Channel Breakout by NatXateThe Channel Breakout by NatXate is a multi-channel technical indicator designed to identify potential breakout opportunities based on a combination of Keltner Channels, Donchian Channels, and Bollinger Bands.
This indicator helps traders pinpoint buy and sell signals by analyzing price behavior around key channel boundaries, while filtering out false signals using volatility and momentum criteria such as the Average True Range (ATR) and Bollinger Bands Width (BBW).
Key Features:
Keltner Channel:
The Keltner Channel is calculated using an Exponential Moving Average (EMA) and ATR to define upper and lower boundaries.
The upper and lower Keltner boundaries serve as potential breakout levels.
Donchian Channel:
The Donchian Channel tracks the highest high and lowest low over a user-defined period.
Price breaking above or below these boundaries indicates a potential long or short opportunity.
Bollinger Bands:
Bollinger Bands use a Simple Moving Average (SMA) and standard deviation to define dynamic support and resistance levels.
The upper and lower Bollinger boundaries provide an additional layer of confirmation for breakouts.
Bollinger Bands Width (BBW) Filter:
Measures the width of the Bollinger Bands, which reflects market volatility.
A minimum BBW threshold (minBBW) ensures signals are only generated during periods of sufficient volatility, helping to avoid false signals in consolidating markets.
ATR Filter:
The ATR is used to measure market volatility.
Only signals with ATR exceeding a user-defined percentage of the current price (atrThresholdPercent) are considered valid.
Buy and Sell Conditions:
Buy Signal:
Price breaks above the upper boundary of any of the three channels (Keltner, Donchian, or Bollinger Bands).
ATR is above its threshold, indicating sufficient volatility.
BBW is above the minBBW threshold.
Sell Signal:
Price breaks below the lower boundary of any of the three channels.
ATR is above its threshold.
BBW is above the minBBW threshold.
Non-Repainting Logic:
Signals are confirmed only after the bar closes (barstate.isconfirmed), preventing repainting and ensuring reliable signal generation.
Visual Signals:
Buy signals are marked with a green "B" label below the bar.
Sell signals are marked with a red "S" label above the bar.
The upper and lower boundaries of the Keltner Channel, Donchian Channel, and Bollinger Bands are plotted for visual clarity.
Alerts:
Separate alerts are available for Buy and Sell signals:
Buy Signal: "Channel Breakout Buy Signal by NatXate detected!"
Sell Signal: "Channel Breakout Sell Signal by NatXate detected!"
Alerts trigger once per bar close, making it suitable for real-time trading or monitoring.
How It Works:
Trend Identification:
The indicator identifies trends based on price breakouts above or below the channel boundaries.
Volatility Filtering:
Both ATR and BBW filters ensure that only high-probability breakout signals are shown, reducing noise in low-volatility environments.
Signal Confirmation:
Signals are confirmed after the bar closes to prevent false positives or premature triggers.
Parameters:
Keltner Channel Parameters:
lengthKC: Period for the Keltner Channel's EMA.
multKC: ATR multiplier for Keltner Channel boundaries.
Donchian Channel Parameters:
lengthDC: Period for calculating the highest high and lowest low.
Bollinger Bands Parameters:
lengthBB: Period for the Bollinger Bands' SMA.
multBB: Standard deviation multiplier for Bollinger Bands boundaries.
ATR Filter:
atrLength: Period for calculating ATR.
atrThresholdPercent: Minimum ATR as a percentage of the price for valid signals.
BBW Filter:
minBBW: Minimum Bollinger Bands Width required for signal generation.
Use Cases:
Breakout Trading:
Detect potential buy and sell opportunities when price breaks key channel boundaries during high volatility.
Trend Following:
Use the indicator to confirm trends and enter trades in the direction of the breakout.
Avoiding Low-Volatility Periods:
The BBW and ATR filters help avoid false signals in consolidating or choppy markets.
Recommended Usage:
Combine this indicator with additional tools such as volume analysis or momentum oscillators (e.g., MACD, RSI) for further confirmation.
Suitable for various timeframes, from intraday to swing trading.
Backtest thoroughly to adjust parameters for the specific market and timeframe you trade.
Volatilität
Dynamic Intensity Transition Oscillator (DITO)The Dynamic Intensity Transition Oscillator (DITO) is a comprehensive indicator designed to identify and visualize the slope of price action normalized by volatility, enabling consistent comparisons across different assets. This indicator calculates and categorizes the intensity of price movement into six states—three positive and three negative—while providing visual cues and alerts for state transitions.
Components and Functionality
1. Slope Calculation
- The slope represents the rate of change in price action over a specified period (Slope Calculation Period).
- It is calculated as the difference between the current price and the simple moving average (SMA) of the price, divided by the length of the period.
2. Normalization Using ATR
- To standardize the slope across assets with different price scales and volatilities, the slope is divided by the Average True Range (ATR).
- The ATR ensures that the slope is comparable across assets with varying price levels and volatility.
3. Intensity Levels
- The normalized slope is categorized into six distinct intensity levels:
High Positive: Strong upward momentum.
Medium Positive: Moderate upward momentum.
Low Positive: Weak upward movement or consolidation.
Low Negative: Weak downward movement or consolidation.
Medium Negative: Moderate downward momentum.
High Negative: Strong downward momentum.
4. Visual Representation
- The oscillator is displayed as a histogram, with each intensity level represented by a unique color:
High Positive: Lime green.
Medium Positive: Aqua.
Low Positive: Blue.
Low Negative: Yellow.
Medium Negative: Purple.
High Negative: Fuchsia.
Threshold levels (Low Intensity, Medium Intensity) are plotted as horizontal dotted lines for visual reference, with separate colors for positive and negative thresholds.
5. Intensity Table
- A dynamic table is displayed on the chart to show the current intensity level.
- The table's text color matches the intensity level color for easy interpretation, and its size and position are customizable.
6. Alerts for State Transitions
- The indicator includes a robust alerting system that triggers when the intensity level transitions from one state to another (e.g., from "Medium Positive" to "High Positive").
- The alert includes both the previous and current states for clarity.
Inputs and Customization
The DITO indicator offers a variety of customizable settings:
Indicator Parameters
Slope Calculation Period: Defines the period over which the slope is calculated.
ATR Calculation Period: Defines the period for the ATR used in normalization.
Low Intensity Threshold: Threshold for categorizing weak momentum.
Medium Intensity Threshold: Threshold for categorizing moderate momentum.
Intensity Table Settings
Table Position: Allows you to position the intensity table anywhere on the chart (e.g., "Bottom Right," "Top Left").
Table Size: Enables customization of table text size (e.g., "Small," "Large").
Use Cases
Trend Identification:
- Quickly assess the strength and direction of price movement with color-coded intensity levels.
Cross-Asset Comparisons:
- Use the normalized slope to compare momentum across different assets, regardless of price scale or volatility.
Dynamic Alerts:
- Receive timely alerts when the intensity transitions, helping you act on significant momentum changes.
Consolidation Detection:
- Identify periods of low intensity, signaling potential reversals or breakout opportunities.
How to Use
- Add the indicator to your chart.
- Configure the input parameters to align with your trading strategy.
Observe:
The Oscillator: Use the color-coded histogram to monitor price action intensity.
The Intensity Table: Track the current intensity level dynamically.
Alerts: Respond to state transitions as notified by the alerts.
Final Notes
The Dynamic Intensity Transition Oscillator (DITO) combines trend strength detection, cross-asset comparability, and real-time alerts to offer traders an insightful tool for analyzing market conditions. Its user-friendly visualization and comprehensive alerting make it suitable for both novice and advanced traders.
Disclaimer: This indicator is for educational purposes and is not financial advice. Always perform your own analysis before making trading decisions.
Uptrick: Volatility Reversion BandsUptrick: Volatility Reversion Bands is an indicator designed to help traders identify potential reversal points in the market by combining volatility and momentum analysis within one comprehensive framework. It calculates dynamic bands around a simple moving average and issues signals when price interacts with these bands. Below is a fully expanded description, structured in multiple sections, detailing originality, usefulness, uniqueness, and the purpose behind blending standard deviation-based and ATR-based concepts. All references to code have been removed to focus on the written explanation only.
Section 1: Overview
Uptrick: Volatility Reversion Bands centers on a moving average around which various bands are constructed. These bands respond to changes in price volatility and can help gauge potential overbought or oversold conditions. Signals occur when the price moves beyond certain thresholds, which may imply a reversal or significant momentum shift.
Section 2: Originality, Usefulness, Uniqness, Purpose
This indicator merges two distinct volatility measurements—Bollinger Bands and ATR—into one cohesive system. Bollinger Bands use standard deviation around a moving average, offering a baseline for what is statistically “normal” price movement relative to a recent mean. When price hovers near the upper band, it may indicate overbought conditions, whereas price near the lower band suggests oversold conditions. This straightforward construction often proves invaluable in moderate-volatility settings, as it pinpoints likely turning points and gauges a market’s typical trading range.
Yet Bollinger Bands alone can falter in conditions marked by abrupt volatility spikes or sudden gaps that deviate from recent norms. Intraday news, earnings releases, or macroeconomic data can alter market behavior so swiftly that standard-deviation bands do not keep pace. This is where ATR (Average True Range) adds an important layer. ATR tracks recent highs, lows, and potential gaps to produce a dynamic gauge of how much price is truly moving from bar to bar. In quieter times, ATR contracts, reflecting subdued market activity. In fast-moving markets, ATR expands, exposing heightened volatility on each new bar.
By overlaying Bollinger Bands and ATR-based calculations, the indicator achieves a broader situational awareness. Bollinger Bands excel at highlighting relative overbought or oversold areas tied to an established average. ATR simultaneously scales up or down based on real-time market swings, signaling whether conditions are calm or turbulent. When combined, this means a price that barely crosses the Bollinger Band but also triggers a high ATR-based threshold is likely experiencing a volatility surge that goes beyond typical market fluctuations. Conversely, a price breach of a Bollinger Band when ATR remains low may still warrant attention, but not necessarily the same urgency as in a high-volatility regime.
The resulting synergy offers balanced, context-rich signals. In a strong trend, the ATR layer helps confirm whether an apparent price breakout really has momentum or if it is just a temporary spike. In a range-bound market, standard deviation-based Bollinger Bands define normal price extremes, while ATR-based extensions highlight whether a breakout attempt has genuine force behind it. Traders gain clarity on when a move is both statistically unusual and accompanied by real volatility expansion, thus carrying a higher probability of a directional follow-through or eventual reversion.
Practical advantages emerge across timeframes. Scalpers in fast-paced markets appreciate how ATR-based thresholds update rapidly, revealing if a sudden price push is routine or exceptional. Swing traders can rely on both indicators to filter out false signals in stable conditions or identify truly notable moves. By calibrating to changes in volatility, the merged system adapts naturally whether the market is trending, ranging, or transitioning between these phases.
In summary, combining Bollinger Bands (for a static sense of standard-deviation-based overbought/oversold zones) with ATR (for a dynamic read on current volatility) yields an adaptive, intuitive indicator. Traders can better distinguish fleeting noise from meaningful expansions, enabling more informed entries, exits, and risk management. Instead of relying on a single yardstick for all market conditions, this fusion provides a layered perspective, encouraging traders to interpret price moves in the broader context of changing volatility.
Section 3: Why Bollinger Bands and ATR are combined
Bollinger Bands provide a static snapshot of volatility by computing a standard deviation range above and below a central average. ATR, on the other hand, adapts in real time to expansions or contractions in market volatility. When combined, these measures offset each other’s limitations: Bollinger Bands add structure (overbought and oversold references), and ATR ensures responsiveness to rapid price shifts. This synergy helps reduce noisy signals, particularly during sudden market turbulence or extended consolidations.
Section 4: User Inputs
Traders can adjust several parameters to suit their preferences and strategies. These typically include:
1. Lookback length for calculating the moving average and standard deviation.
2. Multipliers to control the width of Bollinger Bands.
3. An ATR multiplier to set the distance for additional reversal bands.
4. An option to display weaker signals when the price merely approaches but does not cross the outer bands.
Section 5: Main Calculations
At the core of this indicator are four important steps:
1. Calculate a basis using a simple moving average.
2. Derive Bollinger Bands by adding and subtracting a product of the standard deviation and a user-defined multiplier.
3. Compute ATR over the same lookback period and multiply it by the selected factor.
4. Combine ATR-based distance with the Bollinger Bands to set the outer reversal bands, which serve as stronger signal thresholds.
Section 6: Signal Generation
The script interprets meaningful reversal points when the price:
1. Crosses below the lower outer band, potentially highlighting oversold conditions where a bullish reversal may occur.
2. Crosses above the upper outer band, potentially indicating overbought conditions where a bearish reversal may develop.
Section 7: Visualization
The indicator provides visual clarity through labeled signals and color-coded references:
1. Distinct colors for upper and lower reversal bands.
2. Markers that appear above or below bars to denote possible buying or selling signals.
3. A gradient bar color scheme indicating a bar’s position between the lower and upper bands, helping traders quickly see if the price is near either extreme.
Section 8: Weak Signals (Optional)
For those preferring early cues, the script can highlight areas where the price nears the outer bands. When weak signals are enabled:
1. Bars closer to the upper reversal zone receive a subtle marker suggesting a less robust, yet still noteworthy, potential selling area.
2. Bars closer to the lower reversal zone receive a subtle marker suggesting a less robust, yet still noteworthy, potential buying area.
Section 9: Simplicity, Effectiveness, and Lower Timeframes
Although combining standard deviation and ATR involves sophisticated volatility concepts, this indicator is visually straightforward. Reversal bands and gradient-colored bars make it easy to see at a glance when price approaches or crosses a threshold. Day traders operating on lower timeframes benefit from such clarity because it helps filter out minor fluctuations and focus on more meaningful signals.
Section 10: Adaptability across Market Phases
Because both the standard deviation (for Bollinger Bands) and ATR adapt to changing volatility, the indicator naturally adjusts to various environments:
1. Trending: The additional ATR-based outer bands help distinguish between temporary pullbacks and deeper reversals.
2. Ranging: Bollinger Bands often remain narrower, identifying smaller reversals, while the outer ATR bands remain relatively close to the main bands.
Section 11: Reduced Noise in High-Volatility Scenarios
By factoring ATR into the band calculations, the script widens or narrows the thresholds during rapid market fluctuations. This reduces the amount of false triggers typically found in indicators that rely solely on fixed calculations, preventing overreactions to abrupt but short-lived price spikes.
Section 12: Incorporation with Other Technical Tools
Many traders combine this indicator with oscillators such as RSI, MACD, or Stochastic, as well as volume metrics. Overbought or oversold signals in momentum oscillators can provide additional confirmation when price reaches the outer bands, while volume spikes may reinforce the significance of a breakout or potential reversal.
Section 13: Risk Management Considerations
All trading strategies carry risk. This indicator, like any tool, can and does produce losing trades if price unexpectedly reverses again or if broader market conditions shift rapidly. Prudent traders employ protective measures:
1. Stop-loss orders or trailing stops.
2. Position sizing that accounts for market volatility.
3. Diversification across different asset classes when possible.
Section 14: Overbought and Oversold Identification
Standard Bollinger Bands highlight regions where price might be overextended relative to its recent average. The extended ATR-based reversal bands serve as secondary lines of defense, identifying moments when price truly stretches beyond typical volatility bounds.
Section 15: Parameter Customization for Different Needs
Users can tailor the script to their unique preferences:
1. Shorter lookback settings yield faster signals but risk more noise.
2. Higher multipliers spread the bands further apart, filtering out small moves but generating fewer signals.
3. Longer lookback periods smooth out market noise, often leading to more stable but less frequent trading cues.
Section 16: Examples of Different Trading Styles
1. Day Traders: Often reduce the length to capture quick price swings.
2. Swing Traders: May use moderate lengths such as 20 to 50 bars.
3. Position Traders: Might opt for significantly longer settings to detect macro-level reversals.
Section 17: Performance Limitations and Reality Check
No technical indicator is free from false signals. Sudden fundamental news events, extreme sentiment changes, or low-liquidity conditions can render signals less reliable. Backtesting and forward-testing remain essential steps to gauge whether the indicator aligns well with a trader’s timeframe, risk tolerance, and instrument of choice.
Section 18: Merging Volatility and Momentum
A critical uniqueness of this indicator lies in how it merges Bollinger Bands (standard deviation-based) with ATR (pure volatility measure). Bollinger Bands provide a relative measure of price extremes, while ATR dynamically reacts to market expansions and contractions. Together, they offer an enhanced perspective on potential market turns, ideally reducing random noise and highlighting moments where price has traveled beyond typical bounds.
Section 19: Purpose of this Merger
The fundamental purpose behind blending standard deviation measures with real-time volatility data is to accommodate different market behaviors. Static standard deviation alone can underreact or overreact in abnormally volatile conditions. ATR alone lacks a baseline reference to normality. By merging them, the indicator aims to provide:
1. A versatile dynamic range for both typical and extreme moves.
2. A filter against frequent whipsaws, especially in choppy environments.
3. A visual framework that novices and experts can interpret rapidly.
Section 20: Summary and Practical Tips
Uptrick: Volatility Reversion Bands offers a powerful tool for traders looking to combine volatility-based signals with momentum-derived reversals. It emphasizes clarity through color-coded bars, defined reversal zones, and optional weak signal markers. While potentially useful across all major timeframes, it demands ongoing risk management, realistic expectations, and careful study of how signals behave under different market conditions. No indicator serves as a crystal ball, so integrating this script into an overall strategy—possibly alongside volume data, fundamentals, or momentum oscillators—often yields the best results.
Disclaimer and Educational Use
This script is intended for educational and informational purposes. It does not constitute financial advice, nor does it guarantee trading success. Sudden economic events, low-liquidity times, and unexpected market behaviors can all undermine technical signals. Traders should use proper testing procedures (backtesting and forward-testing) and maintain disciplined risk management measures.
ADX (levels)This Pine Script indicator calculates and displays the Average Directional Index (ADX) along with the DI+ and DI- lines to help identify the strength and direction of a trend. The script is designed for Pine Script v6 and includes customizable settings for a more tailored analysis.
Features:
ADX Calculation:
The ADX measures the strength of a trend without indicating its direction.
It uses a smoothing method for more reliable trend strength detection.
DI+ and DI- Lines (Optional):
The DI+ (Directional Index Plus) and DI- (Directional Index Minus) help determine the direction of the trend:
DI+ indicates upward movement.
DI- indicates downward movement.
These lines are disabled by default but can be enabled via input settings.
Customizable Threshold:
A horizontal line (hline) is plotted at a user-defined threshold level (default: 20) to highlight significant ADX values that indicate a strong trend.
Slope Analysis:
The slope of the ADX is analyzed to classify the trend into:
Strong Trend: Slope is higher than a defined "medium" threshold.
Moderate Trend: Slope falls between "weak" and "medium" thresholds.
Weak Trend: Slope is positive but below the "weak" threshold.
A background color changes dynamically to reflect the strength of the trend:
Green (light or dark) indicates trend strength levels.
Custom Colors:
ADX color is customizable (default: pink #e91e63).
Background colors for trend strength can also be adjusted.
Independent Plot Window:
The indicator is displayed in a separate window below the price chart, making it easier to analyze trend strength without cluttering the main price chart.
Parameters:
ADX Period: Defines the lookback period for calculating the ADX (default: 14).
Threshold (hline): A horizontal line value to differentiate strong trends (default: 20).
Slope Thresholds: Adjustable thresholds for weak, moderate, and strong trend slopes.
Enable DI+ and DI-: Boolean options to display or hide the DI+ and DI- lines.
Colors: Customizable colors for ADX, background gradients, and other elements.
How to Use:
Identify Trend Strength:
Use the ADX value to determine the strength of a trend:
Below 20: Weak trend.
Above 20: Strong trend.
Analyze Trend Direction:
Enable DI+ and DI- to check whether the trend is upward (DI+ > DI-) or downward (DI- > DI+).
Dynamic Slope Detection:
Use the background color as a quick visual cue to assess trend strength changes.
This indicator is ideal for traders who want to measure trend strength and direction dynamically while maintaining a clean and organized chart layout.
Percentage Calculator by Akshay GaurThis indicator calculates and displays percentage levels above and below the current price. It allows you to easily identify any percentage levels which can be used in many things like creating strangles and straddles and make informed trading decisions. The indicator automatically adjusts and redraws the lines and labels on the latest bar to reflect real-time market conditions.
Key Features:
• Calculates percentage levels above and below the current price
• Displays percentage levels on big labels with the horizontal lines on the chart
• Allows you to adjust the percentage value and every details.
• Allows you to see Fluctuation line on the chart.
How to Use:
1. Set the percentage value to the desired level (e.g. 1%, 2%, etc.)
2. If you want to see Fluctuation lines also then turn on it from Input settings.
3. Use the displayed levels to identify desired percentage levels.
4. Make informed trading decisions based on the calculated levels
Implied and Historical VolatilityAbstract
This TradingView indicator visualizes implied volatility (IV) derived from the VIX index and historical volatility (HV) computed from past price data of the S&P 500 (or any selected asset). It enables users to compare market participants' forward-looking volatility expectations (via VIX) with realized past volatility (via historical returns). Such comparisons are pivotal in identifying risk sentiment, volatility regimes, and potential mispricing in derivatives.
Functionality
Implied Volatility (IV):
The implied volatility is extracted from the VIX index, often referred to as the "fear gauge." The VIX represents the market's expectation of 30-day forward volatility, derived from options pricing on the S&P 500. Higher values of VIX indicate increased uncertainty and risk aversion (Whaley, 2000).
Historical Volatility (HV):
The historical volatility is calculated using the standard deviation of logarithmic returns over a user-defined period (default: 20 trading days). The result is annualized using a scaling factor (default: 252 trading days). Historical volatility represents the asset's past price fluctuation intensity, often used as a benchmark for realized risk (Hull, 2018).
Dynamic Background Visualization:
A dynamic background is used to highlight the relationship between IV and HV:
Yellow background: Implied volatility exceeds historical volatility, signaling elevated market expectations relative to past realized risk.
Blue background: Historical volatility exceeds implied volatility, suggesting the market might be underestimating future uncertainty.
Use Cases
Options Pricing and Trading:
The disparity between IV and HV provides insights into whether options are over- or underpriced. For example, when IV is significantly higher than HV, options traders might consider selling volatility-based derivatives to capitalize on elevated premiums (Natenberg, 1994).
Market Sentiment Analysis:
Implied volatility is often used as a proxy for market sentiment. Comparing IV to HV can help identify whether the market is overly optimistic or pessimistic about future risks.
Risk Management:
Institutional and retail investors alike use volatility measures to adjust portfolio risk exposure. Periods of high implied or historical volatility might necessitate rebalancing strategies to mitigate potential drawdowns (Campbell et al., 2001).
Volatility Trading Strategies:
Traders employing volatility arbitrage can benefit from understanding the IV/HV relationship. Strategies such as "long gamma" positions (buying options when IV < HV) or "short gamma" (selling options when IV > HV) are directly informed by these metrics.
Scientific Basis
The indicator leverages established financial principles:
Implied Volatility: Derived from the Black-Scholes-Merton model, implied volatility reflects the market's aggregate expectation of future price fluctuations (Black & Scholes, 1973).
Historical Volatility: Computed as the realized standard deviation of asset returns, historical volatility measures the intensity of past price movements, forming the basis for risk quantification (Jorion, 2007).
Behavioral Implications: IV often deviates from HV due to behavioral biases such as risk aversion and herding, creating opportunities for arbitrage (Baker & Wurgler, 2007).
Practical Considerations
Input Flexibility: Users can modify the length of the HV calculation and the annualization factor to suit specific markets or instruments.
Market Selection: The default ticker for implied volatility is the VIX (CBOE:VIX), but other volatility indices can be substituted for assets outside the S&P 500.
Data Frequency: This indicator is most effective on daily charts, as VIX data typically updates at a daily frequency.
Limitations
Implied volatility reflects the market's consensus but does not guarantee future accuracy, as it is subject to rapid adjustments based on news or events.
Historical volatility assumes a stationary distribution of returns, which might not hold during structural breaks or crises (Engle, 1982).
References
Black, F., & Scholes, M. (1973). "The Pricing of Options and Corporate Liabilities." Journal of Political Economy, 81(3), 637-654.
Whaley, R. E. (2000). "The Investor Fear Gauge." The Journal of Portfolio Management, 26(3), 12-17.
Hull, J. C. (2018). Options, Futures, and Other Derivatives. Pearson Education.
Natenberg, S. (1994). Option Volatility and Pricing: Advanced Trading Strategies and Techniques. McGraw-Hill.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (2001). The Econometrics of Financial Markets. Princeton University Press.
Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk. McGraw-Hill.
Baker, M., & Wurgler, J. (2007). "Investor Sentiment in the Stock Market." Journal of Economic Perspectives, 21(2), 129-151.
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.
Triple Power Stop [CHE]Triple Power Stop
This indicator provides a comprehensive multi-timeframe approach for stop level and trend analysis, tailored for traders who want enhanced precision and adaptability in their trading strategies. Here's what makes the Triple Power Stop (CHE) stand out:
Key Features:
1. ATR-Based Stop Levels:
- Uses the Average True Range (ATR) to dynamically calculate stop levels, ensuring sensitivity to market volatility.
- Adjustable ATR multiplier for fine-tuning the stop levels to fit different trading styles.
2. Multi-Timeframe Analysis:
- Evaluates trends across three different timeframes with user-defined multipliers.
- Enables deeper insight into the market's broader context while keeping the focus on precision.
3. Dynamic Volatility Adjustment:
- Introduces a unique volatility factor to enhance stop-level calculations.
- Adapts to market conditions, offering reliable support for both trending and ranging markets.
4. Clear Trend Visualization:
- Stop levels and trends are visually represented with color-coded lines (green for uptrend, red for downtrend).
- Seamlessly integrates trend changes and helps identify potential reversals.
5. Signal Alerts:
- Long and short entry signals are plotted directly on the chart for actionable insights.
- Eliminates guesswork and provides clarity in decision-making.
6. Customizability:
- Adjustable parameters such as ATR length, multipliers, and label counts, allowing traders to tailor the indicator to their strategies.
Practical Use:
The Triple Power Stop (CHE) is ideal for traders who want to:
- Manage risk effectively: With dynamically calculated stop levels, traders can protect their positions while allowing room for natural market fluctuations.
- Follow the trend: Multi-timeframe trend detection ensures alignment with broader market movements.
- Simplify decisions: Clear visual indicators and signals make trading decisions more intuitive and less stressful.
How to Use:
1. Set the ATR length and multiplier values based on your risk tolerance and trading strategy.
2. Choose multipliers for different timeframes to adapt the indicator to your preferred resolutions.
3. Use the color-coded trend lines and entry signals to time your trades and manage positions efficiently.
Disclaimer:
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
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DAILY ATR LEVELS AND EXPECTED MOVE LEVELSThis Pine Script code is designed to visualize ATR (Average True Range) levels and expected move levels on a chart. It provides useful inputs for customizing how these levels are displayed, such as line width, style, and color. The script is divided into several sections, each focused on a different feature:
1. User Inputs for Customization:
- Line Width and Style: Users can customize the line width, style (solid, dotted, or dashed), and color for various levels.
- Offset for Line Placement: The rightOffset input controls how far in the future the lines extend (measured in minutes).
- Show Labels: Labels can be toggled on/off for ATR levels and expected move lines, with customizable text colors.
2. ATR Levels and ATR Settings:
- The ATR length (atrLength) and the multiplier (atrMultiplier) control the calculation of ATR levels.
- The script plots ATR levels based on the daily open price, including key levels like ATR +25%, ATR +50%, etc., for both positive and negative movements.
- Line Drawing: The script dynamically creates lines for each ATR level, and the lines are customized according to the user's inputs. For each level, the line.new function is used to plot a line from the start of the day (daily open) to a point offset in the future.
- Labels: Labels are added near each ATR level to make them more identifiable, such as "ATR +25%" or "Daily Open."
3. Expected Move Calculation and Logic:
- The script calculates the expected move for the next trading session based on the previous close price and the volatility derived from the VIX (Volatility Index).
- The expected move is calculated as a percentage of the previous close and is added and subtracted from the previous close price to generate upper and lower levels.
- Volatility Adjustment: The VIX value is adjusted by the square root of 252 (the number of average trading days in a year) to calculate the daily volatility.
- Upper and Lower Lines: Lines are drawn for the expected move's upper and lower bounds, showing the potential price movement based on volatility.
4. Customizable Expected Move Lines:
- Line Style and Color: The upper and lower expected move lines can be customized in terms of width, style, and color, as specified by the user.
- Labels for Expected Move Levels: Labels are added for the upper and lower expected move lines, such as "Expected Move Upper" and "Expected Move Lower."
5. Logic for Drawing Lines:
- The script continuously evaluates whether the levels should be displayed based on the user's preferences.
- If showATRLevels or showLineEM is enabled, the script will draw the respective lines and labels on the chart.
- It uses line.new to draw the lines and label.new to position the labels at the correct levels on the chart.
6. Handling Time and Line Deletion:
- The script handles the dynamic nature of the chart by deleting previous lines (using line.delete) to avoid cluttering the chart with outdated lines.
- The time for the lines is set dynamically using the startTime and endTime variables, ensuring that lines are drawn within the correct timeframe.
Summary of Key Features:
- ATR Levels: Plots key levels of ATR, such as daily open, ATR +25%, ATR -25%, etc., with customizable colors and line styles.
- Expected Move Levels: Calculates and plots the upper and lower bounds of the expected move based on the VIX and previous close price.
- Customization Options: Users can control the appearance (line width, style, color) and whether to show labels for the ATR and expected move levels.
- Dynamic Updates: The lines and labels update dynamically throughout the trading day, adjusting based on market conditions.
Overall, this script is designed to help traders visualize volatility and potential price movement on a daily chart by providing ATR-based levels and expected move projections. It offers a high degree of customization to suit different charting preferences.
ADR Table BY @ICT_YEROADR Table BY @ICT_YERO
Created by: @ICT_YERO
This custom indicator is designed to provide the Average Daily Range (ADR) for multiple timeframes, including Daily, 4-Hour, and 1-Hour. The indicator is tailored to assist traders in understanding price volatility and making informed trading decisions.
Key Features
Multi-Timeframe ADR Calculation:
Automatically calculates and displays the ADR for Daily, 4-Hour, and 1-Hour timeframes.
Helps traders identify potential price movement ranges for different trading sessions.
Dynamic Range Visualization:
Clear visual representation of the ADR on the chart, making it easy to spot price extremes.
Real-time updates to reflect changes in price movement.
Custom Alerts:
Option to set alerts when the price approaches the ADR high or low.
Useful for identifying potential reversal zones or breakout opportunities.
User-Friendly Interface:
Simple and intuitive settings to customize colors, levels, and display preferences.
Seamlessly integrates with your existing TradingView setup.
ICT-Inspired Methodology:
Designed for traders who follow ICT concepts, focusing on precision and high-probability setups.
Applications
Range Trading: Helps determine the high and low boundaries for scalping or intraday setups.
Volatility Analysis: Understand market behavior during different times of the day or week.
Reversal Zones: Identify areas where price is likely to reverse, based on ADR extremes.
Whether you're a scalper, day trader, or swing trader, this indicator provides a comprehensive overview of price volatility across multiple timeframes, making it an essential tool for your trading arsenal.
DAILY ATR LEVELSThis script is a custom technical indicator for use in TradingView, designed to display daily Average True Range (ATR) levels on the chart, along with the daily opening price. It provides a customizable way to track price levels relative to the daily ATR, which can be useful for traders looking for volatility-based price targets or ranges.
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Customization Options:
- Line Width: Determines the thickness of the plotted lines for the ATR levels and daily open line, ranging from 1 to 10.
- Right Offset (minutes): A time offset (in minutes) that shifts the end of the daily opening price line to the right for visual clarity.
- Line Style: The user can choose between solid, dashed, or dotted lines for all the plotted levels.
- Display Options: Users can toggle the visibility of the daily opening price line (showDayLevel), labels (showLabels), and ATR levels (showATRLevels).
- Colors: Customizable colors for the daily opening price line (dayLevelColor), labels (labelTextColor), and the ATR levels for both positive and negative values (atrLevelPlusColor and atrLevelMinusColor).
ATR Settings:
- ATR Length: Defines the number of periods (bars) to use when calculating the ATR. The default is 180, which corresponds to the ATR calculated on the daily chart using the last 180 bars.
- ATR Multiplier: Allows the user to scale the ATR levels by a multiplier (from 0.1 to 5.0), adjusting the sensitivity of the levels.
- ATR Levels: Users can toggle visibility for several predefined ATR levels, such as +25%, +50%, +75%, +100%, -25%, -50%, -75%, and -100%. These levels represent price points above or below the daily open based on the ATR.
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ATR Levels Calculation:
- The ATR is calculated based on the daily chart using the ta.atr() function with the specified ATR length, default is set at 180.
- The script computes multiple ATR levels above and below the daily open price, adjusting each level by 25%, 50%, 75%, and 100% of the ATR value (scaled by the ATR multiplier).
ATR Level Plotting:
- For each ATR level (positive and negative), a line is drawn across the chart at the corresponding price level.
- The color, line style, and width of these lines can be customized.
- Each ATR level also has an optional label showing the percentage level (e.g., "ATR +25%") at the specified price, which is positioned at the end of the line.
- The labels are removed from the previous bars to avoid clutter.
Workflow:
- The script first calculates the daily opening price using the request.security() function to pull the open price from the daily chart.
- It then calculates the ATR based on the selected length and multiplier.
- The start time for the daily open line is determined by the bar's timestamp at the start of the day, and the end time is adjusted using the user-defined right offset.
- After determining the relevant price levels (for the opening price and ATR levels), the script plots these levels on the chart as lines. It handles the drawing and deletion of lines to ensure that the chart remains updated in real time.
- If labels are enabled, text labels are displayed next to the ATR levels and the daily open line, providing clear markers for the user.
Practical Use:
- Volatility Analysis: This indicator is useful for identifying key price levels based on daily volatility (ATR). Traders can use it to set potential targets or support/resistance levels that are adjusted for volatility.
- Day Trading or Swing Trading: The daily opening price line helps traders quickly see where the price opened for the day, and the ATR levels give a dynamic range for the day's potential price movement.
Overall, this script is designed to provide a clear, customizable view of daily price levels in relation to the ATR, helping traders make informed decisions based on volatility and price action.
goose's session + killzone indicatorA powerful, multi-functional TradingView script designed for forex traders. It visually delineates major market sessions—Asia, London, and New York—on the chart, provides customizable session ranges, pip range counters, and configurable “killzone” markers. The indicator allows users to switch between a traditional line-and-fill style and a historical box style for session visualization, offering both real-time and historical context of market ranges.
Key Features:
1. Session Visualization Options:
• Lines & Fill Style:
• Highlights active sessions using dynamic lines and filled areas that update in real-time.
• Displays session high, low, and mid-range boundaries with customizable colors, border widths, and line styles.
• Historical Boxes Style:
• Creates and retains boxes for each past session, enabling users to review historical session ranges over extended periods.
• Each session box is drawn with user-defined fill and border colors, opacity, and line styles, anchored to the session’s high, low, and timeframe.
• Supports viewing multiple historical sessions at once, up to a configurable limit.
2. Pip Range Counters:
• Displays the range of pips for each active session, anchored to a fixed position near the bottom-right corner of the session.
• Uses arrow-style labels (label.style_label_up/down) to maintain a stable visual offset relative to the session, minimizing movement during vertical zoom.
• Customizable text size, color, and vertical offset, ensuring the pip counter remains legible and unobtrusive.
3. Killzone Lines:
• Allows users to define up to 15 custom “killzone” times with specific line colors, styles, and optional labels.
• Each killzone is drawn precisely when the price crosses the user-specified time, marking important market events or transitions.
4. User Configurations:
• Session Times: Fully adjustable start and end times for Asian, London, and New York sessions.
• Color & Style Settings:
• Customizable fill and border colors (with adjustable opacity) for each session style.
• User-friendly dropdowns and checkboxes for setting line styles, widths, and text sizes.
• Display Mode Selector:
• A dropdown (sessionStyle) lets users toggle between “Lines & Fill” and “Historical Boxes” for session visualization.
• Pip Counter and Killzone Settings:
• Options to show or hide pip counters, match label colors to session fills, and configure killzone appearance individually.
5. Robust Utility Functions:
• Functions to parse user-input times, determine if the current time falls within a session, and calculate session ranges.
• Historical session logic that detects session transitions and creates/upgrades boxes accordingly.
How It Works:
• Session Style Selection:
• Users choose their preferred visualization style via the sessionStyle dropdown.
• If “Lines & Fill” is selected, the indicator plots dynamic lines and fills during active sessions.
• If “Boxes” is selected, the indicator creates historical boxes that outline the range of each past session, persisting on the chart until session boundaries change.
• Pip Counter Labeling:
• During an active session, the pip counter calculates the range between the session’s high and low and displays it as text positioned just below the bottom-right corner of the session.
• The use of arrow-style labels ensures the text remains at a stable visual distance from the session’s outline, even when zooming vertically.
• Killzones:
• Users can define specific times (“killzones”) where important market events occur.
• When the price crosses these times, the script draws lines and optional labels with user-defined appearance settings.
Ideal Use Cases:
• Real-Time Trading: Quickly identify current session ranges, pip sizes, and crucial killzone markers during live trading.
• Historical Analysis: Switch to Historical Boxes to review past session ranges over days, weeks, or months, aiding in pattern recognition and strategy refinement.
• Customization: Tailor the appearance to match personal preferences or chart themes, including colors, styles, line widths, and label sizes.
This comprehensive indicator combines real-time session tracking with historical range visualization and customizable killzones. Its dual display modes, extensive settings, and stable pip counter labels make it a versatile tool for forex traders seeking to analyze market sessions both in the moment and retrospectively.
Adaptive Momentum Reversion StrategyThe Adaptive Momentum Reversion Strategy: An Empirical Approach to Market Behavior
The Adaptive Momentum Reversion Strategy seeks to capitalize on market price dynamics by combining concepts from momentum and mean reversion theories. This hybrid approach leverages a Rate of Change (ROC) indicator along with Bollinger Bands to identify overbought and oversold conditions, triggering trades based on the crossing of specific thresholds. The strategy aims to detect momentum shifts and exploit price reversions to their mean.
Theoretical Framework
Momentum and Mean Reversion: Momentum trading assumes that assets with a recent history of strong performance will continue in that direction, while mean reversion suggests that assets tend to return to their historical average over time (Fama & French, 1988; Poterba & Summers, 1988). This strategy incorporates elements of both, looking for periods when momentum is either overextended (and likely to revert) or when the asset’s price is temporarily underpriced relative to its historical trend.
Rate of Change (ROC): The ROC is a straightforward momentum indicator that measures the percentage change in price over a specified period (Wilder, 1978). The strategy calculates the ROC over a 2-period window, making it responsive to short-term price changes. By using ROC, the strategy aims to detect price acceleration and deceleration.
Bollinger Bands: Bollinger Bands are used to identify volatility and potential price extremes, often signaling overbought or oversold conditions. The bands consist of a moving average and two standard deviation bounds that adjust dynamically with price volatility (Bollinger, 2002).
The strategy employs two sets of Bollinger Bands: one for short-term volatility (lower band) and another for longer-term trends (upper band), with different lengths and standard deviation multipliers.
Strategy Construction
Indicator Inputs:
ROC Period: The rate of change is computed over a 2-period window, which provides sensitivity to short-term price fluctuations.
Bollinger Bands:
Lower Band: Calculated with a 18-period length and a standard deviation of 1.7.
Upper Band: Calculated with a 21-period length and a standard deviation of 2.1.
Calculations:
ROC Calculation: The ROC is computed by comparing the current close price to the close price from rocPeriod days ago, expressing it as a percentage.
Bollinger Bands: The strategy calculates both upper and lower Bollinger Bands around the ROC, using a simple moving average as the central basis. The lower Bollinger Band is used as a reference for identifying potential long entry points when the ROC crosses above it, while the upper Bollinger Band serves as a reference for exits, when the ROC crosses below it.
Trading Conditions:
Long Entry: A long position is initiated when the ROC crosses above the lower Bollinger Band, signaling a potential shift from a period of low momentum to an increase in price movement.
Exit Condition: A position is closed when the ROC crosses under the upper Bollinger Band, or when the ROC drops below the lower band again, indicating a reversal or weakening of momentum.
Visual Indicators:
ROC Plot: The ROC is plotted as a line to visualize the momentum direction.
Bollinger Bands: The upper and lower bands, along with their basis (simple moving averages), are plotted to delineate the expected range for the ROC.
Background Color: To enhance decision-making, the strategy colors the background when extreme conditions are detected—green for oversold (ROC below the lower band) and red for overbought (ROC above the upper band), indicating potential reversal zones.
Strategy Performance Considerations
The use of Bollinger Bands in this strategy provides an adaptive framework that adjusts to changing market volatility. When volatility increases, the bands widen, allowing for larger price movements, while during quieter periods, the bands contract, reducing trade signals. This adaptiveness is critical in maintaining strategy effectiveness across different market conditions.
The strategy’s pyramiding setting is disabled (pyramiding=0), ensuring that only one position is taken at a time, which is a conservative risk management approach. Additionally, the strategy includes transaction costs and slippage parameters to account for real-world trading conditions.
Empirical Evidence and Relevance
The combination of momentum and mean reversion has been widely studied and shown to provide profitable opportunities under certain market conditions. Studies such as Jegadeesh and Titman (1993) confirm that momentum strategies tend to work well in trending markets, while mean reversion strategies have been effective during periods of high volatility or after sharp price movements (De Bondt & Thaler, 1985). By integrating both strategies into one system, the Adaptive Momentum Reversion Strategy may be able to capitalize on both trending and reverting market behavior.
Furthermore, research by Chan (1996) on momentum-based trading systems demonstrates that adaptive strategies, which adjust to changes in market volatility, often outperform static strategies, providing a compelling rationale for the use of Bollinger Bands in this context.
Conclusion
The Adaptive Momentum Reversion Strategy provides a robust framework for trading based on the dual concepts of momentum and mean reversion. By using ROC in combination with Bollinger Bands, the strategy is capable of identifying overbought and oversold conditions while adapting to changing market conditions. The use of adaptive indicators ensures that the strategy remains flexible and can perform across different market environments, potentially offering a competitive edge for traders who seek to balance risk and reward in their trading approaches.
References
Bollinger, J. (2002). Bollinger on Bollinger Bands. McGraw-Hill Professional.
Chan, L. K. C. (1996). Momentum, Mean Reversion, and the Cross-Section of Stock Returns. Journal of Finance, 51(5), 1681-1713.
De Bondt, W. F., & Thaler, R. H. (1985). Does the Stock Market Overreact? Journal of Finance, 40(3), 793-805.
Fama, E. F., & French, K. R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.
Enhanced HMA 5D standard Deviation - RickSimple hull moving average enhanced with standard deviation bands calculated over a 5 day period to account for volatility in ranging periods.
Possibility to choose the source of the hull calculation, as well as the source to use as threshold for long and short signal.
Two different types of visualization: candle coloring or moving average.
Prime Bands [ChartPrime]The Prime Standard Deviation Bands indicator uses custom-calculated bands based on highest and lowest price values over specific period to analyze price volatility and trend direction. Traders can set the bands to 1, 2, or 3 standard deviations from a central base, providing a dynamic view of price behavior in relation to volatility. The indicator also includes color-coded trend signals, standard deviation labels, and mean reversion signals, offering insights into trend strength and potential reversal points.
⯁ KEY FEATURES AND HOW TO USE
⯌ Standard Deviation Bands :
The indicator plots upper and lower bands based on standard deviation settings (1, 2, or 3 SDs) from a central base, allowing traders to visualize volatility and price extremes. These bands can be used to identify overbought and oversold conditions, as well as potential trend reversals.
Example of 3-standard-deviation bands around price:
⯌ Dynamic Trend Indicator :
The midline of the bands changes color based on trend direction. If the midline is rising, it turns green, indicating an uptrend. When the midline is falling, it turns orange, suggesting a downtrend. This color coding provides a quick visual reference to the current trend.
Trend color examples for rising and falling midlines:
⯌ Standard Deviation Labels :
At the end of the bands, the indicator displays labels with price levels for each standard deviation level (+3, 0, -3, etc.), helping traders quickly reference where price is relative to its statistical boundaries.
Price labels at each standard deviation level on the chart:
⯌ Mean Reversion Signals :
When price moves beyond the upper or lower bands and then reverts back inside, the indicator plots mean reversion signals with diamond icons. These signals indicate potential reversal points where the price may return to the mean after extreme moves.
Example of mean reversion signals near bands:
⯌ Standard Deviation Scale on Chart :
A visual scale on the right side of the chart shows the current price position in relation to the bands, expressed in standard deviations. This scale provides an at-a-glance view of how far price has deviated from the mean, helping traders assess risk and volatility.
⯁ USER INPUTS
Length : Sets the number of bars used in the calculation of the bands.
Standard Deviation Level : Allows selection of 1, 2, or 3 standard deviations for upper and lower bands.
Colors : Customize colors for the uptrend and downtrend midline indicators.
⯁ CONCLUSION
The Prime Standard Deviation Bands indicator provides a comprehensive view of price volatility and trend direction. Its customizable bands, trend coloring, and mean reversion signals allow traders to effectively gauge price behavior, identify extreme conditions, and make informed trading decisions based on statistical boundaries.
Aura Vibes EMA Ribbon + VStop + SAR + Bollinger BandsThe combination of Exponential Moving Averages (EMA), Volatility Stop (VStop), Parabolic SAR (PSAR), and Bollinger Bands (BB) offers a comprehensive approach to technical analysis, each serving a distinct purpose:
Exponential Moving Averages (EMA): EMAs are used to identify the direction of the trend by smoothing price data. Shorter-period EMAs react more quickly to price changes, while longer-period EMAs provide a broader view of the trend.
Volatility Stop (VStop): VStop is a dynamic stop-loss mechanism that adjusts based on market volatility, typically using the Average True Range (ATR). This allows traders to set stop-loss levels that accommodate market fluctuations, potentially reducing the likelihood of premature stop-outs.
Parabolic SAR (PSAR): PSAR is a trend-following indicator that provides potential entry and exit points by plotting dots above or below the price chart. When the dots are below the price, it suggests an uptrend; when above, a downtrend.
Bollinger Bands (BB): BB consists of a middle band (typically a 20-period simple moving average) and two outer bands set at standard deviations above and below the middle band. These bands expand and contract based on market volatility, helping traders identify overbought or oversold conditions.
Integrating these indicators can enhance trading strategies:
Trend Identification: Use EMAs to determine the prevailing market trend. For instance, a short-term EMA crossing above a long-term EMA may signal an uptrend.
Entry and Exit Points: Combine PSAR and BB to pinpoint potential entry and exit points. For example, a PSAR dot appearing below the price during an uptrend, coinciding with the price touching the lower Bollinger Band, might indicate a buying opportunity.
Risk Management: Implement VStop to set adaptive stop-loss levels that adjust with market volatility, providing a buffer against market noise.
By thoughtfully combining these indicators, traders can develop a robust trading system that adapts to various market conditions.
Machine Learning RSI Bands V3The Machine Learning RSI Bands V3 is a cutting-edge trading tool designed to provide actionable insights by combining the strength of machine learning with a traditional RSI framework. It adapts dynamically to changing market conditions, offering traders a robust, data-driven approach to identifying opportunities.
Let’s break down its functionality and the logic behind each input to give you a clear understanding of how it works and how you can use it effectively.
RSI Parameters RSI Source (rsisrc): Choose the data source for RSI calculation, such as the closing price. This allows you to focus on the specific price data that aligns with your trading strategy. RSI Length (rsilen): Set the number of periods used for RSI calculation. A shorter length makes the RSI more reactive to price changes, while a longer length smooths out volatility. These inputs allow you to customize the foundational RSI calculations, ensuring the indicator fits your style of trading.
Band Limits Lower Band Limit (lb): Defines the RSI value below which the market is considered oversold. Upper Band Limit (ub): Defines the RSI value above which the market is considered overbought. These settings give you control over the thresholds for market conditions. By adjusting the band limits, you can tailor the indicator to be more or less sensitive to market movements.
Sampling and Reaction Settings Target Reaction Size (l): Determines the number of bars used to define pivot points. Smaller values react to shorter-term price movements, while larger values focus on broader trends. Backtesting Reaction Size (btw): Sets the number of bars used to validate signal performance. This ensures signals are only considered valid if they perform consistently within the specified range. Data Format (version): Choose between Absolute (ignoring direction) and Directional (incorporating directional price changes). Sampling Method (sm): Select how the data is analyzed—options include Price Movement, Volume Movement, RSI Movement, Trend Movement, or a Hybrid approach. These settings empower you to refine how the indicator processes and interprets data, whether focusing on short-term price shifts or broader market trends.
Signal Settings Signal Confidence Method (cm): Choose between: Threshold: Signals must meet a confidence limit before being generated. Voting: Requires a majority of 5 signal components to confirm a trade. Confidence Limit (cl): Defines the confidence threshold for generating signals when using the Threshold method. Votes Needed (vn): Sets the number of votes required to confirm a trade when using the Voting method. Use All Outputs (fm): If enabled, signals are generated without filtering, providing an unfiltered view of potential opportunities. This section offers a balance between precision and flexibility, enabling you to control the rigor applied to signal generation.
How It Works
The script uses machine learning models to adaptively calculate dynamic RSI bands. These bands adjust based on market conditions, providing a more responsive and nuanced interpretation of overbought and oversold levels.
Dynamic Bands: The lower and upper RSI bands are recalibrated using machine learning to reflect current market conditions. Signals: Long and short signals are generated when RSI crosses these bands, with additional filters applied based on your chosen confidence method and sampling settings. Transparency: Real-time success rates and profit factors are displayed on the chart, giving you clear feedback on the indicator's performance.
Why Use Machine Learning RSI Bands V3?
This indicator is built for traders who want more than static thresholds and generic signals. It offers:
Adaptability: Machine learning dynamically adjusts the indicator to market conditions. Customizability: Each input serves a specific purpose, giving you full control over its behavior. Accountability: With built-in performance metrics, you always know how the tool is performing.
This is a tool designed for those who value precision and adaptability in trading.
AI InfinityAI Infinity – Multidimensional Market Analysis
Overview
The AI Infinity indicator combines multiple analysis tools into a single solution. Alongside dynamic candle coloring based on MACD and Stochastic signals, it features Alligator lines, several RSI lines (including glow effects), and optionally enabled EMAs (20/50, 100, and 200). Every module is individually configurable, allowing traders to tailor the indicator to their personal style and strategy.
Important Note (Disclaimer)
This indicator is provided for educational and informational purposes only.
It does not constitute financial or investment advice and offers no guarantee of profit.
Each trader is responsible for their own trading decisions.
Past performance does not guarantee future results.
Please review the settings thoroughly and adjust them to your personal risk profile; consider supplementary analyses or professional guidance where appropriate.
Functionality & Components
1. Candle Coloring (MACD & Stochastic)
Objective: Provide an immediate visual snapshot of the market’s condition.
Details:
MACD Signal: Used to identify bullish and bearish momentum.
Stochastic: Detects overbought and oversold zones.
Color Modes: Offers both a simple (two-color) mode and a gradient mode.
2. Alligator Lines
Objective: Assist with trend analysis and determining the market’s current phase.
Details:
Dynamic SMMA Lines (Jaw, Teeth, Lips) that adjust based on volatility and market conditions.
Multiple Lengths: Each element uses a separate smoothing period (13, 8, 5).
Transparency: You can show or hide each line independently.
3. RSI Lines & Glow Effects
Objective: Display the RSI values directly on the price chart so critical levels (e.g., 20, 50, 80) remain visible at a glance.
Details:
RSI Scaling: The RSI is plotted in the chart window, eliminating the need to switch panels.
Dynamic Transparency: A pulse effect indicates when the RSI is near critical thresholds.
Glow Mode: Choose between “Direct Glow” or “Dynamic Transparency” (based on ATR distance).
Custom RSI Length: Freely adjustable (default is 14).
4. Optional EMAs (20/50, 100, 200)
Objective: Utilize moving averages for trend assessment and identifying potential support/resistance areas.
Details:
20/50 EMA: Select which one to display via a dropdown menu.
100 EMA & 200 EMA: Independently enabled.
Color Logic: Automatically green (price > EMA) or red (price < EMA). Each EMA’s up/down color is customizable.
Configuration Options
Candle Coloring:
Choose between Gradient or Simple mode.
Adjust the color scheme for bullish/bearish candles.
Transparency is dynamically based on candle body size and Stochastic state.
Alligator Lines:
Toggle each line (Jaw/Teeth/Lips) on or off.
Select individual colors for each line.
RSI Section:
RSI Length can be set as desired.
RSI lines (0, 20, 50, 80, 100) with user-defined colors and transparency (pulse effect).
Additional lines (e.g., RSI 40/60) are also available.
Glow Effects:
Switch between “Dynamic Transparency” (ATR-based) and “Direct Glow”.
Independently applied to the RSI 100 and RSI 0 lines.
EMAs (20/50, 100, 200):
Activate each one as needed.
Each EMA’s up/down color can be customized.
Example Use Cases
Trend Identification:
Enable Alligator lines to gauge general trend direction through SMMA signals.
Timing:
Watch the Candle Colors to spot potential overbought or oversold conditions.
Fine-Tuning:
Utilize the RSI lines to closely monitor important thresholds (50 as a trend barometer, 80/20 as possible reversal zones).
Filtering:
Enable a 50 EMA to quickly see if the market is trading above (bullish) or below (bearish) it.
StdDev of VWAP/MAStdDev Indicator (MA, Smoothed VWAP & Rolling VWAP) v5
Overview: The StdDev Indicator is a comprehensive tool designed to provide traders with multi-term deviation analysis by integrating various Moving Averages (MA) and Volume Weighted Average Price (VWAP) methodologies. This indicator combines different MA types and VWAP calculations across multiple timeframes to offer a nuanced view of market volatility and trend strength.
Key Features:
Multiple Moving Average Types:
Simple Moving Average (SMA): Calculates the average price over a specified period, providing a straightforward trend indicator.
Exponential Moving Average (EMA): Gives more weight to recent prices, making it more responsive to new information.
Weighted Moving Average (WMA): Assigns different weights to each price point, emphasizing specific periods.
Smoothed VWAP: Enhances the traditional VWAP by applying additional smoothing techniques (SMA, EMA, WMA) to reduce volatility.
Rolling VWAP: Continuously recalculates VWAP over a rolling window, offering dynamic support and resistance levels.
Multi-Term Deviation Analysis:
Extra Short Term (30 periods)
Short Term (50 periods)
Medium Term (110 periods)
Long Term (125 periods)
Extra-Long Term (190 periods)
Extremely-Long Term (245 periods)
Each term calculates the deviation of the selected price source (default: Low) from its corresponding MA or VWAP, normalized by the standard deviation. This multi-term approach allows traders to assess volatility and trend consistency across different time horizons.
Composite Upper and Lower Bounds:
Aggregates the upper and lower deviations from all terms to form composite boundaries. These bounds serve as dynamic support and resistance levels, helping traders identify potential reversal points or breakout zones.
Timeframe Customization:
Visibility Settings: Customize which deviation terms are visible on specific timeframes (15m, 1h, 4h, 1d, 1w). This flexibility ensures that the indicator aligns with your trading strategy, whether you're a scalper, day trader, or long-term investor.
Bar Coloring (Optional):
Visual Cues: When enabled, bars are color-coded based on the deviation levels, providing immediate visual feedback on market conditions. For example, bars may turn red when short-term deviations exceed the upper bound, indicating potential overbought conditions.
How It Works:
Deviation Calculation:
For each selected MA or VWAP type and term length, the indicator calculates the deviation of the current price source from the MA/VWAP. This deviation is normalized by the standard deviation to account for volatility.
Channel Offset:
Applies a linear regression and standard deviation to the deviation series to establish upper and lower channels. These channels are adjustable via multipliers, allowing traders to set their sensitivity levels.
Composite Boundaries:
Averages the upper and lower channels across all deviation terms to form composite upper and lower bounds. These bounds provide a holistic view of market volatility and trend strength.
Visualization:
Plots individual deviation lines for each term, along with the composite bounds. Optional bar coloring enhances visual interpretation, making it easier to spot significant market movements.
Usage Instructions:
Setup:
Add the StdDev Indicator to your TradingView chart. By default, it uses the Low price as the source, but this can be customized.
Configuration:
Moving Average Type: Select your preferred MA or VWAP type from the dropdown menu.
Term Lengths: Adjust the lengths for each deviation term as per your trading strategy.
StdDev Multipliers: Set the multipliers for the upper and lower bounds to control sensitivity.
Timeframe Visibility: Choose which deviation terms are visible on specific timeframes to tailor the indicator to your trading style.
Bar Coloring: Enable or disable bar coloring based on deviation thresholds for enhanced visual cues.
Interpretation:
Deviations: Monitor the deviation lines to assess overbought or oversold conditions across different terms.
Composite Bounds: Use the upper and lower bounds as dynamic support and resistance levels.
Bar Colors: Quickly identify significant market movements through color-coded bars.
Why Choose StdDev Indicator?
Comprehensive Analysis: By integrating multiple MA and VWAP types across various terms, the indicator offers a multifaceted view of market conditions.
Customization: Highly configurable settings allow traders to adapt the indicator to their specific strategies and timeframes.
Visual Clarity: Clear plotting and optional bar coloring provide intuitive insights, reducing the need for complex analysis.
Conclusion: The StdDev Indicator (MA, Smoothed VWAP & Rolling VWAP) v5 is a versatile tool that combines advanced moving average and VWAP methodologies to deliver a robust deviation analysis framework. Whether you're looking to fine-tune your scalping strategy or gain a deeper understanding of long-term market trends, this indicator equips you with the necessary tools to make informed trading decisions.
Support & Feedback: If you have any questions or need assistance with the indicator, feel free to reach out through the TradingView community or contact the script author directly.
Average Candle RangeThis indicator calculates and displays the average trading range of candles over a specified period, helping traders identify volatility patterns and potential trading opportunities.
Features:
- Customizable lookback period (1-500 bars)
- Clean visual display in a top-right table overlay
- High-precision calculation showing 10 decimal places
- Real-time updates with each new bar
How it Works:
The indicator calculates the range of each candle (High - Low) and then computes the Simple Moving Average (SMA) of these ranges over your specified lookback period. The result is displayed in an easy-to-read table overlay.
Use Cases:
- Volatility Analysis: Monitor market volatility trends
- Position Sizing: Help determine position sizes based on average price movements
- Trading Strategy Development: Use as a reference for setting stop losses and take profits
- Market Phase Identification: Help identify high vs low volatility market phases
Settings:
- Lookback Period: Default is 140 bars, adjustable from 1 to 500
Note:
The indicator displays values with 10 decimal places for high-precision analysis, particularly useful in markets with small price movements.
Volatility IndicatorThe volatility indicator presented here is based on multiple volatility indices that reflect the market’s expectation of future price fluctuations across different asset classes, including equities, commodities, and currencies. These indices serve as valuable tools for traders and analysts seeking to anticipate potential market movements, as volatility is a key factor influencing asset prices and market dynamics (Bollerslev, 1986).
Volatility, defined as the magnitude of price changes, is often regarded as a measure of market uncertainty or risk. Financial markets exhibit periods of heightened volatility that may precede significant price movements, whether upward or downward (Christoffersen, 1998). The indicator presented in this script tracks several key volatility indices, including the VIX (S&P 500), GVZ (Gold), OVX (Crude Oil), and others, to help identify periods of increased uncertainty that could signal potential market turning points.
Volatility Indices and Their Relevance
Volatility indices like the VIX are considered “fear gauges” as they reflect the market’s expectation of future volatility derived from the pricing of options. A rising VIX typically signals increasing investor uncertainty and fear, which often precedes market corrections or significant price movements. In contrast, a falling VIX may suggest complacency or confidence in continued market stability (Whaley, 2000).
The other volatility indices incorporated in the indicator script, such as the GVZ (Gold Volatility Index) and OVX (Oil Volatility Index), capture the market’s perception of volatility in specific asset classes. For instance, GVZ reflects market expectations for volatility in the gold market, which can be influenced by factors such as geopolitical instability, inflation expectations, and changes in investor sentiment toward safe-haven assets. Similarly, OVX tracks the implied volatility of crude oil options, which is a crucial factor for predicting price movements in energy markets, often driven by geopolitical events, OPEC decisions, and supply-demand imbalances (Pindyck, 2004).
Using the Indicator to Identify Market Movements
The volatility indicator alerts traders when specific volatility indices exceed a defined threshold, which may signal a change in market sentiment or an upcoming price movement. These thresholds, set by the user, are typically based on historical levels of volatility that have preceded significant market changes. When a volatility index exceeds this threshold, it suggests that market participants expect greater uncertainty, which often correlates with increased price volatility and the possibility of a trend reversal.
For example, if the VIX exceeds a pre-determined level (e.g., 30), it could indicate that investors are anticipating heightened volatility in the equity markets, potentially signaling a downturn or correction in the broader market. On the other hand, if the OVX rises significantly, it could point to an upcoming sharp movement in crude oil prices, driven by changing market expectations about supply, demand, or geopolitical risks (Geman, 2005).
Practical Application
To effectively use this volatility indicator in market analysis, traders should monitor the alert signals generated when any of the volatility indices surpass their thresholds. This can be used to identify periods of market uncertainty or potential market turning points across different sectors, including equities, commodities, and currencies. The indicator can help traders prepare for increased price movements, adjust their risk management strategies, or even take advantage of anticipated price swings through options trading or volatility-based strategies (Black & Scholes, 1973).
Traders may also use this indicator in conjunction with other technical analysis tools to validate the potential for significant market movements. For example, if the VIX exceeds its threshold and the market is simultaneously approaching a critical technical support or resistance level, the trader might consider entering a position that capitalizes on the anticipated price breakout or reversal.
Conclusion
This volatility indicator is a robust tool for identifying market conditions that are conducive to significant price movements. By tracking the behavior of key volatility indices, traders can gain insights into the market’s expectations of future price fluctuations, enabling them to make more informed decisions regarding market entries and exits. Understanding and monitoring volatility can be particularly valuable during times of heightened uncertainty, as changes in volatility often precede substantial shifts in market direction (French et al., 1987).
References
• Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
• Christoffersen, P. F. (1998). Evaluating Interval Forecasts. International Economic Review, 39(4), 841-862.
• Whaley, R. E. (2000). Derivatives on Market Volatility. Journal of Derivatives, 7(4), 71-82.
• Pindyck, R. S. (2004). Volatility and the Pricing of Commodity Derivatives. Journal of Futures Markets, 24(11), 973-987.
• Geman, H. (2005). Commodities and Commodity Derivatives: Modeling and Pricing for Agriculturals, Metals and Energy. John Wiley & Sons.
• Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.
• French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected Stock Returns and Volatility. Journal of Financial Economics, 19(1), 3-29.
MarktQuants Supertrend"MarktQuants Supertrend" is an indicator designed to help traders visualize market trends using a combination of moving averages and dynamic range calculations. It adapts to market conditions, providing insights into potential trend directions:
Trend Identification:
Utilizes a customizable moving average (MA Type) with options like SMA, EMA, SMMA, WMA, VWMA, TEMA, DEMA, LSMA, HMA, or ALMA to smooth price action.
Calculates a dynamic range based on the highest high over a specified period (Length), adjusted by multipliers (Multiplier Alpha and Multiplier Beta).
Signal Generation:
The indicator assesses price relative to both the moving average and the calculated range (Average Range or Lookback Alpha and Beta).
Scores are computed to determine if the price action suggests a long (bullish) or short (bearish) trend via crossover signals from these scores.
Visual Indicators:
Candlesticks: The color changes based on the trend direction; greenish for long conditions and purplish for short conditions, enhancing visual trend recognition.
Moving Average Line: Plotted in semi-transparent color matching the trend, with a bold line for clarity.
Range Indicator: A line representing the average range, filled with semi-transparent color to show potential support or resistance levels.
Customization:
Users can toggle between using the average range or specific lookback periods for trend signals via the Use Average Range option.
Adjustable parameters for the moving average and range calculations allow for fine-tuning to various market instruments or trading styles.
Inputs:
Range Settings:
Length: Defines the period for calculating the highest high.
Lookback Alpha & Lookback Beta: Different lookback periods for range calculation.
Multiplier Alpha & Multiplier Beta: Multipliers for adjusting the range.
Use Average Range: Switch to use average or specific range for signals.
Source: Pick the preferred source for the range calculations.
Moving Average Settings:
Type: Choice of moving average type.
Length: Length of the moving average.
Source: The price source for the moving average calculation (default is close price).
Alert Options:
MQ - Supertrend Long for Long trades (Buy) when the Long Condition is met.
MQ - Supertrend Short for Short trades (Sell) when the Short Condition is met.
Note: This indicator is best used alongside other analysis tools to confirm trends and signals. Always consider the broader market context.
Volume 2x Average This script helps traders identify stocks or instruments experiencing unusually high trading volume compared to their average volume over a user-defined period. The key features include:
1. Volume 2x Average Filter:
Highlights bars where the current volume is greater than twice the average volume for the selected period.
2. Dynamic Average Period:
Allows users to specify the period for calculating the average volume (e.g., 1 day, 5 days, etc.).
3. Color-Coded Bars:
• Green Bars: Indicate bullish candlesticks where the closing price is higher than the
opening price.
• Red Bars: Indicate bearish candlesticks where the closing price is lower than the
opening price.
4. Optional Bar Visibility:
Users can toggle the visibility of the highlighted volume bars, providing flexibility for clean chart analysis.
5. Average Volume Line:
Plots the average volume as a blue line for reference.
Use Case:
This script is ideal for traders looking to identify potential breakouts, reversals, or key market movements driven by significant volume spikes. By dynamically adjusting the average period and toggling bar visibility, users can tailor the script to fit various trading strategies and timeframes.
Inputs:
1. Show 2x Volume Bars:
• Toggle to enable or disable the display of the highlighted volume bars.
2. Average Volume Period:
• Specify the number of periods (e.g., 1 for 1 day, 5 for 5 days) to calculate the average
volume.
Disclaimer:
This script is for educational purposes only and does not constitute financial advice. Use it alongside your analysis and trading strategy.
Trend Heuristics (+Signals)Trend Heuristics - Enhanced Rolling VWAP with Smart Signals
This indicator is an enhanced version of the Rolling VWAP (RVWAP) concept, originally based on PineCoders' ConditionalAverages library. It combines volume-weighted average price analysis with advanced signal detection for both sweeps and breakouts.
Core Features
1. Rolling VWAP System
- Implements a dynamic rolling VWAP that adapts to different timeframes
- Includes standard deviation bands for volatility measurement
- Offers flexible time period settings (fixed or auto-adjusting)
- Provides customizable visual elements including bands and fills
2. Dual Signal System
Sweep Signals
Detects high-probability reversal points with these conditions:
- Bullish Sweep:
- Opens above upper band
- Tests below upper band (low)
- Closes above upper band
- Shows stronger lower wick
- Closes above previous high
- Has favorable close position (upper 50% of candle)
- Bearish Sweep:
- Opens below lower band
- Tests above lower band (high)
- Closes below lower band
- Shows stronger upper wick
- Closes below previous low
- Has favorable close position (lower 50% of candle)
Breakout Signals
Identifies potential trend changes with these conditions:
- Bullish Breakout:
- Opens below VWAP
- Closes above upper band
- Indicates strong momentum shift upward
- Bearish Breakout:
- Opens above VWAP
- Closes below lower band
- Indicates strong momentum shift downward
Technical Details
Base Components
- Built upon PineCoders' ConditionalAverages library
- Incorporates custom alert system via CustomAlertLib
- Uses standard deviation for band calculations
Customization Options
- Adjustable standard deviation multiplier
- Flexible time period settings
- Independent controls for sweep and breakout signals
- Customizable visual elements (colors, sizes, positions)
- Custom alert message formatting
Use Cases
1. Trend Following:
- Use VWAP as dynamic support/resistance
- Monitor breakout signals for trend changes
2. Mean Reversion:
- Use sweep signals for counter-trend opportunities
- Standard deviation bands for range identification
3. Volume Analysis:
- VWAP provides volume-weighted price levels
- Helps identify significant price levels
Notes
- Best performed on liquid instruments with consistent volume
- Most effective on timeframes from 1hours to 4 hours and 1D, anything greater isn't very good
- Recommended to use in conjunction with other technical analysis tools
- Signals can be filtered based on higher timeframe trends
Credits
- Original Rolling VWAP concept by PineCoders
