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Break of Structure with Volume Confirmation Multi-Condition Intelligent Trading Strategy

Author: ChaoZhang, Date: 2024-12-20 16:15:43
Tags: BOSSMAATRTPSL

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Overview

This is an intelligent trading strategy based on Break of Structure (BOS) and volume confirmation. The strategy generates trading signals by detecting price breakouts of previous highs or lows, combined with volume expansion confirmation. It employs multiple condition verification mechanisms, including consecutive confirmation requirements and dynamic take-profit/stop-loss settings, to enhance trading reliability and risk control capabilities.

Strategy Principles

The core logic includes the following key elements:

  1. Identifies structural highs and lows by calculating the highest and lowest prices within a specified period
  2. Uses moving averages to calculate volume baseline and determine significant volume expansion
  3. Accumulates bullish confirmation count when price breaks above previous high with increased volume
  4. Accumulates bearish confirmation count when price breaks below previous low with increased volume
  5. Trading signals are only triggered after reaching the specified confirmation count
  6. Sets percentage-based take-profit and stop-loss levels after position entry

Strategy Advantages

  1. Multiple condition verification mechanism improves signal reliability
  2. Volume indicator integration helps avoid false breakout signals
  3. Consecutive confirmation mechanism reduces trading frequency and increases win rate
  4. Dynamic take-profit/stop-loss settings automatically adjust exit positions based on entry price
  5. Clear strategy logic with adjustable parameters offers good adaptability

Strategy Risks

  1. Frequent false breakouts in ranging markets may lead to consecutive losses
  2. Stop-loss positions may not be timely enough in volatile markets
  3. Confirmation mechanism may delay entries, missing optimal price points
  4. Fixed volume judgment criteria may not adapt well to changing market conditions Solutions:
  • Introduce market volatility indicators for dynamic parameter adjustment
  • Add trend filters to reduce false signals in ranging markets
  • Optimize stop-loss logic for improved flexibility
  • Design adaptive volume threshold calculation methods

Strategy Optimization Directions

  1. Add trend identification indicators, such as moving average systems, to trade only in trend direction
  2. Incorporate ATR indicator for dynamic stop-loss distance adjustment
  3. Design volatility-adaptive volume threshold judgment mechanism
  4. Include time filters to avoid high-risk periods
  5. Optimize confirmation mechanism to improve entry timing while maintaining reliability

Summary

This is a strategy system that combines classical technical analysis theory with modern quantitative trading methods. Through multiple condition verification and strict risk control, the strategy demonstrates good stability and reliability. While there are aspects requiring optimization, the overall framework design is reasonable and has practical application value. The strategy’s performance can be further improved through the suggested optimization directions.


/*backtest
start: 2019-12-23 08:00:00
end: 2024-12-18 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("BOS and Volume Strategy with Confirmation", overlay=true)

// Parameters
swingLength = input.int(20, title="Swing Length", minval=1)
volumeMultiplier = input.float(1.1, title="Volume Multiplier", step=0.1)
volumeSMA_length = input.int(10, title="Volume SMA Length", minval=1)
takeProfitPercentage = input.float(0.02, title="Take Profit Percentage", step=0.01)
stopLossPercentage = input.float(0.15, title="Stop Loss Percentage", step=0.01)  // New parameter for stop loss
atrLength = input.int(14, title="ATR Length")
confirmationBars = input.int(2, title="Confirmation Bars", minval=1)

// Calculate Swing Highs and Lows
swingHigh = ta.highest(high, swingLength)[1]
swingLow = ta.lowest(low, swingLength)[1]

// Calculate Volume Moving Average
volumeSMA = ta.sma(volume, volumeSMA_length)
highVolume = volume > (volumeSMA * volumeMultiplier)

// Break of Structure Detection with Confirmation
var int bullishCount = 0
var int bearishCount = 0

if (close > swingHigh and highVolume)
    bullishCount := bullishCount + 1
    bearishCount := 0
else if (close < swingLow and highVolume)
    bearishCount := bearishCount + 1
    bullishCount := 0
else
    bullishCount := 0
    bearishCount := 0

bullishBOSConfirmed = (bullishCount >= confirmationBars)
bearishBOSConfirmed = (bearishCount >= confirmationBars)

// Entry and Exit Conditions
var float entryPrice = na  // Declare entryPrice as a variable

if (bullishBOSConfirmed and strategy.position_size <= 0)
    entryPrice := close  // Use ':=' for assignment
    strategy.entry("Long", strategy.long)

if (strategy.position_size > 0)
    // Calculate stop loss price
    stopLossPrice = entryPrice * (1 - stopLossPercentage)
    strategy.exit("Take Profit Long", from_entry="Long", limit=entryPrice * (1 + takeProfitPercentage), stop=stopLossPrice)

if (bearishBOSConfirmed and strategy.position_size >= 0)
    entryPrice := close  // Use ':=' for assignment
    strategy.entry("Short", strategy.short)

if (strategy.position_size < 0)
    // Calculate stop loss price
    stopLossPrice = entryPrice * (1 + stopLossPercentage)
    strategy.exit("Take Profit Short", from_entry="Short", limit=entryPrice * (1 - takeProfitPercentage), stop=stopLossPrice)

// Plot Swing Highs and Lows for Visualization
plot(swingHigh, title="Swing High", color=color.green, linewidth=1)
plot(swingLow, title="Swing Low", color=color.red, linewidth=1)

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