance
The combination of momentum and mean reversion has been widely studied and shown to provide profitable opportunities under certain market conditions. Studies such as Jegadeesh and Titman (1993) confirm that momentum strategies tend to work well in trending markets, while mean reversion strategies have been effective during periods of high volatility or after sharp price movements (De Bondt & Thaler, 1985). By integrating both strategies into one system, the Adaptive Momentum Reversion Strategy may be able to capitalize on both trending and reverting market behavior.
Furthermore, research by Chan (1996) on momentum-based trading systems demonstrates that adaptive strategies, which adjust to changes in market volatility, often outperform static strategies, providing a compelling rationale for the use of Bollinger Bands in this context.
Conclusion
The Adaptive Momentum Reversion Strategy provides a robust framework for trading based on the dual concepts of momentum and mean reversion. By using ROC in combination with Bollinger Bands, the strategy is capable of identifying overbought and oversold conditions while adapting to changing market conditions. The use of adaptive indicators ensures that the strategy remains flexible and can perform across different market environments, potentially offering a competitive edge for traders who seek to balance risk and reward in their trading approaches.
References
Bollinger, J. (2002). Bollinger on Bollinger Bands. McGraw-Hill Professional.
Chan, L. K. C. (1996). Momentum, Mean Reversion, and the Cross-Section of Stock Returns. Journal of Finance, 51(5), 1681-1713.
De Bondt, W. F., & Thaler, R. H. (1985). Does the Stock Market Overreact? Journal of Finance, 40(3), 793-805.
Fama, E. F., & French, K. R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.
Range Channel by Atilla YurtsevenThis script creates a dynamic channel around a user-selected moving average (MA). It calculates the relative difference between price and the MA, then finds the average of the positive differences and the negative differences separately. Using these averages, it plots upper and lower bands around the MA as well as a histogram-like oscillator to show when price moves above or below the average thresholds.
How It Works
Moving Average Selection
The indicator allows you to choose among multiple MA types (SMA, EMA, WMA, Linear Regression, etc.). Depending on your preference, it calculates the chosen MA for the selected lookback period.
Relative Difference Calculation
It then computes the percentage difference between the source (typically the closing price) and the MA. (diff = (src / ma - 1) * 100)
Positive & Negative Averages
- Positive differences are averaged and represent how far the price typically moves above the MA.
- Negative differences are similarly averaged for when price moves below the MA.
Range Channel & Oscillator
- The channel is plotted around the MA using the average positive and negative differences (Upper Edge and Lower Edge).
- The “Untrended” histogram plots the difference (diff). Green bars occur when price is above the MA on average, and red bars when below. Two additional lines mark the upper and lower average thresholds on this histogram.
How to Use
Identify Overbought/Oversold Zones: The upper edge can serve as a dynamic overbought level, while the lower edge can suggest potential oversold conditions. When the histogram approaches or crosses these levels, it may signal price extremes relative to its average movement.
Trend Confirmation: Compare price action relative to the channel. If price and the histogram consistently remain above the MA and upper threshold, it could indicate a stronger bullish trend. If they remain below, it might signal a prolonged bearish trend.
Entry/Exit Timings:
- Entry: Traders can look for moments when price breaks back inside the channel from an extreme, anticipating a mean reversion.
- Exit: Watching how price interacts with these dynamic edges can help define stop-loss or take-profit points.
Because these thresholds adapt over time based on actual price behavior, they can be more responsive than fixed-percentage bands. However, like all indicators, it’s most effective when used in conjunction with other technical and fundamental tools.
Disclaimer
This script is provided for educational and informational purposes only. It does not guarantee any specific outcome or profit. Use it at your own discretion and risk.
Trade smart, stay safe.
Atilla Yurtseven
McClellan A-D Volume Integration ModelThe strategy integrates the McClellan A-D Oscillator with an adjustment based on the Advance/Decline (A-D) volume data. The McClellan Oscillator is calculated by taking the difference between the short-term and long-term exponential moving averages (EMAs) of the A-D line. This strategy introduces an enhancement where the A-D volume (the difference between the advancing and declining volume) is factored in to adjust the oscillator value.
Inputs:
• ema_short_length: The length for the short-term EMA of the A-D line.
• ema_long_length: The length for the long-term EMA of the A-D line.
• osc_threshold_long: The threshold below which the oscillator must drop for an entry signal to trigger.
• exit_periods: The number of periods after which the position is closed.
• Data Sources:
• ad_advance and ad_decline are the data sources for advancing and declining issues, respectively.
• vol_advance and vol_decline are the volume data for the advancing and declining issues. If volume data is unavailable, it defaults to na (Not Available), and the fallback logic ensures that the strategy continues to function.
McClellan Oscillator with Volume Adjustment:
• The A-D line is calculated by subtracting the declining issues from the advancing issues. Then, the volume difference is applied to this line, creating a “weighted” A-D line.
• The short and long EMAs are calculated for the weighted A-D line to generate the McClellan Oscillator.
Entry Condition:
• The strategy looks for a reversal signal, where the oscillator falls below the threshold and then rises above it again. The condition is designed to trigger a long position when this reversal happens.
Exit Condition:
• The position is closed after a set number of periods (exit_periods) have passed since the entry.
Plotting:
• The McClellan Oscillator and the threshold are plotted on the chart for visual reference.
• Entry and exit signals are highlighted with background colors to make the signals more visible.
Scientific Background:
The McClellan A-D Oscillator is a popular market breadth indicator developed by Sherman and Marian McClellan. It is used to gauge the underlying strength of a market by analyzing the difference between the number of advancing and declining stocks. The oscillator is typically calculated using exponential moving averages (EMAs) of the A-D line, with the idea being that crossovers of these EMAs indicate potential changes in the market’s direction.
The integration of A-D volume into this model adds another layer of analysis, as volume is often considered a leading indicator of price movement. By factoring in volume, the strategy becomes more sensitive to not just the number of advancing or declining stocks but also how significant those movements are based on trading volume, as discussed in Schwager, J. D. (1999). Technical Analysis of the Financial Markets. This enhanced version aims to capture stronger and more sustainable trends in the market, helping to filter out false signals.
Additionally, volume analysis is often used to confirm price movements, as described in Wyckoff, R. (1931). The Day Trading System. Therefore, incorporating the volume of advancing and declining stocks in the McClellan Oscillator offers a more robust signal for trading decisions.
VIX Spike StrategyThis script implements a trading strategy based on the Volatility Index (VIX) and its standard deviation. It aims to enter a long position when the VIX exceeds a certain number of standard deviations above its moving average, which is a signal of a volatility spike. The position is then exited after a set number of periods.
VIX Symbol (vix_symbol): The input allows the user to specify the symbol for the VIX index (typically "CBOE:VIX").
Standard Deviation Length (stddev_length): The number of periods used to calculate the standard deviation of the VIX. This can be adjusted by the user.
Standard Deviation Multiplier (stddev_multiple): This multiplier is used to determine how many standard deviations above the moving average the VIX must exceed to trigger a long entry.
Exit Periods (exit_periods): The user specifies how many periods after entering the position the strategy will exit the trade.
Strategy Logic:
Data Loading: The script loads the VIX data, both for the current timeframe and as a rescaled version for calculation purposes.
Standard Deviation Calculation: It calculates both the moving average (SMA) and the standard deviation of the VIX over the specified period (stddev_length).
Entry Condition: A long position is entered when the VIX exceeds the moving average by a specified multiple of its standard deviation (calculated as vix_mean + stddev_multiple * vix_stddev).
Exit Condition: After the position is entered, it will be closed after the user-defined number of periods (exit_periods).
Visualization:
The VIX is plotted in blue.
The moving average of the VIX is plotted in orange.
The threshold for the VIX, which is the moving average plus the standard deviation multiplier, is plotted in red.
The background turns green when the entry condition is met, providing a visual cue.
Sources:
The VIX is often used as a measure of market volatility, with high values indicating increased uncertainty in the market.
Standard deviation is a statistical measure of the variability or dispersion of a set of data points. In financial markets, it is used to measure the volatility of asset prices.
References:
Bollerslev, T. (1986). "Generalized Autoregressive Conditional Heteroskedasticity." Journal of Econometrics.
Black, F., & Scholes, M. (1973). "The Pricing of Options and Corporate Liabilities." Journal of Political Economy.
Smart DCA Strategy (Public)INSPIRATION
While Dollar Cost Averaging (DCA) is a popular and stress-free investment approach, I noticed an opportunity for enhancement. Standard DCA involves buying consistently, regardless of market conditions, which can sometimes mean missing out on optimal investment opportunities. This led me to develop the Smart DCA Strategy – a 'set and forget' method like traditional DCA, but with an intelligent twist to boost its effectiveness.
The goal was to build something more profitable than a standard DCA strategy so it was equally important that this indicator could backtest its own results in an A/B test manner against the regular DCA strategy.
WHY IS IT SMART?
The key to this strategy is its dynamic approach: buying aggressively when the market shows signs of being oversold, and sitting on the sidelines when it's not. This approach aims to optimize entry points, enhancing the potential for better returns while maintaining the simplicity and low stress of DCA.
WHAT THIS STRATEGY IS, AND IS NOT
This is an investment style strategy. It is designed to improve upon the common standard DCA investment strategy. It is therefore NOT a day trading strategy. Feel free to experiment with various timeframes, but it was designed to be used on a daily timeframe and that's how I recommend it to be used.
You may also go months without any buy signals during bull markets, but remember that is exactly the point of the strategy - to keep your buying power on the sidelines until the markets have significantly pulled back. You need to be patient and trust in the historical backtesting you have performed.
HOW IT WORKS
The Smart DCA Strategy leverages a creative approach to using Moving Averages to identify the most opportune moments to buy. A trigger occurs when a daily candle, in its entirety including the high wick, closes below the threshold line or box plotted on the chart. The indicator is designed to facilitate both backtesting and live trading.
HOW TO USE
Settings:
The input parameters for tuning have been intentionally simplified in an effort to prevent users falling into the overfitting trap.
The main control is the Buying strictness scale setting. Setting this to a lower value will provide more buying days (less strict) while higher values mean less buying days (more strict). In my testing I've found level 9 to provide good all round results.
Validation days is a setting to prevent triggering entries until the asset has spent a given number of days (candles) in the overbought state. Increasing this makes entries stricter. I've found 0 to give the best results across most assets.
In the backtest settings you can also configure how much to buy for each day an entry triggers. Blind buy size is the amount you would buy every day in a standard DCA strategy. Smart buy size is the amount you would buy each day a Smart DCA entry is triggered.
You can also experiment with backtesting your strategy over different historical datasets by using the Start date and End date settings. The results table will not calculate for any trades outside what you've set in the date range settings.
Backtesting:
When backtesting you should use the results table on the top right to tune and optimise the results of your strategy. As with all backtests, be careful to avoid overfitting the parameters. It's better to have a setup which works well across many currencies and historical periods than a setup which is excellent on one dataset but bad on most others. This gives a much higher probability that it will be effective when you move to live trading.
The results table provides a clear visual representation as to which strategy, standard or smart, is more profitable for the given dataset. You will notice the columns are dynamically coloured red and green. Their colour changes based on which strategy is more profitable in the A/B style backtest - green wins, red loses. The key metrics to focus on are GOA (Gain on Account) and Avg Cost.
Live Trading:
After you've finished backtesting you can proceed with configuring your alerts for live trading.
But first, you need to estimate the amount you should buy on each Smart DCA entry. We can use the Total invested row in the results table to calculate this. Assuming we're looking to trade on
BTCUSD
Decide how much USD you would spend each day to buy BTC if you were using a standard DCA strategy. Lets say that is $5 per day
Enter that USD amount in the Blind buy size settings box
Check the Blind Buy column in the results table. If we set the backtest date range to the last 10 years, we would expect the amount spent on blind buys over 10 years to be $18,250 given $5 each day
Next we need to tweak the value of the Smart buy size parameter in setting to get it as close as we can to the Total Invested amount for Blind Buy
By following this approach it means we will invest roughly the same amount into our Smart DCA strategy as we would have into a standard DCA strategy over any given time period.
After you have calculated the Smart buy size, you can go ahead and set up alerts on Smart DCA buy triggers.
BOT AUTOMATION
In an effort to maintain the 'set and forget' stress-free benefits of a standard DCA strategy, I have set my personal Smart DCA Strategy up to be automated. The bot runs on AWS and I have a fully functional project for the bot on my GitHub account. Just reach out if you would like me to point you towards it. You can also hook this into any other 3rd party trade automation system of your choice using the pre-configured alerts within the indicator.
PLANNED FUTURE DEVELOPMENTS
Currently this is purely an accumulation strategy. It does not have any sell signals right now but I have ideas on how I will build upon it to incorporate an algorithm for selling. The strategy should gradually offload profits in bull markets which generates more USD which gives more buying power to rinse and repeat the same process in the next cycle only with a bigger starting capital. Watch this space!
MARKETS
Crypto:
This strategy has been specifically built to work on the crypto markets. It has been developed, backtested and tuned against crypto markets and I personally only run it on crypto markets to accumulate more of the coins I believe in for the long term. In the section below I will provide some backtest results from some of the top crypto assets.
Stocks:
I've found it is generally more profitable than a standard DCA strategy on the majority of stocks, however the results proved to be a lot more impressive on crypto. This is mainly due to the volatility and cycles found in crypto markets. The strategy makes its profits from capitalising on pullbacks in price. Good stocks on the other hand tend to move up and to the right with less significant pullbacks, therefore giving this strategy less opportunity to flourish.
Forex:
As this is an accumulation style investment strategy, I do not recommend that you use it to trade Forex.
For more info about this strategy including backtest results, please see the full description on the invite only version of this strategy named "Smart DCA Strategy"
Gauti Market Maker Killzone EMA1. Identifying the Trend
Use Daily (1D) and Hourly (1H) Exponential Moving Averages (EMAs) to define the overall trend:
Bullish Trend: Both 1D and 1H EMAs are upward sloping, and the price is above these EMAs.
Bearish Trend: Both 1D and 1H EMAs are downward sloping, and the price is below these EMAs.
2. Confirmation with Higher Timeframes
Bullish Conditions:
Check 1D and 4H charts for price action above the EMA bands.
Look for price forming higher highs and higher lows or respecting support at the EMA bands.
Bearish Conditions:
Check 1D and 4H charts for price action below the EMA bands.
Look for price forming lower highs and lower lows or respecting resistance at the EMA bands.
Note: Crossover of EMAs on higher timeframes is an optional extra confirmation, but not mandatory for entry.
3. Entry Strategy
Use the 15-Minute (15M) timeframe for entries.
Entries are taken only during Killzones:
Killzones: London Open, New York Open, or other intraday key trading sessions. (Define the time ranges for these zones based on your trading hours.)
Wait for the price to touch or pull back to the EMA band during the Killzones in the direction of the overall trend:
In a bullish trend, enter long when the price touches the EMA band and shows signs of rejection or reversal.
In a bearish trend, enter short when the price touches the EMA band and shows signs of rejection or reversal.
4. Checklist for Entry
Confirm the following before entering:
1D Trend aligns with the 1H Trend.
Price Action in 1D and 4H supports the trend.
Killzone session is active.
Price is reacting to the EMA band on the 15M chart in the trend direction.
Lot Size & Risk Calculator (All Pairs)this indicator is designed to simplify and optimize risk management. It automatically calculates the ideal lot size based on your account balance, risk percentage, and defined entry and exit levels. Additionally, it includes visual tools to represent stop-loss (SL) and take-profit (TP) levels, helping you trade with precision and consistency.
WHAT IS THIS INDICATOR FOR?
This indicator is essential for traders who want to:
Maintain consistent risk in their trades.
Quickly calculate lot sizes for Forex, XAUUSD, BTCUSD, and US100.
Visualize key levels (Entry, SL, and TP) on the chart.
Monitor potential losses and gains in real time.
COMPATIBLE ASSETS
The Lot Size Calculator works with the following assets:
Forex: Standard currency pairs.
XAUUSD: Gold versus the US dollar.
BTCUSD: Bitcoin versus the US dollar.
US100: Nasdaq 100 index.
Calculations adjust automatically based on the selected asset.
TAKE-PROFIT (TP) LEVELS
The indicator allows you to define up to three take-profit levels:
TP1
TP2
TP3
.
Each level is configurable based on your exit strategy.
DASHBOARD
The dashboard is a visual tool that consolidates key information about your trade:
Account balance: Total amount available in your account.
Lot size: Calculated based on your risk and parameters.
Potential loss (SL): Amount you could lose if the price hits your stop-loss.
Potential gain (TP): Expected profit if the take-profit level is reached.
SETTINGS
The indicator offers multiple configurable options to adapt to your trading style:
Levels
Entry: Initial trade price.
Stop-Loss (SL): Maximum allowed loss level.
Take-Profit (TP): Up to three configurable levels.
Risk Management
Account balance ($): Enter your total available balance.
Risk percentage: Define how much you're willing to risk per trade
.
Visual Options
Visualization style: Choose between simple lines or visual fills.
Colors: Customize the colors of lines and labels.
Dashboard Settings
Statistics: Enable or disable key data display.
Size and position: Adjust the dashboard's size and location on the chart.
HOW TO CHANGE AN ENTRY?
Open the indicator settings in TradingView and entering the new data manually
Removing and re-adding the indicator to the chart
Scalp System# Scalp System
A premium scalping system designed specifically for 2-minute charts, combining multiple timeframe analysis with trend-based trading decisions. This indicator helps identify high-probability scalping opportunities through color-coded moving averages and their crossovers.
## Strategy Overview
### Entry Signals
- ONLY trade LONG when price is above RED line
- ONLY trade SHORT when price is below RED line
- Primary entry: BLUE/GREEN crosses
- Strong trend confirmation: YELLOW/PURPLE crosses
### Best Practices
1. Trade with the trend (follow RED line direction)
2. Wait for price pullbacks of faster lines
3. Combine crosses with support/resistance levels
4. Use smaller targets
5. Quick exits on failed breakouts
6. Monitor volume for confirmation
### Color Guide
- YELLOW: Fast trend identifier
- BLUE: Very short-term momentum (1min)
- GREEN: Short-term momentum (3min)
- RED: Trend filter
- PURPLE: Strong trend baseline
### Risk Management
- Place stops beyond the RED line
- Scale out at key levels
- Use 1:1.5 minimum risk/reward
- Avoid trading during major news
- Reduce position size in choppy markets
### Best Trading Hours
- Most effective during first 2 hours after market open
- Good opportunities during power hour (last hour)
- Avoid lunch hour chop (11:30-1:30 EST)
## Tips
- Less is more - wait for clean setups
- Respect the RED line as your trend filter
- Multiple timeframe confirmation increases success rate
- Use crosses as triggers, not absolute signals
- Practice in simulator before live trading
TFMTFM Strategy Explanation
Overview
The TFM (Timeframe Multiplier) strategy is a PineScript trading bot that utilizes multiple timeframes to identify entry and exit points.
Inputs
1. tfm (Timeframe Multiplier): Multiplies the chart's timeframe to create a higher timeframe for analysis.
2. lns (Long and Short): Enables or disables short positions.
Logic
Calculations
1. chartTf: Gets the chart's timeframe in seconds.
2. tfTimes: Calculates the higher timeframe by multiplying chartTf with tfm.
3. MintickerClose and MaxtickerClose: Retrieve the minimum and maximum closing prices from the higher timeframe using request.security.
- MintickerClose: Finds the lowest low when the higher timeframe's close is below its open.
- MaxtickerClose: Finds the highest high when the higher timeframe's close is above its open.
Entries and Exits
1. Long Entry: When the current close price crosses above MaxtickerClose.
2. Short Entry (if lns is true): When the current close price crosses below MintickerClose.
3. Exit Long: When the short condition is met (if lns is false) or when the trade is manually closed.
Strategy
1. Attach the script to a chart.
2. Adjust tfm and lns inputs.
3. Monitor entries and exits.
Example Use Cases
1. Intraday trading with tfm = 2-5.
2. Swing trading with tfm = 10-30.
Tips
1. Experiment with different tfm values.
2. Use lns to control short positions.
3. Combine with other indicators for confirmation.
Easy CotHow to Use the Commitment of Traders (COT) Report for Market Analysis
The Commitment of Traders (COT) report is a weekly publication by the Commodity Futures Trading Commission (CFTC) that breaks down the open interest in various futures markets. It categorizes traders into three main groups: Commercials, Non-Commercials, and Retail Traders (Non-Reportable positions). Understanding and analyzing the COT report can provide insights into market sentiment and potential reversals, especially in commodity, currency, and stock index futures.
Key Components of the COT Report
Commercials (Hedgers)
These are entities involved in the production or consumption of the underlying asset. For example, oil producers might hedge by selling oil futures to lock in prices, while airlines might buy futures to hedge against rising prices.
Commercials typically act as hedgers, so their positions can indicate the need for protection rather than speculative intent. Because they are less price-sensitive, their positions are usually opposite to the trend near market reversals.
Non-Commercials (Large Speculators)
This group includes hedge funds, asset managers, and large traders who take speculative positions to profit from price movements.
Non-Commercials are often trend-followers, meaning they increase long positions in an uptrend and short positions in a downtrend. When Non-Commercials become extremely bullish or bearish, it may signal a potential market reversal.
Retail Traders (Non-Reportable Positions)
These are smaller individual traders whose positions are too small to be reported individually.
Retail traders tend to be less experienced and are often on the wrong side of major market moves, so extreme positions by retail traders can sometimes signal a market turning point.
How to Interpret the COT Data
1. Identify Extreme Positions
Extreme Long or Short Positions: When a group reaches a historically extreme level of long or short positions, it often signals a potential reversal. For instance, if Non-Commercials are overwhelmingly long, it may indicate that the uptrend is overextended, and a reversal could be near.
Contrarian Indicator: Since Retail Traders are often on the wrong side, you may look for signals where they are extremely long or short, indicating a possible reversal in the opposite direction.
2. Look for Divergences
Divergence Between Groups: If Non-Commercials (speculators) and Retail Traders are moving in opposite directions, it could indicate that a trend is losing momentum and a reversal is possible.
Commercials vs. Non-Commercials: Commercials are often positioned opposite to Non-Commercials. If there’s a divergence where Non-Commercials are highly bullish, but Commercials are increasingly bearish, it might suggest a coming reversal.
3. Trend Confirmation and Reversal Signals
Trend Confirmation: If both Non-Commercials and Retail Traders are aligned in one direction, it might confirm the trend. However, keep in mind that such alignment may signal the later stages of a trend.
Reversal Signals: Look for signs when Non-Commercials are reaching a peak in one direction while Retail Traders peak in the opposite. Such situations can often indicate that the current trend is close to exhaustion.
Using the COT Report in Trading Strategies
Contrarian Trading Strategy
Extreme Positions as Reversal Signals: Use COT data to identify extreme positions. For instance, if Non-Commercials have a very high long position in a commodity, it might suggest that a bullish trend is overextended and a bearish reversal could be near.
Retail Trader Extremes: If Retail Traders are heavily long or short, consider taking the opposite position once you have additional confirmation signals (e.g., technical indicators).
Following the Trend with Large Speculators
Non-Commercials tend to be trend-followers, so if you see them increasingly long (or short) on an asset, it could be a signal to follow the trend until extreme levels are reached.
Using Divergences for Entry and Exit Points
Entry: If Non-Commercials are long, but Retail Traders are heavily short, consider entering a long position as it may confirm the trend.
Exit: If Non-Commercials begin to reduce their positions while Retail Traders increase theirs, it might be time to consider exiting, as the trend could be losing momentum.
VOLUME DIRECTION INDICATORDesigned for the 1-hour chart, this indicator shows:
Green Line: Volume when price rises, suggesting buying.
Red Line: Volume when price falls, indicating selling.
How to Use:
Watch for Crossover: When the Green Line moves above the Red, it might signal a budding uptrend.
Check Retracement: If the Green Line pulls back but stays above the Red, the uptrend could be strengthening.
Price Check: Look for a small price drop but not a reversal.
Trade Entry:
Enter at the high of the retracement candle.
Or wait for the Green Line to rise again.
For Precision: Draw a line at the retracement peak and switch to a shorter timeframe to find entry patterns above this line.
Remember: Use this with other tools for better trading decisions.
The Volume Direction Indicator provides a visual representation of market activity by assuming volume can be attributed to buying or selling based on price action within each bar. When the price closes higher than it opened, the volume for that period is considered as 'Bought Shares', plotted in green. Conversely, if the price closes lower, the volume is treated as 'Sold Shares', shown in red. This indicator resets daily to give a fresh perspective on trading activity each day.
Key Features:
Buying Pressure: Green line represents the cumulative volume during periods where the price increased.
Selling Pressure: Red line indicates the cumulative volume during price decreases.
Daily Reset: Accumulated values reset at the start of each new trading day, focusing on daily market sentiment.
Note: This indicator simplifies market dynamics by linking volume directly to price changes. It does not account for complex trading scenarios like short selling or market manipulations. Use this indicator as a tool to gauge general market direction and activity, not for precise transaction data.
Confluence StrategyOverview of Confluence Strategy
The Confluence Strategy in trading refers to the combination of multiple technical indicators, support/resistance levels, and chart patterns to identify high-probability trading opportunities. The idea is that when several indicators agree on a price movement, the likelihood of that movement being successful increases.
Key Components
Technical Indicators:
Moving Averages (MA): Commonly used to determine the trend direction. Look for crossovers (e.g., the 50-day MA crossing above the 200-day MA).
Relative Strength Index (RSI): Helps identify overbought or oversold conditions. A reading above 70 may indicate overbought conditions, while below 30 suggests oversold.
MACD (Moving Average Convergence Divergence): Useful for spotting changes in momentum. Look for MACD crossovers and divergence from price.
Support and Resistance Levels:
Identify key levels where price has historically reversed. These can be drawn from previous highs/lows, Fibonacci retracement levels, or psychological price levels.
Chart Patterns:
Patterns like head and shoulders, double tops/bottoms, or flags can indicate potential reversals or continuations in price.
Strategy Implementation
Set Up Your Chart:
Add the desired indicators (e.g., MA, RSI, MACD) to your TradingView chart.
Mark significant support and resistance levels.
Identify Confluence Points:
Look for situations where multiple indicators align. For instance, if the price is near a support level, the RSI is below 30, and the MACD shows bullish divergence, this may signal a buying opportunity.
Entry and Exit Points:
Entry: Place a trade when your confluence conditions are met. Use limit orders for better prices.
Exit: Set profit targets based on resistance levels or use trailing stops. Consider the risk-reward ratio to ensure your trades are favorable.
Risk Management:
Always implement stop-loss orders to protect against unexpected market moves. Position size should reflect your risk tolerance.
Example of a Confluence Trade
Setup:
Price approaches a strong support level.
RSI shows oversold conditions (below 30).
The 50-day MA is about to cross above the 200-day MA (bullish crossover).
Action:
Enter a long position as the conditions align.
Set a stop loss just below the support level and a take profit at the next resistance level.
Conclusion
The Confluence Strategy can significantly enhance trading accuracy by ensuring that multiple indicators support a trade decision. Traders on TradingView can customize their indicators and charts to fit their personal trading styles, making it a flexible approach to technical analysis.
Gold Scalping Strategy with Precise EntriesThe Gold Scalping Strategy with Precise Entries is designed to take advantage of short-term price movements in the gold market (XAU/USD). This strategy uses a combination of technical indicators and chart patterns to identify precise buy and sell opportunities during times of consolidation and trend continuation.
Key Elements of the Strategy:
Exponential Moving Averages (EMAs):
50 EMA: Used as the shorter-term moving average to detect the recent price trend.
200 EMA: Used as the longer-term moving average to determine the overall market trend.
Trend Identification:
A bullish trend is identified when the 50 EMA is above the 200 EMA.
A bearish trend is identified when the 50 EMA is below the 200 EMA.
Average True Range (ATR):
ATR (14) is used to calculate the market's volatility and to set a dynamic stop loss based on recent price movements. Higher ATR values indicate higher volatility.
ATR helps define a suitable stop-loss distance from the entry point.
Relative Strength Index (RSI):
RSI (14) is used as a momentum oscillator to detect overbought or oversold conditions.
However, in this strategy, the RSI is primarily used as a consolidation filter to look for neutral zones (between 45 and 55), which may indicate a potential breakout or trend continuation after a consolidation phase.
Engulfing Patterns:
Bullish Engulfing: A bullish signal is generated when the current candle fully engulfs the previous bearish candle, indicating potential upward momentum.
Bearish Engulfing: A bearish signal is generated when the current candle fully engulfs the previous bullish candle, signaling potential downward momentum.
Precise Entry Conditions:
Long (Buy):
The 50 EMA is above the 200 EMA (bullish trend).
The RSI is between 45 and 55 (neutral/consolidation zone).
A bullish engulfing pattern occurs.
The price closes above the 50 EMA.
Short (Sell):
The 50 EMA is below the 200 EMA (bearish trend).
The RSI is between 45 and 55 (neutral/consolidation zone).
A bearish engulfing pattern occurs.
The price closes below the 50 EMA.
Take Profit and Stop Loss:
Take Profit: A fixed 20-pip target (where 1 pip = 0.10 movement in gold) is used for each trade.
Stop Loss: The stop-loss is dynamically set based on the ATR, ensuring that it adapts to current market volatility.
Visual Signals:
Buy and sell signals are visually plotted on the chart using green and red labels, indicating precise points of entry.
Advantages of This Strategy:
Trend Alignment: The strategy ensures that trades are taken in the direction of the overall trend, as indicated by the 50 and 200 EMAs.
Volatility Adaptation: The use of ATR allows the stop loss to adapt to the current market conditions, reducing the risk of premature exits in volatile markets.
Precise Entries: The combination of engulfing patterns and the neutral RSI zone provides a high-probability entry signal that captures momentum after consolidation.
Quick Scalping: With a fixed 20-pip profit target, the strategy is designed to capture small price movements quickly, which is ideal for scalping.
This strategy can be applied to lower timeframes (such as 1-minute, 5-minute, or 15-minute charts) for frequent trade opportunities in gold trading, making it suitable for day traders or scalpers. However, proper risk management should always be used due to the inherent volatility of gold.
Adaptive MA Scalping StrategyAdaptive MA Scalping Strategy
The Adaptive MA Scalping Strategy is an innovative trading approach that merges the strengths of the Kaufman's Adaptive Moving Average (KAMA) with the Moving Average Convergence Divergence (MACD) histogram. This combination results in a momentum-adaptive moving average that dynamically adjusts to market conditions, providing traders with timely and reliable signals.
How It Works
Kaufman's Adaptive Moving Average (KAMA): Unlike traditional moving averages, KAMA adjusts its sensitivity based on market volatility. It becomes more responsive during trending markets and less sensitive during periods of consolidation, effectively filtering out market noise.
MACD Histogram Integration: The strategy incorporates the MACD histogram, a momentum indicator that measures the difference between a fast and a slow exponential moving average (EMA). By adding the MACD histogram values to the KAMA, the strategy creates a new line—the momentum-adaptive moving average (MOMA)—which captures both trend direction and momentum.
Signal Generation:
Long Entry: The strategy enters a long position when the closing price crosses above the MOMA. This indicates a potential upward momentum shift.
Exit Position: The position is closed when the closing price crosses below the MOMA, signaling a potential decline in momentum.
Cloud Calculation Detail
The MOMA is calculated by adding the MACD histogram value to the KAMA of the price. This addition effectively adjusts the KAMA based on the momentum indicated by the MACD histogram. When momentum is strong, the MACD histogram will have higher values, causing the MOMA to adjust accordingly and provide earlier entry or exit signals.
Performance on Stocks
This strategy has demonstrated excellent performance on stocks when applied to the 1-hour timeframe. Its adaptive nature allows it to respond swiftly to market changes, capturing profitable trends while minimizing the impact of false signals caused by market noise. The combination of KAMA's adaptability and MACD's momentum detection makes it particularly effective in volatile market conditions commonly seen in stock trading.
Key Parameters
KAMA Length (malen): Determines the sensitivity of the KAMA. A length of 100 is used to balance responsiveness with noise reduction.
MACD Fast Length (fast): Sets the period for the fast EMA in the MACD calculation. A value of 24 helps in capturing short-term momentum changes.
MACD Slow Length (slow): Sets the period for the slow EMA in the MACD calculation. A value of 52 smooths out longer-term trends.
MACD Signal Length (signal): Determines the period for the signal line in the MACD calculation. An 18-period signal line is used for timely crossovers.
Advantages of the Strategy
Adaptive to Market Conditions: By adjusting to both volatility and momentum, the strategy remains effective across different market phases.
Enhanced Signal Accuracy: The fusion of KAMA and MACD reduces false signals, improving the accuracy of trade entries and exits.
Simplicity in Execution: With straightforward entry and exit rules based on price crossovers, the strategy is user-friendly for traders at all experience levels
Crypto Volatility Bitcoin Correlation Strategy Description:
The Crypto Volatility Bitcoin Correlation Strategy is designed to leverage market volatility specifically in Bitcoin (BTC) using a combination of volatility indicators and trend-following techniques. This strategy utilizes the VIXFix (a volatility indicator adapted for crypto markets) and the BVOL7D (Bitcoin 7-Day Volatility Index from BitMEX) to identify periods of high volatility, while confirming trends with the Exponential Moving Average (EMA). These components work together to offer a comprehensive system that traders can use to enter positions when volatility and trends are aligned in their favor.
Key Features:
VIXFix (Volatility Index for Crypto Markets): This indicator measures the highest price of Bitcoin over a set period and compares it with the current low price to gauge market volatility. A rise in VIXFix indicates increasing market volatility, signaling that large price movements could occur.
BVOL7D (Bitcoin 7-Day Volatility Index): This volatility index, provided by BitMEX, measures the volatility of Bitcoin over the past 7 days. It helps traders monitor the recent volatility trend in the market, particularly useful when making short-term trading decisions.
Exponential Moving Average (EMA): The 50-period EMA acts as a trend indicator. When the price is above the EMA, it suggests the market is in an uptrend, and when the price is below the EMA, it suggests a downtrend.
How It Works:
Long Entry: A long position is triggered when both the VIXFix and BVOL7D indicators are rising, signaling increased volatility, and the price is above the 50-period EMA, confirming that the market is trending upward.
Exit: The strategy exits the position when the price crosses below the 50-period EMA, which signals a potential weakening of the uptrend and a decrease in volatility.
This strategy ensures that traders only enter positions when the volatility aligns with a clear trend, minimizing the risk of entering trades during periods of market uncertainty.
Testing and Timeframe:
This strategy has been tested on Bitcoin using the daily timeframe, which provides a longer-term perspective on market trends and volatility. However, users can adjust the timeframe according to their trading preferences. It is crucial to note that this strategy does not include comprehensive risk management, aside from the exit condition when the price crosses below the EMA. Users are strongly advised to implement their own risk management techniques, such as setting appropriate stop-loss levels, to safeguard their positions during high volatility periods.
Utility:
The Crypto Volatility Bitcoin Correlation Strategy is particularly well-suited for traders who aim to capitalize on the high volatility often seen in the Bitcoin market. By combining volatility measurements (VIXFix and BVOL7D) with a trend-following mechanism (EMA), this strategy helps identify optimal moments for entering and exiting trades. This approach ensures that traders participate in potentially profitable market moves while minimizing exposure during times of uncertainty.
Use Cases:
Volatility-Based Entries: Traders looking to take advantage of market volatility spikes will find this strategy useful for timing entry points during market swings.
Trend Confirmation: By using the EMA as a confirmation tool, traders can avoid entering trades that go against the trend, which can result in significant losses during volatile market conditions.
Risk Management: While the strategy exits when price falls below the EMA, it is important to recognize that this is not a full risk management system. Traders should use caution and integrate additional risk measures, such as stop-losses and position sizing, to better manage potential losses.
How to Use:
Step 1: Monitor the VIXFix and BVOL7D indicators. When both are rising and the Bitcoin price is above the EMA, the strategy will trigger a long entry, indicating that the market is experiencing increased volatility with a confirmed uptrend.
Step 2: Exit the position when the price drops below the 50-period EMA, signaling that the trend may be reversing or weakening, reducing the likelihood of continued upward price movement.
This strategy is open-source and is intended to help traders navigate volatile market conditions, particularly in Bitcoin, using proven indicators for volatility and trend confirmation.
Risk Disclaimer:
This strategy has been tested on the daily timeframe of Bitcoin, but users should be aware that it does not include built-in risk management except for the below-EMA exit condition. Users should be extremely cautious when using this strategy and are encouraged to implement their own risk management, such as using stop-losses, position sizing, and setting appropriate limits. Trading involves significant risk, and this strategy does not guarantee profits or prevent losses. Past performance is not indicative of future results. Always test any strategy in a demo environment before applying it to live markets.
