Dynamic Wave Indicator Combination Strategy

Pine ScriptBreakout Mean reversion Momentum RSI MACD EMA ATR ADX / DMI

Author: ChaoZhang
Complexity: Starter · 63 lines
Parameters: 0
Backtest bar: 3h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is a comprehensive trading system based on multiple technical indicators, combining momentum indicators, trend indicators, and volatility indicators to capture short-term market opportunities. The strategy identifies trading opportunities through MACD crossover signals, EMA trend confirmation, RSI overbought/oversold conditions, and ADX trend strength filtering, while using ATR-based dynamic stop-loss and take-profit for risk management.

Strategy Principles

The core logic of the strategy is based on the following key components:

MACD indicator for capturing momentum changes through fast and slow line crossovers
200-period EMA for overall trend confirmation, with price above the line indicating bullish trend and vice versa
RSI indicator for price momentum confirmation, RSI>50 supports long positions, RSI<50 supports short positions
ADX indicator for weak trend filtering, only considering entry when ADX is above the set threshold
ATR indicator for dynamic calculation of stop-loss and take-profit levels, adapting to market volatility

Strategy Advantages

Multiple indicator cross-validation improves signal reliability
Dynamic risk management system automatically adjusts stops based on market volatility
High adaptability with adjustable parameters for different market conditions
Complete trend confirmation mechanism reduces false breakout risks
Systematic entry and exit logic reduces subjective judgment

Strategy Risks

Multiple indicators may lead to signal lag
Short timeframes are susceptible to market noise
Parameter optimization may result in overfitting
High-frequency trading may incur higher transaction costs
Frequent stop-losses may be triggered during extreme market volatility

Strategy Optimization Directions

Incorporate volume indicators for additional confirmation
Optimize ADX threshold to improve trend filtering efficiency
Add time filters to avoid low liquidity periods
Develop adaptive parameter system to enhance strategy stability
Implement market volatility filters to handle different market conditions

Summary

This strategy constructs a complete trading system through the comprehensive use of multiple technical indicators. While it faces challenges such as lag and parameter optimization, the strategy demonstrates good adaptability and reliability through proper risk management and continuous optimization. Traders are advised to conduct thorough backtesting and parameter optimization before live implementation.

Source Pine Script

/*backtest
start: 2024-02-18 00:00:00
end: 2025-02-16 08:00:00
period: 3h
basePeriod: 3h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("Optimized Impulse Wave Strategy", overlay=true)

// === INPUT PARAMETERS ===
fast_length = input(12, title="MACD Fast Length")
slow_length = input(26, title="MACD Slow Length")
signal_smoothing = input(9, title="MACD Signal Smoothing")
ema_length = input(200, title="EMA Length")
rsi_length = input(14, title="RSI Length")
adx_length = input(14, title="ADX Length")
adx_smoothing = input(14, title="ADX Smoothing")
atr_length = input(14, title="ATR Length")
risk_reward_ratio = input(2, title="Risk-Reward Ratio")
adx_threshold = input(20, title="ADX Threshold")

// === INDICATORS ===
[macdLine, signalLine, _] = ta.macd(close, fast_length, slow_length, signal_smoothing)
ema = ta.ema(close, ema_length)
rsi = ta.rsi(close, rsi_length)
[dmiPlus, dmiMinus, adx] = ta.dmi(adx_length, adx_smoothing)

// === ENTRY CONDITIONS ===
bullishTrend = ta.crossover(macdLine, signalLine) and close > ema and adx > adx_threshold and rsi > 50
bearishTrend = ta.crossunder(macdLine, signalLine) and close < ema and adx > adx_threshold and rsi < 50

// === STOP-LOSS & TAKE-PROFIT CALCULATION ===
longStopLoss = close - ta.atr(atr_length) * 1.5
longTakeProfit = close + (ta.atr(atr_length) * 1.5 * risk_reward_ratio)
shortStopLoss = close + ta.atr(atr_length) * 1.5
shortTakeProfit = close - (ta.atr(atr_length) * 1.5 * risk_reward_ratio)

// === STRATEGY EXECUTION ===
// Enter Long
if bullishTrend
    strategy.entry("Long", strategy.long)
    strategy.exit("TakeProfitLong", from_entry="Long", limit=longTakeProfit, stop=longStopLoss)

// Enter Short
if bearishTrend
    strategy.entry("Short", strategy.short)
    strategy.exit("TakeProfitShort", from_entry="Short", limit=shortTakeProfit, stop=shortStopLoss)

// === PLOTTING ===
plot(ema, title="EMA 200", color=color.blue, linewidth=2)
plotshape(series=bullishTrend, location=location.belowbar, color=color.green, size=size.small, title="Buy Signal")
plotshape(series=bearishTrend, location=location.abovebar, color=color.red, size=size.small, title="Sell Signal")

// === ALERTS ===
alertcondition(bullishTrend, title="Bullish Entry", message="Buy Signal Triggered!")
alertcondition(bearishTrend, title="Bearish Entry", message="Sell Signal Triggered!")

// === DEBUGGING LOG ===
label.new(bar_index, high, "ADX: " + str.tostring(adx), color=color.white, textcolor=color.black)
label.new(bar_index, low, "MACD Cross: " + str.tostring(macdLine), color=color.white, textcolor=color.black)

Published by ChaoZhang on the FMZ strategy square · last modified 2025-02-18. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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