Overview
The Supply and Demand Order Block Breakout Strategy with Volume Filter is a quantitative trading strategy that combines fractal theory, volume confirmation, and order block concepts from technical analysis. The strategy identifies key fractal points in historical price data and uses volume filtering mechanisms to determine potential supply and demand zones. Trading signals are executed when price breaks through these critical zones. The core concept leverages order block effects in the market, where large capital creates buying and selling pressure zones at specific price levels, with volume validation to enhance signal reliability.
Strategy Principles
The strategy's core principle is based on fractal theory and order block concepts. First, the strategy identifies potential supply and demand zones by calculating price fractal points within specified periods. An up fractal is defined as the highest price level within the specified period, while a down fractal represents the lowest price level within the same timeframe. To improve signal quality, the strategy incorporates a volume filtering mechanism that requires the fractal point's volume to exceed a specified multiple of the 20-period simple moving average volume.
When a valid up fractal is identified, the strategy marks this level as a resistance zone and waits for an upward price breakout. When price breaks above the up fractal, the strategy interprets this as a change in supply and demand dynamics, where the former resistance zone transforms into a support zone, triggering a long position entry. Conversely, when a valid down fractal is identified, the strategy marks this level as a support zone. When price breaks below this level, the former support zone becomes a resistance zone, triggering a short position entry.
The strategy allows users to choose between two breakout confirmation methods: wick plus body breakout and pure body breakout. The wick plus body breakout mode uses high and low prices as breakout confirmation criteria, while the pure body breakout mode uses closing prices for confirmation. The volume filtering mechanism validates breakout effectiveness by comparing current volume against historical average volume multiples.
Strategy Advantages
The strategy possesses several significant advantages. First, the volume filtering mechanism substantially improves signal reliability. Traditional fractal breakout strategies are prone to false breakout signals, while volume confirmation effectively filters out weak breakouts with insufficient volume, ensuring trading signals only occur under adequate market participation.
Second, the strategy is based on order block theory, providing a solid market logic foundation. Order blocks represent concentrated buying and selling activities by large capital at specific price levels, often forming important support and resistance levels. When price breaks through these critical zones, it typically indicates significant market structure changes, providing traders with high-probability entry opportunities.
Third, the strategy demonstrates excellent adaptability and configurability. Users can adjust parameters such as fractal periods and volume multiples according to different market environments and personal preferences. The choice of breakout types also provides additional flexibility, allowing traders to select the most suitable confirmation method based on market characteristics.
Finally, the strategy maintains clear and concise logic that is easy to understand and implement. Through well-defined fractal identification, volume filtering, and breakout confirmation processes, the strategy avoids complex technical indicator combinations, reducing over-optimization risks.
Strategy Risks
Despite numerous advantages, the strategy also presents certain potential risks requiring attention. First, the strategy heavily depends on volume data. In situations where volume data is inaccurate or market liquidity is poor, the volume filtering mechanism may produce misjudgments, leading to missed valid trading opportunities or false signals.
Second, the strategy suffers from lag issues. Due to the need to wait for fractal point confirmation and breakout occurrence, the strategy's entry timing may lag behind optimal entry points. This lag could affect profitability in rapidly changing market environments.
Third, the strategy lacks explicit stop-loss and take-profit mechanisms. While the strategy can identify entry timing, it doesn't provide corresponding risk management measures. During severe market volatility or breakout failures, traders may face significant loss risks.
To mitigate these risks, traders should combine other technical analysis tools for signal confirmation, set reasonable stop-loss and take-profit levels, and dynamically adjust strategy parameters based on market conditions. Additionally, comprehensive backtesting and validation across different market environments are recommended.
Strategy Optimization Directions
The strategy offers multiple optimization directions. First, implementing a dynamic volume threshold mechanism could be beneficial. The current strategy uses fixed volume multiples as filtering conditions, but market volume characteristics change over time. By introducing adaptive volume thresholds, the strategy can dynamically adjust filtering standards based on actual market conditions, improving adaptability.
Second, adding comprehensive risk management modules is recommended. Stop-loss and take-profit levels can be set based on volatility, support and resistance levels, or fixed percentages. Position management mechanisms could also be considered, adjusting trade sizes based on signal strength and market volatility.
Third, incorporating multi-timeframe analysis could enhance performance. The current strategy operates within a single timeframe, but combining higher timeframe trend analysis could improve success rates. For example, executing trading signals only when higher timeframe trends align.
Fourth, optimizing fractal identification algorithms presents opportunities for improvement. Current fractal identification is relatively simple; more complex methods could be considered, such as pattern-based fractal identification or composite fractal identification combining other technical indicators.
Finally, adding signal filtering mechanisms is advisable. Additional technical indicators (such as RSI, MACD) could filter trading signals, or market sentiment indicators could adjust strategy sensitivity.
Summary
The Supply and Demand Order Block Breakout Strategy with Volume Filter is a comprehensive quantitative trading strategy combining fractal theory, volume analysis, and order block concepts. The strategy identifies key price fractal points, incorporates volume confirmation mechanisms, and executes trading operations when price breaks through important supply and demand zones.
The strategy's main advantages include solid theoretical foundation, good signal quality, and high configurability. The volume filtering mechanism effectively improves signal reliability, while order block theory provides clear market logic support. However, the strategy also presents potential risks, including dependency on volume data, signal lag, and lack of risk management mechanisms.
Through optimization measures such as introducing dynamic volume thresholds, comprehensive risk management modules, multi-timeframe analysis, and signal filtering mechanisms, the strategy's performance and stability can be further enhanced. For quantitative traders, this strategy provides an effective market structure analysis tool that helps identify high-probability trading opportunities.
Strategy source code
/*backtest
start: 2024-07-07 00:00:00
end: 2025-07-05 10:18:00
period: 3m
basePeriod: 3m
exchanges: [{"eid":"Futures_Binance","currency":"ETH_USDT"}]
*/
//@version=5
strategy("Supply and Demand - Order Block Strategy with Volume Filter", overlay=true)
// ═══════════════════════════════════════════════════════════════════════════════════
// 📊 INPUT SETTINGS
// ═══════════════════════════════════════════════════════════════════════════════════
breakType = input.string("Wick+Body", title="Fractal Break Type:", options=["Wick+Body", "Body"])
n = input.int(title="Periods", defval=5, minval=3, tooltip="Number of periods for fractal lookback")
// 🔊 Volume Filter
enableVolumeFilter = input.bool(true, "Enable Volume Filter", group="Volume Filter")
volumeMultiplier = input.float(1.5, "Volume Multiplier", minval=1.0, maxval=3.0, step=0.1, group="Volume Filter", tooltip="Fractal must have volume > average volume * this multiplier")
// ═══════════════════════════════════════════════════════════════════════════════════
// 📈 TECHNICAL INDICATORS
// ═══════════════════════════════════════════════════════════════════════════════════
avgVolume = ta.sma(volume, 20)
// ═══════════════════════════════════════════════════════════════════════════════════
// 📦 FRACTAL CALCULATION WITH VOLUME FILTER
// ═══════════════════════════════════════════════════════════════════════════════════
// Original fractal calculation
upFractal = high[n] == ta.highest(high, n)
downFractal = low[n] == ta.lowest(low, n)
// 🔊 Enhanced fractal with volume confirmation
upFractalValid = upFractal and (not enableVolumeFilter or volume[n] > avgVolume * volumeMultiplier)
downFractalValid = downFractal and (not enableVolumeFilter or volume[n] > avgVolume * volumeMultiplier)
var float topValue = na
var float bottomValue = na
var topBreakBlock = false
var bottomBreakBlock = false
topBreakCheckSource = breakType == "Wick+Body" ? high : close
bottomBreakCheckSource = breakType == "Wick+Body" ? low : close
// New up fractal - only if volume criteria met
if upFractalValid
topBreakBlock := false
topValue := high[n]
// New down fractal - only if volume criteria met
if downFractalValid
bottomBreakBlock := false
bottomValue := low[n]
// ═══════════════════════════════════════════════════════════════════════════════════
// 🚀 ENTRY LOGIC
// ═══════════════════════════════════════════════════════════════════════════════════
// Top break
if ta.crossover(topBreakCheckSource, topValue) and not topBreakBlock
topBreakBlock := true
if strategy.position_size <= 0
strategy.entry("Long", strategy.long)
// Bottom break
if ta.crossunder(bottomBreakCheckSource, bottomValue) and not bottomBreakBlock
bottomBreakBlock := true
if strategy.position_size >= 0
strategy.entry("Short", strategy.short)
// ═══════════════════════════════════════════════════════════════════════════════════
// 🎨 PLOTS
// ═══════════════════════════════════════════════════════════════════════════════════
plotshape(downFractalValid, style=shape.triangleup, location=location.belowbar, offset=-n, color=color.new(color.gray,80), size = size.tiny)
plotshape(upFractalValid, style=shape.triangledown, location=location.abovebar, offset=-n, color=color.new(color.gray,80), size = size.tiny)
Strategy parameters
The original address: Supply and Demand Order Block Breakout Strategy with Volume Filter
Top comments (1)
Really appreciate the breakdown of this strategy — combining order blocks with a volume filter adds a solid layer of confirmation. I’ve been experimenting with similar setups on lower timeframes, and the volume breakout trigger definitely helps reduce false signals. Thanks for sharing this, looking forward to testing it on different pairs!