ATR BandsThe ATR Bands indicator is a volatility-based tool that plots dynamic support and resistance levels around the price using the Average True Range (ATR). It consists of two bands:
Upper Band: Calculated as current price + ATR, representing an upper volatility threshold.
Lower Band: Calculated as current price - ATR, serving as a lower volatility threshold.
Key Features:
✅ Measures Volatility: Expands and contracts based on market volatility.
✅ Dynamic Support & Resistance: Helps identify potential breakout or reversal zones.
✅ Customizable Smoothing: Supports multiple moving average methods (RMA, SMA, EMA, WMA) for ATR calculation.
How to Use:
Trend Confirmation: If the price consistently touches or exceeds the upper band, it may indicate strong bullish momentum.
Reversal Signals: A price approaching the lower band may suggest a potential reversal or increased selling pressure.
Volatility Assessment: Wide bands indicate high volatility, while narrow bands suggest consolidation.
This indicator is useful for traders looking to incorporate volatility-based strategies into their trading decisions
In den Scripts nach "Volatility" suchen
Trend Analysis with Volatility and MomentumVolatility and Momentum Trend Analyzer
The Volatility and Momentum Trend Analyzer is a multi-faceted TradingView indicator designed to provide a comprehensive analysis of market trends, volatility, and momentum. It incorporates key features to identify trend direction (uptrend, downtrend, or sideways), visualize weekly support and resistance levels, and offer a detailed assessment of market strength and activity. Below is a breakdown of its functionality:
1. Input Parameters
The indicator provides customizable settings for precision and adaptability:
Volatility Lookback Period: Configurable period (default: 14) for calculating Average True Range (ATR), which measures market volatility.
Momentum Lookback Period: Configurable period (default: 14) for calculating the Rate of Change (ROC), which measures the speed and strength of price movements.
Support/Resistance Lookback Period: Configurable period (default: 7 weeks) to determine critical support and resistance levels based on weekly high and low prices.
2. Volatility Analysis (ATR)
The Average True Range (ATR) is calculated to quantify the market's volatility:
What It Does: ATR measures the average range of price movement over the specified lookback period.
Visualization: Plotted as a purple line in a separate panel below the price chart, with values amplified (multiplied by 10) for better visibility.
3. Momentum Analysis (ROC)
The Rate of Change (ROC) evaluates the momentum of price movements:
What It Does: ROC calculates the percentage change in closing prices over the specified lookback period, indicating the strength and direction of market moves.
Visualization: Plotted as a yellow line in a separate panel below the price chart, with values amplified (multiplied by 10) for better visibility.
4. Trend Detection
The indicator identifies the current market trend based on momentum and the position of the price relative to its moving average:
Uptrend: Occurs when momentum is positive, and the closing price is above the simple moving average (SMA) of the specified lookback period.
Downtrend: Occurs when momentum is negative, and the closing price is below the SMA.
Sideways Trend: Occurs when neither of the above conditions is met.
Visualization: The background of the price chart changes color to reflect the detected trend:
Green: Uptrend.
Red: Downtrend.
Gray: Sideways trend.
5. Weekly Support and Resistance
Critical levels are calculated based on weekly high and low prices:
Support: The lowest price observed over the last specified number of weeks.
Resistance: The highest price observed over the last specified number of weeks.
Visualization:
Blue Line: Indicates the support level.
Orange Line: Indicates the resistance level.
Both lines are displayed on the main price chart, dynamically updating as new data becomes available.
6. Alerts
The indicator provides configurable alerts for trend changes, helping traders stay informed without constant monitoring:
Uptrend Alert: Notifies when the market enters an uptrend.
Downtrend Alert: Notifies when the market enters a downtrend.
Sideways Alert: Notifies when the market moves sideways.
7. Key Use Cases
Trend Following: Identify and follow the dominant trend to capitalize on sustained price movements.
Volatility Assessment: Measure market activity to determine potential breakouts or quiet consolidation phases.
Support and Resistance: Highlight key levels where price is likely to react, assisting in decision-making for entries, exits, or stop-loss placement.
Momentum Tracking: Gauge the strength and speed of price moves to validate trends or anticipate reversals.
8. Visualization Summary
Main Chart:
Background color-coded for trend direction (green, red, gray).
Blue and orange lines for weekly support and resistance.
Lower Panels:
Purple line for volatility (ATR).
Yellow line for momentum (ROC).
VIX Bars [CrossTrade]In simple terms, this indicator colors your chart bars based on the VIX levels. We know that high volatility is unstainable and will naturally regress to a calmer market, therefore highlighting the bars where VIX is at extreme highs can sometimes indicate a market turning point. Consider pairing this indicator with my VIX Heatmap indicator for a complete picture of volatility.
Customizable VIX Levels: You can set your own thresholds for when the bars turn green or red. Green bars pop up when the VIX is above your set upper level (default is 30) - kind of like a heads-up that things might get bumpy. Red bars show up when the VIX dips below your lower threshold (default is 15), signaling calmer waters.
Optional Donchian Channel Filter: The Donchian Channel filter looks at the highest highs and lowest lows over your chosen period (default's 52 days) and only colors the bars if they match the filter's criteria. This adds an extra layer of confirmation that the colored bars at at a major high or low.
Visual Simplicity: The indicator keeps things visually straightforward. No cluttered screen, just colored bars telling you a story about market vibes. Alert come standard to signal those potential bottom or top bars based on the VIX being at your preferred extreme levels.
In essence, "VIX Bars" is like having a volatility radar on your chart. It doesn't make predictions, but it sure gives you a neat, color-coded heads-up on market sentiment. Great for adding an extra dimension to your analysis without getting all tangled up in complex indicators!
I11L - Risk Adjusted LeveragingThis trading system, called "I11L - Risk Adjusted Leveraging", is designed to manage trades based on the current market volatility relative to its historical average. The system calculates the target number of open trades based on the ATR (Average True Range) indicator and adjusts the leverage accordingly. The system opens and closes trades using a pyramiding approach, allowing multiple positions to be opened at the same time.
Here's a step-by-step explanation of the system:
1. Calculate the ATR with a 14-day period and normalize it by dividing it by the current closing price.
2. Calculate the 100-day simple moving average (SMA) of the normalized ATR.
3. Calculate the ratio of the normalized ATR to its 100-day SMA.
4. Determine the target leverage based on the inverse of the ratio (2 / ratio).
5. Calculate the target number of open trades by multiplying the target leverage by 5.
6. Plot the target number of open trades and the current number of open trades on the chart.
7. Check if there's an opportunity to buy (if the current number of open trades is less than the target) or close a trade (if the current number of open trades is more than the target plus 1).
8. If there's an opportunity to buy, open a long trade and add the trade's name to the openTrades array.
9. If there's an opportunity to close a trade and there are trades in the openTrades array, close the most recent trade by referencing the array and remove it from the array.
This system aims to capture trends in the market by dynamically adjusting the number of open trades and leverage based on the market's volatility. It uses an array to keep track of open trades, allowing for better control over the opening and closing of individual trades.
vol_rangesThis script shows three measures of volatility:
historical (hv): realized volatility of the recent past
median (mv): a long run average of realized volatility
implied (iv): a user-defined volatility
Historical and median volatility are based on the EWMA, rather than standard deviation, method of calculating volatility. Since Tradingview's built in ema function uses a window, the "window" parameter determines how much historical data is used to calculate these volatility measures. E.g. 30 on a daily chart means the previous 30 days.
The plots above and below historical candles show past projections based on these measures. The "periods to expiration" dictates how far the projection extends. At 30 periods to expiration (default), the plot will indicate the one standard deviation range from 30 periods ago. This is calculated by multiplying the volatility measure by the square root of time. For example, if the historical volatility (hv) was 20% and the window is 30, then the plot is drawn over: close * 1.2 * sqrt(30/252).
At the most recent candle, this same calculation is simply drawn as a line projecting into the future.
This script is intended to be used with a particular options contract in mind. For example, if the option expires in 15 days and has an implied volatility of 25%, choose 15 for the window and 25 for the implied volatility options. The ranges drawn will reflect the two standard deviation range both in the future (lines) and at any point in the past (plots) for HV (blue), MV (red), and IV (grey).
Chandelier ExitChandelier Exit (CE) is a volatility-based indicator developed by "Chuck Le Beau", ATR is used to measure the Volatility.
It identifies stop loss exit points for long and short trading positions.
Configuring the ATR period = 1 and Multiplier = (say) 1.25 or 1.5, it can be used for readily available buffer Stop Loss value from previous high/low.
Bollinger Bands Fibonacci Ratios StrategyHello, everyone!
We have just released an innovative strategy for TradingView. It allows you to identify price pivot points and volatility.
This strategy is:
User-friendly
Configurable
Equipped with Bollinger Bands and smoothed ATR to measure volatility
Features
Thanks to the BB Fibo strategy, you can:
Trade stocks and commodities.
Identify price pivot points.
Choose any band for trading Long or Short positions.
Swap upper and lower bands applying Use Reverse Buy/Sell parameters.
Note! The upper bands are for the Long position. The lower bands are for the Short positions.
Parameters
We have equipped our strategy with more than 14 additional parameters. So, you can configure the EA according to your needs!
Inputs:
Length
Source: Open, High, Low, Close, HL2, HLC3, OHLC4
Offset
Fibonacci Ratio 1 — a Fibonacci factor for the 1st upper and lower indicator lines calculating.
Fibonacci Ratio 2 — a Fibonacci factor for the 2nd upper and lower indicator lines calculating.
Fibonacci Ratio 3 — a Fibonacci factor for the 3d upper and lower indicator lines calculating.
Use Reverse Buy — the strategy will use lower Bollinger bands instead of upper ones.
Fibonacci Buy — band selection for opening Long positions conditions.
Use Reverse Sell — the strategy will use upper Bollinger bands instead of lower ones.
Fibonacci Sell — band selection for opening Short positions conditions.
Style:
Basis — baseline color and style settings.
Upper 3 — the 3d upper line color and style.
Upper 2 — the 2nd upper line color and style.
Upper 1 — the 1st upper line color and style.
Lower 1 — the 1st lower line color and style.
Lower 2 — the 2nd lower line color and style.
Lower 3 — the 3d upper line color and style.
Background — the background color within the 3d upper and 3d lower indicator band.
Precision — the number of decimals for BB Fibo values.
Note! Try BB Fibo on your demo account first before going live.
VoVix DEVMA🌌 VoVix DEVMA: A Deep Dive into Second-Order Volatility Dynamics
Welcome to VoVix+, a sophisticated trading framework that transcends traditional price analysis. This is not merely another indicator; it is a complete system designed to dissect and interpret the very fabric of market volatility. VoVix+ operates on the principle that the most powerful signals are not found in price alone, but in the behavior of volatility itself. It analyzes the rate of change, the momentum, and the structure of market volatility to identify periods of expansion and contraction, providing a unique edge in anticipating major market moves.
This document will serve as your comprehensive guide, breaking down every mathematical component, every user input, and every visual element to empower you with a profound understanding of how to harness its capabilities.
🔬 THEORETICAL FOUNDATION: THE MATHEMATICS OF MARKET DYNAMICS
VoVix+ is built upon a multi-layered mathematical engine designed to measure what we call "second-order volatility." While standard indicators analyze price, and first-order volatility indicators (like ATR) analyze the range of price, VoVix+ analyzes the dynamics of the volatility itself. This provides insight into the market's underlying state of stability or chaos.
1. The VoVix Score: Measuring Volatility Thrust
The core of the system begins with the VoVix Score. This is a normalized measure of volatility acceleration or deceleration.
Mathematical Formula:
VoVix Score = (ATR(fast) - ATR(slow)) / (StDev(ATR(fast)) + ε)
Where:
ATR(fast) is the Average True Range over a short period, representing current, immediate volatility.
ATR(slow) is the Average True Range over a longer period, representing the baseline or established volatility.
StDev(ATR(fast)) is the Standard Deviation of the fast ATR, which measures the "noisiness" or consistency of recent volatility.
ε (epsilon) is a very small number to prevent division by zero.
Market Implementation:
Positive Score (Expansion): When the fast ATR is significantly higher than the slow ATR, it indicates a rapid increase in volatility. The market is "stretching" or expanding.
Negative Score (Contraction): When the fast ATR falls below the slow ATR, it indicates a decrease in volatility. The market is "coiling" or contracting.
Normalization: By dividing by the standard deviation, we normalize the score. This turns it into a standardized measure, allowing us to compare volatility thrust across different market conditions and timeframes. A score of 2.0 in a quiet market means the same, relatively, as a score of 2.0 in a volatile market.
2. Deviation Analysis (DEV): Gauging Volatility's Own Volatility
The script then takes the analysis a step further. It calculates the standard deviation of the VoVix Score itself.
Mathematical Formula:
DEV = StDev(VoVix Score, lookback_period)
Market Implementation:
This DEV value represents the magnitude of chaos or stability in the market's volatility dynamics. A high DEV value means the volatility thrust is erratic and unpredictable. A low DEV value suggests the change in volatility is smooth and directional.
3. The DEVMA Crossover: Identifying Regime Shifts
This is the primary signal generator. We take two moving averages of the DEV value.
Mathematical Formula:
fastDEVMA = SMA(DEV, fast_period)
slowDEVMA = SMA(DEV, slow_period)
The Core Signal:
The strategy triggers on the crossover and crossunder of these two DEVMA lines. This is a profound concept: we are not looking at a moving average of price or even of volatility, but a moving average of the standard deviation of the normalized rate of change of volatility.
Bullish Crossover (fastDEVMA > slowDEVMA): This signals that the short-term measure of volatility's chaos is increasing relative to the long-term measure. This often precedes a significant market expansion and is interpreted as a bullish volatility regime.
Bearish Crossunder (fastDEVMA < slowDEVMA): This signals that the short-term measure of volatility's chaos is decreasing. The market is settling down or contracting, often leading to trending moves or range consolidation.
⚙️ INPUTS MENU: CONFIGURING YOUR ANALYSIS ENGINE
Every input has been meticulously designed to give you full control over the strategy's behavior. Understanding these settings is key to adapting VoVix+ to your specific instrument, timeframe, and trading style.
🌀 VoVix DEVMA Configuration
🧬 Deviation Lookback: This sets the lookback period for calculating the DEV value. It defines the window for measuring the stability of the VoVix Score. A shorter value makes the system highly reactive to recent changes in volatility's character, ideal for scalping. A longer value provides a smoother, more stable reading, better for identifying major, long-term regime shifts.
⚡ Fast VoVix Length: This is the lookback period for the fastDEVMA. It represents the short-term trend of volatility's chaos. A smaller number will result in a faster, more sensitive signal line that reacts quickly to market shifts.
🐌 Slow VoVix Length: This is the lookback period for the slowDEVMA. It represents the long-term, baseline trend of volatility's chaos. A larger number creates a more stable, slower-moving anchor against which the fast line is compared.
How to Optimize: The relationship between the Fast and Slow lengths is crucial. A wider gap (e.g., 20 and 60) will result in fewer, but potentially more significant, signals. A narrower gap (e.g., 25 and 40) will generate more frequent signals, suitable for more active trading styles.
🧠 Adaptive Intelligence
🧠 Enable Adaptive Features: When enabled, this activates the strategy's performance tracking module. The script will analyze the outcome of its last 50 trades to calculate a dynamic win rate.
⏰ Adaptive Time-Based Exit: If Enable Adaptive Features is on, this allows the strategy to adjust its Maximum Bars in Trade setting based on performance. It learns from the average duration of winning trades. If winning trades tend to be short, it may shorten the time exit to lock in profits. If winners tend to run, it will extend the time exit, allowing trades more room to develop. This helps prevent the strategy from cutting winning trades short or holding losing trades for too long.
⚡ Intelligent Execution
📊 Trade Quantity: A straightforward input that defines the number of contracts or shares for each trade. This is a fixed value for consistent position sizing.
🛡️ Smart Stop Loss: Enables the dynamic stop-loss mechanism.
🎯 Stop Loss ATR Multiplier: Determines the distance of the stop loss from the entry price, calculated as a multiple of the current 14-period ATR. A higher multiplier gives the trade more room to breathe but increases risk per trade. A lower multiplier creates a tighter stop, reducing risk but increasing the chance of being stopped out by normal market noise.
💰 Take Profit ATR Multiplier: Sets the take profit target, also as a multiple of the ATR. A common practice is to set this higher than the Stop Loss multiplier (e.g., a 2:1 or 3:1 reward-to-risk ratio).
🏃 Use Trailing Stop: This is a powerful feature for trend-following. When enabled, instead of a fixed stop loss, the stop will trail behind the price as the trade moves into profit, helping to lock in gains while letting winners run.
🎯 Trail Points & 📏 Trail Offset ATR Multipliers: These control the trailing stop's behavior. Trail Points defines how much profit is needed before the trail activates. Trail Offset defines how far the stop will trail behind the current price. Both are based on ATR, making them fully adaptive to market volatility.
⏰ Maximum Bars in Trade: This is a time-based stop. It forces an exit if a trade has been open for a specified number of bars, preventing positions from being held indefinitely in stagnant markets.
⏰ Session Management
These inputs allow you to confine the strategy's trading activity to specific market hours, which is crucial for day trading instruments that have defined high-volume sessions (e.g., stock market open).
🎨 Visual Effects & Dashboard
These toggles give you complete control over the on-chart visuals and the dashboard. You can disable any element to declutter your chart or focus only on the information that matters most to you.
📊 THE DASHBOARD: YOUR AT-A-GLANCE COMMAND CENTER
The dashboard centralizes all critical information into one compact, easy-to-read panel. It provides a real-time summary of the market state and strategy performance.
🎯 VOVIX ANALYSIS
Fast & Slow: Displays the current numerical values of the fastDEVMA and slowDEVMA. The color indicates their direction: green for rising, red for falling. This lets you see the underlying momentum of each line.
Regime: This is your most important environmental cue. It tells you the market's current state based on the DEVMA relationship. 🚀 EXPANSION (Green) signifies a bullish volatility regime where explosive moves are more likely. ⚛️ CONTRACTION (Purple) signifies a bearish volatility regime, where the market may be consolidating or entering a smoother trend.
Quality: Measures the strength of the last signal based on the magnitude of the DEVMA difference. An ELITE or STRONG signal indicates a high-conviction setup where the crossover had significant force.
PERFORMANCE
Win Rate & Trades: Displays the historical win rate of the strategy from the backtest, along with the total number of closed trades. This provides immediate feedback on the strategy's historical effectiveness on the current chart.
EXECUTION
Trade Qty: Shows your configured position size per trade.
Session: Indicates whether trading is currently OPEN (allowed) or CLOSED based on your session management settings.
POSITION
Position & PnL: Displays your current position (LONG, SHORT, or FLAT) and the real-time Profit or Loss of the open trade.
🧠 ADAPTIVE STATUS
Stop/Profit Mult: In this simplified version, these are placeholders. The primary adaptive feature currently modifies the time-based exit, which is reflected in how long trades are held on the chart.
🎨 THE VISUAL UNIVERSE: DECIPHERING MARKET GEOMETRY
The visuals are not mere decorations; they are geometric representations of the underlying mathematical concepts, designed to give you an intuitive feel for the market's state.
The Core Lines:
FastDEVMA (Green/Maroon Line): The primary signal line. Green when rising, indicating an increase in short-term volatility chaos. Maroon when falling.
SlowDEVMA (Aqua/Orange Line): The baseline. Aqua when rising, indicating a long-term increase in volatility chaos. Orange when falling.
🌊 Morphism Flow (Flowing Lines with Circles):
What it represents: This visualizes the momentum and strength of the fastDEVMA. The width and intensity of the "beam" are proportional to the signal strength.
Interpretation: A thick, steep, and vibrant flow indicates powerful, committed momentum in the current volatility regime. The floating '●' particles represent kinetic energy; more particles suggest stronger underlying force.
📐 Homotopy Paths (Layered Transparent Boxes):
What it represents: These layered boxes are centered between the two DEVMA lines. Their height is determined by the DEV value.
Interpretation: This visualizes the overall "volatility of volatility." Wider boxes indicate a chaotic, unpredictable market. Narrower boxes suggest a more stable, predictable environment.
🧠 Consciousness Field (The Grid):
What it represents: This grid provides a historical lookback at the DEV range.
Interpretation: It maps the recent "consciousness" or character of the market's volatility. A consistently wide grid suggests a prolonged period of chaos, while a narrowing grid can signal a transition to a more stable state.
📏 Functorial Levels (Projected Horizontal Lines):
What it represents: These lines extend from the current fastDEVMA and slowDEVMA values into the future.
Interpretation: Think of these as dynamic support and resistance levels for the volatility structure itself. A crossover becomes more significant if it breaks cleanly through a prior established level.
🌊 Flow Boxes (Spaced Out Boxes):
What it represents: These are compact visual footprints of the current regime, colored green for Expansion and red for Contraction.
Interpretation: They provide a quick, at-a-glance confirmation of the dominant volatility flow, reinforcing the background color.
Background Color:
This provides an immediate, unmistakable indication of the current volatility regime. Light Green for Expansion and Light Aqua/Blue for Contraction, allowing you to assess the market environment in a split second.
📊 BACKTESTING PERFORMANCE REVIEW & ANALYSIS
The following is a factual, transparent review of a backtest conducted using the strategy's default settings on a specific instrument and timeframe. This information is presented for educational purposes to demonstrate how the strategy's mechanics performed over a historical period. It is crucial to understand that these results are historical, apply only to the specific conditions of this test, and are not a guarantee or promise of future performance. Market conditions are dynamic and constantly change.
Test Parameters & Conditions
To ensure the backtest reflects a degree of real-world conditions, the following parameters were used. The goal is to provide a transparent baseline, not an over-optimized or unrealistic scenario.
Instrument: CME E-mini Nasdaq 100 Futures (NQ1!)
Timeframe: 5-Minute Chart
Backtesting Range: March 24, 2024, to July 09, 2024
Initial Capital: $100,000
Commission: $0.62 per contract (A realistic cost for futures trading).
Slippage: 3 ticks per trade (A conservative setting to account for potential price discrepancies between order placement and execution).
Trade Size: 1 contract per trade.
Performance Overview (Historical Data)
The test period generated 465 total trades , providing a statistically significant sample size for analysis, which is well above the recommended minimum of 100 trades for a strategy evaluation.
Profit Factor: The historical Profit Factor was 2.663 . This metric represents the gross profit divided by the gross loss. In this test, it indicates that for every dollar lost, $2.663 was gained.
Percent Profitable: Across all 465 trades, the strategy had a historical win rate of 84.09% . While a high figure, this is a historical artifact of this specific data set and settings, and should not be the sole basis for future expectations.
Risk & Trade Characteristics
Beyond the headline numbers, the following metrics provide deeper insight into the strategy's historical behavior.
Sortino Ratio (Downside Risk): The Sortino Ratio was 6.828 . Unlike the Sharpe Ratio, this metric only measures the volatility of negative returns. A higher value, such as this one, suggests that during this test period, the strategy was highly efficient at managing downside volatility and large losing trades relative to the profits it generated.
Average Trade Duration: A critical characteristic to understand is the strategy's holding period. With an average of only 2 bars per trade , this configuration operates as a very short-term, or scalping-style, system. Winning trades averaged 2 bars, while losing trades averaged 4 bars. This indicates the strategy's logic is designed to capture quick, high-probability moves and exit rapidly, either at a profit target or a stop loss.
Conclusion and Final Disclaimer
This backtest demonstrates one specific application of the VoVix+ framework. It highlights the strategy's behavior as a short-term system that, in this historical test on NQ1!, exhibited a high win rate and effective management of downside risk. Users are strongly encouraged to conduct their own backtests on different instruments, timeframes, and date ranges to understand how the strategy adapts to varying market structures. Past performance is not indicative of future results, and all trading involves significant risk.
🔧 THE DEVELOPMENT PHILOSOPHY: FROM VOLATILITY TO CLARITY
The journey to create VoVix+ began with a simple question: "What drives major market moves?" The answer is often not a change in price direction, but a fundamental shift in market volatility. Standard indicators are reactive to price. We wanted to create a system that was predictive of market state. VoVix+ was designed to go one level deeper—to analyze the behavior, character, and momentum of volatility itself.
The challenge was twofold. First, to create a robust mathematical model to quantify these abstract concepts. This led to the multi-layered analysis of ATR differentials and standard deviations. Second, to make this complex data intuitive and actionable. This drove the creation of the "Visual Universe," where abstract mathematical values are translated into geometric shapes, flows, and fields. The adaptive system was intentionally kept simple and transparent, focusing on a single, impactful parameter (time-based exits) to provide performance feedback without becoming an inscrutable "black box." The result is a tool that is both profoundly deep in its analysis and remarkably clear in its presentation.
⚠️ RISK DISCLAIMER AND BEST PRACTICES
VoVix+ is an advanced analytical tool, not a guarantee of future profits. All financial markets carry inherent risk. The backtesting results shown by the strategy are historical and do not guarantee future performance. This strategy incorporates realistic commission and slippage settings by default, but market conditions can vary. Always practice sound risk management, use position sizes appropriate for your account equity, and never risk more than you can afford to lose. It is recommended to use this strategy as part of a comprehensive trading plan. This was developed specifically for Futures
"The prevailing wisdom is that markets are always right. I take the opposite view. I assume that markets are always wrong. Even if my assumption is occasionally wrong, I use it as a working hypothesis."
— George Soros
— Dskyz, Trade with insight. Trade with anticipation.
Futures Beta Overview with Different BenchmarksBeta Trading and Its Implementation with Futures
Understanding Beta
Beta is a measure of a security's volatility in relation to the overall market. It represents the sensitivity of the asset's returns to movements in the market, typically benchmarked against an index like the S&P 500. A beta of 1 indicates that the asset moves in line with the market, while a beta greater than 1 suggests higher volatility and potential risk, and a beta less than 1 indicates lower volatility.
The Beta Trading Strategy
Beta trading involves creating positions that exploit the discrepancies between the theoretical (or expected) beta of an asset and its actual market performance. The strategy often includes:
Long Positions on High Beta Assets: Investors might take long positions in assets with high beta when they expect market conditions to improve, as these assets have the potential to generate higher returns.
Short Positions on Low Beta Assets: Conversely, shorting low beta assets can be a strategy when the market is expected to decline, as these assets tend to perform better in down markets compared to high beta assets.
Betting Against (Bad) Beta
The paper "Betting Against Beta" by Frazzini and Pedersen (2014) provides insights into a trading strategy that involves betting against high beta stocks in favor of low beta stocks. The authors argue that high beta stocks do not provide the expected return premium over time, and that low beta stocks can yield higher risk-adjusted returns.
Key Points from the Paper:
Risk Premium: The authors assert that investors irrationally demand a higher risk premium for holding high beta stocks, leading to an overpricing of these assets. Conversely, low beta stocks are often undervalued.
Empirical Evidence: The paper presents empirical evidence showing that portfolios of low beta stocks outperform portfolios of high beta stocks over long periods. The performance difference is attributed to the irrational behavior of investors who overvalue riskier assets.
Market Conditions: The paper suggests that the underperformance of high beta stocks is particularly pronounced during market downturns, making low beta stocks a more attractive investment during volatile periods.
Implementation of the Strategy with Futures
Futures contracts can be used to implement the betting against beta strategy due to their ability to provide leveraged exposure to various asset classes. Here’s how the strategy can be executed using futures:
Identify High and Low Beta Futures: The first step involves identifying futures contracts that have high beta characteristics (more sensitive to market movements) and those with low beta characteristics (less sensitive). For example, commodity futures like crude oil or agricultural products might exhibit high beta due to their price volatility, while Treasury bond futures might show lower beta.
Construct a Portfolio: Investors can construct a portfolio that goes long on low beta futures and short on high beta futures. This can involve trading contracts on stock indices for high beta stocks and bonds for low beta exposures.
Leverage and Risk Management: Futures allow for leverage, which means that a small movement in the underlying asset can lead to significant gains or losses. Proper risk management is essential, using stop-loss orders and position sizing to mitigate the inherent risks associated with leveraged trading.
Adjusting Positions: The positions may need to be adjusted based on market conditions and the ongoing performance of the futures contracts. Continuous monitoring and rebalancing of the portfolio are essential to maintain the desired risk profile.
Performance Evaluation: Finally, investors should regularly evaluate the performance of the portfolio to ensure it aligns with the expected outcomes of the betting against beta strategy. Metrics like the Sharpe ratio can be used to assess the risk-adjusted returns of the portfolio.
Conclusion
Beta trading, particularly the strategy of betting against high beta assets, presents a compelling approach to capitalizing on market inefficiencies. The research by Frazzini and Pedersen emphasizes the benefits of focusing on low beta assets, which can yield more favorable risk-adjusted returns over time. When implemented using futures, this strategy can provide a flexible and efficient means to execute trades while managing risks effectively.
References
Frazzini, A., & Pedersen, L. H. (2014). Betting against beta. Journal of Financial Economics, 111(1), 1-25.
Fama, E. F., & French, K. R. (1992). The cross-section of expected stock returns. Journal of Finance, 47(2), 427-465.
Black, F. (1972). Capital Market Equilibrium with Restricted Borrowing. Journal of Business, 45(3), 444-454.
Ang, A., & Chen, J. (2010). Asymmetric volatility: Evidence from the stock and bond markets. Journal of Financial Economics, 99(1), 60-80.
By utilizing the insights from academic literature and implementing a disciplined trading strategy, investors can effectively navigate the complexities of beta trading in the futures market.
Average True Range with EMAIncreasing and decreasing volatility in respect to ATR crossing an ema of ATR.
Ema acts as a proxy for look-back period as per Historical Volatility Percentile.
ATR is a proxy for Volatility as per standard deviation.
Divergence below ema means low volatility: the more divergence, the lower.
Divergence above the ema means high volatility.
Volatility MeterThis is my third published indicator; its simple, but don't underestimate it. As the name suggests, it measures volatility. Specifically, it measures this through the incremental difference in closing prices. It then uses an SMA to smooth out the indicator's values, this will allow the trader to see the trend in volatility: Is it increasing? decreasing? etc.
If you have any thoughts or ideas about changing the indicator, let me know.
-racer8
Ultimate Moving Average Bands [CC+RedK]The Ultimate Moving Average Bands were created by me and @RedKTrader and this converts our Ultimate Moving Average into volatility bands that use the same adaptive logic to create the bands. I have enabled everything to be fully adjustable so please let me know if you find a more useful setting than what I have here by default. I'm sure everyone is familiar with volatility bands but generally speaking if a price goes above the volatility bands then this is either a sign of an extremely strong uptrend or a potential reversal point and vice versa. I have included strong buy and sell signals in addition to normal ones so darker colors are strong signals and lighter colors are normal ones. Buy when the lines turn green and sell when they turn red.
Let me know if there are any other scripts you would like to see me publish!
ER-Adaptive ATR, STD-Adaptive Damiani Volatmeter [Loxx]ER-Adaptive ATR, STD-Adaptive Damiani Volatmeter is a Damiani Volatmeter with both Efficiency-Ratio Adaptive ATR, used in place of ATR, and Adaptive Deviation, used in place of Standard Deviation.
What is Adaptive Deviation?
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis we usually use it to measure the level of current volatility .
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA , we can call it EMA deviation. And added to that, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
The green line is the Adaptive Deviation, the white line is regular Standard Deviation. This concept will be used in future indicators to further reduce noise and adapt to price volatility .
See here for a comparison between Adaptive Deviation and Standard Deviation
What is Efficiency Ratio Adaptive ATR?
Average True Range (ATR) is widely used indicator in many occasions for technical analysis . It is calculated as the RMA of true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range
See here for a comparison between Efficiency-Ratio Adaptive ATR, and ATR.
What is the Damiani Volatmeter?
Damiani Volatmeter uses ATR and Standard deviation to tease out ticker volatility so you can better understand when it's the ideal time to trade. The idea here is that you only take trades when volatility is high so this indicator is to be coupled with various other indicators to validate the other indicator's signals. This is also useful for detecting crabbing and chopping markets.
Shoutout to user @xinolia for the DV function used here.
Anything red means that volatility is low. Remember volatility doesn't have a direction. Anything green means volatility high despite the direction of price. The core signal line here is the green and red line that dips below two while threshold lines to "recharge". Maximum recharge happen when the core signal line shows a yellow ping. Soon after one or many yellow pings you should expect a massive upthrust of volatility . The idea here is you don't trade unless volatility is rising or green. This means that the Volatmeter has to dip into the recharge zone, recharge and then spike upward. You can also attempt to buy or sell reversals with confluence indicators when volatility is in the recharge zone, but I wouldn't recommend this. However, if you so choose to do this, then use the following indicator for confluence.
And last reminder, volatility doesn't have a direction! Red doesn't mean short, and green doesn't mean long, Red means don't trade period regardless of direction long/short, and green means trade no matter the direction long/short. This means you'll have to add an indicator that does show direction such as a mean reversion indicator like Fisher Transform or a Gaussian Filter. You can search my public scripts for various Fisher Transform and Gaussian Filter indicators.
Price-Filtered Spearman Rank Correl. w/ Floating Levels is considered the Mercedes Benz of reversal indicators
Comparison between this indicator, ER-Adaptive ATR, STD-Adaptive Damiani Volatmeter , and the regular Damiani Volatmeter . Notice that the adaptive version catches more volatility than the regular version.
How signals work
RV = Rising Volatility
VD = Volatility Dump
Plots
White line is signal
Thick red/green line is the Volatmeter line
The dotted lower lines are the zero line and minimum recharging line
Included
Bar coloring
Alerts
Signals
Related indicators
Variety Moving Average Waddah Attar Explosion (WAE)
Damiani Volatmeter
rv_iv_vrpThis script provides realized volatility (rv), implied volatility (iv), and volatility risk premium (vrp) information for each of CBOE's volatility indices. The individual outputs are:
- Blue/red line: the realized volatility. This is an annualized, 20-period moving average estimate of realized volatility--in other words, the variability in the instrument's actual returns. The line is blue when realized volatility is below implied volatility, red otherwise.
- Fuchsia line (opaque): the median of realized volatility. The median is based on all data between the "start" and "end" dates.
- Gray line (transparent): the implied volatility (iv). According to CBOE's volatility methodology, this is similar to a weighted average of out-of-the-money ivs for options with approximately 30 calendar days to expiration. Notice that we compare rv20 to iv30 because there are about twenty trading periods in thirty calendar days.
- Fuchsia line (transparent): the median of implied volatility.
- Lightly shaded gray background: the background between "start" and "end" is shaded a very light gray.
- Table: the table shows the current, percentile, and median values for iv, rv, and vrp. Percentile means the value is greater than "N" percent of all values for that measure.
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Volatility risk premium (vrp) is simply the difference between implied and realized volatility. Along with implied and realized volatility, traders interpret this measure in various ways. Some prefer to be buying options when there volatility, implied or realized, reaches absolute levels, or low risk premium, whereas others have the opposite opinion. However, all volatility traders like to look at these measures in relation to their past values, which this script assists with.
By the way, this script is similar to my "vol premia," which provides the vrp data for all of these instruments on one page. However, this script loads faster and lets you see historical data. I recommend viewing the indicator and the corresponding instrument at the same time, to see how volatility reacts to changes in the underlying price.
Ehlers AM Detector [CC]The AM Detector was created by John Ehlers (Stocks and Commodities May 2021 pg 14) and this is his first volatility indicator I believe. Since this is a more informational indicator rather than a buy or sell signal generator, I have included buy and sell signals for a simple moving average but feel free to use this in combo with any other system you use. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators you would like to see me publish!
Gaussian Smooth Trend | QuantEdgeB🧠 Introducing Gaussian Smooth Trend (GST) by QuantEdgeB
🛠️ Overview
Gaussian Smooth Trend (GST) is an advanced volatility-filtered trend-following system that blends multiple smoothing techniques into a single directional bias tool. It is purpose-built to reduce noise, isolate meaningful price shifts, and deliver early trend detection while dynamically adapting to market volatility.
GST leverages the Gaussian filter as its core engine, wrapped in a layered framework of DEMA smoothing, SMMA signal tracking, and standard deviation-based breakout thresholds, producing a powerful toolset for trend confirmation and momentum-based decision-making.
🔍 How It Works
1️⃣ DEMA Smoothing Engine
The indicator begins by calculating a Double Exponential Moving Average (DEMA), which provides a responsive and noise-resistant base input for subsequent filtering.
2️⃣ Gaussian Filter
A custom Gaussian kernel is applied to the DEMA signal, allowing the system to detect smooth momentum shifts while filtering out short-term volatility.
This is especially powerful during low-volume or sideways markets where traditional MAs struggle.
3️⃣ SMMA Layer with Z-Filtering
The filtered Gaussian signal is then passed through a custom Smoothed Moving Average (SMMA). A standard deviation envelope is constructed around this SMMA, dynamically expanding/contracting based on market volatility.
4️⃣ Signal Generation
• ✅ Long Signal: Price closes above Upper SD Band
• ❌ Short Signal: Price closes below Lower SD Band
• ➖ No trade: Price stays within the band → market indecision
✨ Key Features
🔹 Multi-Stage Trend Detection
Combines DEMA → Gaussian Kernel → SMMA → SD Bands for robust signal integrity across market conditions.
🔹 Gaussian Adaptive Filtering
Applies a tunable sigma parameter for the Gaussian kernel, enabling you to fine-tune smoothness vs. responsiveness.
🔹 Volatility-Aware Trend Zones
Price must close outside of dynamic SD envelopes to trigger signals — reducing whipsaws and increasing signal quality.
🔹 Dynamic Color-Coded Visualization
Candle coloring and band fills reflect live trend state, making the chart intuitive and fast to read.
⚙️ Custom Settings
• DEMA Source: Price stream used for smoothing (default: close)
• DEMA Length: Period for initial exponential smoothing (default: 7)
• Gaussian Length / Sigma: Controls smoothing strength of kernel filter
• SMMA Length: Final smoothing layer (default: 12)
• SD Length: Lookback period for standard deviation filtering (default: 30)
• SD Mult Up / Down: Adjusts distance of upper/lower breakout zones (default: 2.5 / 1.8)
• Color Modes: Six distinct color palettes (e.g., Strategy, Warm, Cool)
• Signal Labels: Toggle on/off entry markers ("𝓛𝓸𝓷𝓰", "𝓢𝓱𝓸𝓻𝓽")
📌 Trading Applications
✅ Trend-Following → Enter on confirmed breakouts from Gaussian-smoothed volatility zones
✅ Breakout Validation → Use SD bands to avoid false breakouts during chop
✅ Volatility Compression Monitoring → Narrowing bands often precede large directional moves
✅ Overlay-Based Confirmation → Can complement other QuantEdgeB indicators like K-DMI, BMD, or Z-SMMA
📌 Conclusion
Gaussian Smooth Trend (GST) delivers a precision-built trend model tailored for modern traders who demand both clarity and control. The layered signal architecture, combined with volatility awareness and Gaussian signal enhancement, ensures accurate entries, clean visualizations, and actionable trend structure — all in real-time.
🔹 Summary Highlights
1️⃣ Multi-stage Smoothing — DEMA → Gaussian → SMMA for deep signal integrity
2️⃣ Volatility-Aware Filtering — SD bands prevent false entries
3️⃣ Visual Trend Mapping — Gradient fills + candle coloring for clean charts
4️⃣ Highly Customizable — Adapt to your timeframe, style, and volatility
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Volatility-Adjusted DEMA Supertrend [QuantAlgo]Introducing the Volatility-Adjusted DEMA Supertrend by QuantAlgo 📈💫
Take your trading and investing strategies to the next level with the Volatility-Adjusted DEMA Supertrend , a dynamic tool designed to adapt to market volatility and provide clear, actionable trend signals. This innovative indicator is ideal for both traders and investors looking for a more responsive approach to market trends, helping you capture potential shifts with greater precision.
🌟 Key Features:
🛠 Customizable Trend Settings: Adjust the period for trend calculation and fine-tune the sensitivity to price movements. This flexibility allows you to tailor the Supertrend to your unique trading or investing strategy, whether you're focusing on shorter or longer timeframes.
📊 Volatility-Responsive Multiplier: The Supertrend dynamically adjusts its sensitivity based on real-time market volatility. This could help filter out noise in calmer markets and provide more accurate signals during periods of heightened volatility.
✨ Trend-Based Color-Coding: Visualize bullish and bearish trends with ease. The indicator paints candles and plots trend lines with distinct colors based on the current market direction, offering quick, clear insights into potential opportunities.
🔔 Custom Alerts: Set up alerts for key trend shifts to ensure you're notified of significant market changes. These alerts would allow you to act swiftly, potentially capturing opportunities without needing to constantly monitor the charts.
📈 How to Use:
✅ Add the Indicator: Add the Volatility-Adjusted DEMA Supertrend to your chart. Customize the trend period, volatility settings, and price source to match your trading or investing style. This ensures the indicator aligns with your market strategy.
👀 Monitor Trend Shifts: Watch the color-coded trend lines and candles as they dynamically shift based on real-time market conditions. These visual cues help you spot potential trend reversals and confirm your entries and exits with greater confidence.
🔔 Set Alerts: Configure alerts for key trend shifts, allowing you to stay informed of potential market reversals or continuation patterns, even when you're not actively watching the market.
⚙️ How It Works:
The Volatility-Adjusted DEMA Supertrend is designed to adapt to changes in market conditions, making it highly responsive to price volatility. The indicator calculates a trend line based on price and volatility, dynamically adjusting it to reflect recent market behavior. When the market experiences higher volatility, the trend line becomes more flexible, potentially allowing for greater sensitivity to rapid price movements. Conversely, during periods of low volatility, the indicator tightens its range, helping to reduce noise and avoid false signals.
The indicator includes a volatility-responsive multiplier, which further enhances its adaptability to market conditions. This means the trend direction would always be based on the latest market data, potentially helping you stay ahead of shifts or continuation trends. The Supertrend's visual color-coding simplifies the process of identifying bullish or bearish trends, while customizable alerts ensure you can stay on top of significant changes in market direction.
This tool is versatile and could be applied across various markets and timeframes, making it a valuable addition for both traders and investors. Whether you’re trading in fast-moving markets or focusing on longer-term investments, the Volatility-Adjusted DEMA Supertrend could help you remain aligned with the current market environment.
Disclaimer:
This indicator is designed to enhance your analysis by providing trend information, but it should not be used as the sole basis for making trading or investing decisions. Always combine it with other forms of analysis and risk management practices. No statements or claims aim to be financial advice, and no signals from us or our indicators should be interpreted as such. Past performance is not indicative of future results.
VIX Percentile Rank HistogramVIX Percentile Rank Histogram
The VIX Percentile Rank Histogram provides a visual representation of the CBOE Volatility Index (VIX) percentile rank over a customizable lookback period, helping traders gauge market sentiment and make informed trading decisions.
Overview:
This indicator calculates the percentile rank of the VIX over a specified lookback period and displays it as a histogram. The histogram helps traders understand whether the current VIX level is relatively high or low compared to its recent history. This information is particularly useful for timing entries and exits in the S&P 500 or related ETFs and Mega Caps.
How It Works:
VIX Data Integration: The script fetches daily VIX close prices, regardless of the chart you are viewing, to analyze market volatility.
Percentile Rank Calculation: The indicator calculates the rank percentile of the VIX over the chosen lookback period.
Histogram Visualization: The histogram plots the difference between the flipped VIX percentile rank and 50, showing green bars for ranks below 50 (indicating lower market volatility) and red bars for ranks above 50 (indicating higher market volatility).
Usage:
This indicator is most effective when trading the S&P 500 (SPX, SPY, ES1!) or ETFs and Mega Caps that closely follow the S&P 500. It provides insight into market sentiment, helping traders make more informed decisions.
Timing Entries and Exits: Green histogram readings suggest it's a good time to enter or hold long positions, while red readings suggest considering exits or short positions.
Market Sentiment: A high VIX percentile rank (red bars) indicates market fear and uncertainty, while a low percentile rank (green bars) suggests investor confidence and reduced volatility.
Key Features:
Customizable Lookback Period: The default lookback period is set to 20 days, but can be adjusted based on the trader's average trade duration. For example, if your trades typically last 20 days, a 20-day lookback period helps contextualize the VIX level relative to its recent history.
Histogram Visualization: The histogram provides a clear visual representation of market volatility.
Green Bars: Indicate a lower-than-median VIX percentile rank, suggesting reduced market volatility.
Red Bars: Indicate a higher-than-median VIX percentile rank, suggesting increased market volatility.
Threshold Line: A dashed gray line at the 0 level serves as a visual reference for the median VIX rank.
Important Note:
This indicator always shows readings from the VIX, regardless of the chart you are viewing. For example, if you are looking at Natural Gas futures, this indicator will provide no relevant data. It works best when trading the S&P 500 or related ETFs and Mega Caps.
Volatility Zones (VStop + Bands) — Fixed (v2)📝 What this indicator is
This script is called “Volatility Zones (VStop + Bands)”.
It is an ATR-based volatility indicator that combines dynamic volatility bands, a Volatility Stop line (VStop), and volatility spike detection into a single tool.
Unlike moving average–based indicators, this tool does not rely on averages of price direction. Instead, it measures the market’s true volatility and reacts to expansions or contractions in price ranges.
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⚙️ How it is built
The indicator uses several volatility-based components:
1. Average True Range (ATR)
o ATR is calculated over a user-defined length.
o It measures how much price typically moves in a given number of bars, making it the foundation of this indicator.
2. Volatility Bands
o Upper band = close + ATR × factor
o Lower band = close - ATR × factor
o The area between them is shaded.
o This gives traders an immediate visual sense of market volatility width — wide bands = high volatility, narrow bands = quiet market.
3. Volatility Stop (VStop)
o A stateful trailing stop based on ATR.
o It tracks the highest (or lowest) price in the current trend and places a stop offset by ATR × multiplier.
o When price crosses this stop, the indicator flips trend direction.
o This creates a dynamic stop-and-reverse mechanism that adapts to volatility.
4. Trend Zones
o When the trend is bullish, the stop is green and the chart background is shaded softly green.
o When bearish, the stop is red and the background is shaded softly red.
o This makes the market’s directional bias visually clear at all times.
5. Flip Signals (Buy/Sell Arrows)
o Whenever the VStop flips, arrows appear:
Green BUY arrows below price when the trend turns bullish.
Red SELL arrows above price when the trend turns bearish.
o These are also tied to built-in alerts for automation.
6. Volatility Spike Detection
o The script compares current ATR to its recent average.
o If ATR suddenly expands above a threshold, a small yellow “VOL” marker appears at the top of the chart.
o This highlights potential breakout phases or unusual volatility events.
7. Stop Labels
o At every trend flip, a small label appears at the bar, showing the exact stop level.
o This makes it easy to use the stop as a reference for risk management.
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📊 How it works in practice
• When price is above the VStop line, the market is considered in an uptrend.
• When price is below the VStop line, the market is in a downtrend.
• The bands expand/contract with volatility, helping traders gauge risk and position sizing.
• Flip arrows signal when trend direction changes.
• Volatility spikes warn traders that the market is entering a higher-risk phase, often before strong moves.
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🎯 How it may help traders
• Trend following → Helps traders identify whether the market is trending up or down.
• Stop placement → Provides a dynamic stop level that adjusts to volatility.
• Volatility awareness → Shaded bands and spike markers show when the market is likely to become unstable.
• Trade timing → Flip arrows and labels help identify potential entry or exit points.
• Risk management → Wide bands indicate higher risk; narrow bands suggest safer, tighter ranges.
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🌍 In what markets it is useful
Because the indicator is based purely on volatility, it works across all asset classes and timeframes:
• Stocks & ETFs → Helps identify breakouts and long-term trends.
• Forex → Very useful in spot FX where volatility shifts frequently.
• Crypto → ATR reacts strongly to high volatility, helping traders adapt stops dynamically.
• Futures & Commodities → Great for tracking trending commodities and managing risk.
Scalpers, swing traders, and position traders can all benefit by adjusting the ATR length and multipliers to suit their trading style.
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💡 Originality of this script
This is not just a mashup of existing indicators. It integrates:
• ATR-based Volatility Bands for context,
• A stateful Volatility Stop (adapted and rewritten cleanly),
• Flip arrows and labels for actionable trading signals,
• Volatility spike detection to highlight regime shifts.
The result is a comprehensive volatility-aware trading tool that goes beyond just plotting ATR or trend stops.
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🔔 Alerts
• Buy Flip → triggers when the trend changes bullish.
• Sell Flip → triggers when the trend changes bearish.
Traders can connect these alerts to automated strategies, bots, or notification systems.
ATR Strength Index~~~~~~~ATRRSI~~~~~~~~~
Understanding the ATR Strength IndexThe "ATR Strength Index" (ATR SI) is a custom technical indicator derived by applying the calculation methodology of the Relative Strength Index (RSI) to the values of the Average True Range (ATR).
While the standard RSI measures the momentum of price changes, the ATR SI measures the momentum of volatility itself, as represented by the ATR.It is important to note that this is not a standard, widely recognised indicator like the traditional RSI or ATR.
It's a custom construction designed to provide a different perspective on market dynamics – specifically, the speed and magnitude of changes in volatility.
How it is Calculated
The calculation of the ATR Strength Index follows the same steps as the standard RSI, but the input data is the ATR value for each period, rather than the price.Let ATRi be the Average True Range value for the current period i.Let ATRi−1 be the Average True Range value for the previous period i−1.Calculate the period-over-period change in ATR:ΔATRi=ATRi−ATRi−1Separate ATR Gains and ATR Losses:If ΔATRi>0, then ATR,Gaini=ΔATRi and ATR,Lossi=0.If ΔATRi<0, then ATR,Gaini=0 and ATR,Lossi=∣ΔATRi∣.If ΔATRi=0, then ATR,Gaini=0 and ATR,Lossi=0.Calculate the Smoothed Average ATR Gain and Average ATR Loss over a specified lookback period (let's call this the "RSI Length" or n).
This typically uses a smoothing method similar to Wilder's original RSI calculation (a modified moving average or exponential moving average).Average,ATR,Gainn=Smoothed Average of ATR,Gain over n periodsAverage,ATR,Lossn=Smoothed Average of ATR,Loss over n periodsCalculate the ATR Relative Strength (ATR RS):ATR,RSn=Average,ATR,LossnAverage,ATR,GainnCalculate the ATR Strength Index:ATR,SIn=100−1+ATR,RSn100The resulting index oscillates between 0 and 100, just like the standard RSI.
How to Use It
Interpreting the ATR Strength Index focuses on the momentum of volatility rather than price momentum:High Values (e.g., above 70): Indicate that volatility (as measured by ATR) has been increasing rapidly over the chosen period.
This could suggest a market transitioning from a period of low volatility to high volatility, potentially preceding or accompanying strong directional price moves or increased choppiness.Low Values (e.g., below 30): Indicate that volatility has been decreasing rapidly.
This could suggest a market transitioning from high volatility to low volatility, potentially entering a period of consolidation or ranging price action.Midline (50): Represents a balance between increasing and decreasing volatility momentum.Divergence: You could potentially look for divergence between the ATR value itself and the ATR Strength Index. For example, if ATR is making higher highs but the ATR SI is making lower highs, it might suggest that while volatility is still increasing, the speed of that increase is slowing down. The interpretation and reliability of such divergence would need careful testing.
This indicator is best used as a supplementary tool to gain insight into the underlying volatility dynamics of the market, rather than as a primary signal generator for price direction.
It can help in understanding the current market environment – whether volatility is picking up or dying down – which can inform the suitability of different trading strategies (e.g., trend-following strategies might be more effective when volatility momentum is high, while range-bound strategies might suit periods of low volatility momentum).
Uniqueness
The ATR Strength Index is unique because it applies a momentum oscillator's logic (RSI) to a volatility indicator's output (ATR).Standard RSI: Focuses on the directional force of price movements.Standard ATR: Measures the amount of volatility, regardless of direction.ATR Strength Index: Measures the speed and direction of change in volatility.
It provides a perspective that neither the standard RSI nor ATR offers on their own – a quantified measure of how quickly the market's choppiness or range is expanding or contracting. This can be valuable for traders who incorporate volatility analysis into their decision-making process.In summary, the ATR Strength Index is a custom indicator that adapts the RSI calculation to measure the momentum of volatility, offering a unique view on market dynamics by showing how rapidly volatility is increasing or decreasing.
Amplitude [Anan]The Amplitude indicator calculates and visualizes both the amplitude and cumulative amplitude of price movements, providing traders with insights into price volatility and trend strength. By distinguishing between positive and negative amplitude movements, this indicator aids in identifying bullish and bearish sentiments, potential reversal points, and confirming trend directions.
█ Main Formulas
‣ Amplitude = High - Low
‣ Cumulative Amplitude = sum of Amplitude over the specified lookback period
‣ Percentage Amplitude = (Amplitude / Open) × 100%
High: Candle high (or highest high when lookback > 1)
Low: Candle low (or lowest low when lookback > 1)
Open: Open price of the first candle in the lookback period
█ Key Features
✦Dual Amplitude Calculations:
Amplitude: Reflects price range and direction over a short-term period.
Cumulative Amplitude: Aggregates amplitude over a longer period for broader trend analysis.
✦Customizable Parameters: Adjust lookback periods, smoothing options, moving averages and Alerts.
✦Direction Separation: Distinguish between positive and negative amplitude movements to identify market sentiment.
✦Flexible Visualization: Customizable colors and plot styles for enhanced chart readability.
✦Alert System: Generate signals based on amplitude direction and moving average crossovers
█ How to Use and Interpret
✦Understanding Amplitude and Cumulative Amplitude:
‣Amplitude: Measures the price range (high - low) over a specified short-term period.
‣Cumulative Amplitude: Aggregates amplitude over a defined longer-term period.
‣Percentage Representation: shows amplitude relative to the open price from `amp_length` bars ago, providing a normalized view.
‣Interpretation:
Large Amplitude Values: Indicate high volatility.
Small Amplitude Values: Indicate low volatility.
✦Trend Identification:
‣Uptrend: Consistently positive amplitudes and upward-moving averages.
‣Downtrend: Consistently negative amplitudes and downward-moving averages.
✦Overbought/Oversold Conditions:
‣High Positive Amplitude: May indicate overbought conditions and potential reversals.
‣High Negative Amplitude: May indicate oversold conditions and potential reversals.
✦Volatility Analysis:
‣High Amplitude Values: Suggest increased market volatility.
‣Low Amplitude Values: Suggest reduced market volatility.
✦Signal Confirmation:
‣Moving Average Crossovers: Confirm the strength and direction of trends, aiding in informed trading decisions.
✦Trading Strategies:
‣ Breakout Trading: Large increases in amplitude can signal potential breakouts.
‣ Mean Reversion: Extreme amplitude values may indicate upcoming price corrections.
‣ Volatility-Based Strategies: Adjust position sizes or trading frequency based on amplitude magnitudes.
‣ Multi-Timeframe Analysis: Compare amplitudes across different timeframes for a comprehensive market view.
█ Customization Tips
‣ Lookback Periods: Experiment with different periods to suit your trading style and asset characteristics.
‣ Smoothing Settings: Adjust to balance responsiveness and noise reduction.
‣ Percentage Amplitude: Use for normalized comparisons across different price levels.
Risk Management: Position Size & Risk RewardHere is a Risk Management Indicator that calculates stop loss and position sizing based on the volatility of the stock. Most traders use a basic 1 or 2% Risk Rule, where they will not risk more than 1 or 2% of their capital on any one trade. I went further and applied four levels of risk: 0.25%, 0.50%, 1% and 2%. How you apply these different levels of risk is what makes this indicator extremely useful. Here are some common ways to apply this script:
• If the stock is extremely volatile and has a better than 50% chance of hitting the stop loss, then risk only 0.25% of your capital on that trade.
• If a stock has low volatility and has less than 20% change of hitting the stop loss, then risk 2% of your capital on that trade.
• Risking anywhere between 0.25% and 2% is purely based on your intuition and assessment of the market.
• If you are on a losing streak and you want to cut back on your position sizing, then lowering the Risk % can help you weather the storm.
• If you are on a winning streak and your entries are experiencing a higher level of success, then gradually increase the Risk % to reap bigger profits.
• If you want to trade outside the noise of the market or take on more noise/risk, you can adjust the ATR Factor.
• … and whatever else you can imagine using it to benefit your trading.
The position size is calculated using the Capital and Risk % fields, which is the percentage of your total trading capital (a.k.a net liquidity or Capital at Risk). If you instead want to calculate the position size based on a specific amount of money, then enter the amount in the Custom Risk Amt input box. Any amount greater than 0 in the Custom Risk Amt field will override the values in the Capital and Risk % fields.
The stop loss is calculated by using the ATR. The default setting is the 14 RMA, but you can change the length and smoothing of the true range moving average to your liking. Selecting a different length and smoothing affects the stop loss and position size, so choose these values very carefully.
The ATR Factor is a multiplier of the ATR. The ATR Factor can be used to adjust the stop loss and move it outside of the market noise. For the more volatile stock, increase the factor to lower the stop loss and reduce the chance of getting stopped out. For stocks with less volatility , you can lower the factor to raise the stop loss and increase position size. Adjusting the ATR Factor can also be useful when you want the stop loss to be at or below key levels of support.
The Market Session is the hours the market is open. The Market Session only affects the Opening Range Breakout (ORB) option, so it’s important to change these values if you’re trading the ORB and you’re outside of Eastern Standard Time or you’re trading in a foreign exchange.
The ORB is a bonus to the script. When enabled, the indicator will only appear in the first green candle of the day (09:30:00 or 09:30 AM EST or the start time specified in Market Session). When using the ORB, the stop loss is based on the spread of the first candle at the Open. The spread is the difference between the High and Low of the green candle. On 1-day or higher timeframes, the indicator will be the spread of the last (or current) candle.
The output of the indicator is a label overlaying the chart:
1. ATR (14 RMA x2) – This indicated that the stop loss is determined by the ATR. The x2 is the ATR Factor. If ORB is selected, then the first line will show SPREAD, instead of ATR.
2. Capital – This is your total capital or capital at risk.
3. Risk X% of Capital – The amount you’re risking on a % of the Capital. If a Custom Risk Amt is entered, then Risk Amount will be shown in place of Capital and Risk % of Capital.
4. Entry – The current price.
5. Stop Loss – The stop loss price.
6. -1R – The stop loss price and the amount that will be lost of the stop loss is hit.
7. – These are the target prices, or levels where you will want to take profit.
This script is primarily meant for people who are new to active trading and who are looking for a sound risk management strategy based on market volatility . This script can also be used by the more experienced trader who is using a similar system, but also wants to see it applied as an indicator on TradingView. I’m looking forward to maintaining this script and making it better in future revisions. If you want to include or change anything you believe will be a good change or feature, then please contact me in TradingView.
Seasonality Monte Carlo Forecaster [BackQuant]Seasonality Monte Carlo Forecaster
Plain-English overview
This tool projects a cone of plausible future prices by combining two ideas that traders already use intuitively: seasonality and uncertainty. It watches how your market typically behaves around this calendar date, turns that seasonal tendency into a small daily “drift,” then runs many randomized price paths forward to estimate where price could land tomorrow, next week, or a month from now. The result is a probability cone with a clear expected path, plus optional overlays that show how past years tended to move from this point on the calendar. It is a planning tool, not a crystal ball: the goal is to quantify ranges and odds so you can size, place stops, set targets, and time entries with more realism.
What Monte Carlo is and why quants rely on it
• Definition . Monte Carlo simulation is a way to answer “what might happen next?” when there is randomness in the system. Instead of producing a single forecast, it generates thousands of alternate futures by repeatedly sampling random shocks and adding them to a model of how prices evolve.
• Why it is used . Markets are noisy. A single point forecast hides risk. Monte Carlo gives a distribution of outcomes so you can reason in probabilities: the median path, the 68% band, the 95% band, tail risks, and the chance of hitting a specific level within a horizon.
• Core strengths in quant finance .
– Path-dependent questions : “What is the probability we touch a stop before a target?” “What is the expected drawdown on the way to my objective?”
– Pricing and risk : Useful for path-dependent options, Value-at-Risk (VaR), expected shortfall (CVaR), stress paths, and scenario analysis when closed-form formulas are unrealistic.
– Planning under uncertainty : Portfolio construction and rebalancing rules can be tested against a cloud of plausible futures rather than a single guess.
• Why it fits trading workflows . It turns gut feel like “seasonality is supportive here” into quantitative ranges: “median path suggests +X% with a 68% band of ±Y%; stop at Z has only ~16% odds of being tagged in N days.”
How this indicator builds its probability cone
1) Seasonal pattern discovery
The script builds two day-of-year maps as new data arrives:
• A return map where each calendar day stores an exponentially smoothed average of that day’s log return (yesterday→today). The smoothing (90% old, 10% new) behaves like an EWMA, letting older seasons matter while adapting to new information.
• A volatility map that tracks the typical absolute return for the same calendar day.
It calculates the day-of-year carefully (with leap-year adjustment) and indexes into a 365-slot seasonal array so “March 18” is compared with past March 18ths. This becomes the seasonal bias that gently nudges simulations up or down on each forecast day.
2) Choice of randomness engine
You can pick how the future shocks are generated:
• Daily mode uses a Gaussian draw with the seasonal bias as the mean and a volatility that comes from realized returns, scaled down to avoid over-fitting. It relies on the Box–Muller transform internally to turn two uniform random numbers into one normal shock.
• Weekly mode uses bootstrap sampling from the seasonal return history (resampling actual historical daily drifts and then blending in a fraction of the seasonal bias). Bootstrapping is robust when the empirical distribution has asymmetry or fatter tails than a normal distribution.
Both modes seed their random draws deterministically per path and day, which makes plots reproducible bar-to-bar and avoids flickering bands.
3) Volatility scaling to current conditions
Markets do not always live in average volatility. The engine computes a simple volatility factor from ATR(20)/price and scales the simulated shocks up or down within sensible bounds (clamped between 0.5× and 2.0×). When the current regime is quiet, the cone narrows; when ranges expand, the cone widens. This prevents the classic mistake of projecting calm markets into a storm or vice versa.
4) Many futures, summarized by percentiles
The model generates a matrix of price paths (capped at 100 runs for performance inside TradingView), each path stepping forward for your selected horizon. For each forecast day it sorts the simulated prices and pulls key percentiles:
• 5th and 95th → approximate 95% band (outer cone).
• 16th and 84th → approximate 68% band (inner cone).
• 50th → the median or “expected path.”
These are drawn as polylines so you can immediately see central tendency and dispersion.
5) A historical overlay (optional)
Turn on the overlay to sketch a dotted path of what a purely seasonal projection would look like for the next ~30 days using only the return map, no randomness. This is not a forecast; it is a visual reminder of the seasonal drift you are biasing toward.
Inputs you control and how to think about them
Monte Carlo Simulation
• Price Series for Calculation . The source series, typically close.
• Enable Probability Forecasts . Master switch for simulation and drawing.
• Simulation Iterations . Requested number of paths to run. Internally capped at 100 to protect performance, which is generally enough to estimate the percentiles for a trading chart. If you need ultra-smooth bands, shorten the horizon.
• Forecast Days Ahead . The length of the cone. Longer horizons dilute seasonal signal and widen uncertainty.
• Probability Bands . Draw all bands, just 95%, just 68%, or a custom level (display logic remains 68/95 internally; the custom number is for labeling and color choice).
• Pattern Resolution . Daily leans on day-of-year effects like “turn-of-month” or holiday patterns. Weekly biases toward day-of-week tendencies and bootstraps from history.
• Volatility Scaling . On by default so the cone respects today’s range context.
Plotting & UI
• Probability Cone . Plots the outer and inner percentile envelopes.
• Expected Path . Plots the median line through the cone.
• Historical Overlay . Dotted seasonal-only projection for context.
• Band Transparency/Colors . Customize primary (outer) and secondary (inner) band colors and the mean path color. Use higher transparency for cleaner charts.
What appears on your chart
• A cone starting at the most recent bar, fanning outward. The outer lines are the ~95% band; the inner lines are the ~68% band.
• A median path (default blue) running through the center of the cone.
• An info panel on the final historical bar that summarizes simulation count, forecast days, number of seasonal patterns learned, the current day-of-year, expected percentage return to the median, and the approximate 95% half-range in percent.
• Optional historical seasonal path drawn as dotted segments for the next 30 bars.
How to use it in trading
1) Position sizing and stop logic
The cone translates “volatility plus seasonality” into distances.
• Put stops outside the inner band if you want only ~16% odds of a stop-out due to noise before your thesis can play.
• Size positions so that a test of the inner band is survivable and a test of the outer band is rare but acceptable.
• If your target sits inside the 68% band at your horizon, the payoff is likely modest; outside the 68% but inside the 95% can justify “one-good-push” trades; beyond the 95% band is a low-probability flyer—consider scaling plans or optionality.
2) Entry timing with seasonal bias
When the median path slopes up from this calendar date and the cone is relatively narrow, a pullback toward the lower inner band can be a high-quality entry with a tight invalidation. If the median slopes down, fade rallies toward the upper band or step aside if it clashes with your system.
3) Target selection
Project your time horizon to N bars ahead, then pick targets around the median or the opposite inner band depending on your style. You can also anchor dynamic take-profits to the moving median as new bars arrive.
4) Scenario planning & “what-ifs”
Before events, glance at the cone: if the 95% band already spans a huge range, trade smaller, expect whips, and avoid placing stops at obvious band edges. If the cone is unusually tight, consider breakout tactics and be ready to add if volatility expands beyond the inner band with follow-through.
5) Options and vol tactics
• When the cone is tight : Prefer long gamma structures (debit spreads) only if you expect a regime shift; otherwise premium selling may dominate.
• When the cone is wide : Debit structures benefit from range; credit spreads need wider wings or smaller size. Align with your separate IV metrics.
Reading the probability cone like a pro
• Cone slope = seasonal drift. Upward slope means the calendar has historically favored positive drift from this date, downward slope the opposite.
• Cone width = regime volatility. A widening fan tells you that uncertainty grows fast; a narrow cone says the market typically stays contained.
• Mean vs. price gap . If spot trades well above the median path and the upper band, mean-reversion risk is high. If spot presses the lower inner band in an up-sloping cone, you are in the “buy fear” zone.
• Touches and pierces . Touching the inner band is common noise; piercing it with momentum signals potential regime change; the outer band should be rare and often brings snap-backs unless there is a structural catalyst.
Methodological notes (what the code actually does)
• Log returns are used for additivity and better statistical behavior: sim_ret is applied via exp(sim_ret) to evolve price.
• Seasonal arrays are updated online with EWMA (90/10) so the model keeps learning as each bar arrives.
• Leap years are handled; indexing still normalizes into a 365-slot map so the seasonal pattern remains stable.
• Gaussian engine (Daily mode) centers shocks on the seasonal bias with a conservative standard deviation.
• Bootstrap engine (Weekly mode) resamples from observed seasonal returns and adds a fraction of the bias, which captures skew and fat tails better.
• Volatility adjustment multiplies each daily shock by a factor derived from ATR(20)/price, clamped between 0.5 and 2.0 to avoid extreme cones.
• Performance guardrails : simulations are capped at 100 paths; the probability cone uses polylines (no heavy fills) and only draws on the last confirmed bar to keep charts responsive.
• Prerequisite data : at least ~30 seasonal entries are required before the model will draw a cone; otherwise it waits for more history.
Strengths and limitations
• Strengths :
– Probabilistic thinking replaces single-point guessing.
– Seasonality adds a small but meaningful directional bias that many markets exhibit.
– Volatility scaling adapts to the current regime so the cone stays realistic.
• Limitations :
– Seasonality can break around structural changes, policy shifts, or one-off events.
– The number of paths is performance-limited; percentile estimates are good for trading, not for academic precision.
– The model assumes tomorrow’s randomness resembles recent randomness; if regime shifts violently, the cone will lag until the EWMA adapts.
– Holidays and missing sessions can thin the seasonal sample for some assets; be cautious with very short histories.
Tuning guide
• Horizon : 10–20 bars for tactical trades; 30+ for swing planning when you care more about broad ranges than precise targets.
• Iterations : The default 100 is enough for stable 5/16/50/84/95 percentiles. If you crave smoother lines, shorten the horizon or run on higher timeframes.
• Daily vs. Weekly : Daily for equities and crypto where month-end and turn-of-month effects matter; Weekly for futures and FX where day-of-week behavior is strong.
• Volatility scaling : Keep it on. Turn off only when you intentionally want a “pure seasonality” cone unaffected by current turbulence.
Workflow examples
• Swing continuation : Cone slopes up, price pulls into the lower inner band, your system fires. Enter near the band, stop just outside the outer line for the next 3–5 bars, target near the median or the opposite inner band.
• Fade extremes : Cone is flat or down, price gaps to the upper outer band on news, then stalls. Favor mean-reversion toward the median, size small if volatility scaling is elevated.
• Event play : Before CPI or earnings on a proxy index, check cone width. If the inner band is already wide, cut size or prefer options structures that benefit from range.
Good habits
• Pair the cone with your entry engine (breakout, pullback, order flow). Let Monte Carlo do range math; let your system do signal quality.
• Do not anchor blindly to the median; recalc after each bar. When the cone’s slope flips or width jumps, the plan should adapt.
• Validate seasonality for your symbol and timeframe; not every market has strong calendar effects.
Summary
The Seasonality Monte Carlo Forecaster wraps institutional risk planning into a single overlay: a data-driven seasonal drift, realistic volatility scaling, and a probabilistic cone that answers “where could we be, with what odds?” within your trading horizon. Use it to place stops where randomness is less likely to take you out, to set targets aligned with realistic travel, and to size positions with confidence born from distributions rather than hunches. It will not predict the future, but it will keep your decisions anchored to probabilities—the language markets actually speak.
