Dual EMA RSI Divergence Strategy A Trend Capture System Based on Exponential Moving Average and Relative Strength

Pine ScriptTrend following Breakout Mean reversion RSI EMA

Author: ChaoZhang
Complexity: Starter · 70 lines
Parameters: 0
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

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Overview

This is a trend following strategy that combines Exponential Moving Average (EMA) and Relative Strength Index (RSI). The strategy identifies trading signals by monitoring the crossover of fast and slow EMAs while incorporating RSI overbought/oversold levels and RSI divergence to effectively capture market trends. Operating on a 1-hour timeframe, it enhances trading accuracy through multiple technical indicator verification.

Strategy Principles

The core logic includes the following key elements:

Uses 9-period and 26-period EMAs to determine trend direction, with uptrend indicated when fast line is above slow line
Employs 14-period RSI with 65 and 35 as thresholds for long and short signals
Detects RSI divergence on 1-hour timeframe by comparing price highs/lows with RSI highs/lows
Long entry requires: fast EMA above slow EMA, RSI above 65, and no bearish RSI divergence
Short entry requires: fast EMA below slow EMA, RSI below 35, and no bullish RSI divergence

Strategy Advantages

Cross-validation of multiple technical indicators improves signal reliability
RSI divergence detection reduces false breakout risks
Combines benefits of trend following and overbought/oversold conditions
Parameters can be optimized for different market characteristics
Clear strategy logic that's easy to understand and implement

Strategy Risks

EMA as a lagging indicator may lead to suboptimal entry points
RSI may generate excessive signals in ranging markets
Divergence detection may produce false readings, especially in volatile markets
Significant drawdowns possible during quick market reversals Mitigation measures:

Add stop-loss and take-profit settings
Consider adding volume indicator verification
Adjust RSI thresholds in ranging markets

Optimization Directions

Introduce adaptive RSI thresholds based on market volatility
Incorporate volume indicators for signal confirmation
Develop more precise divergence detection algorithms
Add stop-loss and profit management mechanisms
Consider adding market volatility filters

Summary

This strategy builds a relatively complete trading system by combining moving averages, momentum indicators, and divergence analysis. It emphasizes multiple signal verification to effectively reduce false judgment risks. While there is some inherent lag, the strategy holds practical value through parameter optimization and risk management improvements.

Source Pine Script

/*backtest
start: 2024-12-10 00:00:00
end: 2025-01-08 08:00:00
period: 1h
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("EMA9_RSI_Strategy_LongShort", overlay=true)

// Parameters
fastLength = input.int(9, minval=1, title="Fast EMA Length")
slowLength = input.int(26, minval=1, title="Slow EMA Length")
rsiPeriod = input.int(14, minval=1, title="RSI Period")
rsiLevelLong = input.int(65, minval=1, title="RSI Level (Long)")
rsiLevelShort = input.int(35, minval=1, title="RSI Level (Short)")

// Define 1-hour timeframe
timeframe_1h = "60"

// Fetch 1-hour data
high_1h = request.security(syminfo.tickerid, timeframe_1h, high)
low_1h = request.security(syminfo.tickerid, timeframe_1h, low)
rsi_1h = request.security(syminfo.tickerid, timeframe_1h, ta.rsi(close, rsiPeriod))

// Current RSI
rsi = ta.rsi(close, rsiPeriod)

// Find highest/lowest price and corresponding RSI in the 1-hour timeframe
highestPrice_1h = ta.highest(high_1h, 1) // ราคาสูงสุดใน 1 ช่วงของ timeframe 1 ชั่วโมง
lowestPrice_1h = ta.lowest(low_1h, 1)   // ราคาต่ำสุดใน 1 ช่วงของ timeframe 1 ชั่วโมง
highestRsi_1h = ta.valuewhen(high_1h == highestPrice_1h, rsi_1h, 0)
lowestRsi_1h = ta.valuewhen(low_1h == lowestPrice_1h, rsi_1h, 0)

// Detect RSI Divergence for Long
bearishDivLong = high > highestPrice_1h and rsi < highestRsi_1h
bullishDivLong = low < lowestPrice_1h and rsi > lowestRsi_1h
divergenceLong = bearishDivLong or bullishDivLong

// Detect RSI Divergence for Short (switch to low price for divergence check)
bearishDivShort = low > lowestPrice_1h and rsi < lowestRsi_1h
bullishDivShort = high < highestPrice_1h and rsi > highestRsi_1h
divergenceShort = bearishDivShort or bullishDivShort

// Calculate EMA
emaFast = ta.ema(close, fastLength)
emaSlow = ta.ema(close, slowLength)

// Long Conditions
longCondition = emaFast > emaSlow and rsi > rsiLevelLong and not divergenceLong

// Short Conditions
shortCondition = emaFast < emaSlow and rsi < rsiLevelShort and not divergenceShort

// Plot conditions
plotshape(longCondition, title="Buy", location=location.belowbar, color=color.green, style=shape.labelup, text="Buy")
plotshape(shortCondition, title="Sell", location=location.abovebar, color=color.red, style=shape.labeldown, text="Sell")

// Execute the strategy
if (longCondition)
    strategy.entry("Long", strategy.long, comment="entry long")

if (shortCondition)
    strategy.entry("Short", strategy.short, comment="entry short")

// Alert
alertcondition(longCondition, title="Buy Signal", message="Buy signal triggered!")
alertcondition(shortCondition, title="Sell Signal", message="Sell signal triggered!")

Published by ChaoZhang on the FMZ strategy square · last modified 2025-01-10. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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