Cross Period Momentum EMA Strategy with RSI and Dynamic ATR Stop Loss

Pine ScriptMomentum Breakout Mean reversion RSI EMA ATR Volume

Author: ChaoZhang
Complexity: Starter · 54 lines
Parameters: 0
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is a comprehensive intraday trading system that combines multiple technical indicators. It primarily uses the crossover signals of fast and slow Exponential Moving Averages (EMA) as the main entry criteria, incorporates the Relative Strength Index (RSI) for momentum filtering, and utilizes the Average True Range (ATR) for dynamic stop-loss placement, forming a complete trading system. Through strict risk control and dynamic profit/loss settings, the strategy aims to capture short-term market fluctuations.

Strategy Principles

The core logic includes the following aspects:

Trend Determination: Using 9-period and 21-period EMA crossovers to identify market trend direction
Momentum Filtering: Employing 14-period RSI for overbought/oversold judgments to prevent counter-trend entries
Risk Control: Setting dynamic stop-loss levels based on 14-period ATR with a multiplier of 1.5
Profit Targets: Setting dynamic take-profit levels at 2 times ATR from entry point

Specific trading rules:

Long Entry: Fast EMA crosses above Slow EMA with RSI below 70
Short Entry: Fast EMA crosses below Slow EMA with RSI above 30
Stop-Loss: Long positions set at 1.5 times ATR below entry, short positions at 1.5 times ATR above entry
Take-Profit: Dynamic levels set at 2 times ATR from entry price

Strategy Advantages

Multiple Indicator Confirmation: Combines trend and momentum indicators for improved signal reliability
Dynamic Risk Management: Adapts stop-loss levels to market volatility using ATR
Systematic Trading: Clear entry and exit conditions reduce subjective judgment
Reasonable Risk-Reward Ratio: Balanced profit and loss settings for long-term stability
High Adaptability: Parameters can be adjusted for different market characteristics

Strategy Risks

Rapid Oscillation Risk: May generate frequent false breakout signals in ranging markets
Slippage Impact: Intraday trading requires high execution efficiency
Parameter Sensitivity: Optimal parameters may vary across different market environments
Transaction Costs: Frequent trading may incur higher costs

Risk Control Suggestions:

Conduct thorough historical data backtesting
Consider adding trading filters
Control position sizing appropriately
Regular parameter effectiveness evaluation

Strategy Optimization Directions

Enhanced Market Environment Filtering:

Add volatility indicators to judge market characteristics
Dynamically adjust parameters based on market conditions

Refined Trading Rules:

Consider adding time filters
Implement volume confirmation mechanisms
Optimize profit/loss ratios

Strengthened Risk Control:

Implement dynamic position sizing
Add maximum drawdown controls
Design comprehensive money management plans

Summary

This strategy constructs a relatively complete trading system by combining EMA trend following, RSI momentum filtering, and ATR dynamic risk control. Its main feature is the synergistic effect of multiple technical indicators while emphasizing risk management. While there is room for optimization, the overall design philosophy aligns with systematic quantitative trading principles. Traders are advised to conduct thorough parameter optimization and backtesting before live implementation, while making appropriate adjustments based on their risk tolerance and money management requirements.

Source Pine Script

/*backtest
start: 2024-02-10 00:00:00
end: 2025-02-08 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("Day Trading EMA/RSI Strategy", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=200)

// Ulazni parametri
fastEmaPeriod   = input.int(9, "Fast EMA Period", minval=1)
slowEmaPeriod   = input.int(21, "Slow EMA Period", minval=1)
rsiPeriod       = input.int(14, "RSI Period", minval=1)
rsiOversold     = input.int(30, "RSI Oversold Level")
rsiOverbought   = input.int(70, "RSI Overbought Level")
atrPeriod       = input.int(14, "ATR Period", minval=1)
atrMultiplier   = input.float(1.5, "ATR Multiplier za Stop Loss", step=0.1)
takeProfitFactor= input.float(2.0, "Take Profit Factor", step=0.1)

// Izračun indikatora
fastEMA = ta.ema(close, fastEmaPeriod)
slowEMA = ta.ema(close, slowEmaPeriod)
rsiValue = ta.rsi(close, rsiPeriod)
atrValue = ta.atr(atrPeriod)

// Definicija trenda: ako je fastEMA iznad slowEMA, smatramo da je trend uzlazan, inače silazni.
trendUp   = fastEMA > slowEMA
trendDown = fastEMA < slowEMA

// Uvjeti za ulaz:
// Ulaz u long poziciju: crossover fastEMA i slowEMA, uz filtriranje da RSI nije prekupovan (manje od rsiOverbought)
longCondition  = ta.crossover(fastEMA, slowEMA) and (rsiValue < rsiOverbought)
// Ulaz u short poziciju: crossunder fastEMA i slowEMA, uz filtriranje da RSI nije preprodavan (više od rsiOversold)
shortCondition = ta.crossunder(fastEMA, slowEMA) and (rsiValue > rsiOversold)

// Definicija dinamičnih stop-loss razina (ATR-based)
stopLossLong  = close - (atrMultiplier * atrValue)
stopLossShort = close + (atrMultiplier * atrValue)

// Izvršenje naloga
if (longCondition)
    strategy.entry("Long", strategy.long)
    strategy.exit("Exit Long", "Long", stop=stopLossLong, limit=close + (takeProfitFactor * atrValue))

if (shortCondition)
    strategy.entry("Short", strategy.short)
    strategy.exit("Exit Short", "Short", stop=stopLossShort, limit=close - (takeProfitFactor * atrValue))

// Plotanje indikatora za preglednost
plot(fastEMA, title="Fast EMA", color=color.green)
plot(slowEMA, title="Slow EMA", color=color.red)

Published by ChaoZhang on the FMZ strategy square · last modified 2025-02-10. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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