Dynamic Volatility Differential Model (DVDM)The Dynamic Volatility Differential Model (DVDM) is a quantitative trading strategy designed to exploit the spread between implied volatility (IV) and historical (realized) volatility (HV). This strategy identifies trading opportunities by dynamically adjusting thresholds based on the standard deviation of the volatility spread. The DVDM is versatile and applicable across various markets, including equity indices, commodities, and derivatives such as the FDAX (DAX Futures).
Key Components of the DVDM:
1. Implied Volatility (IV):
The IV is derived from options markets and reflects the market’s expectation of future price volatility. For instance, the strategy uses volatility indices such as the VIX (S&P 500), VXN (Nasdaq 100), or RVX (Russell 2000), depending on the target market. These indices serve as proxies for market sentiment and risk perception (Whaley, 2000).
2. Historical Volatility (HV):
The HV is computed from the log returns of the underlying asset’s price. It represents the actual volatility observed in the market over a defined lookback period, adjusted to annualized levels using a multiplier of \sqrt{252} for daily data (Hull, 2012).
3. Volatility Spread:
The difference between IV and HV forms the volatility spread, which is a measure of divergence between market expectations and actual market behavior.
4. Dynamic Thresholds:
Unlike static thresholds, the DVDM employs dynamic thresholds derived from the standard deviation of the volatility spread. The thresholds are scaled by a user-defined multiplier, ensuring adaptability to market conditions and volatility regimes (Christoffersen & Jacobs, 2004).
Trading Logic:
1. Long Entry:
A long position is initiated when the volatility spread exceeds the upper dynamic threshold, signaling that implied volatility is significantly higher than realized volatility. This condition suggests potential mean reversion, as markets may correct inflated risk premiums.
2. Short Entry:
A short position is initiated when the volatility spread falls below the lower dynamic threshold, indicating that implied volatility is significantly undervalued relative to realized volatility. This signals the possibility of increased market uncertainty.
3. Exit Conditions:
Positions are closed when the volatility spread crosses the zero line, signifying a normalization of the divergence.
Advantages of the DVDM:
1. Adaptability:
Dynamic thresholds allow the strategy to adjust to changing market conditions, making it suitable for both low-volatility and high-volatility environments.
2. Quantitative Precision:
The use of standard deviation-based thresholds enhances statistical reliability and reduces subjectivity in decision-making.
3. Market Versatility:
The strategy’s reliance on volatility metrics makes it universally applicable across asset classes and markets, ensuring robust performance.
Scientific Relevance:
The strategy builds on empirical research into the predictive power of implied volatility over realized volatility (Poon & Granger, 2003). By leveraging the divergence between these measures, the DVDM aligns with findings that IV often overestimates future volatility, creating opportunities for mean-reversion trades. Furthermore, the inclusion of dynamic thresholds aligns with risk management best practices by adapting to volatility clustering, a well-documented phenomenon in financial markets (Engle, 1982).
References:
1. Christoffersen, P., & Jacobs, K. (2004). The importance of the volatility risk premium for volatility forecasting. Journal of Financial and Quantitative Analysis, 39(2), 375-397.
2. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
3. Hull, J. C. (2012). Options, Futures, and Other Derivatives. Pearson Education.
4. Poon, S. H., & Granger, C. W. J. (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature, 41(2), 478-539.
5. Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
This strategy leverages quantitative techniques and statistical rigor to provide a systematic approach to volatility trading, making it a valuable tool for professional traders and quantitative analysts.
Educational
Market Regime DetectorMarket Regime Detector
The Market Regime Detector is a tool designed to help traders identify and adapt to the prevailing market environment by analyzing price action in relation to key macro timeframe levels. This indicator categorizes the market into distinct regimes—Bullish, Bearish, or Reverting—providing actionable insights to set trading expectations, manage volatility, and align strategies with broader market conditions.
What is a Market Regime?
A market regime refers to the overarching state or condition of the market at a given time. Understanding the market regime is critical for traders as it determines the most effective trading approach. The three main regimes are:
Bullish Regime:
Characterized by upward momentum where prices are consistently trending higher.
Trading strategies often focus on buying opportunities and trend-following setups.
Bearish Regime:
Defined by downward price pressure and declining trends.
Traders typically look for selling opportunities or adopt risk-off strategies.
Reverting Regime:
Represents a consolidation phase where prices move within a defined range.
Ideal for mean-reversion strategies or range-bound trading setups.
Key Features of the Market Regime Detector:
Dynamic Market Regime Detection:
Identifies the market regime based on macro timeframe high and low levels (e.g., weekly or monthly).
Provides clear and actionable insights for each regime to align trading strategies.
Visual Context for Price Levels:
Plots the macro high and low levels on the chart, allowing traders to visualize critical support and resistance zones.
Enhances understanding of volatility and trend boundaries.
Regime Transition Alerts:
Sends alerts only when the market transitions into a new regime, ensuring traders are notified of meaningful changes without redundant signals.
Alert messages include clear regime descriptions, such as "Market entered a Bullish Regime: Price is above the macro high."
Customizable Visualization:
Background colors dynamically adjust to the current regime:
Blue for Reverting.
Aqua for Bullish.
Fuchsia for Bearish.
Option to toggle high/low line plotting and background highlights for a tailored experience.
Volatility and Expectation Management:
Offers insights into market volatility by showing when price action approaches, exceeds, or reverts within macro timeframe levels.
Helps traders set realistic expectations and adjust their strategies accordingly.
Use Cases:
Trend Traders: Identify bullish or bearish regimes to capture sustained price movements.
Range Traders: Leverage reverting regimes to trade between defined support and resistance zones.
Risk Managers: Use macro high and low levels as dynamic stop-loss or take-profit zones to optimize trade management.
The Market Regime Detector equips traders with a deeper understanding of the market environment, making it an essential tool for informed decision-making and strategic planning. Whether you're trading trends, ranges, or managing risk, this indicator provides the clarity and insights needed to navigate any market condition.
Renko Chart EmulationRenko charts are a popular tool in technical analysis, known for their ability to filter out market noise and focus purely on price movements. Unlike traditional candlestick or bar charts, Renko charts are not time-based but are constructed using bricks that represent a fixed price movement. This makes them particularly useful for identifying trends and key levels of support and resistance. While Renko charts are commonly found on platforms with specialized charting capabilities, they can also be emulated in Pine Script as a line indicator.
The Renko emulation indicator in Pine Script calculates the movement of price based on a user-defined brick size. Whenever the price moves up or down by an amount equal to or greater than the brick size, a new level is plotted, indicating a shift in price direction. This approach helps traders visualize significant price moves without the distractions of smaller fluctuations. By plotting the Renko levels as a continuous line and coloring it based on direction, this indicator provides a clean and straightforward representation of market trends.
Traders can use this Renko emulation line to identify potential entry and exit points, as well as to confirm ongoing trends. The simplicity of Renko charts makes them a favorite among those who prefer a minimalist approach to technical analysis. However, it is essential to choose an appropriate brick size that aligns with the volatility of the trading instrument. A smaller brick size may result in frequent signals, while a larger one can smooth out the chart, focusing only on the most substantial price movements. This script offers a flexible solution for incorporating Renko-style analysis into any trading strategy.
Adaptive Sentiment-Volume MomentumThis is a simple breakout approach using ATR bands and an EMA filter. Test this strategy and let me know how it performs!
Correlation Coefficient Master TableThe Correlation Coefficient Master Table is a comprehensive tool designed to calculate and visualize the correlation coefficient between a selected base asset and multiple other assets over various time periods. It provides traders and analysts with a clear understanding of the relationships between assets, enabling them to analyze trends, diversification opportunities, and market dynamics. You can define key parameters such as the base asset’s data source (e.g., close price), the assets to compare against (up to six symbols), and multiple lookback periods for granular analysis.
The indicator calculates the covariance and normalizes it by the product of the standard deviations. The correlation coefficient ranges from -1 to +1, with +1 indicating a perfect positive relationship, -1 a perfect negative relationship, and 0 no relationship.
You can specify the lookback periods (e.g., 15, 30, 90, or 120 bars) to tailor the calculation to their analysis needs. The results are visualized as both a line plot and a table. The line plot shows the correlation over the primary lookback period (the Chart Length), which can be used to inspect a certain length close up, or could be used in conjunction with the table to provide you with five lookback periods at once for the same base asset. The dynamically created table provides a detailed breakdown of correlation values for up to six target assets across the four user-defined lengths. The table’s cells are formatted with rounded values and color-coded for easy interpretation.
This indicator is ideal for traders, portfolio managers, and market researchers who need an in-depth understanding of asset interdependencies. By providing both the numerical correlation coefficients and their visual representation, users can easily identify patterns, assess diversification strategies, and monitor correlations across multiple timeframes, making it a valuable tool for decision-making.
AVP 259 alertsits a mixture of indicators that merges the famous indicators in one single form to easily get explained with their study and mastery
SPX Lin Reg with SD -1 +1 -2 +2 -3 +3 (Extented)Linear regression for a given period with standard deviations -1 +1 -2 +2 -3 +3
EMA CHILL 9/20/50/100/200this is a ema of 9,20,50,100 and 200, ema stands for exponential moving average, which tells us price trends in the market, we can use it in any chart to find market trends,
EMA CHILL 9/20/50/100this is a ema of 9,20,50 and 100, ema stands for exponential moving average, which tells us price trends in the market, we can use it in any chart to find market trends,
Fibonacci Trend - Aynet1. Inputs
lookbackPeriod: Defines the number of bars to consider for calculating swing highs and lows. Default is 20.
fibLevel1 to fibLevel5: Fibonacci retracement levels to calculate price levels (23.6%, 38.2%, 50%, 61.8%, 78.6%).
useTime: Enables or disables time-based Fibonacci projections.
riskPercent: Defines the percentage of risk for trading purposes (currently not used in calculations).
2. Functions
isSwingHigh(index): Identifies a swing high at the given index, where the high of that candle is higher than both its previous and subsequent candles.
isSwingLow(index): Identifies a swing low at the given index, where the low of that candle is lower than both its previous and subsequent candles.
3. Variables
swingHigh and swingLow: Store the most recent swing high and swing low prices.
swingHighTime and swingLowTime: Store the timestamps of the swing high and swing low.
fib1 to fib5: Fibonacci levels based on the difference between swingHigh and swingLow.
4. Swing Point Detection
The script checks if the last bar is a swing high or swing low using the isSwingHigh() and isSwingLow() functions.
If a swing high is detected:
The high price is stored in swingHigh.
The timestamp of the swing high is stored in swingHighTime.
If a swing low is detected:
The low price is stored in swingLow.
The timestamp of the swing low is stored in swingLowTime.
5. Fibonacci Levels Calculation
If both swingHigh and swingLow are defined, the script calculates the Fibonacci retracement levels (fib1 to fib5) based on the price difference (priceDiff = swingHigh - swingLow).
6. Plotting Fibonacci Levels
Fibonacci levels (fib1 to fib5) are plotted as horizontal lines using the line.new() function.
Labels (e.g., "23.6%") are added near the lines to indicate the level.
Lines and labels are color-coded:
23.6% → Blue
38.2% → Green
50.0% → Yellow
61.8% → Orange
78.6% → Red
7. Filling Between Fibonacci Levels
The plot() function creates lines for each Fibonacci level.
The fill() function is used to fill the space between two levels with semi-transparent colors:
Blue → Between fib1 and fib2
Green → Between fib2 and fib3
Yellow → Between fib3 and fib4
Orange → Between fib4 and fib5
8. Time-Based Fibonacci Projections
If useTime is enabled:
The time difference (timeDiff) between the swing high and swing low is calculated.
Fibonacci time projections are added based on multiples of 23.6%.
If the current time reaches a projected time, a label (e.g., "T1", "T2") is displayed near the high price.
9. Trading Logic
Two placeholder variables are defined for trading logic:
longCondition: Tracks whether a condition for a long trade is met (currently not implemented).
shortCondition: Tracks whether a condition for a short trade is met (currently not implemented).
These variables can be extended to define entry/exit signals based on Fibonacci levels.
How It Works
Detect Swing Points: It identifies recent swing high and swing low points on the chart.
Calculate Fibonacci Levels: Based on the swing points, it computes retracement levels.
Visualize Levels: Plots the levels on the chart with labels and fills between them.
Time Projections: Optionally calculates time-based projections for future price movements.
Trading Opportunities: The framework provides tools for detecting potential reversal or breakout zones using Fibonacci levels.
Forex Hammer and Hanging Man StrategyThe strategy is based on two key candlestick chart patterns: Hammer and Hanging Man. These chart patterns are widely used in technical analysis to identify potential reversal points in the market. Their relevance in the Forex market, known for its high liquidity and volatile price movements, is particularly pronounced. Both patterns provide insights into market sentiment and trader psychology, which are critical in currency trading, where short-term volatility plays a significant role.
1. Hammer:
• Typically occurs after a downtrend.
• Signals a potential trend reversal to the upside.
• A Hammer has:
• A small body (close and open are close to each other).
• A long lower shadow, at least twice as long as the body.
• No or a very short upper shadow.
2. Hanging Man:
• Typically occurs after an uptrend.
• Signals a potential reversal to the downside.
• A Hanging Man has:
• A small body, similar to the Hammer.
• A long lower shadow, at least twice as long as the body.
• A small or no upper shadow.
These patterns are a manifestation of market psychology, specifically the tug-of-war between buyers and sellers. The Hammer reflects a situation where sellers tried to push the price down but were overpowered by buyers, while the Hanging Man shows that buyers failed to maintain the upward movement, and sellers could take control.
Relevance of Chart Patterns in Forex
In the Forex market, chart patterns are vital tools because they offer insights into price action and market sentiment. Since Forex trading often involves large volumes of trades, chart patterns like the Hammer and Hanging Man are important for recognizing potential shifts in market momentum. These patterns are a part of technical analysis, which aims to forecast future price movements based on historical data, relying on the psychology of market participants.
Scientific Literature on the Relevance of Candlestick Patterns
1. Behavioral Finance and Candlestick Patterns:
Research on behavioral finance supports the idea that candlestick patterns, such as the Hammer and Hanging Man, are relevant because they reflect shifts in trader psychology and sentiment. According to Lo, Mamaysky, and Wang (2000), patterns like these could be seen as representations of collective investor behavior, influenced by overreaction, optimism, or pessimism, and can often signal reversals in market trends.
2. Statistical Validation of Chart Patterns:
Studies by Brock, Lakonishok, and LeBaron (1992) explored the profitability of technical analysis strategies, including candlestick patterns, and found evidence that certain patterns, such as the Hammer, can have predictive value in financial markets. While their study primarily focused on stock markets, their findings are generally applicable to the Forex market as well.
3. Market Efficiency and Candlestick Patterns:
The efficient market hypothesis (EMH) posits that all available information is reflected in asset prices, but some studies suggest that markets may not always be perfectly efficient, allowing for profitable exploitation of certain chart patterns. For instance, Jegadeesh and Titman (1993) found that momentum strategies, which often rely on price patterns and trends, could generate significant returns, suggesting that patterns like the Hammer or Hanging Man may provide a slight edge, particularly in short-term Forex trading.
Testing the Strategy in Forex Using the Provided Script
The provided script allows traders to test and evaluate the Hammer and Hanging Man patterns in Forex trading by entering positions when these patterns appear and holding the position for a specified number of periods. This strategy can be tested to assess its performance across different currency pairs and timeframes.
1. Testing on Different Timeframes:
• The effectiveness of candlestick patterns can vary across different timeframes, as market dynamics change with the level of detail in each timeframe. Shorter timeframes may provide more frequent signals, but with higher noise, while longer timeframes may produce more reliable signals, but with fewer opportunities. This multi-timeframe analysis could be an area to explore to enhance the strategy’s robustness.
2. Exit Strategies:
• The script incorporates an exit strategy where positions are closed after holding them for a specified number of periods. This is useful for testing how long the reversal patterns typically take to play out and when the optimal exit occurs for maximum profitability. It can also help to adjust the exit logic based on real-time market behavior.
Conclusion
The Hammer and Hanging Man patterns are widely recognized in technical analysis as potential reversal signals, and their application in Forex trading is valuable due to the market’s high volatility and liquidity. This strategy leverages these candlestick patterns to enter and exit trades based on shifts in market sentiment and psychology. Testing and optimization, as offered by the script, can help refine the strategy and improve its effectiveness.
For further refinement, it could be valuable to consider combining candlestick patterns with other technical indicators or using multi-timeframe analysis to confirm patterns and increase the probability of successful trades.
References:
• Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. The Journal of Finance, 55(4), 1705-1770.
• Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. The Journal of Finance, 47(5), 1731-1764.
• Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
This provides a theoretical basis for the use of candlestick patterns in trading, supported by academic literature and research on market psychology and efficiency.
RVM - MA Bounce Low Cheat Strategy with LLS SignalsYellow triangle represent candles which bounc off of 10d MA or 21d EMA or 50d Ma with long lower wick and small body bullish candle. respresnting potential upside.
If the price breaches the clandle close price of the yellow candle in the next successive 5 sessions there will be a buy green triangle triggered.
Center of Candle Trendline### **Center of Candle Trendline**
This script dynamically plots a trendline through the center of each candlestick's body. The "center" is calculated as the average of the open and close prices for each candle. The trendline updates in real-time as new candles form, providing a clean and straightforward way to track the market's midline movement.
#### **Features:**
1. **Dynamic Trendline:** The trendline connects the center points of consecutive candlestick bodies, giving a clear visual representation of price movements.
2. **Accurate Center Calculation:** The center is determined as `(open + close) / 2`, ensuring the trendline reflects the true midpoint of each candlestick body.
3. **Real-Time Updates:** The trendline updates automatically as new bars form, keeping your chart up to date with the latest price action.
4. **Customization-Ready:** Adjust the line’s color, width, or style easily to fit your chart preferences.
#### **How to Use:**
- Add this script to your chart to monitor the price movement relative to the center of candlestick bodies.
- Use the trendline to identify trends, reversals, or price consolidation zones.
#### **Applications:**
- **Trend Analysis:** Visualize how the market trends around the center of candlesticks.
- **Reversal Identification:** Detect potential reversal zones when the price deviates significantly from the trendline.
- **Support and Resistance Zones:** Use the trendline as a dynamic support or resistance reference.
This tool is perfect for traders who want a clean and minimalistic approach to tracking price action. Whether you're a beginner or an experienced trader, this script provides valuable insights without overwhelming your chart.
#### **Note:**
This is not a standalone trading strategy but a visual aid to complement your analysis. Always combine it with other tools and techniques for better trading decisions.
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Feel free to tweak this description based on your preferences or style!
SPX Lin Reg with SD -1 +1 -2 +2 -3 +3 (Extented) V1.0Here is a script to plot a linear regression for a given period and add standard deviations.
SPX Lin Reg with SD -1 +1 -2 +2 (Extented) V1.0Here is a script to plot a linear regression for a given period and add standard deviations.
SPX Lin Reg with SD -1 +1 (Extented) V 1.0Here is a script to plot a linear regression for a given period and add standard deviations.
SPX Lin Reg with SD -1 +1 V1.0
110 / 5 000
Here is a script to plot a linear regression for a given period and add standard deviations.
Price Down from Monthly High (%)//@version=5
indicator("Price Down from Monthly High (%)", overlay=true)
// Get the highest price of the current month
monthlyHigh = request.security(syminfo.tickerid, "M", high)
// Calculate the percentage price down from the monthly high
percentageDown = ((monthlyHigh - close) / monthlyHigh) * 100
// Plot the percentage down from the monthly high
plot(percentageDown, color=color.red, title="Percentage Down from Monthly High", linewidth=2)
Relative Performance Indicator by ComLucro - 2025_V01The "Relative Performance Indicator by ComLucro - 2025_V01" is a powerful tool designed to analyze an asset's performance relative to a benchmark index over multiple timeframes. This indicator provides traders with a clear view of how their chosen asset compares to a market index in short, medium, and long-term periods.
Key Features:
Customizable Lookback Periods: Analyze performance across three adjustable periods (default: 20, 50, and 200 bars).
Relative Performance Analysis: Calculate and visualize the difference in percentage performance between the asset and the benchmark index.
Dynamic Summary Label: Displays a detailed breakdown of the asset's and index's performance for the latest bar.
User-Friendly Interface: Includes customizable colors and display options for clear visualization.
How It Works:
The script fetches closing prices of both the asset and a benchmark index.
It calculates percentage changes over the selected lookback periods.
The indicator then computes the relative performance difference between the asset and the index, plotting it on the chart for easy trend analysis.
Who Is This For?:
Traders and investors who want to compare an asset’s performance against a benchmark index.
Those looking to identify trends and deviations between an asset and the broader market.
Disclaimer:
This tool is for educational purposes only and does not constitute financial or trading advice. Always use it alongside proper risk management strategies and backtest thoroughly before applying it to live trading.
Chart Recommendation:
Use this script on clean charts for better clarity. Combine it with other technical indicators like moving averages or trendlines to enhance your analysis. Ensure you adjust the lookback periods to match your trading style and the timeframe of your analysis.
Additional Notes:
For optimal performance, ensure the benchmark index's data is available on your TradingView subscription. The script uses fallback mechanisms to avoid interruptions when index data is unavailable. Always validate the settings and test them to suit your trading strategy.
Crypto Market Caps / Global GDP %This indicator compares the total market capitalization of various crypto sectors to the global Gross Domestic Product (GDP), expressed as a percentage. The purpose of this indicator is to provide a visual representation of the relative size of the crypto market compared to the global economy, allowing traders and analysts to understand how the market is growing in relation to the overall economy.
Key Features
Crypto Market Caps -
TOTAL: Represents the total market capitalization of all cryptocurrencies.
TOTAL3: Represents the market capitalization of all cryptocurrencies, excluding Bitcoin and Ethereum.
OTHERS: Represents the market capitalization of all cryptocurrencies excluding the top 10.
Global GDP -
The indicator uses a combination of GDP data from multiple regions across the world, including:
GDP from the EU, North America (NA), and other regions.
GDP data from Asia, Latin America (LATAM), and the Middle East & North Africa (MENA).
Percentage Representation -
The market caps (TOTAL, TOTAL3, OTHERS) are compared to the global GDP, and the result is expressed as a percentage. This allows you to easily see how the size of the cryptocurrency market compares to the entire global economy at any given time.
Plotting and Visualization
The indicator plots the market cap to global GDP ratio for each category (TOTAL, TOTAL3, OTHERS) on the chart.
You can choose which plots to display through user inputs.
The percentage scale makes it easy to compare how much of the global GDP is represented by different parts of the crypto market.
Labels can be added for additional clarity, showing the exact percentage value on the chart.
How to Use
The indicator provides a clear view of the cryptocurrency market's relative size compared to the global economy.
Higher values indicate that the crypto market (or a segment of it) is becoming a larger portion of the global economy.
Lower values suggest the crypto market is still a smaller segment of the global economic activity.
User Inputs
TOTAL/GlobalGDP: Toggle visibility for the total market capitalization of all cryptocurrencies.
TOTAL3/GlobalGDP: Toggle visibility for the market cap of cryptocurrencies excluding Bitcoin and Ethereum.
OTHERS/GlobalGDP: Toggle visibility for the market cap of cryptocurrencies excluding the top 10.
Labels: Enable or disable the display of labels showing the exact percentage values.
Practical Use Cases
Market Sentiment: Gauge the overall market sentiment and potential growth relative to global economic conditions.
Investment Decisions: Help identify when the crypto market is becoming more or less significant in the context of the global economy.
Macro Analysis: Combine this indicator with other macroeconomic indicators to gain deeper insights into the broader economic landscape.
By providing an easy-to-understand percentage representation, this indicator offers valuable insights for anyone interested in tracking the relationship between cryptocurrency market cap and global economic activity.
GL_Prev Week HighThe GL_Prev Week High Indicator is a powerful tool designed to enhance your trading analysis by displaying the previous week's high price directly on your chart. With clear and customizable visuals, this indicator helps traders quickly identify critical price levels, enabling more informed decision-making.
Key Features:
Previous Week's High Line:
Displays the previous week's high as a red line on your chart for easy reference.
Customizable Horizontal Line:
Includes a white horizontal line for enhanced clarity, with adjustable length, color, and width settings.
All-Time High Tracking:
Automatically tracks the all-time high from the chart's history and places a dynamic label above it.
Real-Time Updates:
The indicator updates in real-time to ensure accuracy as new bars are added.
User Inputs for Personalization:
Adjust the left and right span of the horizontal line.
Customize line width and color to suit your preferences.
Use Case:
This indicator is ideal for traders looking to integrate the previous week's high as a key support or resistance level in their trading strategy. Whether you are analyzing trends, identifying breakout zones, or planning entry/exit points, this tool provides valuable insights directly on the chart.
How to Use:
Add the indicator to your chart.
Customize the settings (line length, width, and color) through the input panel to match your preferences.
Use the red line to track the previous week's high and the label to monitor all-time highs effortlessly.
License:
This script is shared under the Mozilla Public License 2.0. Feel free to use and adapt the script as per the license terms.
Adaptive Momentum Reversion StrategyThe Adaptive Momentum Reversion Strategy: An Empirical Approach to Market Behavior
The Adaptive Momentum Reversion Strategy seeks to capitalize on market price dynamics by combining concepts from momentum and mean reversion theories. This hybrid approach leverages a Rate of Change (ROC) indicator along with Bollinger Bands to identify overbought and oversold conditions, triggering trades based on the crossing of specific thresholds. The strategy aims to detect momentum shifts and exploit price reversions to their mean.
Theoretical Framework
Momentum and Mean Reversion: Momentum trading assumes that assets with a recent history of strong performance will continue in that direction, while mean reversion suggests that assets tend to return to their historical average over time (Fama & French, 1988; Poterba & Summers, 1988). This strategy incorporates elements of both, looking for periods when momentum is either overextended (and likely to revert) or when the asset’s price is temporarily underpriced relative to its historical trend.
Rate of Change (ROC): The ROC is a straightforward momentum indicator that measures the percentage change in price over a specified period (Wilder, 1978). The strategy calculates the ROC over a 2-period window, making it responsive to short-term price changes. By using ROC, the strategy aims to detect price acceleration and deceleration.
Bollinger Bands: Bollinger Bands are used to identify volatility and potential price extremes, often signaling overbought or oversold conditions. The bands consist of a moving average and two standard deviation bounds that adjust dynamically with price volatility (Bollinger, 2002).
The strategy employs two sets of Bollinger Bands: one for short-term volatility (lower band) and another for longer-term trends (upper band), with different lengths and standard deviation multipliers.
Strategy Construction
Indicator Inputs:
ROC Period: The rate of change is computed over a 2-period window, which provides sensitivity to short-term price fluctuations.
Bollinger Bands:
Lower Band: Calculated with a 18-period length and a standard deviation of 1.7.
Upper Band: Calculated with a 21-period length and a standard deviation of 2.1.
Calculations:
ROC Calculation: The ROC is computed by comparing the current close price to the close price from rocPeriod days ago, expressing it as a percentage.
Bollinger Bands: The strategy calculates both upper and lower Bollinger Bands around the ROC, using a simple moving average as the central basis. The lower Bollinger Band is used as a reference for identifying potential long entry points when the ROC crosses above it, while the upper Bollinger Band serves as a reference for exits, when the ROC crosses below it.
Trading Conditions:
Long Entry: A long position is initiated when the ROC crosses above the lower Bollinger Band, signaling a potential shift from a period of low momentum to an increase in price movement.
Exit Condition: A position is closed when the ROC crosses under the upper Bollinger Band, or when the ROC drops below the lower band again, indicating a reversal or weakening of momentum.
Visual Indicators:
ROC Plot: The ROC is plotted as a line to visualize the momentum direction.
Bollinger Bands: The upper and lower bands, along with their basis (simple moving averages), are plotted to delineate the expected range for the ROC.
Background Color: To enhance decision-making, the strategy colors the background when extreme conditions are detected—green for oversold (ROC below the lower band) and red for overbought (ROC above the upper band), indicating potential reversal zones.
Strategy Performance Considerations
The use of Bollinger Bands in this strategy provides an adaptive framework that adjusts to changing market volatility. When volatility increases, the bands widen, allowing for larger price movements, while during quieter periods, the bands contract, reducing trade signals. This adaptiveness is critical in maintaining strategy effectiveness across different market conditions.
The strategy’s pyramiding setting is disabled (pyramiding=0), ensuring that only one position is taken at a time, which is a conservative risk management approach. Additionally, the strategy includes transaction costs and slippage parameters to account for real-world trading conditions.
Empirical Evidence and Relevance
The combination of momentum and mean reversion has been widely studied and shown to provide profitable opportunities under certain market conditions. Studies such as Jegadeesh and Titman (1993) confirm that momentum strategies tend to work well in trending markets, while mean reversion strategies have been effective during periods of high volatility or after sharp price movements (De Bondt & Thaler, 1985). By integrating both strategies into one system, the Adaptive Momentum Reversion Strategy may be able to capitalize on both trending and reverting market behavior.
Furthermore, research by Chan (1996) on momentum-based trading systems demonstrates that adaptive strategies, which adjust to changes in market volatility, often outperform static strategies, providing a compelling rationale for the use of Bollinger Bands in this context.
Conclusion
The Adaptive Momentum Reversion Strategy provides a robust framework for trading based on the dual concepts of momentum and mean reversion. By using ROC in combination with Bollinger Bands, the strategy is capable of identifying overbought and oversold conditions while adapting to changing market conditions. The use of adaptive indicators ensures that the strategy remains flexible and can perform across different market environments, potentially offering a competitive edge for traders who seek to balance risk and reward in their trading approaches.
References
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