Multi Timeframe Trading Strategy Combining Harmonic Patterns and Williams R

Pine ScriptMulti-timeframe Mean reversion Momentum Pivots / S&R

Author: ChaoZhang
Complexity: Starter · 61 lines
Parameters: 0
Backtest bar: 1m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is an advanced trading system that combines Harmonic Patterns with the Williams Percent Range (WPR) indicator. It identifies harmonic formations (such as Gartley, Bat, Crab, and Butterfly patterns) in the market and uses WPR's overbought/oversold levels to determine trade entry and exit points. The strategy employs multiple confirmation mechanisms, utilizing the synergy of technical indicators to enhance trading accuracy and reliability.

Strategy Principles

The core logic includes several key components:

Harmonic Pattern Recognition: Uses price pivot points to identify potential harmonic formations by analyzing the relationships between highs and lows.
Williams %R Calculation: Employs a custom period for calculating WPR, analyzing relationships between high, low, and closing prices to determine market conditions.
Entry Conditions:
- Long Entry: When a bullish harmonic pattern appears and WPR is in oversold territory
- Short Entry: When a bearish harmonic pattern appears and WPR is in overbought territory
Risk Management: Implements dynamic stop-loss based on recent lows/highs and sets take-profit levels using risk-reward ratios.

Strategy Advantages

Multi-dimensional Analysis: Combines pattern analysis with momentum indicators for more reliable trading signals.
Robust Risk Control: Uses dynamic stop-loss and risk-reward based take-profit settings to effectively control risk per trade.
High Adaptability: Can be adapted to different market environments and instruments through parameter optimization.
Signal Confirmation Mechanism: Reduces false signals through dual confirmation using harmonic patterns and WPR.

Strategy Risks

Pattern Recognition Risk: Simplified harmonic pattern recognition may lead to misidentification of some formations.
Parameter Sensitivity: Multiple parameters require careful optimization, as improper settings can affect strategy performance.
Market Environment Dependency: May underperform in highly volatile or ranging markets.
Signal Lag: Technical indicator-based signals may have inherent lag.

Strategy Optimization Directions

Enhanced Pattern Recognition:
- Add stricter harmonic ratio validation
- Incorporate price structure analysis for improved pattern identification
Signal Filtering:
- Add trend filters
- Consider volatility indicators for market environment adaptation
Risk Management Optimization:
- Implement dynamic risk-reward ratio adjustments
- Add volatility-based position sizing

Summary

This strategy builds a comprehensive trading system by combining harmonic patterns with the Williams %R indicator. Its strengths lie in its multi-dimensional analysis approach and robust risk control mechanisms, though attention must be paid to parameter optimization and market environment adaptation. Through the suggested optimization directions, the strategy's stability and reliability can be further enhanced.

Source Pine Script

/*backtest
start: 2025-01-09 00:00:00
end: 2025-01-16 00:00:00
period: 1m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT","balance":49999}]
*/

//@version=5
strategy("Harmonic Pattern with WPR Backtest", overlay=true)

// === Inputs ===
patternLength = input.int(5, title="Pattern Length")
wprLength = input.int(14, title="WPR Length")
wprOverbought = input.float(-20, title="WPR Overbought Level")
wprOversold = input.float(-80, title="WPR Oversold Level")
riskRewardMultiplier = input.float(0.618, title="Take-Profit Risk/Reward Multiplier")
stopLossBuffer = input.float(0.005, title="Stop-Loss Buffer (%)")

// === Manual Calculation of William Percent Range (WPR) ===
highestHigh = ta.highest(high, wprLength)
lowestLow = ta.lowest(low, wprLength)
wpr = ((highestHigh - close) / (highestHigh - lowestLow)) * -100

// === Harmonic Pattern Detection (Simplified Approximation) ===
// Calculate price pivots
pivotHigh = ta.pivothigh(high, patternLength, patternLength)
pivotLow = ta.pivotlow(low, patternLength, patternLength)

// Detect Bullish and Bearish Harmonic Patterns
bullishPattern = pivotLow and close > ta.lowest(close, patternLength)  // Simplified detection for bullish patterns
bearishPattern = pivotHigh and close < ta.highest(close, patternLength)  // Simplified detection for bearish patterns

// === Entry Conditions ===
longCondition = bullishPattern and wpr < wprOversold
shortCondition = bearishPattern and wpr > wprOverbought

// === Stop-Loss and Take-Profit Levels ===
longEntryPrice = close
longSL = ta.valuewhen(longCondition, low, 0) * (1 - stopLossBuffer)  // Stop-loss for long trades
longTP = longEntryPrice * (1 + riskRewardMultiplier)  // Take-profit for long trades

shortEntryPrice = close
shortSL = ta.valuewhen(shortCondition, high, 0) * (1 + stopLossBuffer)  // Stop-loss for short trades
shortTP = shortEntryPrice * (1 - riskRewardMultiplier)  // Take-profit for short trades

// === Backtesting Logic ===
// Long Trade
if longCondition
    strategy.entry("Long", strategy.long)
    strategy.exit("Long Exit", "Long", stop=longSL, limit=longTP)

// Short Trade
if shortCondition
    strategy.entry("Short", strategy.short)
    strategy.exit("Short Exit", "Short", stop=shortSL, limit=shortTP)

// === Visualization ===
bgcolor(longCondition ? color.new(color.green, 90) : na, title="Long Entry Signal")
bgcolor(shortCondition ? color.new(color.red, 90) : na, title="Short Entry Signal")

Published by ChaoZhang on the FMZ strategy square · last modified 2025-01-17. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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