TOL LANGIT ATR v7 - AI EnhancedThe TOL LANGIT ATR v7 is an adaptive technical indicator designed to identify market trends, support and resistance levels, and breakout points. It uses the Average True Range (ATR) and volatility to dynamically adjust trend bands, with visual markers for buy and sell signals. The indicator also highlights key support (blue) and resistance (orange) levels, and alerts users when these levels are broken. It’s perfect for trend following, breakout trading, and reversal strategies, and includes easy-to-set alerts for key market changes.
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Market Cycles
The Market Cycles indicator transforms market price data into a stochastic wave, offering a unique perspective on market cycles. The wave is bounded between positive and negative values, providing clear visual cues for potential bullish and bearish trends. When the wave turns green, it signals a bullish cycle, while red indicates a bearish cycle.
Designed to show clarity and precision, this tool helps identify market momentum and cyclical behavior in an intuitive way. Ideal for fine-tuning entries or analyzing broader trends, this indicator aims to enhance the decision-making process with simplicity and elegance.
Inside Bar Breakout/Fakeout with AI Scenarios [Yosiet]Inside Bar Breakout/Fakeout Indicator with Scenarios
The Indicator is a powerful tool for traders looking to identify potential breakout and fakeout opportunities based on inside bar patterns. This indicator combines multiple technical analysis concepts to provide a comprehensive view of market behavior, helping traders make more informed decisions.
Key Features
Inside bar detection with filtering
Breakout and fakeout identification
Three distinct scenario detections
Customizable moving average calculations
Flexible visualization options
Alert conditions for various events
How It Works
The indicator identifies inside bars and filters them based on a maximum number of consecutive inside bars. It then detects breakouts and fakeouts using user-defined parameters. The script also calculates moving averages to determine trend direction.
Three specific scenarios are detected:
Strong breakout followed by a strong reversal
Weak breakout with multiple doji/weak candles
Strong breakout without reversal
These scenarios are visually represented on the chart, allowing traders to quickly identify potential trading opportunities.
How to Use
Apply the indicator to your chart
Adjust the input parameters to suit your trading style
Look for inside bar patterns and subsequent breakouts/fakeouts
Pay attention to the three scenario markers for additional context
Use the alert conditions to stay informed of potential opportunities
EMD Oscillator (Zeiierman)█ Overview
The Empirical Mode Decomposition (EMD) Oscillator is an advanced indicator designed to analyze market trends and cycles with high precision. It breaks down complex price data into simpler parts called Intrinsic Mode Functions (IMFs), allowing traders to see underlying patterns and trends that aren’t visible with traditional indicators. The result is a dynamic oscillator that provides insights into overbought and oversold conditions, as well as trend direction and strength. This indicator is suitable for all types of traders, from beginners to advanced, looking to gain deeper insights into market behavior.
█ How It Works
The core of this indicator is the Empirical Mode Decomposition (EMD) process, a method typically used in signal processing and advanced scientific fields. It works by breaking down price data into various “layers,” each representing different frequencies in the market’s movement. Imagine peeling layers off an onion: each layer (or IMF) reveals a different aspect of the price action.
⚪ Data Decomposition (Sifting): The indicator “sifts” through historical price data to detect natural oscillations within it. Each oscillation (or IMF) highlights a unique rhythm in price behavior, from rapid fluctuations to broader, slower trends.
⚪ Adaptive Signal Reconstruction: The EMD Oscillator allows traders to select specific IMFs for a custom signal reconstruction. This reconstructed signal provides a composite view of market behavior, showing both short-term cycles and long-term trends based on which IMFs are included.
⚪ Normalization: To make the oscillator easy to interpret, the reconstructed signal is scaled between -1 and 1. This normalization lets traders quickly spot overbought and oversold conditions, as well as trend direction, without worrying about the raw magnitude of price changes.
The indicator adapts to changing market conditions, making it effective for identifying real-time market cycles and potential turning points.
█ Key Calculations: The Math Behind the EMD Oscillator
The EMD Oscillator’s advanced nature lies in its high-level mathematical operations:
⚪ Intrinsic Mode Functions (IMFs)
IMFs are extracted from the data and act as the building blocks of this indicator. Each IMF is a unique oscillation within the price data, similar to how a band might be divided into treble, mid, and bass frequencies. In the EMD Oscillator:
Higher-Frequency IMFs: Represent short-term market “noise” and quick fluctuations.
Lower-Frequency IMFs: Capture broader market trends, showing more stable and long-term patterns.
⚪ Sifting Process: The Heart of EMD
The sifting process isolates each IMF by repeatedly separating and refining the data. Think of this as filtering water through finer and finer mesh sieves until only the clearest parts remain. Mathematically, it involves:
Extrema Detection: Finding all peaks and troughs (local maxima and minima) in the data.
Envelope Calculation: Smoothing these peaks and troughs into upper and lower envelopes using cubic spline interpolation (a method for creating smooth curves between data points).
Mean Removal: Calculating the average between these envelopes and subtracting it from the data to isolate one IMF. This process repeats until the IMF criteria are met, resulting in a clean oscillation without trend influences.
⚪ Spline Interpolation
The cubic spline interpolation is an advanced mathematical technique that allows smooth curves between points, which is essential for creating the upper and lower envelopes around each IMF. This interpolation solves a tridiagonal matrix (a specialized mathematical problem) to ensure that the envelopes align smoothly with the data’s natural oscillations.
To give a relatable example: imagine drawing a smooth line that passes through each peak and trough of a mountain range on a map. Spline interpolation ensures that line is as smooth and close to reality as possible. Achieving this in Pine Script is technically demanding and demonstrates a high level of mathematical coding.
⚪ Amplitude Normalization
To make the oscillator more readable, the final signal is scaled by its maximum amplitude. This amplitude normalization brings the oscillator into a range of -1 to 1, creating consistent signals regardless of price level or volatility.
█ Comparison with Other Signal Processing Methods
Unlike standard technical indicators that often rely on fixed parameters or pre-defined mathematical functions, the EMD adapts to the data itself, capturing natural cycles and irregularities in real-time. For example, if the market becomes more volatile, EMD adjusts automatically to reflect this without requiring parameter changes from the trader. In this way, it behaves more like a “smart” indicator, intuitively adapting to the market, unlike most traditional methods. EMD’s adaptive approach is akin to AI’s ability to learn from data, making it both resilient and robust in non-linear markets. This makes it a great alternative to methods that struggle in volatile environments, such as fixed-parameter oscillators or moving averages.
█ How to Use
Identify Market Cycles and Trends: Use the EMD Oscillator to spot market cycles that represent phases of buying or selling pressure. The smoothed version of the oscillator can help highlight broader trends, while the main oscillator reveals immediate cycles.
Spot Overbought and Oversold Levels: When the oscillator approaches +1 or -1, it may indicate that the market is overbought or oversold, signaling potential entry or exit points.
Confirm Divergences: If the price movement diverges from the oscillator's direction, it may indicate a potential reversal. For example, if prices make higher highs while the oscillator makes lower highs, it could be a sign of weakening trend strength.
█ Settings
Window Length (N): Defines the number of historical bars used for EMD analysis. A larger window captures more data but may slow down performance.
Number of IMFs (M): Sets how many IMFs to extract. Higher values allow for a more detailed decomposition, isolating smaller cycles within the data.
Amplitude Window (L): Controls the length of the window used for amplitude calculation, affecting the smoothness of the normalized oscillator.
Extraction Range (IMF Start and End): Allows you to select which IMFs to include in the reconstructed signal. Starting with lower IMFs captures faster cycles, while ending with higher IMFs includes slower, trend-based components.
Sifting Stopping Criterion (S-number): Sets how precisely each IMF should be refined. Higher values yield more accurate IMFs but take longer to compute.
Max Sifting Iterations (num_siftings): Limits the number of sifting iterations for each IMF extraction, balancing between performance and accuracy.
Source: The price data used for the analysis, such as close or open prices. This determines which price movements are decomposed by the indicator.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Option Delta Candles [Luxmi AI]Introduction
In the world of options trading, understanding how an option’s price changes with various factors is crucial. One of the key metrics traders use is **Delta**, which measures the sensitivity of an option’s price to changes in the price of the underlying asset. This blog explores an Option Delta Indicator with an Exponential Moving Average (EMA), including its uses, how it works, and its potential limitations.
What is the Option Delta Indicator?
Delta is one of the "Greeks" used in options trading to gauge the risk and behavior of options. It indicates how much an option's price is expected to change for a one-point move in the underlying asset's price. Specifically:
- Call Option Delta: A positive value indicating that the option's price increases as the underlying price increases.
- Put Option Delta: A negative value indicating that the option's price decreases as the underlying price increases.
Key Features of the Indicator
Delta Calculation
The Option Delta Indicator calculates the delta of a call option using the Black-Scholes model, a widely accepted method for pricing European-style options. The formula for delta in the context of a call option is:
Delta = N(d1)
Where:
d1 is calculated as:
d1 = (ln(S / K) + (r + (σ^2 / 2)) * T) / (σ * sqrt(T))
Here, S is the current market price of the option (used as the strike price in this case), K is the strike price, r is the risk-free interest rate, σ is the volatility, and T is the time to expiry in years.
EMA of Delta
The Exponential Moving Average (EMA) of the delta is also plotted. The EMA is a smoothing function that helps identify trends by giving more weight to recent data points. It is calculated as:
EMA = ta.ema(delta_call, emaLength)
Where `emaLength` is the user-defined period for the EMA.
Uses of the Option Delta Indicator
Trend Analysis
The EMA helps smooth out delta values, making it easier to identify trends in the delta over time. This can be useful for traders looking to understand whether the delta is increasing or decreasing, which may indicate how the option’s sensitivity to price changes is evolving.
Decision-Making Tool
By observing both delta and its EMA, traders can make more informed decisions. For instance, if the delta is rising and the EMA confirms this trend, it might indicate bullish momentum in the underlying asset. Conversely, a declining delta with a falling EMA could suggest bearish trends.
Risk Management
Understanding the delta can help traders manage their risk by assessing how sensitive their options positions are to movements in the underlying asset. By using the EMA of delta, traders can better gauge changes in sensitivity and adjust their positions accordingly.
Limitations and Disadvantages
Dependence on Model Assumptions
The Black-Scholes model, which is used to calculate delta, relies on several assumptions including constant volatility and interest rates, and the absence of dividends. These assumptions may not hold in real-world markets, potentially affecting the accuracy of delta calculations.
No Consideration of Market Conditions
The indicator does not account for broader market conditions or liquidity factors. Delta and its EMA are calculated based purely on price and time to expiry, without incorporating market news or events that might impact the option's price.
Lag in EMA
The EMA, while smoothing data, introduces a lag because it is based on past prices. This means that the EMA may not react immediately to sudden price changes, potentially causing delayed signals.
Simplified Strike Price
In this indicator, the strike price is set to the current market price of the option. This simplification might not be suitable for all trading strategies, particularly if a different strike price is more relevant to the trader's strategy.
Limited Scope
This indicator focuses solely on delta and its EMA. While useful, it does not provide a comprehensive view of an option’s overall risk profile. Traders should consider using additional indicators and analyses for a more complete understanding.
Conclusion
The Option Delta Indicator with EMA offers a useful tool for traders to analyze how the sensitivity of an option’s price to changes in the underlying asset’s price evolves over time. The inclusion of an EMA helps to smooth out the delta values and identify trends. However, traders should be aware of the limitations, including the model’s assumptions, potential lag in EMA signals, and the simplified approach to the strike price.
As with any trading tool, it's crucial to use this indicator as part of a broader trading strategy that includes other analyses and risk management practices. Understanding its strengths and limitations will help traders make more informed decisions and enhance their overall trading effectiveness.
Spaghetti - Custom Cryptocurrency Index IndicatorDescription:
Spaghetti is a highly customizable cryptocurrency index indicator designed to let you track an average price of up to 15 different cryptocurrencies in one convenient line. Whether you're interested in a mix of meme coins, AI projects, or any other specific subset of coins, Spaghetti allows you to create your own personalized index.
Features:
Customizable Coin List: Input up to 15 different cryptocurrencies of your choice, allowing you to tailor the indicator to your preferred assets and strategies.
Dynamic Labeling: Features a label on the chart that displays a user-defined name, so you can personalize the indicator's label to match your theme or trading strategy.
Color Customization: The line color is fully customizable, enabling better visual integration with your charts.
Average Calculation: Calculates and plots the average price of all selected coins, providing an easy way to visualize overall market movement for your customized selection.
How to Use Spaghetti:
In the indicator settings, enter the tickers for up to 15 coins you want to include (e.g., BINANCE:BTCUSDT).
Customize the line color and the label text to match your style or preferences.
The indicator will plot the average price of all selected coins, with a dynamic label that follows the price for easy reference.
Spaghetti makes it easy to create and track custom crypto indices, providing a broader perspective of your selected market segments. Perfect for traders who want to stay on top of multiple assets without the clutter!
Backside Bubble ScalpingFrom LIHKG
Pine from Perplexity AI
以下是Backside Bubble Scalping策略的使用說明，旨在幫助交易者理解如何在美股交易中應用這一策略。
使用說明：Backside Bubble Scalping 策略
1. 前提條件
交易時間：此策略適用於香港時間晚上9:30 PM至12:00 AM。
圖表類型：使用1分鐘圖表進行交易。
2. 策略概述
Backside Bubble Scalping策略包含兩種主要的設置：尖backside和鈍backside。這些設置通常在10:00 PM至12:00 AM之間出現。
3. 指標設定
VWAP（粉紅色）：成交量加權平均價格，用於識別市場趨勢。
9 EMA（綠色）：9期指數移動平均線，用於捕捉短期價格變化。
4. 識別 Backside 設置
尖backside
特徵：
當市場趨勢為純紅色下跌，並形成尖尖的V形底部。
入場條件：
當價格突破9 EMA並經過小幅盤整後，進場做多。
鈍backside
特徵：
在混合顏色的趨勢中，形成鈍鈍的V形底部。
入場條件：
在盤整期間進場做多。
5. 止損和止盈設置
止損位置：
尖backside：設置在9 EMA上方的盤整範圍底部加上0.2。
鈍backside：設置在V底部的最低點加上0.2。
止盈位置：
尖backside：當價格跌破VWAP或出現一根K線沒有跟隨時出場。
鈍backside：當一根K線的三分之二身體向下突破9 EMA時出場。
6. 操作步驟
監控市場動態：在指定的交易時間內，觀察VWAP和9 EMA的變化。
識別入場信號：根據尖backside或鈍backside的條件進行判斷，確定何時進場。
設置止損和止盈：根據上述條件設置止損和止盈位，以管理風險。
執行交易：根據信號執行交易，並持續監控市場情況以調整策略。
7. 注意事項
避免在VWAP附近進行交易，以減少失敗風險。
如果出現影線（wick bar），建議不要進行交易，因為這可能表示該設置失敗。
Simple Price Action [Luxmi AI]Introducing the Simple Price Action Indicator
The Simple Price Action Indicator is designed to help traders quickly identify market trends and make informed decisions. This custom-built Pine Script tool changes candle colors on your chart based on price movement:
- Lime Green Candles indicate bullish momentum when the current price closes above the previous candle’s high.
- Red Candles signal bearish momentum when the price closes below the previous candle’s low.
Alongside these visual cues, the indicator generates Buy and Sell signals based on color changes:
- A buy signal appears when a red candle turns green.
- A sell signal shows up when a green candle turns red.
These signals are displayed directly on the chart as small labels ("B" for buy and "S" for sell), helping you easily spot trading opportunities. You can also set up alerts to notify you whenever a new signal is triggered, ensuring you never miss a trade.
The Simple Price Action Indicator is a straightforward yet effective tool for traders looking to enhance their price action analysis.
How It Works: Under the Hood
The script begins by defining two key colors—lime green for bullish candles and red for bearish candles. It then determines the candle color based on the closing price relative to the previous candle's high and low. If a bullish or bearish condition is met, the candle is colored accordingly.
Next, the script checks for a change in candle color to generate buy and sell signals. If a candle turns green after being red, a buy signal is plotted below the candle. If a candle turns red after being green, a sell signal is plotted above the candle.
Finally, the script includes alert conditions that correspond to these buy and sell signals, ensuring you can react quickly to potential trades.
Machine Learning Signal FilterIntroducing the "Machine Learning Signal Filter," an innovative trading indicator designed to leverage the power of machine learning to enhance trading strategies. This tool combines advanced data processing capabilities with user-friendly customization options, offering traders a sophisticated yet accessible means to optimize their market analysis and decision-making processes. Importantly, this indicator does not repaint, ensuring that signals remain consistent and reliable after they are generated.
Machine Learning Integration
The "Machine Learning Signal Filter" employs machine learning algorithms to analyze historical price data and identify patterns that may not be immediately apparent through traditional technical analysis. By utilizing techniques such as regression analysis and neural networks, the indicator continuously learns from new data, refining its predictive capabilities over time. This dynamic adaptability allows the indicator to adjust to changing market conditions, potentially improving the accuracy of trading signals.
Key Features and Benefits
Dynamic Signal Generation: The indicator uses machine learning to generate buy and sell signals based on complex data patterns. This approach enables it to adapt to evolving market trends, offering traders timely and relevant insights. Crucially, the indicator does not repaint, providing reliable signals that traders can trust.
Customizable Parameters: Users can fine-tune the indicator to suit their specific trading styles by adjusting settings such as the temporal synchronization and neural pulse rate. This flexibility ensures that the indicator can be tailored to different market environments.
Visual Clarity and Usability: The indicator provides clear visual cues on the chart, including color-coded signals and optional display of signal curves. Users can also customize the table's position and text size, enhancing readability and ease of use.
Comprehensive Performance Metrics: The indicator includes a detailed metrics table that displays key performance indicators such as return rates, trade counts, and win/loss ratios. This feature helps traders assess the effectiveness of their strategies and make data-driven decisions.
How It Works
The core of the "Machine Learning Signal Filter" is its ability to process and learn from large datasets. By applying machine learning models, the indicator identifies potential trading opportunities based on historical data patterns. It uses regression techniques to predict future price movements and neural networks to enhance pattern recognition. As new data is introduced, the indicator refines its algorithms, improving its accuracy and reliability over time.
Use Cases
Trend Following: Ideal for traders seeking to capitalize on market trends, the indicator helps identify the direction and strength of price movements.
Scalping: With its ability to provide quick signals, the indicator is suitable for scalpers aiming for rapid profits in volatile markets.
Risk Management: By offering insights into trade performance, the indicator aids in managing risk and optimizing trade setups.
In summary, the "Machine Learning Signal Filter" is a powerful tool that combines the analytical strength of machine learning with the practical needs of traders. Its ability to adapt and provide actionable insights makes it an invaluable asset for navigating the complexities of financial markets.
The "Machine Learning Signal Filter" is a tool designed to assist traders by providing insights based on historical data and machine learning techniques. It does not guarantee profitable trades and should be used as part of a comprehensive trading strategy. Users are encouraged to conduct their own research and consider their financial situation before making trading decisions. Trading involves significant risk, and it is possible to lose more than the initial investment. Always trade responsibly and be aware of the risks involved.
MACD Screener [Luxmi AI] MTFMulti-Timeframe Stock Screener with MACD
Introduction
In the world of trading, having a reliable stock screener is crucial for identifying potential trading opportunities. One of the most effective tools for this purpose is the Moving Average Convergence Divergence (MACD) indicator. By using MACD crossovers and crossunders with the signal line as trend change indicators, traders can make informed decisions. This guide explores how to utilize a multi-timeframe stock screener built in Pine Script v5 that leverages the MACD indicator to its fullest potential.
Understanding the MACD Indicator
The MACD is a momentum indicator that shows the relationship between two moving averages of a security’s price. It consists of three main components:
MACD Line - The difference between the 12-period EMA (Exponential Moving Average) and the 26-period EMA.
Signal Line - A 9-period EMA of the MACD line.
Histogram - The difference between the MACD line and the signal line.
A crossover occurs when the MACD line crosses above the signal line, indicating a potential bullish trend. Conversely, a crossunder occurs when the MACD line crosses below the signal line, signaling a potential bearish trend.
Why Multi-Timeframe Analysis Matters
A multi-timeframe approach provides a more comprehensive view of the market by analyzing trends across different timeframes. This method enhances the reliability of trading signals, as it reduces the likelihood of false signals. For instance, a MACD crossover on both daily and weekly charts offers a stronger indication of a trend change than a single timeframe signal.
Using Your Multi-Timeframe Stock Screener
Here’s how to effectively use it:
1. Setting Up Your Screener
Ensure that your stock screener is configured correctly to analyze multiple timeframes. You should be able to input the desired timeframes (e.g., daily and weekly) and set the conditions for MACD crossovers and crossunders.
2. Selecting Stocks for Analysis
Start by choosing a universe of stocks to analyze. This can be a list of stocks from major indices like the S&P 500, Nifty50 or specific sectors you are interested in. The screener will then apply the MACD criteria to these stocks.
3. Interpreting the Signals
- Bullish Signal (UP): A MACD crossover on both the daily and weekly charts suggests a strong bullish trend. This indicates that the stock is likely to move upward in the near future.
- Bearish Signal (DOWN): A MACD crossunder on both the daily and weekly charts signals a strong bearish trend. This indicates that the stock is likely to decline.
4. Confirming Signals with Other Indicators
While the MACD is a powerful indicator, it’s always a good idea to confirm its signals with other technical indicators such as the Relative Strength Index (RSI) or moving averages. This multi-indicator approach can help you make more informed decisions and reduce the risk of false signals.
5. Monitoring and Adjusting
Regularly monitor the performance of the stocks' trend identified by your screener. Adjust the screener settings if necessary to improve its accuracy. Market conditions can change, and it’s important to ensure your screener adapts to these changes.
6. Backtesting and Validation
Before fully relying on the signals from your screener, backtest it using historical data. This will help you validate its effectiveness and fine-tune the parameters to achieve the best results.
Conclusion
Your multi-timeframe stock screener with MACD crossover and crossunder as trend change indicators is a powerful tool for identifying potential trading opportunities. By analyzing trends across different timeframes, you can gain a comprehensive view of the market and make more informed trading decisions. Remember to confirm signals with other indicators and regularly monitor the screener’s performance to ensure it remains effective in different market conditions. Happy trading!
CVD with Moving Average (Trend Colors) [SYNC & TRADE]Yesterday I wrote a simple and easy code for the indicator "Cumulative Delta Volume with a moving average" using AI.
Introduction:
Delta is the difference between buys and sells. If there are more purchases, the delta is positive, if there are more sales, the delta is negative. We look at each candle separately on a particular time frame, which does not give us an overall picture over time.
Cumulative volume delta is in many ways an extension of volume delta, but it covers longer periods of time and provides different trading signals. Like the volume delta indicator, the Cumulative Volume Delta (CVD) indicator measures the relationship between buying and selling pressure, but does not focus on one specific candle (or other chart element), but rather gives a picture over time.
What did you want to get?
I have often seen that they tried to attach RSI and the Ichimoku cloud to the cumulative delta of volume, but I have never seen a cumulative delta of volume with a moving average. A moving average that takes data from the cumulative volume delta will be different from the moving average of the underlying asset. It has been noted that often at the intersection of the cumulative volume delta and the moving average, this is a more accurate signal to buy or sell than the same intersections for the underlying asset.
Initially, 5 moving averages were made with values of 21, 55, 89, 144 and 233, but I realized that this overloads the chart. It is easier to change the length of the moving average depending on the time frame you are using than to overload the chart. The final version with one moving SMA, EMA, RMA, WMA, HMA.
The logic for applying a moving average to a cumulative volume delta:
You choose a moving average, just like you would on your underlying asset. Use the moving average you like and the period you are used to working with. Each TF has its own settings.
What we see on the graph:
This is not an oscillator, but an adapted version for a candlestick chart (line only). Using it, you can clearly see where the market is moving based on the cumulative volume delta. The cool thing is that you can include your moving average applied to the cumulative volume delta. Thanks to this, you can see a trend movement, a return to the moving average to continue the trend.
Opportunities not lost:
The most interesting thing is that it remains possible to observe the divergence of the asset and the cumulative delta of the volume. This gives a great advantage. Those who have not worked with divergence do not rush into it right away. There may be 3 peaks in divergence (as with oversold/overbought), but it works many times more clearly than RSI and MACD.
Here's a good example on the daily chart. The moment we were all waiting for 75,000. The cumulative Delta Volume fell with each peak, while the price chart (tops) were approximately level.
Usually they throw (allow to buy) without volume for sales (delta down, price up) in order to merge at a more interesting price. And they also drain without the volume of purchases for a squeeze (price down / delta up) and again I buy back at a more interesting price. There are more complex estimation options; you can read about the divergence of the cumulative delta of the CVD volume. I just recommend doing a backtest.
Recommendations:
One more moment. Use the indicator on the stock exchange, where there is the most money, by turnover and by asset. Choose Binance, not Bybit. Those. choose the BTC asset, for example, but on the Binance exchange. Not futures, but spot.
The greater the turnover on the exchange for an asset, and the fewer opportunities to enter with leverage, the less volatile the price and the more beautiful and accurate the chart.
Works on all assets. There is a subscription limit (the number of calculated bars) that has little effect on anything. Can be applied to any asset where there is volume (not SPX, but ES1, not MOEX, but MX1!).
Перевод на русский.
Вчера написал с помощью AI простой и легкий код индикатора "Кумулятивная Дельта Объема со скользящей средней".
Введение:
Дельта (Delta) — это разница между покупками и продажами. Если покупок больше — дельта положительная, если больше продаж — дельта отрицательная. Мы смотрим на каждую свечу отдельно на том или ином таймфрейме, что не дает нам общей картины во времени.
Кумулятивная дельта объема — во многом продолжение дельты объёмов, но она включает более длительные периоды времени и дает другие торговые сигналы. Как и индикатор дельты объёма, индикатор кумулятивной дельты объема (Cumulative Volume Delta, CVD) измеряет связь между давлением покупателей и продавцов, но при этом не фокусируется на одной конкретной свече (или другом элементе графика), а дает картину во времени.
Что хотел получить?
Часто видел, что к кумулятивной детьте объема пытались прикрепить RSI и облако ишимоку, но никогда не видел кумулятивную дельту объема со скользящей средней. Скользящая средняя которая берет данные от кумулятивной дельты объема будет отличатся от скользящей средней основного актива. Было замечено, что часто в местах пересечения кумулятивной дельты объема и скользящей средней - это более точный сигнал к покупке или продаже, чем такие же пересечения по основному активу.
Изначально было сделанно 5 скользящих со значениями 21, 55, 89, 144 и 233, но я понял, что это перегружает график. Проще менять длину скользящей средней от используемого таймфрейма, чем перегружать график. Финальный вариант с одной скользящей SMA, EMA, RMA, WMA, HMA.
Логика применения скользящей средней к кумулятивной дельте объема:
Вы выбираете скользящую среднюю, так же как и на основном активе. Применяйте ту скользящую среднюю, которая вам нравится и период, с которым привыкли работать. На каждом TF свои настройки.
Что мы видим на графике:
Это не осциллятор, а адаптированная версия к свечному графику (только линия). С помощью него вы можете наглядно посмотреть куда движется рынок по кумулятивной дельте объема. Самое интересное, что вы можете включить свою скользящую среднюю, применимую к кумулятивной дельте объема. Благодаря этому вы можете видеть трендовое движение, возврат к средней скользящей для продолжения тренда.
Не потерянные возможности:
Самое интересное, что осталась возможность наблюдать за дивергенцией актива и кумулятивной дельтой объема. Это дает большое преимущество. Те кто не работал с дивергенцией не бросайтесь на нее сразу. Может быть и 3 пика в дивергенции (как с перепроданностью / перекупленностью), но работает в разы четче чем RSI и MACD.
Вот хороший пример на дневном графике. Момент когда мы все ждали 75000. Кумулятивная Дельта Объема падала с каждым пиком, в то время как ценовой график (вершины) были примерно на уровне.
Обычно закидывают (разрешают покупать) без объема на продажи (дельта вниз цена вверх), чтобы слить по более интересной цене. И также сливают без объема покупок для сквиза (цена вниз / дельта вверх) и опять откупаю по более интересной цене. Существуют более сложные варианты оценки, можете почитать про дивергенцию кумулятивной дельты объема CVD. Только рекомендую сделать бэктест.
Рекомендации:
Еще момент. Используйте индикатор, на бирже, там где больше всего денег, по обороту и по активу. Выбирайте не Bybit, а Binance. Т.е. выбираете актив BTC, к примеру, но на бирже Binance. Не фьючерс, а спот.
Чем более большие обороты на бирже, по активу, и меньше возможностей заходить с плечами, тем менее волатильная цена и более красивый и точный график.
Работает на всех активах. Есть ограничение по подписке (количество рассчитываемых баров) мало влияет на что. Можно применить к любому активу где есть объем (не SPX, а ES1, не MOEX, а MX1!).
Support/Resistance v2 (ML) KmeanKmean with Standard Deviation Channel
1. Description of Kmean
Kmean (or K-means) is a popular clustering algorithm used to divide data into K groups based on their similarity. In the context of financial markets, Kmean can be applied to find the average price values over a specific period, allowing the identification of major trends and levels of support and resistance.
2. Application in Trading
In trading, Kmean is used to smooth out the price series and determine long-term trends. This helps traders make more informed decisions by avoiding noise and short-term fluctuations. Kmean can serve as a baseline around which other analytical tools, such as channels and bands, are constructed.
3. Description of Standard Deviation (stdev)
Standard deviation (stdev) is a statistical measure that indicates how much the values of data deviate from their mean value. In finance, standard deviation is often used to assess price volatility. A high standard deviation indicates strong price fluctuations, while a low standard deviation indicates stable movements.
4. Combining Kmean and Standard Deviation to Predict Short-Term Price Behavior
Combining Kmean and standard deviation creates a powerful tool for analyzing market conditions. Kmean shows the average price trend, while the standard deviation channels demonstrate the boundaries within which the price can fluctuate. This combination helps traders to:
Identify support and resistance levels.
Predict potential price reversals.
Assess risks and set stop-losses and take-profits.
Should you have any questions about code, please reach me at Tradingview directly.
Hope you find this script helpful!
Moving Average Crossover Strategy by AI and Anton ThomasDescription:
Indicator Name: Moving Average Crossover Strategy
Overview:
The "Moving Average Crossover Strategy" is a technical analysis indicator that combines moving averages and the Relative Strength Index (RSI) to identify potential buy and sell signals. It aims to capture trend reversals and momentum shifts in the market.
Key Components:
Moving Averages:
The indicator calculates two moving averages:
Fast Moving Average (10-period SMA): This average reacts more quickly to price changes.
Slow Moving Average (30-period SMA): This average provides a smoother trend indication.
A bullish signal occurs when the fast moving average crosses above the slow moving average (golden cross), indicating a potential uptrend.
A bearish signal occurs when the fast moving average crosses below the slow moving average (death cross), indicating a potential downtrend.
Relative Strength Index (RSI):
The RSI measures the strength and momentum of price movements on a scale of 0 to 100.
A reading above 70 indicates overbought conditions, suggesting a potential reversal to the downside.
A reading below 30 indicates oversold conditions, suggesting a potential reversal to the upside.
Trading Signals:
Buy Signal:
Generated when the fast moving average crosses above the slow moving average (longCondition).
Additionally, a buy signal is identified when the RSI is oversold (below 30) and then crosses above the oversold threshold.
The indicator plots a green triangle above the bar to indicate the buy signal.
Sell Signal:
Generated when the fast moving average crosses below the slow moving average (shortCondition).
Additionally, a sell signal is identified when the RSI is overbought (above 70) and then crosses below the overbought threshold.
The indicator plots a red triangle below the bar to indicate the sell signal.
Additional Features:
Bullish Engulfing Pattern:
Detects bullish engulfing candlestick patterns, indicating potential bullish reversals.
Plots a green triangle below the bar to highlight the bullish engulfing pattern.
Bearish Engulfing Pattern:
Detects bearish engulfing candlestick patterns, indicating potential bearish reversals.
Plots a red triangle above the bar to highlight the bearish engulfing pattern.
Oversold and Overbought Levels:
Plots horizontal dashed lines at 30 (oversold) and 70 (overbought) on the RSI indicator.
Usage:
Traders can use this indicator to identify potential entry and exit points in the market based on moving average crossovers, RSI conditions, and candlestick patterns. It provides a comprehensive view of trend direction and momentum.
Considerations:
Always confirm signals with other technical analysis tools and market conditions.
Implement proper risk management strategies to minimize potential losses.
Example:
A buy signal is generated when the fast moving average crosses above the slow moving average, and the RSI is below 30 but crosses above it.
A sell signal is generated when the fast moving average crosses below the slow moving average, and the RSI is above 70 but crosses below it.
If you find this indicator profitable, please support by gifting some USDT (BSC NETWORK): 0x2c5c2dd39bbcc9453dd1428d881da37dacd9bf47
or just a thank you email at antonthomasfull(at)gmail.com
Luxmi AI Directional Option Buying (Long Only)Introduction:
"Option premium charts typically exhibit a predisposition towards bearish sentiment in higher timeframes"
In the dynamic world of options trading, navigating through the complexities of market trends and price movements is essential for making informed decisions. Among the arsenal of tools available to traders, option premium charts stand out as a pivotal source of insight, particularly in higher timeframes. However, their inherent bearish inclination in such timeframes necessitates a keen eye for identifying bullish pullbacks, especially in lower timeframes, to optimize buying strategies effectively.
Understanding the interplay between different data points becomes paramount in this endeavor. Traders embark on a journey of analysis, delving into metrics such as Implementation Shortfall, the performance of underlying index constituents, and bullish trends observed in lower timeframes like the 1-minute and 3-minute charts. These data points serve as guiding beacons, illuminating potential opportunities amidst the market's ever-shifting landscape.
Using this indicator, we will dissect the significance of option premium charts and their nuanced portrayal of market sentiment. Furthermore, we will unveil the art of discerning bullish pullbacks in lower timeframes, leveraging a multifaceted approach that amalgamates quantitative analysis with qualitative insights. Through this holistic perspective, traders can refine their decision-making processes, striving towards efficiency and efficacy in their options trading endeavors.
Major Features:
Implementation Shortfall (IS) Candles:
Working Principle:
TWAP (Time-Weighted Average Price) and EMA (Exponential Moving Average) are both commonly used in calculating Implementation Shortfall, a metric that measures the difference between the actual execution price of a trade and the benchmark price.
TWAP calculates the average price of a security over a specified time period, giving equal weight to each interval. On the other hand, EMA places more weight on recent prices, making it more responsive to current market conditions.
To calculate Implementation Shortfall using TWAP, the difference between the average execution price and the benchmark price is determined over the trading period. Similarly, with EMA, the difference is calculated using the exponential moving average price instead of a simple average.
By employing TWAP and EMA, traders can gauge the effectiveness of their trading strategies and identify areas for improvement in executing trades relative to a benchmark.
Benefits of using Implementation Shortfall:
By visualizing the implementation shortfall and its comparison with the EMA on the chart, traders can quickly assess whether current trading activity is deviating from recent trends.
Green bars suggest potential buying opportunities or bullish sentiment, while red bars suggest potential selling opportunities or bearish sentiment.
Traders can use this visualization to make more informed decisions about their trading strategies, such as adjusting position sizes, entering or exiting trades, or managing risk based on the observed deviations from the moving average.
How to use this feature:
This feature calculates Implementation Shortfall (IS) and visually represents it by coloring the candles in either bullish (green) or bearish (red) hues. This color-coding system provides traders with a quick and intuitive way to assess market sentiment and potential entry points. Specifically, a long entry is signaled when both the candle color and the trend cloud color align as green, indicating a bullish market outlook. This integrated approach enables traders to make informed decisions, leveraging IS insights alongside visual cues for more effective trading strategies.
Micro Trend Candles:
Working Principle:
This feature begins by initializing variables to determine trend channel width and track price movements. Average True Range (ATR) is then calculated to measure market volatility, influencing the channel's size. Highs and lows are identified within a specified range, and trends are assessed based on price breaches, with potential changes signaled accordingly. The price channel is continually updated to adapt to market shifts, and arrows are placed to indicate potential entry points. Colors are assigned to represent bullish and bearish trends, dynamically adjusting based on current market conditions. Finally, candles on the chart are colored to visually depict the identified micro trend, offering traders an intuitive way to interpret market sentiment and potential entry opportunities.
Benefits of using Micro Trend Candles:
Traders can use these identified micro trends to spot potential short-term trading opportunities. For example:
Trend Following: Traders may decide to enter trades aligned with the prevailing micro trend. If the candles are consistently colored in a certain direction, traders may consider entering positions in that direction.
Reversals: Conversely, if the script signals a potential reversal by changing the candle colors, traders may anticipate trend reversals and adjust their trading strategies accordingly. For instance, they might close existing positions or enter new positions in anticipation of a trend reversal.
It's important to note that these micro trends are short-term in nature and may not always align with broader market trends. Therefore, traders utilizing this script should consider their trading timeframes and adjust their strategies accordingly.
How to use this feature:
This feature assigns colors to candles to represent bullish and bearish trends, with adjustments made based on current market conditions. Green candles accompanied by a green trend cloud signal a potential long entry, while red candles suggest caution, indicating a bearish trend. This visual representation allows traders to interpret market sentiment intuitively, identifying optimal entry points and exercising caution during potential downtrends.
Scalping Candles (Inspired by Elliott Wave):
Working Principle:
This feature draws inspiration from the Elliot Wave method, utilizing technical analysis techniques to discern potential market trends and sentiment shifts. It begins by calculating the variance between two Exponential Moving Averages (EMAs) of closing prices, mimicking Elliot Wave's focus on wave and trend analysis. The shorter-term EMA captures immediate price momentum, while the longer-term EMA reflects broader market trends. A smoother Exponential Moving Average (EMA) line, derived from the difference between these EMAs, aids in identifying short-term trend shifts or momentum reversals.
Benefits of using Scalping Candles Inspired by Elliott Wave:
The Elliott Wave principle is a form of technical analysis that attempts to predict future price movements by identifying patterns in market charts. It suggests that markets move in repetitive waves or cycles, and traders can potentially profit by recognizing these patterns.
While this script does not explicitly analyze Elliot Wave patterns, it is inspired by the principle's emphasis on trend analysis and market sentiment. By calculating and visualizing the difference between EMAs and assigning colors to candles based on this analysis, the script aims to provide traders with insights into potential market sentiment shifts, which can align with the broader philosophy of Elliott Wave analysis.
How to use this feature:
Candlestick colors are assigned based on the relationship between the EMA line and the variance. When the variance is below or equal to the EMA line, candles are colored red, suggesting a bearish sentiment. Conversely, when the variance is above the EMA line, candles are tinted green, indicating a bullish outlook. Though not explicitly analyzing Elliot Wave patterns, the script aligns with its principles of trend analysis and market sentiment interpretation. By offering visual cues on sentiment shifts, it provides traders with insights into potential trading opportunities, echoing Elliot Wave's emphasis on pattern recognition and trend analysis.
Volume Candles:
Working Principle:
This feature introduces a custom volume calculation method tailored for bullish and bearish bars, enabling a granular analysis of volume dynamics specific to different price movements. By summing volumes over specified periods for bullish and bearish bars, traders gain insights into the intensity of buying and selling pressures during these periods, facilitating a deeper understanding of market sentiment. Subsequently, the script computes the net volume, revealing the overall balance between buying and selling pressures. Positive net volume signifies prevailing bullish sentiment, while negative net volume indicates bearish sentiment.
Benefits of Using Volume candles:
Enhanced Volume Analysis: Traders gain a deeper understanding of volume dynamics specific to bullish and bearish price movements, allowing them to assess the intensity of buying and selling pressures with greater precision.
Insight into Market Sentiment: By computing net volume and analyzing its relationship with the Exponential Moving Average (EMA), traders obtain valuable insights into prevailing market sentiment. This helps in identifying potential shifts in sentiment and anticipating market movements.
Visual Representation of Sentiment: The color-coded candle bodies based on volume dynamics provide traders with a visual representation of market sentiment. This intuitive visualization helps in quickly interpreting sentiment shifts and making timely trading decisions.
How to use this feature:
This visual representation allows traders to quickly interpret market sentiment based on volume dynamics. Green candles indicate potential bullish sentiment, while red candles suggest bearish sentiment. The color-coded candle bodies help traders identify shifts in market sentiment and make informed trading decisions.
Smart Sentimeter Candles:
Working Principle:
The "Smart Sentimeter Candles" feature is a tool designed for market sentiment analysis using technical indicators. It begins by defining stock symbols from various sectors, allowing traders to select specific indices for sentiment analysis. The script then calculates the difference between two Exponential Moving Averages (EMAs) of the High-Low midpoint, capturing short-term momentum changes in the market. It computes the difference between current and previous values to capture momentum shifts over time.
Additionally, it calculates the Exponential Moving Average (EMA) of this difference to provide a smoothed representation of the prevailing trend in market momentum. Another EMA of this difference is calculated to offer an alternative perspective on longer-term momentum trends. Bar colors are determined based on the difference between current and previous values, with bullish and bearish sentiment represented by custom colors. Finally, sentiment candles are visualized on the chart, providing traders with a clear representation of market sentiment changes.
Benefits of Using Sentimeter Candles:
By analyzing index constituents, traders gain insights into the individual stocks that collectively influence the index's performance. This understanding is crucial for trading options as it helps traders tailor their strategies to specific sectors or stocks within the index.
Sector-Specific Analysis: Traders can focus on specific sectors by selecting relevant indices for sentiment analysis.
Momentum Identification: The script identifies short-term momentum changes in the market, aiding traders in spotting potential trend reversals or continuations.
Clear Visualization: Sentiment candles visually represent market sentiment changes, making it easier for traders to interpret and act upon sentiment trends.
How to use this feature:
Select Indices: Toggle the inputs to choose which indices (e.g., NIFTY, BANKNIFTY, FINNIFTY) to analyze.
Interpret Sentiment Candles: Monitor the color of sentiment candles on the chart. Green candles indicate bullish sentiment, while red candles suggest bearish sentiment.
Observe Momentum Changes: Pay attention to momentum changes identified by the difference between EMAs and their respective EMAs. Increasing bullish momentum may present buying opportunities, while increasing bearish momentum could signal potential sell-offs.
Trend Cloud:
Working Principle:
The script utilizes the Relative Strength Index (RSI) to assess market momentum, identifying bullish and bearish phases based on RSI readings. It calculates two boolean variables, bullmove and bearmove, which signal shifts in momentum direction by considering changes in the Exponential Moving Average (EMA) of the closing price. When RSI indicates bullish momentum and the closing price's EMA exhibits positive changes, bullmove is triggered, signifying the start of a bullish phase. Conversely, when RSI suggests bearish momentum and the closing price's EMA shows negative changes, bearmove is activated, marking the beginning of a bearish phase. This systematic approach helps in understanding the current trend of the price. The script visually emphasizes these phases on the chart using plot shape markers, providing traders with clear indications of trend shifts.
Benefits of Using Trend Cloud:
Comprehensive Momentum Assessment: The script offers a holistic view of market momentum by incorporating RSI readings and changes in the closing price's EMA, enabling traders to identify both bullish and bearish phases effectively.
Structured Trend Recognition: With the calculation of boolean variables, the script provides a structured approach to recognizing shifts in momentum direction, enhancing traders' ability to interpret market dynamics.
Visual Clarity: Plotshape markers visually highlight the start and end of bullish and bearish phases on the chart, facilitating easy identification of trend shifts and helping traders to stay informed.
Prompt Response: Traders can promptly react to changing market conditions as the script triggers alerts when bullish or bearish phases begin, allowing them to seize potential trading opportunities swiftly.
Informed Decision-Making: By integrating various indicators and visual cues, the script enables traders to make well-informed decisions and adapt their strategies according to prevailing market sentiment, ultimately enhancing their trading performance.
How to use this feature:
The most effective way to maximize the benefits of this feature is to use it in conjunction with other key indicators and visual cues. By combining the color-coded clouds, which indicate bullish and bearish sentiment, with other features such as IS candles, microtrend candles, volume candles, and sentimeter candles, traders can gain a comprehensive understanding of market dynamics. For instance, aligning the color of the clouds with the trend direction indicated by IS candles, microtrend candles, and sentimeter candles can provide confirmation of trend strength or potential reversals.
Furthermore, traders can leverage the trend cloud as a trailing stop-loss tool for long entries, enhancing risk management strategies. By adjusting the stop-loss level based on the color of the cloud, traders can trail their positions to capture potential profits while minimizing losses. For long entries, maintaining the position as long as the cloud remains green can help traders stay aligned with the prevailing bullish sentiment. Conversely, a shift in color from green to red serves as a signal to exit the position, indicating a potential reversal in market sentiment and minimizing potential losses. This integration of the trend cloud as a trailing stop-loss mechanism adds an additional layer of risk management to trading strategies, increasing the likelihood of successful trades while reducing exposure to adverse market movements.
Moreover, the red cloud serves as an indicator of decay in option premiums and potential theta effect, particularly relevant for options traders. When the cloud turns red, it suggests a decline in option prices and an increase in theta decay, highlighting the importance of managing options positions accordingly. Traders may consider adjusting their options strategies, such as rolling positions or closing out contracts, to mitigate the impact of theta decay and preserve capital. By incorporating this insight into options pricing dynamics, traders can make more informed decisions about their options trades.
Scalping Opportunities (UpArrow and DownArrow):
Working Principle:
The feature calculates candlestick values based on the open, high, low, and close prices of each bar. By comparing these derived candlestick values, it determines whether the current candlestick is bullish or bearish. Additionally, it signals when there is a change in the color (bullish or bearish) of the derived candlesticks compared to the previous bar, enabling traders to identify potential shifts in market sentiment. This is a long only strategy, hence the signals are plotted only when the Trend Cloud is Green (Bullish).
Benefits of using UpArrow and DownArrow:
Clear Visualization: By employing color-coded candlesticks, the script offers traders a visually intuitive representation of market sentiment, enabling quick interpretation of prevailing conditions.
Signal Identification: Its capability to detect shifts in market sentiment serves as a valuable tool for identifying potential trading opportunities, facilitating timely decision-making and execution.
Long-Only Strategy: The script selectively plots signals only when the trend cloud is green, aligning with a bullish bias and enabling traders to focus on long positions during favorable market conditions.
Up arrows indicate potential long entry points, complementing the bullish bias of the trend cloud. Conversely, down arrows signify an active pullback in progress, signaling caution and prompting traders to refrain from entering long positions during such periods.
How to use this feature:
Confirmation: Confirm bullish market conditions with the Trend Cloud indicator. Ensure alignment between trend cloud signals, candlestick colors, and arrow indicators for confident trading decisions.
Entry Signals: Look for buy signals within a green trend cloud, indicated by bullish candlestick color changes and up arrows, suggesting potential long entry points aligned with the prevailing bullish sentiment.
Wait Signals: Exercise caution when encountering down arrows, which signify wait signals or active pullbacks in progress. Avoid entering long positions during these periods to avoid potential losses.
Exit Strategy: Use trend cloud color changes as signals to exit long positions. When the trend cloud shifts color, consider closing out long positions to lock in profits or minimize losses.
Profit Management: It's important to book or lock in some profits early on in option buying. Consider taking partial profits when the trade is in your favor and trail the remaining position to maximize gains on favorable trades.
Risk Management: Implement stop-loss orders or trailing stops to manage risk effectively. Exit positions promptly if sentiment shifts or if price movements deviate from the established trend, safeguarding capital.
Up and Down Signals:
Working Principle:
This feature calculates Trailing Stoploss (TSL) using the Average True Range (ATR) to dynamically adjust the stop level based on price movements. It generates buy signals when the price crosses above the trailing stop and sell signals when it crosses below. These signals are plotted on the chart and trigger alerts, signaling potential trading opportunities. Additionally, the script selectively plots Up and Down signals only when the Implementation Shortfall Calculation identifies scalp opportunities, independent of the prevailing price trend.
Benefits of using Up and Down Signals:
Trailing Stoploss: The script employs an ATR-based trailing stop, allowing traders to adjust stop levels dynamically in response to changing market conditions, thereby maximizing profit potential and minimizing losses.
Clear Signal Generation: Buy and sell signals are generated based on price interactions with the trailing stop, providing clear indications of entry and exit points for traders to act upon.
Alert Notifications: The script triggers alerts when buy or sell signals are generated, ensuring traders remain informed of potential trading opportunities even when not actively monitoring the charts.
Scalping Opportunities: By incorporating Implementation Shortfall Calculation, the script identifies scalp opportunities, enabling traders to capitalize on short-term price movements irrespective of the prevailing trend.
How to use this feature:
Signal Interpretation: Interpret Up signals as opportunities to enter long positions when the price crosses above the trailing stop, and Down signals as cues to exit.
Alert Monitoring: Pay attention to alert notifications triggered by the script, indicating potential trading opportunities based on signal generation.
Scalping Strategy: When Up and Down signals are plotted alongside scalp opportunities identified by the Implementation Shortfall Calculation, consider scalping trades aligned with these signals for short-term profit-taking, regardless of the overall market trend.
Consideration of Trend Cloud: Remember that this feature does not account for the underlying trend provided by the Trend Cloud feature. Consequently, the take profit levels generated by the trailing stop may be smaller than those derived from trend-following strategies. It's advisable to supplement this feature with additional trend analysis to optimize profit-taking levels and enhance overall trading performance.
Chart Timeframe Support and Resistance:
Working Principle:
This feature serves to identify and visualize support and resistance levels on the chart, primarily based on the chosen Chart Timeframe (CTF). It allows users to specify parameters such as the number of bars considered on the left and right sides of each pivot point, as well as line width and label color. Moreover, users have the option to enable or disable the display of these levels. By utilizing functions to calculate pivot highs and lows within the specified timeframe, the script determines the highest high and lowest low surrounding each pivot point.
Additionally, it defines functions to create lines and labels for each detected support and resistance level. Notably, this feature incorporates a trading method that emphasizes the concept of resistance turning into support after breakouts, thereby providing valuable insights for traders employing such strategies. These lines are drawn on the chart, with colors indicating whether the level is above or below the current close price, aiding traders in visualizing key levels and making informed trading decisions.
Benefits of Chart Timeframe Support and Resistance:
Identification of Price Levels: Support and resistance levels help traders identify significant price levels where buying (support) and selling (resistance) pressure may intensify. These levels are often formed based on historical price movements and are regarded as areas of interest for traders.
Decision Making: Support and resistance levels assist traders in making informed trading decisions. By observing price reactions near these levels, traders can gauge market sentiment and adjust their strategies accordingly. For example, traders may choose to enter or exit positions, set stop-loss orders, or take profit targets based on price behavior around these levels.
Risk Management: Support and resistance levels aid in risk management by providing reference points for setting stop-loss orders. Traders often place stop-loss orders below support levels for long positions and above resistance levels for short positions to limit potential losses if the market moves against them.
How to use this feature:
Planning Long Positions: When considering long positions, it's advantageous to strategize when the price is in proximity to a support level identified by the script. This suggests a potential area of buying interest where traders may expect a bounce or reversal in price. Additionally, confirm the bullish bias by ensuring that the trend cloud is green, indicating favorable market conditions for long trades.
Waiting for Breakout: If long signals are generated near resistance levels detected by the script, exercise patience and wait for a breakout above the resistance. A breakout above resistance signifies potential strength in the upward momentum and may present a more opportune moment to enter long positions. This approach aligns with trading methodologies that emphasize confirmation of bullish momentum before initiating trades.
Settings:
The Index Constituent Analysis setting empowers users to input the constituents of a specific index, facilitating the analysis of market sentiments based on the performance of these individual components. An index serves as a statistical measure of changes in a portfolio of securities representing a particular market or sector, with constituents representing the individual assets or securities comprising the index.
By providing the constituent list, users gain insights into market sentiments by observing how each constituent performs within the broader index. This analysis aids traders and investors in understanding the underlying dynamics driving the index's movements, identifying trends or anomalies, and making informed decisions regarding their investment strategies.
This setting empowers users to customize their analysis based on specific indexes relevant to their trading or investment objectives, whether tracking a benchmark index, sector-specific index, or custom index. Analyzing constituent performance offers a valuable tool for market assessment and decision-making.
Example: BankNifty Index and Its Constituents
Illustratively, the BankNifty index represents the performance of the banking sector in India and includes major banks and financial institutions listed on the National Stock Exchange of India (NSE). Prominent constituents of the BankNifty index include:
State Bank of India (SBIN)
HDFC Bank
ICICI Bank
Kotak Mahindra Bank
Axis Bank
IndusInd Bank
Punjab National Bank (PNB)
Yes Bank
Federal Bank
IDFC First Bank
By utilizing the Index Constituent Analysis setting and inputting these constituent stocks of the BankNifty index, traders and investors can assess the individual performance of these banking stocks within the broader banking sector index. This analysis enables them to gauge market sentiments, identify trends, and make well-informed decisions regarding their trading or investment strategies in the banking sector.
Example: NAS100 Index and Its Constituents
Similarly, the NAS100 index, known as the NASDAQ-100, tracks the performance of the largest non-financial companies listed on the NASDAQ stock exchange. Prominent constituents of the NAS100 index include technology and consumer discretionary stocks such as:
Apple Inc. (AAPL)
Microsoft Corporation (MSFT)
Amazon.com Inc. (AMZN)
Alphabet Inc. (GOOGL)
Facebook Inc. (FB)
Tesla Inc. (TSLA)
NVIDIA Corporation (NVDA)
PayPal Holdings Inc. (PYPL)
Netflix Inc. (NFLX)
Adobe Inc. (ADBE)
By inputting these constituent stocks of the NAS100 index into the Index Constituent Analysis setting, traders and investors can analyze the individual performance of these technology and consumer discretionary stocks within the broader NASDAQ-100 index. This analysis facilitates the evaluation of market sentiments, identification of trends, and informed decision-making regarding trading or investment strategies in the technology and consumer sectors.
Example: FTSE 100 Index and Its Constituents
The FTSE 100 index represents the performance of the 100 largest companies listed on the London Stock Exchange (LSE) by market capitalization. Some notable constituents of the FTSE 100 index include:
HSBC Holdings plc
BP plc
GlaxoSmithKline plc
Unilever plc
Royal Dutch Shell plc
AstraZeneca plc
Diageo plc
Rio Tinto plc
British American Tobacco plc
Reckitt Benckiser Group plc
By inputting these constituent stocks of the FTSE 100 index into the Index Constituent Analysis setting, traders and investors can analyze the individual performance of these diverse companies within the broader UK market index. This analysis facilitates the evaluation of market sentiments, identification of trends, and informed decision-making regarding trading or investment strategies in the UK market.
This comprehensive approach enables users to dissect index performance effectively, providing valuable insights for investors and traders across different markets and sectors.
Index Selection - Index Selection allows traders to specify the index for Sentimeter calculations, enabling customization for Call and Put Option charts corresponding to the chosen index.
Support and Resistance Levels - Set the left and right bars to consider pivot high and low to draw Support and resistance lines. Linewidth setting to help increase the width of the Support and Resistance lines. Label Color to change the color of the labels.
Style Section Colors to allow users to customize the color scheme to their liking.
Crypto Narratives: Relative StrengthThis indicator offers a unique perspective on the crypto market by focusing on the relative strength of different narratives. It aggregates RSI data from multiple tokens associated with each narrative, providing a comprehensive view of the sentiment and momentum behind these themes. You can use it to take profit, find W bottoms or M tops to enter and exit narratives. and generally see what hot at the moment with lots of pretty colours.
This indicator tracks the relative strength of various crypto narratives using the Relative Strength Index (RSI) of representative tokens. It allows users to gauge the momentum and sentiment behind different themes in the cryptocurrency market.
Functionality:
The indicator calculates the average RSI values for the current leading tokens associated with ten different crypto narratives:
- AI (Artificial Intelligence)
- Ordinals
- DeFi (Decentralized Finance)
- Memes
- Gaming
- Level 1 (Layer 1 Protocols)
- Sol Betas (Solana Ecosystem)
- Storage/DePin
- RWA (Real-World Assets)
- ReStaking
he average RSI values for each narrative are calculated by summing the RSI values of the associated tokens and dividing by the number of tokens. The indicator plots the 3-period simple moving average (SMA) of each narrative's RSI using different colors and line styles.
Users can customize the RSI length, line width, and label offset through the input options. If the "Show Labels" option is enabled, the indicator displays labels for each narrative's RSI value on the most recent bar.
The indicator also includes horizontal lines representing overbought and oversold levels, which can be adjusted through the input options. Alerts are triggered when a narrative's RSI crosses above the overbought level or below the oversold level. The alerts include the narrative name, RSI value, and a suggestion to consider selling or buying.
Machine Learning: Multiple Logistic Regression
Multiple Logistic Regression Indicator
The Logistic Regression Indicator for TradingView is a versatile tool that employs multiple logistic regression based on various technical indicators to generate potential buy and sell signals. By utilizing key indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend, the indicator aims to provide a systematic approach to decision-making in financial markets.
How It Works:
Technical Indicators:
The script uses multiple technical indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend as input variables for the logistic regression model.
These indicators are normalized to create categorical variables, providing a consistent scale for the model.
Logistic Regression:
The logistic regression function is applied to the normalized input variables (x1 to x6) with user-defined coefficients (b0 to b6).
The logistic regression model predicts the probability of a binary outcome, with values closer to 1 indicating a bullish signal and values closer to 0 indicating a bearish signal.
Loss Function (Cross-Entropy Loss):
The cross-entropy loss function is calculated to quantify the difference between the predicted probability and the actual outcome.
The goal is to minimize this loss, which essentially measures the model's accuracy.
// Error Function (cross-entropy loss)
loss(y, p) =>
-y * math.log(p) - (1 - y) * math.log(1 - p)
// y - depended variable
// p - multiple logistic regression
Gradient Descent:
Gradient descent is an optimization algorithm used to minimize the loss function by adjusting the weights of the logistic regression model.
The script iteratively updates the weights (b1 to b6) based on the negative gradient of the loss function with respect to each weight.
// Adjusting model weights using gradient descent
b1 -= lr * (p + loss) * x1
b2 -= lr * (p + loss) * x2
b3 -= lr * (p + loss) * x3
b4 -= lr * (p + loss) * x4
b5 -= lr * (p + loss) * x5
b6 -= lr * (p + loss) * x6
// lr - learning rate or step of learning
// p - multiple logistic regression
// x_n - variables
Learning Rate:
The learning rate (lr) determines the step size in the weight adjustment process. It prevents the algorithm from overshooting the minimum of the loss function.
Users can set the learning rate to control the speed and stability of the optimization process.
Visualization:
The script visualizes the output of the logistic regression model by coloring the SMA.
Arrows are plotted at crossover and crossunder points, indicating potential buy and sell signals.
Lables are showing logistic regression values from 1 to 0 above and below bars
Table Display:
A table is displayed on the chart, providing real-time information about the input variables, their values, and the learned coefficients.
This allows traders to monitor the model's interpretation of the technical indicators and observe how the coefficients change over time.
How to Use:
Parameter Adjustment:
Users can adjust the length of technical indicators (rsi_length, cci_length, etc.) and the Z score length based on their preference and market characteristics.
Set the initial values for the regression coefficients (b0 to b6) and the learning rate (lr) according to your trading strategy.
Signal Interpretation:
Buy signals are indicated by an upward arrow (▲), and sell signals are indicated by a downward arrow (▼).
The color-coded SMA provides a visual representation of the logistic regression output by color.
Table Information:
Monitor the table for real-time information on the input variables, their values, and the learned coefficients.
Keep an eye on the learning rate to ensure a balance between model adjustment speed and stability.
Backtesting and Validation:
Before using the script in live trading, conduct thorough backtesting to evaluate its performance under different market conditions.
Validate the model against historical data to ensure its reliability.
TradesAI - Elite (Premium)This is an all-inclusive, premium indicator that focuses mainly on price action analysis, a form of looking at raw price data and market structure to analyze and capture areas of interest where price could react.
This indicator is a perfect trading companion that saves you a lot of time in trading price action. Some of the popular methods that use price action analysis are "Smart Money Concepts (SMC)", "Inner Circle Trader (ICT)", and "Institutional Trading".
🔶 POWERFUL TOOLS
The indicator combines three main tools as a trading suite:
Trendlines
Market Structure Breakouts (MSB)
Order Blocks (OBs) and Reversal Order Blocks (ROBs)
These 3 main tools are interconnected together. Below we go over each, and then explain how and why they are brought in together. Please also note that the indicator's settings have tooltips next to most of them, with more detailed information.
🔶 TRENDLINES
This indicator automatically draws the most relevant Trendlines from pivot high/pivot low (based on the defined settings) as origins, while keeping track of candle closes across these Trendlines to adjust or invalidate accordingly.
The indicator will draw all possible Trendlines up to the maximum allowed by TradingView's PineScript. It uses a bullish pivot high candle to draw downtrends, and a bearish pivot low candle to draw uptrends. The algorithm will draw the most suitable active Trendlines from those origin points.
The indicator takes the origin point as the first point of the Trendline, then starts looking for the immediate next same-type candle (bullish to bullish or bearish to bearish), to draw the Trendline between the origin candle and this newer candle.
An uptrend is a ray connecting two bearish candles, as long as the second candle has a Low higher than the low of the origin (first) candle. A downtrend is a ray connecting two bullish candles, as long as the second candle has a high lower than the high of the origin (first) candle.
Upon drawing, the indicator then starts monitoring and adjusting this Trendline, by keeping the origin always the same but changing the second point. The goal is to keep reducing the slope of the Trendline till it is at 0 degrees (horizontal line). That then makes the Trendline "final". Note that you have the option to keep all Trendlines or just show the final, in the settings.
So, the algorithm has three states for the Trendlines:
Initial: not tested, meaning price hasn't yet broken through it and closed a candle beyond it, to cause a re-adjustment of this Trendline.
Broken: a candle hard closed (opened and closed) across it but still, the direction of the trend is maintained with a new Trendline from the same origin – could be replaced (or kept on the chart as a "backside", which is what we call a broken Trendline to be tested from the opposite side) with a new Trendline from the same origin, to the newest candle that caused the break to happen, as then it becomes the new second point of that Trendline.
Final: a candle hard closed (opened and closed) across it and can't draw a new Trendline from the same origin maintaining the direction of the trend (so an uptrend becomes a downtrend or a downtrend becomes an uptrend at this point, which is not allowed). This marks the end of the Trendline adjustment for that origin.
To summarize the Trendlines algorithm, imagine starting from a candle and drawing the Trendline, then keep re-adjusting it to make its slope less and less, till it becomes a horizontal line. That's the final state.
Here is a step-by-step scenario to demonstrate the algorithm:
Notice how first an Uptrend (green ray) is drawn between point A origin pivot (picked by our smart algorithm) and point B, both marked by green arrows:
Uptrend then turned into backside (where it flips from diagonal support to resistance where liquidity potentially resides):
Then a new uptrend is drawn from the same point A origin pivot to a new point B matching the filters in settings.
Finally, it turns also into a backside and is considered final because no more uptrends could be drawn from the same point A origin point.
Unlike traditional Trendline tools, this indicator takes into account numerous rules for each candlestick to determine valid support and resistance levels, which act as liquidity zones.
Unlike conventional Trendline tools, this indicator allows the user to define the pivot point left and right length to capture the proper ones as origins, then automatically recognizes and extends lines from them as liquidity zones where a reaction is expected. Moreover, the indicator monitors those Trendlines in real-time to switch them from buying to selling zones, and vice-versa, as the price structure changes.
Features
Log vs. Linear scale switch to show different Trendlines accordingly. When updating the Trendlines, or deciding whether Touches/Hard Closes are met, it makes a difference.
Ability to show all forms of Trendlines, final Trendlines or just backside Trendlines.
Why is it used?
For experienced traders, it offers the advantage of time efficiency, while new traders can bypass the steep learning curve of drawing Trendlines manually, which could practically be drawn between any two candlesticks on the chart (many variations).
🔶 MARKET STRUCTURE BREAKOUT (MSB)
The Market Structure Breakouts (MSB) tool is a trading tool that detects specific patterns on trading charts and provides ‘take profit’ regions based on the extended direction of the identified pattern. A breakout is a potential trading opportunity that presents itself when an asset's price moves away from a zone of accumulation (i.e. above a resistance level or below a support level) on increasing volume. The most famous form of market structure breakout is double/triple tops/bottoms, or what is referred to as W or M breakouts.
See this example below of how our MSB smart algorithm picked the local bottom of INDEX:BTCUSD
Here is a step-by-step scenario to demonstrate the algorithm:
First, the algorithm picks the pivot points according to our Machine Learning (ML) model, which uses Average True Range (ATR) and Moving Averages of various types to decide. It will then signal a Market Structure Breakout (MSB):
You may either short (sell) this MSB towards the targets (dotted green lines) and/or buy (long) at the targets (dotted green lines). Usually, these targets provide scalp moves, according to our model, but they may also act as strong reversal points on the chart.
Unlike standard indicators, the MSB tool identifies patterns that may not appear in every time frame due to specific conditions that need to be met, including Average True Range (ATR) and Moving Averages at the time of creation. Once these patterns are identified, the tool gives ‘take profit’ regions in the direction of the trading pattern and even allows for trading in the opposite direction (contrarian/counter-trend scalps) once those regions are reached. A confirmed breakout has the potential to drive the price to these specific targets, calculated based on our Machine Learning (ML) model. The Targets are the measured moves placed from the breakout point.
Features
Log vs. Linear scale switch to show different MSBs accordingly based on the ratios.
Detects trading patterns with specific conditions.
Ability to specify how sensitive the pivot points are for capturing market structure breakouts.
Provides take profit regions in the extended direction of the pattern.
Allows for versatile trading styles by permitting trades in the opposite direction (contrarian or counter-trend) once the take profit region is reached.
Highlights 2 levels of interest for potential trade initiation (or as targets of the MSB move).
🔶 ORDER BLOCK (OB) and REVERSAL ORDER BLOCK (ROB)
Before diving deeper into OBs and ROBs, you may consider the following chart for a general understanding of price ladders, and how they break. This is a bearish price ladder leaving Lower Lows and Lower Highs after an initial Low and High (L->H->LL->LH). Bullish ladders are the opposite (H->L->HH->HL).
In this bearish ladder case, notice the numbers representing the highs made (being lower). While this is a clean structure, markets don't always create such clean ladders, but you may switch to a higher timeframe to see it in a clearer form (usually, you will be able to spot it there).
In SMC or ICT concepts, the "Break Of Structure (BOS)" is pretty much creating a new lower low (LL) for the bearish ladder (and the creation of a higher high (HH) for the bullish ladder). By doing so, markets are grabbing liquidity below these levels and could either continue the ladder or stop/flip it. This gives you the context of how the ladder prints.
Price usually ends the ladder with a "Change of Character (CHoCH)", which represents a BOS (to grab liquidity) followed by an aggressive move in the opposite direction, which could lead the market to close the gaps and balance out. It is considered a good practice to then target liquidity in the opposite direction when a CHoCH happens, meaning for a bearish ladder you may target the pivots marked by 3, 2 and 1 at the top (start of the ladder).
Now we move to Order Blocks (OBs) and Reversal Order Blocks (ROBs). Think of them as sniper zones or micro ladders inside the bigger ladder/structure.
Order Blocks are usually used as zones of support and resistance on a trading chart where liquidity is present, or what some traders call "potential institutional interest zones". Order Blocks can be observed at the beginning of these strong moves of BOS or the CHoCH, leaving behind a zone (one or more candles) to be revisited later to balance the market. Therefore, these are interesting levels to place Limit/Market orders (sell the peaks or buy the valleys) instead of doing so at the swing highs or swing lows of the ladder (where BOS or CHoCH happened). The idea here is that the price could go deep into the ladder's step (peak or valley), and by doing so, it usually goes to these zones.
A bullish Order Block (Valley-OB) is the last bearish candle of a downtrend before a sequence of bullish candles (thus forming a "Valley"). A bearish Order Block (Peak-OB) is the last bullish candle of an uptrend before a sequence of bearish candles (thus forming a "Peak"). Our indicator captures the full range zones of the OB meaning not only the last candle but the sequence of same-type candles immediately next to it, which creates a zone, thus the name "OB/ROB Zone". Not only does the tool mark those levels on the chart, but it also has a smart tracking algorithm to remove the appropriate levels dynamically. It will monitor, candle by candle, what is happening to all the OBs/ROBs, and update them according to how they are being tested/visited (eg. weak testing being a touch, and strong testing being a touch of the same colour candle).
Bullish Valley-OB:
Bearish Peak-OB:
The indicator follows our concept of "Zone Activation" to determine whether to mark zones with dashed or solid lines.
If we take a bearish Peak-OB as an example, notice how it first gets drawn with a dashed red line (as the algorithm monitors how far the price moved away from the zone):
As price moves away (distance based on our Machin Learning (ML) model), it turns into solid lines:
Some people prefer to enter market orders or limit (pending) orders close to the zone, while others wait for it to hit. You may wait for these zones to turn into solid lines (meaning that the price made a decent move away from it before revisiting it). It depends on your trading strategy.
When Order Block (OB) zones break instead of holding the ladder, they turn into what we call Reversal Order Blocks (ROB); our algorithm of flipping these zones where price could react from the other side of the OB. Our algorithm monitor and highlight the most suitable ones to trade, based on +30 conditions and variables by our Machine Learning (ML) models. Examples of ROBs in the SMC or ICT trading community are a "Breaker Block", a "Mitigation Block" or a "Unicorn Setup". However, our algorithm filters the zones based on many factors such as ratios of price movement before, inside and after these zones, along with many other factors.
The algorithm monitors the ratios of how price moved into and away from the OB/ROB, as well as the type of move happening, to then filter the ones that are considered of high probability to break/not do a reaction.
A bullish Valley-OB (green) turns into a bearish Valley-ROB (neon red) where you may short (sell), while a bearish Peak-OB (red) turns into a bullish Peak-ROB (neon green) where you may long (buy).
Example of a bullish Valley-OB that turned into a bearish Valley-ROB:
Features
Log vs. Linear scale switch to show OBs/ROBs accordingly based on the ratios and the price action around these zones (before and after creation).
Uses our Machine Learning (ML) model to determine relevant Order Blocks (OBs) to show or hide based on price action.
Considers distribution and accumulation candles to find relevant Order Blocks.
Various types of triggers to mark those Order Blocks and their zones: breakout, close, hard close (open and close) or full close (low, high, open and close).
Monitors the 1:1 expansion of price from key areas of interest, which would change the importance of the zones through our concept of “Zone Activation”.
Allows for customization in the settings to display different types of Order Blocks (e.g., tested or untested).
Marking and invalidating levels based on many variables, including single or multiple candle zones, touching/closing beyond specific levels, weak/strong testing criteria, price tolerance % (near a level), and many more.
Provides color-coded visual representation for easier interpretation.
Why is it used?
Order Blocks (OB) and Reversal Order Blocks (ROB) represent the building blocks of price ladders, in conjunction with Swing Highs and Swing Lows. By identifying where liquidity is potentially present, they become common targets for big market players. Additionally, they provide clear invalidation points based on various types of candle closes, such as hard closes or simply a candle close.
One strategy that could be used is to open positions at these OB or ROB Levels as long as the chart maintains the trend (ladder), for a potentially higher win rate (or against it for a quick scalp). Be mindful of the breaking of a ladder or the building of a new one. A ladder breaks with a hard close (open and close) of a candle across the closest two levels; a ladder builds by not breaking back down across the levels it has tested. By definition, strong ladders will have a few untested levels and come back to wick them but still retain the structure of the laddering direction (trending with Lower Lows + Lower Highs or Higher Lows + Higher Highs).
🔶 COMBINING ALL TOOLS
In summary, Trendlines could be great tools to give you a general context of whether the price is laddering up or down. Once you spot the ladder, your goal is to either trade in its direction (not to go against the trend) or to counter-trend trade (contrarian). To do so, you could use the MSB tool to spot these BOS/CHoCH. And to give you more precise entries, you may rely on the OB/ROB zones which usually mesh over the ladder, to provide a sniper entry!
🔶 RISK DISCLAIMER
Trading is risky, and most day traders lose money. The risk of loss in trading can be substantial. Decisions to buy, sell, hold or trade in securities, commodities and other investments involve risk and are best made based on the advice of qualified financial professionals. Past performance does not guarantee future results. All content is to be considered hypothetical, selected after the fact, in order to demonstrate our product and should not be construed as financial advice. You should therefore carefully consider whether such trading is suitable for you in light of your financial condition.
Trendlines [TradesAI]What is it?
This indicator allows the user to pick any Candle (preferably a Pivot, for better results) to draw the most relevant Trendlines from it as Origin, while keeping track of candle closes across these Trendlines to adjust or invalidate accordingly.
It allows for up to 2 Origins to be picked on chart. Remember to pick a Bullish candle to draw Downtrends, and a Bearish candle to draw Uptrends. The algorithm will draw the most suitable Active Trendlines from those Origin points.
How does it do it?
The indicator takes the Origin point as the first point of the Trendline, then starts looking for the immediate next same-type candle (Bullish to Bullish or Bearish to Bearish), to draw the Trendline between the Origin candle and this newer candle.
An Uptrend is a ray connecting two Bearish candles, as long as the second candle has a Low higher than the Low of the Origin (first) candle. A Downtrend is a ray connecting two Bullish candles, as long as the second candle has a High lower than the High of the Origin (first) candle.
Upon drawing, the indicator then starts monitoring and adjusting this Trendline, by keeping the Origin always the same, but changing the second point. The goal is to keep reducing the slope of the Trendline till it is at 0 degrees (horizontal line). That then makes the Trendline "Final".
So, the algorithm has 3 States for the Trendlines:
Initial: not tested, meaning price hasn't yet broken through it and closed a candle beyond it, to cause a re-adjustment of this Trendline.
Broken: candle Hard Closed (its Open and Close) across it but still the direction of the Trend is maintained with a new Trendline from the same Origin – could be replaced (or kept on chart as "Backside", which is what we call a Broken Trendline to be tested from the opposite side) with a new Trendline from the same Origin, to the newest candle that caused the break to happen, as then it becomes the new second point of that trendline.
Final: candle Hard Closed across it and can't draw a new Trendline from the same Origin maintaining the direction of the Trend (so an uptrend becomes a downtrend or a downtrend becomes an uptrend at this point, which is not allowed). This marks the end of Trendline adjustment for that Origin.
To summarize the algorithm, imagine starting from a candle and drawing the trendline, then keep re-adjusting it to make its slope less and less, till it becomes a horizontal line. That's the final state.
Unlike traditional trendline tools, this indicator takes into account numerous rules for each candlestick to determine valid support and resistance levels, which act as Liquidity Zones.
What does it do differently?
Unlike conventional trendline tools, this indicator allows the user to pick the Pivot point as Origin, then automatically recognizes and extends lines from them as Liquidity Zones where a reaction is expected. Moreover, the indicator monitors those trendlines in real-time to switch them from Buying to Selling zones, and vice-versa, as price structure changes.
Features
Log vs. Linear scale switch to show different trendlines accordingly. When updating the Trendlines, or deciding whether Touches/Hard Closes are met, it makes a difference.
Ability to show all forms of Trendlines, Final Trendlines or just Backside Trendlines.
Why is it used?
For experienced traders, it offers the advantage of time-efficiency, while new traders can bypass the steep learning curve of drawing trendlines manually, which could practically be drawn between any two candlesticks on the chart (unlimited variations).
Defensive Nexus ShieldIndicator: Defensive Nexus Shield , capturing profits in the breakout trend.
Defensive Nexus Shield is a trend signal and support resistance display. Identify the short-term bullish and bearish defensive area through the effective extreme value of bulls and bears, and trigger trading opportunities when there are characteristics of breaking through the defensive area.
Usage：
Signal direction: "B" means that the bulls attacked and the bears failed, and entered a bullish trend. "S" means that the bears attacked and the bulls failed, entering a bearish trend.
Defense point of bulls and bears: "Blue line" represents the bearish defense line. The "green line" represents the bullish defensive line. The "purple line" represents the junction of bulls and bears.
Tip I：
Trend signal. When the signal "B" appears, it means that the bulls are attacking, and the market is bullish. Please refer to the signal for corresponding operations.
Tip II：
Breakout signal. After the trend signal appears, if the trend is confirmed, it will continue to enter the breakthrough signal.
Take the bull signal as an example. When B appears, the price continues to rise and breaks through the blue line, the bearish defense line, which triggers the bullish breakthrough signal. At this time, the bulls will strengthen. Provide signal reference for traders who do short-term breakthrough transactions.
*The signals in the indicators are for reference only and not intended as investment advice. Past performance of a strategy is not indicative of future earnings results.
Update - 2023.09.05
Optimize the alarm function. If you need to monitor the "B" or "S" signal, when creating an alarm, set the condition bar to:
Defensive Nexus Shield --> "B" or "S" --> Crossing Up --> value -> 0.5
KD Momentum MatrixI believe many traders think that fluctuation is very troublesome. The money earned in the trends is easily lost in the fluctuation. Because it is hard to find the high and low points of range.
Indicator: KD Momentum Matrix is the best choice for analyzing fluctuation, with potential volatility reminder.
KD Momentum Matrix is not only a momentum indicator, but also a short-term indicator. It divides the movement of the candle into long and short term trends, as well as bullish and bearish momentum. It identifies the points where the bullish and bearish momentum increases and weakens, and effectively capture profits.
💠Usage：
Potential volatility reminder:
"strong" represents an increase in potential volatility, indicating that the fluctuation of the candles may increase in the future.
"weak" represents a decrease in potential volatility, indicating that the fluctuation of the candles may decrease in the future.
Momentum column:
·The short-term momentum column, the "green and red columns", represents the short-term bullish and bearish momentum, and is the main reference feature of this indicator.
·Long term momentum columns, known as "dark green and purple columns", represent long-term bullish and bearish momentum and serve as auxiliary reference feature.
Note: Long and short term momentum columns usually have the same direction, and in rare cases, they may deviate. Sometimes there may be overlapping long and short term columns. The reference bullish and bearish directions are consistent regardless of the long and short term.
🎈Tip I：
When there is a potential volatility reminder: "weak" or "strong", it is important to note that there may be something different on amplitude of fluctuation in the future. If you have a position, you need to think new about the direction of your position.
🎈Tip II：
Taking the main reference feature - the short-term momentum column as an example, when the momentum column changes from red to green, it indicates short-term bullishness, and there may be a small upward trend. If the price happens to be near the bottom of the visible range at this time, consider executing a round of opening long positions or closing short positions.
When holding a long position, the bearish signal indicated by the momentum bar is used for departure, i.e. the momentum bar changes from green to red.
🎈Advanced tip I：
Deviation. The long and short term momentum columns are mostly consistent, but occasionally there may be deviations, indicating intense competition between bulls and bears. In the short term, it is recommended not to engage in trading because of its high uncertainty.
🎈Advanced tip II：
Volatility indicators can also be used in trends, but it is important to remember the idea of following the trend. For example, when there is a callback during an upward trend, we choose to buy or add a long position when the momentum bar becomes a long signal.
*The signals in the indicators are for reference only and not intended as investment advice. Past performance of a strategy is not indicative of future earnings results.
Update -
Optimize the alarm function. If you need to monitor the "strong " or "weak" signal, when creating an alarm, set the condition bar to:
KD Momentum Matrix --> "strong " or "weak" --> Crossing Up --> value -> 1
Machine Learning: STDEV Oscillator [YinYangAlgorithms]This Indicator aims to fill a gap within traditional Standard Deviation Analysis. Rather than its usual applications, this Indicator focuses on applying Standard Deviation within an Oscillator and likewise applying a Machine Learning approach to it. By doing so, we may hope to achieve an Adaptive Oscillator which can help display when the price is deviating from its standard movement. This Indicator may help display both when the price is Overbought or Underbought, and likewise, where the price may face Support and Resistance. The reason for this is that rather than simply plotting a Machine Learning Standard Deviation (STDEV), we instead create a High and a Low variant of STDEV, and then use its Highest and Lowest values calculated within another Deviation to create Deviation Zones. These zones may help to display these Support and Resistance locations; and likewise may help to show if the price is Overbought or Oversold based on its placement within these zones. This Oscillator may also help display Momentum when the High and/or Low STDEV crosses the midline (0). Lastly, this Oscillator may also be useful for seeing the spacing between the High and Low of the STDEV; large spacing may represent volatility within the STDEV which may be helpful for seeing when there is Momentum in the form of volatility.
Tutorial:
Above is an example of how this Indicator looks on BTC/USDT 1 Day. As you may see, when the price has parabolic movement, so does the STDEV. This is due to this price movement deviating from the mean of the data. Therefore when these parabolic movements occur, we create the Deviation Zones accordingly, in hopes that it may help to project future Support and Resistance locations as well as helping to display when the price is Overbought and Oversold.
If we zoom in a little bit, you may notice that the Support Zone (Blue) is smaller than the Resistance Zone (Orange). This is simply because during the last Bull Market there was more parabolic price deviation than there was during the Bear Market. You may see this if you refer to their values; the Resistance Zone goes to ~18k whereas the Support Zone is ~10.5k. This is completely normal and the way it is supposed to work. Due to the nature of how STDEV works, this Oscillator doesn’t use a 1:1 ratio and instead can develop and expand as exponential price action occurs.
The Neutral (0) line may also act as a Support and Resistance location. In the example above we can see how when the STDEV is below it, it acts as Resistance; and when it’s above it, it acts as Support.
This Neutral line may also provide us with insight as towards the momentum within the market and when it has shifted. When the STDEV is below the Neutral line, the market may be considered Bearish. When the STDEV is above the Neutral line, the market may be considered Bullish.
The Red Line represents the STDEV’s High and the Green Line represents the STDEV’s Low. When the STDEV’s High and Low get tight and close together, this may represent there is currently Low Volatility in the market. Low Volatility may cause consolidation to occur, however it also leaves room for expansion.
However, when the STDEV’s High and Low are quite spaced apart, this may represent High levels of Volatility in the market. This may mean the market is more prone to parabolic movements and expansion.
We will conclude our Tutorial here. Hopefully this has given you some insight into how applying Machine Learning to a High and Low STDEV then creating Deviation Zones based on it may help project when the Momentum of the Market is Bullish or Bearish; likewise when the price is Overbought or Oversold; and lastly where the price may face Support and Resistance in the form of STDEV.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Machine Learning: Optimal Length [YinYangAlgorithms]This Indicator aims to solve an issue that most others face; static lengths. This Indicator will scan lengths from the Min to Max setting (1 - 400 by default) to calculate which is the most Optimal Length in the current market condition. Almost every Indicator uses a length in some part of their calculation, and this length is usually adjustable via the Settings; however it is generally a static fixed length. Static non changing lengths may not always produce optimal results. As market conditions change generally the optimal length will too. For this reason we have created this indicator.
This Indicator will create a Neutral (Min - Max Length), Fast (Min - Mid Length ((Max - Min) / 2)) and Slow (Mid Length ((Max - Min) / 2) - Max Length). This allows you to understand which the Optimal Fast, Slow and Neutral lengths are within the given Mix and Max length settings.
This Indicator then plots these Optimal Lengths as an Oscillator which can then be used within ANOTHER Indicator as a Source within its Settings. Stand alone this Indicator may not prove all that useful, however when its Lengths are inputted into another Indicator it may prove very useful. This allows other Indicators to use the Optimal Length within its calculations from the Settings rather than relying on simply a fixed length. Unfortunately this results in users needing to manually plug the Optimal Length plots into the second Indicator; but it also allows for endless possibilities with applying Machine Learning Optimal Lengths within both Traditional and Non-Traditional Indicators and may give other Pine Coders an easy and effective way to add Machine Learning auto adjustable lengths within their already created Indicators.
The beautiful part about this Indicator is that aside from inputting the Optimal Length Plot into another Indicator, there is no manual updating needed. When the Optimal Length changes, the change will automatically reflect in the other Indicator without the need for you to manually adjust its length. This may be very useful with both time preservation, as well as if there is an automated strategy based upon said Indicator that now won’t need manual intervention.
Tutorial:
By default this is what the Machine Learning: Optimal Length Indicator looks like. It is simply a way of both Displaying and Plotting our current Optimal Length so that we may then use it as a source within ANOTHER Indicator. This will allow the automation of an Optimal Length to be updated, rather than needing any manual input from yourself (aside from set up).
For instance if you set the start length to 1 and the end length to 400 (default settings), it will scan to find the optimal Length setting between 1 and 400. This features 3 types of lengths:
Fast (Green Line): 1-199 (from start length to half way of total)
Slow (Red Line): 200 - 400 (mid way to end length)
Neutral (Blue Line): 1 - 400 (start to end length)
By breaking down the Optimal Length detection into these 3 different types, we can see how the Optimal Length compares and changes based on the lengths allotted to them and how performance changes.
For instance, you may notice that both the Fast and Slow Optimal Length didn’t change much in the example above; however the Neutral Optimal Length changed quite a bit. This is due to the fact that the Neutral is inclusive of all lengths available and may be considered the more accurate due to that. However, this doesn’t mean the Fast and Slow lengths aren’t important and should be used. They may be useful for seeing how something fairs in a Fast and Slow standpoint.
If you change your TimeFrame from 15 minute to 1 Day, you’ll notice that the Optimal Lengths gravitate towards their upper bounds:
199 is max for Fast, it’s at 195
400 is max for Slow, its at 393
400 is max for Neutral, its at 399
The Optimal Length may move up to its upper bounds on Higher Time Frames because there is a lot of price action and long term data being displayed. This may lead to higher lengths performing better in a profitability standpoint since its data is based on so far back and such drastic price movements.
Below we’re going to go through a few examples, including the code so you may reproduce the example and have an understanding of how versatile Inputting an Optimal Length as a source may be within Traditional Indicators.
Adding the Machine Learning: Optimal Length to another Indicator:
You may add the Optimal Length to another Indicator as shown in the example above. In the example we are adding the ‘Machine Learning: Optimal Length - Neutral’ to our Neutral Length within the Settings. The external Indicator needs to have the ability to input the Optimal Length as a Source, this way it can automatically change within the external Indicator when the Optimal Length Indicator changes its Optimal Length.
Please note you may get an error within an external Indicator that accepts the Length as a Source if you don’t select the Machine Learning: Optimal Length. For instance, if you use ‘Close’ within BTC/USDT the length used would be ~36,000. This length is too long and will throw an error.
For this reason, we will ensure the Max Length that may be used is 1000.
Please note, on lower Time Frames you may need to adjust the Max Length. For instance if 20k bar data is used, the Max Length ‘may’ fail to load when going by default Min: 1 and Max: 400. Generally with most pairs it will load if your TradingView subscription is Premium or greater; however if it is less there is a chance it may fail. If it fails for you too often please lower the Max Length Amount; or send us a message we can look into a fix for this.
*** If it fails to load, please try removing the external Indicator and re-adding it and adding the Lengths back as a Source within the Settings. Sometimes it fails, but re-adding may fix it. If it keeps failing afterwards, reduce the Max Length Amount as mentioned above. ***
Simple Moving Average:
In this example above have the Fast, Slow and Neutral Optimal Length formatted as a Slow Moving Average. The first example is on the 15 minute Time Frame and the second is on the 1 Day Time Frame, demonstrating how the length changes based on the Time Frame and the effects it may have.
Here is the code for the example Indicator shown above. This example shows how you may use the Optimal Length as a Source and then use that Optimal Length and plot it as a Simple Moving Average:
//@version=5
indicator("Optimal Length - Backtesting - MA", overlay=true, max_bars_back=5000)
outputType = input.string("All", "Output Type", options= )
lengthSource = input.source(close, "Neutral Length")
lengthSource_fast = input.source(close, "Fast Length")
lengthSource_slow = input.source(close, "Slow Length")
showNeutral = outputType == "Neutral" or outputType == "Fast + Neutral" or outputType == "Slow + Neutral" or outputType == "All"
showFast = outputType == "Fast" or outputType == "Fast + Neutral" or outputType == "Fast + Slow" or outputType == "All"
showSlow = outputType == "Slow" or outputType == "Slow + Neutral" or outputType == "Fast + Slow" or outputType == "All"
//Neutral
optimalLength = math.min(math.max(math.round(lengthSource), 1), 1000)
optimalMA = ta.sma(close, optimalLength)
//Fast
optimalLength_fast = math.min(math.max(math.round(lengthSource_fast), 1), 1000)
optimalMA_fast = ta.sma(close, optimalLength_fast)
//Slow
optimalLength_slow = math.min(math.max(math.round(lengthSource_slow), 1), 1000)
optimalMA_slow = ta.sma(close, optimalLength_slow)
plot(showNeutral ? optimalMA : na, color=color.blue)
plot(showFast ? optimalMA_fast : na, color=color.green)
plot(showSlow ? optimalMA_slow : na, color=color.red)
Bollinger Bands:
In the two examples above for Bollinger Bands we have first the 15 Minute Time Frame and then the 1 Day Time Frame. As described above in ‘Adding the Machine Learning: Optimal Length to another Indicator’ sometimes it may fail to load, for this reason in the 15 Minute it was reduced to a max of 300 Length.
Bollinger Bands are a way to see a Simple Moving Average (SMA) that then uses Standard Deviation to identify how much deviation has occurred. This Deviation is than Added and Subtracted from the SMA to create the Bollinger Bands which help Identify possible movement zones that are ‘within range’. This may mean that the price may face Support / Resistance when it reaches the Outer / Inner bounds of the Bollinger Bands. Likewise, it may mean the Price is ‘Overbought’ when outside and above or ‘Underbought’ when outside and below the Bollinger Bands.
By applying All 3 different types of Optimal Lengths towards a Traditional Bollinger Band calculation we may hope to see different ranges of Bollinger Bands and how different lookback lengths may imply possible movement ranges on both a Short Term, Long Term and Neutral perspective. By seeing these possible ranges you may have the ability to identify more levels of Support and Resistance over different lengths and Trading Styles.
Below is the code for the Bollinger Bands example above:
//@version=5
indicator("Optimal Length - Backtesting - Bollinger Bands", overlay=true, max_bars_back=5000)
outputType = input.string("All", "Output Type", options= )
lengthSource = input.source(close, "Neutral Length")
lengthSource_fast = input.source(close, "Fast Length")
lengthSource_slow = input.source(close, "Slow Length")
showNeutral = outputType == "Neutral" or outputType == "Fast + Neutral" or outputType == "Slow + Neutral" or outputType == "All"
showFast = outputType == "Fast" or outputType == "Fast + Neutral" or outputType == "Fast + Slow" or outputType == "All"
showSlow = outputType == "Slow" or outputType == "Slow + Neutral" or outputType == "Fast + Slow" or outputType == "All"
mult = 2.0
src = close
neutralColor = color.blue
slowColor = color.red
fastColor = color.green
//Neutral
optimalLength = math.min(math.max(math.round(lengthSource), 1), 1000)
optimalMA = ta.sma(close, optimalLength)
//Fast
optimalLength_fast = math.min(math.max(math.round(lengthSource_fast), 1), 1000)
optimalMA_fast = ta.sma(close, optimalLength_fast)
//Slow
optimalLength_slow = math.min(math.max(math.round(lengthSource_slow), 1), 1000)
optimalMA_slow = ta.sma(close, optimalLength_slow)
//Neutral Bollinger Bands
dev = mult * ta.stdev(src, math.round(optimalLength))
upper = optimalMA + dev
lower = optimalMA - dev
plot(showNeutral ? optimalMA : na, "Neutral Basis", color=color.new(neutralColor, 0))
p1 = plot(showNeutral ? upper : na, "Neutral Upper", color=color.new(neutralColor, 50))
p2 = plot(showNeutral ? lower : na, "Neutral Lower", color=color.new(neutralColor, 50))
fill(p1, p2, title = "Neutral Background", color=color.new(neutralColor, 96))
//Slow Bollinger Bands
dev_slow = mult * ta.stdev(src, math.round(optimalLength_slow))
upper_slow = optimalMA_slow + dev_slow
lower_slow = optimalMA_slow - dev_slow
plot(showFast ? optimalMA_slow : na, "Slow Basis", color=color.new(slowColor, 0))
p1_slow = plot(showFast ? upper_slow : na, "Slow Upper", color=color.new(slowColor, 50))
p2_slow = plot(showFast ? lower_slow : na, "Slow Lower", color=color.new(slowColor, 50))
fill(p1_slow, p2_slow, title = "Slow Background", color=color.new(slowColor, 96))
//Fast Bollinger Bands
dev_fast = mult * ta.stdev(src, math.round(optimalLength_fast))
upper_fast = optimalMA_fast + dev_fast
lower_fast = optimalMA_fast - dev_fast
plot(showSlow ? optimalMA_fast : na, "Fast Basis", color=color.new(fastColor, 0))
p1_fast = plot(showSlow ? upper_fast : na, "Fast Upper", color=color.new(fastColor, 50))
p2_fast = plot(showSlow ? lower_fast : na, "Fast Lower", color=color.new(fastColor, 50))
fill(p1_fast, p2_fast, title = "Fast Background", color=color.new(fastColor, 96))
Donchian Channels:
Above you’ll see two examples of Machine Learning: Optimal Length applied to Donchian Channels. These are displayed with both the 15 Minute Time Frame and the 1 Day Time Frame.
Donchian Channels are a way of seeing potential Support and Resistance within a given lookback length. They are a way of withholding the High’s and Low’s of a specific lookback length and looking for deviation within this length. By applying our Fast, Slow and Neutral Machine Learning: Optimal Length to these Donchian Channels way may hope to achieve a viable range of High’s and Low’s that one may use to Identify Support and Resistance locations for different ranges of Optimal Lengths and likewise potentially different Trading Strategies.
The code to reproduce these Donchian Channels as displayed above is so:
//@version=5
indicator("Optimal Length - Backtesting - Donchian Channels", overlay=true, max_bars_back=5000)
outputType = input.string("All", "Output Type", options= )
lengthSource = input.source(close, "Neutral Length")
lengthSource_fast = input.source(close, "Fast Length")
lengthSource_slow = input.source(close, "Slow Length")
showNeutral = outputType == "Neutral" or outputType == "Fast + Neutral" or outputType == "Slow + Neutral" or outputType == "All"
showFast = outputType == "Fast" or outputType == "Fast + Neutral" or outputType == "Fast + Slow" or outputType == "All"
showSlow = outputType == "Slow" or outputType == "Slow + Neutral" or outputType == "Fast + Slow" or outputType == "All"
mult = 2.0
src = close
neutralColor = color.blue
slowColor = color.red
fastColor = color.green
//Neutral
optimalLength = math.min(math.max(math.round(lengthSource), 1), 1000)
optimalMA = ta.sma(close, optimalLength)
//Fast
optimalLength_fast = math.min(math.max(math.round(lengthSource_fast), 1), 1000)
optimalMA_fast = ta.sma(close, optimalLength_fast)
//Slow
optimalLength_slow = math.min(math.max(math.round(lengthSource_slow), 1), 1000)
optimalMA_slow = ta.sma(close, optimalLength_slow)
//Neutral Donchian Channels
lower_dc = ta.lowest(optimalLength)
upper_dc = ta.highest(optimalLength)
basis_dc = math.avg(upper_dc, lower_dc)
plot(showNeutral ? basis_dc : na, "Donchain Channel - Neutral Basis", color=color.new(neutralColor, 0))
u = plot(showNeutral ? upper_dc : na, "Donchain Channel - Neutral Upper", color=color.new(neutralColor, 50))
l = plot(showNeutral ? lower_dc : na, "Donchain Channel - Neutral Lower", color=color.new(neutralColor, 50))
fill(u, l, color=color.new(neutralColor, 96), title = "Donchain Channel - Neutral Background")
//Fast Donchian Channels
lower_dc_fast = ta.lowest(optimalLength_fast)
upper_dc_fast = ta.highest(optimalLength_fast)
basis_dc_fast = math.avg(upper_dc_fast, lower_dc_fast)
plot(showFast ? basis_dc_fast : na, "Donchain Channel - Fast Neutral Basis", color=color.new(fastColor, 0))
u_fast = plot(showFast ? upper_dc_fast : na, "Donchain Channel - Fast Upper", color=color.new(fastColor, 50))
l_fast = plot(showFast ? lower_dc_fast : na, "Donchain Channel - Fast Lower", color=color.new(fastColor, 50))
fill(u_fast, l_fast, color=color.new(fastColor, 96), title = "Donchain Channel - Fast Background")
//Slow Donchian Channels
lower_dc_slow = ta.lowest(optimalLength_slow)
upper_dc_slow = ta.highest(optimalLength_slow)
basis_dc_slow = math.avg(upper_dc_slow, lower_dc_slow)
plot(showSlow ? basis_dc_slow : na, "Donchain Channel - Slow Neutral Basis", color=color.new(slowColor, 0))
u_slow = plot(showSlow ? upper_dc_slow : na, "Donchain Channel - Slow Upper", color=color.new(slowColor, 50))
l_slow = plot(showSlow ? lower_dc_slow : na, "Donchain Channel - Slow Lower", color=color.new(slowColor, 50))
fill(u_slow, l_slow, color=color.new(slowColor, 96), title = "Donchain Channel - Slow Background")
Envelopes / Envelopes Adjusted:
Envelopes are an interesting one in the sense that they both may be perceived as useful; however we deem that with the use of an ‘Optimal Length’ that the ‘Envelopes Adjusted’ may work best. We will start with examples of the Traditional Envelope then showcase the Adjusted version.
Envelopes:
As you may see, a Traditional form of Envelopes even produced with our Machine Learning: Optimal Length may not produce optimal results. Unfortunately this may occur with some Traditional Indicators and they may need some adjustments as you’ll notice with the ‘Envelopes Adjusted’ version. However, even without the adjustments, these Envelopes may be useful for seeing ‘Overbought’ and ‘Oversold’ locations within a Machine Learning: Optimal Length standpoint.
Envelopes Adjusted:
By adding an adjustment to these Envelopes, we may hope to better reflect out Optimal Length within it. This is caused by adding a ratio reflection towards the current length of the Optimal Length and the max Length used. This allows for the Fast and Neutral (and potentially Slow if Neutral is greater) to achieve a potentially more accurate result.
Envelopes, much like Bollinger Bands are a way of seeing potential movement zones along with potential Support and Resistance. However, unlike Bollinger Bands which are based on Standard Deviation, Envelopes are based on percentages +/- from the Simple Moving Average.
The code used to reproduce the example above is as follows:
//@version=5
indicator("Optimal Length - Backtesting - Envelopes", overlay=true, max_bars_back=5000)
outputType = input.string("All", "Output Type", options= )
displayType = input.string("Envelope Adjusted", "Display Type", options= )
lengthSource = input.source(close, "Neutral Length")
lengthSource_fast = input.source(close, "Fast Length")
lengthSource_slow = input.source(close, "Slow Length")
showNeutral = outputType == "Neutral" or outputType == "Fast + Neutral" or outputType == "Slow + Neutral" or outputType == "All"
showFast = outputType == "Fast" or outputType == "Fast + Neutral" or outputType == "Fast + Slow" or outputType == "All"
showSlow = outputType == "Slow" or outputType == "Slow + Neutral" or outputType == "Fast + Slow" or outputType == "All"
mult = 2.0
src = close
neutralColor = color.blue
slowColor = color.red
fastColor = color.green
//Neutral
optimalLength = math.min(math.max(math.round(lengthSource), 1), 1000)
optimalMA = ta.sma(close, optimalLength)
//Fast
optimalLength_fast = math.min(math.max(math.round(lengthSource_fast), 1), 1000)
optimalMA_fast = ta.sma(close, optimalLength_fast)
//Slow
optimalLength_slow = math.min(math.max(math.round(lengthSource_slow), 1), 1000)
optimalMA_slow = ta.sma(close, optimalLength_slow)
percent = 10.0
maxAmount = math.max(optimalLength, optimalLength_fast, optimalLength_slow)
//Neutral
k = displayType == "Envelope" ? percent/100.0 : (percent/100.0) / (optimalLength / maxAmount)
upper_env = optimalMA * (1 + k)
lower_env = optimalMA * (1 - k)
plot(showNeutral ? optimalMA : na, "Envelope - Neutral Basis", color=color.new(neutralColor, 0))
u_env = plot(showNeutral ? upper_env : na, "Envelope - Neutral Upper", color=color.new(neutralColor, 50))
l_env = plot(showNeutral ? lower_env : na, "Envelope - Neutral Lower", color=color.new(neutralColor, 50))
fill(u_env, l_env, color=color.new(neutralColor, 96), title = "Envelope - Neutral Background")
//Fast
k_fast = displayType == "Envelope" ? percent/100.0 : (percent/100.0) / (optimalLength_fast / maxAmount)
upper_env_fast = optimalMA_fast * (1 + k_fast)
lower_env_fast = optimalMA_fast * (1 - k_fast)
plot(showFast ? optimalMA_fast : na, "Envelope - Fast Basis", color=color.new(fastColor, 0))
u_env_fast = plot(showFast ? upper_env_fast : na, "Envelope - Fast Upper", color=color.new(fastColor, 50))
l_env_fast = plot(showFast ? lower_env_fast : na, "Envelope - Fast Lower", color=color.new(fastColor, 50))
fill(u_env_fast, l_env_fast, color=color.new(fastColor, 96), title = "Envelope - Fast Background")
//Slow
k_slow = displayType == "Envelope" ? percent/100.0 : (percent/100.0) / (optimalLength_slow / maxAmount)
upper_env_slow = optimalMA_slow * (1 + k_slow)
lower_env_slow = optimalMA_slow * (1 - k_slow)
plot(showSlow ? optimalMA_slow : na, "Envelope - Slow Basis", color=color.new(slowColor, 0))
u_env_slow = plot(showSlow ? upper_env_slow : na, "Envelope - Slow Upper", color=color.new(slowColor, 50))
l_env_slow = plot(showSlow ? lower_env_slow : na, "Envelope - Slow Lower", color=color.new(slowColor, 50))
fill(u_env_slow, l_env_slow, color=color.new(slowColor, 96), title = "Envelope - Slow Background")
Hopefully these examples, including reproducing code, have given you some insight as to how useful this Machine Learning: Optimal Length may be and how another Indicator may easily modify their existing code to incorporate the usage of such Machine Learning: Optimal Length. We likewise will publish a Backtesting Indicator which incorporates all of the concepts we’ve gone over within here; in case you wish to take advantage of the Traditional Indicators mentioned above that allow the input of Machine Learning: Optimal Length and don’t wish to code them.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Backtest Strategy Optimizer AdapterBacktest Strategy Optimizer Adapter
With this library, you will be able to run one or multiple backtests with different variables (combinations). For example, you can run 100 backtests of Supertrend at once with an increment factor of 0.1. This way, you can easily fetch the most profitable settings and apply them to your strategy.
To get a better understanding of the code, you can check the code below.
Single backtest results
= backtest.results(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Add backtest results to a table
backtest.table(initial_capital, profit_and_loss, open_balance, winrate, entries, exits, wins, losses, backtest_table_position, backtest_table_margin, backtest_table_transparency, backtest_table_cell_color, backtest_table_title_cell_color, backtest_table_text_color)
Backtest result without chart labels
= backtest.run(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Backtest result profit
profit = backtest.profit(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Backtest result winrate
winrate = backtest.winrate(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Start Date
You can set the start date either by using a timestamp or a number that refers to the number of bars back.
Stop Loss / Take Profit Issue
Unfortunately, I did not manage to achieve 100% accuracy for the take profit and stop loss. The original TradingView backtest can stop at the correct position within a bar using the strategy.exit stop and limit variables. However, it seems unachievable with a crossunder/crossover function in PineScript unless it is calculated on every tick (which would make the backtesting results invalid). So far, I have not found a workaround, and I would be grateful if someone could solve this issue, if it is even possible. If you have any solutions or fixes, please let me know!
Multiple Backtest Results / Optimizer
You can run multiple backtests in a single strategy or indicator, but there are certain requirements for placing the correct code in the right way. To view examples of running multiple backtests, you can refer to the links provided in the updates I posted below. In the samples I have also explained how you can auto-generate code for your backtest strategy.
