Triple EMA Trend Following Multi Indicator Quantitative Trading Strategy

Pine ScriptTrend following Breakout Mean reversion RSI EMA SMA ATR ADX / DMI

Author: ChaoZhang
Complexity: Starter · 59 lines
Parameters: 0
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

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Overview

This strategy is a trend following system based on multiple technical indicators, combining Moving Averages (EMA), Directional Movement Index (DMI), Detrended Price Oscillator (DPO), Relative Strength Index (RSI), and Average True Range (ATR). The core concept is to execute trades only after confirming multiple market characteristics including trend direction, momentum, and volatility to improve trading success rate.

Strategy Principles

The strategy employs a Triple Exponential Moving Average (EMA) system as its core trend identification mechanism, combined with other technical indicators for multiple signal confirmation:

Fast EMA (10-day) captures short-term price momentum
Medium EMA (25-day) serves as a medium-term trend filter
Slow EMA (50-day) defines the overall trend direction
DMI (14-day) confirms trend directional strength
DPO confirms price deviation from trend
RSI (14-day) measures momentum and overbought/oversold conditions
ATR (14-day) sets stop-loss and profit targets

Trade Signal Conditions:

Long: Fast EMA crosses above Medium EMA with both above Slow EMA, ADX>25, RSI>50, DPO>0
Short: Fast EMA crosses below Medium EMA with both below Slow EMA, ADX>25, RSI<50, DPO<0

Strategy Advantages

Multiple signal confirmation improves reliability and reduces false signals
Combines trend following and momentum characteristics for effective trend capture
Dynamic adjustment of stops and targets through ATR adapts to market volatility
Systematic risk management limits each trade risk to 2% of account
Clear strategy logic with well-defined component functions facilitates debugging and optimization

Strategy Risks

May generate frequent false breakout signals in ranging markets
Multiple indicator confirmation can lead to delayed entries
Fixed ADX threshold may perform inconsistently across different market conditions
Potentially significant drawdowns during quick market reversals
Parameter optimization risks overfitting to historical data

Risk Control Measures:

Dynamic ATR-based stops adapt to market volatility
Fixed proportion risk management
Multiple indicator cross-confirmation reduces false signals

Strategy Optimization Directions

Introduce adaptive parameter mechanisms to dynamically adjust indicator parameters based on market conditions
Add market environment recognition module to apply different trading rules in different market conditions
Optimize exit mechanism by incorporating trend reversal signals and partial profit-taking
Incorporate volume analysis to improve signal reliability
Develop drawdown control mechanism to reduce position size or pause trading during consecutive losses

Summary

This strategy constructs a complete trend following trading system through the combination of multiple technical indicators. Its main features are strict signal confirmation and reasonable risk control, suitable for tracking medium to long-term trends on daily timeframes. While there is some lag in signals, the strategy demonstrates robust overall performance through strict risk control and multiple signal confirmation. When applying to live trading, careful consideration should be given to market environment selection and parameter optimization for specific instruments.

Source Pine Script

/*backtest
start: 2019-12-23 08:00:00
end: 2025-01-15 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT","balance":49999}]
*/

//@version=5
strategy("Daily Strategy with Triple EMA, DMI, DPO, RSI, and ATR", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=100)

// Input parameters
fastEmaLength = input.int(10, title="Fast EMA Length")
mediumEmaLength = input.int(25, title="Medium EMA Length")
slowEmaLength = input.int(50, title="Slow EMA Length")
dmiLength = input.int(14, title="DMI Length")
adxSmoothing = input.int(14, title="ADX Smoothing")
dpoLength = input.int(14, title="DPO Length")
rsiLength = input.int(14, title="RSI Length")
atrLength = input.int(14, title="ATR Length")
riskPercentage = input.float(2.0, title="Risk Percentage", step=0.1)
atrMultiplier = input.float(1.5, title="ATR Multiplier for Stop Loss", step=0.1)
tpMultiplier = input.float(2.0, title="ATR Multiplier for Take Profit", step=0.1)

// Calculate EMAs
fastEma = ta.ema(close, fastEmaLength)
mediumEma = ta.ema(close, mediumEmaLength)
slowEma = ta.ema(close, slowEmaLength)

// Calculate other indicators
[adx, diPlus, diMinus] = ta.dmi(dmiLength, adxSmoothing)
dpo = close - ta.sma(close, dpoLength)
rsi = ta.rsi(close, rsiLength)
atr = ta.atr(atrLength)

// Trading logic
longCondition = ta.crossover(fastEma, mediumEma) and fastEma > slowEma and mediumEma > slowEma and adx > 25 and rsi > 50 and dpo > 0
shortCondition = ta.crossunder(fastEma, mediumEma) and fastEma < slowEma and mediumEma < slowEma and adx > 25 and rsi < 50 and dpo < 0

// Risk management
riskAmount = (strategy.equity * riskPercentage) / 100
stopLoss = atr * atrMultiplier
takeProfit = atr * tpMultiplier

// Entry and exit logic
if (longCondition)
    strategy.entry("Buy", strategy.long)
    strategy.exit("Exit Long", "Buy", stop=close - stopLoss, limit=close + takeProfit)

if (shortCondition)
    strategy.entry("Sell", strategy.short)
    strategy.exit("Exit Short", "Sell", stop=close + stopLoss, limit=close - takeProfit)

// Plot indicators
plot(fastEma, color=color.green, title="Fast EMA")
plot(mediumEma, color=color.orange, title="Medium EMA")
plot(slowEma, color=color.red, title="Slow EMA")
hline(25, "ADX Threshold", color=color.gray, linestyle=hline.style_dotted)

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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