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Refactor documentation for various filters and indicators to enhance clarity and consistency
- Updated Bessel, Bilateral, Blma, Butter, Conv, Ema, Kama, LSMA, MAMA, MGDI, SSF, USF, ATR, ADL, and ADOSC documentation to use bullet points for key concepts and features. - Added a new Qodana configuration file for code analysis. - Removed coverage configuration from Quantower.Tests.csproj to streamline testing setup.
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Miha Kralj
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# VWAPBANDS: VWAP Bands
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## Overview and Purpose
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VWAP Bands (VWAPBANDS) is a channel indicator that extends the Volume Weighted Average Price (VWAP) concept by adding standard deviation bands above and below the central VWAP line. This indicator combines the volume-weighted fairness concept of VWAP with statistical volatility measurements, creating dynamic support and resistance levels that reflect both price-volume relationships and market volatility.
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Unlike traditional moving average-based bands, VWAPBANDS uses volume-weighted variance calculations to determine band width, making the indicator particularly sensitive to volume-driven price movements. The bands automatically adjust to market conditions while maintaining their statistical significance, providing traders with reliable levels for identifying overbought/oversold conditions and potential reversal points.
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## Core Concepts
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* **Volume-weighted statistics:** Uses volume data to weight price observations, giving more importance to high-volume periods
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* **Session-based calculation:** Resets calculations based on configurable time periods (daily, hourly, etc.)
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* **Statistical significance:** Bands represent 1 and 2 standard deviations from the volume-weighted mean
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* **Dynamic adaptation:** Band width adjusts automatically based on volume-weighted price variance
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* **Multi-timeframe flexibility:** Supports various reset intervals from minutes to months
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* **Institutional relevance:** Reflects the same VWAP calculations used by institutional traders
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The key advantage of VWAPBANDS is its ability to combine the fairness concept of VWAP (where institutional orders are often benchmarked) with volatility-based support and resistance levels, making it particularly valuable for understanding institutional price levels and market structure.
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## Common Settings and Parameters
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| Parameter | Default | Function | When to Adjust |
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| ------ | ------ | ------ | ------ |
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| Source | HLC3 | Price data used for VWAP calculation | Use Close for end-of-period analysis, HLC3 for comprehensive price representation |
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| Session Reset | 1D | Time period for VWAP calculation reset | Match to trading strategy timeframe: intraday (1H, 4H), swing (1D), position (1W) |
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| StdDev Multiplier | 1.0 | Distance of primary bands from VWAP in standard deviations | Increase for wider bands in volatile markets, decrease for tighter levels |
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| Show 2nd Bands | True | Display secondary bands at 2x multiplier distance | Disable for cleaner charts, enable for additional confluence levels |
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**Pro Tip:** Use daily reset for swing trading strategies, hourly reset for intraday scalping, and weekly reset for position trading to align the indicator with your trading timeframe.
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## Calculation and Mathematical Foundation
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**Simplified explanation:**
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VWAPBANDS calculates the volume-weighted average price from the session start, then computes the volume-weighted variance of prices around this average. Standard deviation bands are plotted at 1x and 2x the multiplier distance from VWAP.
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**Technical formula:**
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1. VWAP = Σ(Price × Volume) / Σ(Volume)
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2. Volume-Weighted Variance = Σ(Price² × Volume) / Σ(Volume) - VWAP²
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3. Standard Deviation = √(Volume-Weighted Variance)
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4. Upper Band = VWAP + (Multiplier × Standard Deviation)
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5. Lower Band = VWAP - (Multiplier × Standard Deviation)
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**Detailed calculation steps:**
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1. Initialize cumulative sums at session start (price×volume, volume, price²×volume)
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2. For each bar, add current values to cumulative sums if volume > 0
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3. Calculate VWAP as ratio of cumulative price×volume to cumulative volume
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4. Compute volume-weighted second moment and subtract VWAP squared for variance
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5. Take square root of variance to get standard deviation
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6. Plot bands at specified multiples of standard deviation from VWAP
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> 🔍 **Technical Note:** The implementation uses session-based resets to ensure VWAP calculations align with market structure. Volume-weighted variance provides more accurate volatility measurement than simple price variance, as it reflects the actual trading intensity at different price levels.
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## Interpretation Details
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VWAPBANDS provides multiple layers of market analysis:
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* **VWAP Line Analysis:**
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* Price above VWAP: Bullish bias, buyers in control above fair value
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* Price below VWAP: Bearish bias, sellers in control below fair value
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* Price oscillating around VWAP: Balanced market, fair value region
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* **Band Interaction Signals:**
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* Price touching upper 1σ band: Potential resistance, consider profit-taking
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* Price touching lower 1σ band: Potential support, consider accumulation
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* Price beyond 2σ bands: Extreme conditions, potential mean reversion opportunity
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* Price consistently above/below bands: Strong trend continuation signal
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* **Band Width Analysis:**
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* Expanding bands: Increasing volatility, larger price movements expected
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* Contracting bands: Decreasing volatility, potential breakout setup
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* Stable band width: Consistent volatility environment
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* **Volume-Price Relationship:**
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* High volume near bands: Increased significance of support/resistance levels
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* Low volume near bands: Potential for false breakouts or weak reversals
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* Volume expansion with band breaks: Confirmation of directional moves
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## Trading Applications
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**Mean Reversion Strategy:**
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* Buy when price touches or exceeds lower 1σ band with volume confirmation
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* Sell when price reaches VWAP or upper bands
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* Use 2σ bands for extreme mean reversion opportunities
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* Set stops beyond 2σ levels to account for extended moves
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**Trend Following Strategy:**
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* Enter long positions when price breaks above upper bands with volume
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* Enter short positions when price breaks below lower bands with volume
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* Use VWAP as dynamic support/resistance in trending markets
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* Trail stops using the opposite band or VWAP line
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**Institutional Level Trading:**
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* Monitor price action around VWAP for institutional interest
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* Look for volume spikes when price approaches VWAP after extended moves
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* Use VWAP as benchmark for order execution efficiency
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* Identify accumulation/distribution phases based on VWAP interaction
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**Breakout Strategy:**
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* Monitor periods of contracting bands for potential breakouts
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* Enter positions on volume-confirmed breaks beyond 1σ bands
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* Target 2σ bands for profit-taking on breakout moves
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* Use failed breakouts as contrarian signals
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## Signal Combinations
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**High-Probability Long Signals:**
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* Price bounces off lower 1σ band with increasing volume
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* Price reclaims VWAP after period below with strong volume
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* Bullish divergence between price and volume at lower bands
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* Multiple timeframe VWAP alignment supporting upward bias
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**High-Probability Short Signals:**
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* Price fails at upper 1σ band with declining volume
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* Price breaks below VWAP after period above with strong volume
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* Bearish divergence between price and volume at upper bands
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* Multiple timeframe VWAP alignment supporting downward bias
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**Consolidation Warnings:**
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* Price oscillating between narrow bands around VWAP
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* Decreasing volume with price approaching bands
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* Multiple false breakouts beyond bands
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* Band width contracting significantly
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## Advanced Techniques
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**Multi-Timeframe Analysis:**
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* Use higher timeframe VWAPBANDS for major support/resistance levels
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* Combine daily VWAP with intraday bands for precision timing
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* Look for confluence between different session VWAP levels
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* Identify key levels where multiple timeframe VWAPs converge
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**Volume Profile Integration:**
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* Combine VWAPBANDS with volume profile for enhanced context
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* Identify high-volume nodes near VWAP levels
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* Use volume-at-price data to validate band significance
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* Monitor institutional order flow around VWAP levels
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**Session-Specific Analysis:**
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* Analyze different session reset periods for various market conditions
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* Use overnight VWAP for gap analysis and fair value assessment
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* Apply weekly VWAP for longer-term institutional benchmarking
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* Implement monthly VWAP for portfolio rebalancing levels
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## Limitations and Considerations
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* **Session dependency:** Reset timing significantly affects indicator behavior and relevance
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* **Volume quality:** Requires accurate volume data; may be less reliable in low-volume periods
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* **Lag component:** VWAP calculations create some lag, especially early in sessions
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* **Market structure:** Most effective in liquid markets with consistent volume patterns
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* **Gap handling:** Overnight gaps can affect VWAP relevance at session open
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* **False signals:** Low-volume periods may produce unreliable band interactions
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## Comparison with Related Indicators
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**VWAPBANDS vs. Bollinger Bands:**
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* VWAPBANDS: Volume-weighted center line with volume-weighted variance
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* Bollinger Bands: Simple moving average center with price-based standard deviation
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**VWAPBANDS vs. Keltner Channels:**
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* VWAPBANDS: VWAP-based with statistical variance measurements
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* Keltner Channels: EMA-based with ATR-derived band width
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**VWAPBANDS vs. Standard VWAP:**
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* VWAPBANDS: Adds volatility context with standard deviation bands
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* Standard VWAP: Single line without volatility or support/resistance context
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## Best Practices
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**Parameter Optimization:**
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* Match session reset to trading strategy timeframe
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* Adjust multiplier based on asset volatility characteristics
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* Test different source prices (close vs. HLC3) for optimal results
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* Consider market hours and session boundaries for reset timing
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**Risk Management:**
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* Use bands for position sizing (larger positions near support bands)
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* Set stops beyond 2σ levels to avoid normal volatility whipsaws
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* Monitor volume confirmation for all band interaction signals
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* Avoid trading during low-volume periods when bands may be unreliable
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**Market Context:**
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* Consider overall market regime (trending vs. ranging)
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* Account for news events and earnings that may affect volume patterns
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* Monitor correlation with institutional trading patterns
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* Adjust expectations based on market volatility environment
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## References
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* Harris, L. (2003). Trading and Exchanges: Market Microstructure for Practitioners. Oxford University Press.
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* Berkowitz, S. A. (1993). The Advantages of Volume Weighted Average Price Trading. Journal of Portfolio Management.
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@@ -0,0 +1,92 @@
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// The MIT License (MIT)
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// © mihakralj
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//@version=6
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indicator("VWAP Bands (VWAPBANDS)", "VWAPBANDS", overlay=true)
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//@function Calculates VWAP Bands with standard deviation bands
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//@param src Source price series (typically hlc3)
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//@param vol Volume series
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//@param reset_condition Condition to reset VWAP calculation
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//@param multiplier Standard deviation multiplier for bands
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//@returns [vwap_value, upper_band1, lower_band1, upper_band2, lower_band2, stdev] VWAP and band values
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//@optimized for performance and dirty data
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vwapbands(series float src, series float vol, series bool reset_condition, series float multiplier) =>
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var float sum_pv = 0.0, var float sum_vol = 0.0, var float sum_pv2 = 0.0, var int count = 0
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float current_price = nz(src), float current_vol = nz(vol, 0.0)
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if reset_condition
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if current_vol > 0.0
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sum_pv := current_price * current_vol
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sum_vol := current_vol
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sum_pv2 := current_price * current_price * current_vol
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count := 1
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else
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sum_pv := 0.0, sum_vol := 0.0, sum_pv2 := 0.0, count := 0
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else
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if current_vol > 0.0
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sum_pv += current_price * current_vol
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sum_vol += current_vol
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sum_pv2 += current_price * current_price * current_vol
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count += 1
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float vwap_val = sum_vol > 0.0 ? sum_pv / sum_vol : src
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float variance = 0.0
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if sum_vol > 0.0 and count > 1
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mean_p2 = sum_pv2 / sum_vol
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vwap_squared = vwap_val * vwap_val
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variance := math.max(0.0, mean_p2 - vwap_squared)
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float stdev = math.sqrt(variance)
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float upper1 = vwap_val + multiplier * stdev
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float lower1 = vwap_val - multiplier * stdev
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float upper2 = vwap_val + 2.0 * multiplier * stdev
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float lower2 = vwap_val - 2.0 * multiplier * stdev
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[vwap_val, upper1, lower1, upper2, lower2, stdev]
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// ---------- Main loop ----------
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// Inputs
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i_source = input.source(hlc3, "Source")
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i_session_type = input.string("1D", "Session Reset", options=["1m", "2m", "3m", "5m", "10m", "15m", "30m", "45m", "1H", "2H", "3H", "4H", "1D", "1W", "1M", "3M", "6M", "12M", "Never"])
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i_multiplier = input.float(1.0, "Standard Deviation Multiplier", minval=0.1, step=0.1)
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i_show_bands2 = input.bool(true, "Show 2nd Standard Deviation Bands")
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// Calculate reset condition
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reset_condition = switch i_session_type
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"1m" => ta.change(time("1")) != 0
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"2m" => ta.change(time("2")) != 0
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"3m" => ta.change(time("3")) != 0
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"5m" => ta.change(time("5")) != 0
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"10m" => ta.change(time("10")) != 0
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"15m" => ta.change(time("15")) != 0
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"30m" => ta.change(time("30")) != 0
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"45m" => ta.change(time("45")) != 0
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"1H" => ta.change(time("60")) != 0
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"2H" => ta.change(time("120")) != 0
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"3H" => ta.change(time("180")) != 0
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"4H" => ta.change(time("240")) != 0
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"1D" => ta.change(time("1D")) != 0
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"1W" => ta.change(time("1W")) != 0
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"1M" => ta.change(time("1M")) != 0
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"3M" => ta.change(time("3M")) != 0
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"6M" => ta.change(time("6M")) != 0
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"12M" => ta.change(time("12M")) != 0
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"Never" => bar_index == 0
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=> false
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// Calculation
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[vwap_value, upper_band1, lower_band1, upper_band2, lower_band2, stdev] = vwapbands(i_source, volume, reset_condition, i_multiplier)
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// Colors
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vwap_color = color.yellow
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band1_color = color.blue
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band2_color = color.purple
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fill_color1 = color.blue
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fill_color2 = color.purple
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// Plot
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p_vwap = plot(vwap_value, "VWAP", color=color.yellow, linewidth=2)
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p_upper1 = plot(upper_band1, "Upper Band 1σ", color=color.yellow, linewidth=2)
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p_lower1 = plot(lower_band1, "Lower Band 1σ", color=color.yellow, linewidth=2)
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p_upper2 = plot(i_show_bands2 ? upper_band2 : na, "Upper Band 2σ", color=color.yellow, linewidth=2)
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p_lower2 = plot(i_show_bands2 ? lower_band2 : na, "Lower Band 2σ", color=color.yellow, linewidth=2)
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fill(p_upper1, p_lower1, color=color.new(color.blue, 90), title="1σ Band Fill")
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fill(p_upper2, p_upper1, color=i_show_bands2 ? color.new(color.purple, 90) : na, title="Upper 2σ Fill")
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fill(p_lower1, p_lower2, color=i_show_bands2 ? color.new(color.purple, 90) : na, title="Lower 2σ Fill")
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