Risk Reward Ratio Optimized Strategy Based on Moving Average Crossover

This strategy is an automated trading system based on moving average crossover signals, optimized through a fixed risk-reward ratio. The strategy uses the crossover of Fast MA and Slow MA to determine market trend…

Pine ScriptTrend following Breakout Volatility SMA Volume

Author: ChaoZhang
Complexity: Starter · 72 lines
Parameters: 0
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Principle

The core logic relies on crossover signals generated by two moving averages (10-period and 30-period). The system generates long signals when the fast MA crosses above the slow MA, and short signals when the fast MA crosses below. After each entry, the system automatically calculates stop-loss levels based on a preset 2% loss percentage and sets take-profit targets according to a 2.5 risk-reward ratio. This approach ensures each trade has consistent risk-reward characteristics.

Strategy Advantages

Systematic Risk Management: Achieves standardized capital management through fixed stop-loss percentages and risk-reward ratios
Objective Trading Mechanism: Signal system based on MA crossovers eliminates subjective judgment bias
Strong Parameter Adaptability: Key parameters like stop-loss percentage and risk-reward ratio can be flexibly adjusted
High Automation Level: Automated processes from signal generation to position management reduce human error

Strategy Risks

Choppy Market Risk: MA crossover signals may generate frequent false breakouts in ranging markets
Slippage Risk: Actual execution prices may significantly deviate from signal prices in fast-moving markets
Fixed Stop-Loss Risk: Single stop-loss percentage may not suit all market conditions
Commission Costs: Frequent trading may result in high transaction costs

Strategy Optimization Directions

Implement Trend Filters: Add longer-period moving averages or other trend indicators to filter false signals
Dynamic Stop-Loss Mechanism: Adjust stop-loss percentages based on market volatility for better adaptability
Volume Confirmation: Incorporate volume indicators to verify breakout validity
Entry Timing Optimization: Wait for pullbacks after MA crossovers before entering positions

Summary

This strategy combines classical technical analysis methods with modern risk management concepts to build a complete trading system. While it has certain limitations, continuous optimization and improvement allow the strategy to maintain stable performance across different market conditions. The key lies in constantly adjusting parameter settings based on actual trading results to find the most suitable configuration for the current market environment.

Source Pine Script

/*backtest
start: 2019-12-23 08:00:00
end: 2024-12-25 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("SOL 15m 2.5 R:R Strategy", overlay=true, margin_long=100, margin_short=100, initial_capital=10000, commission_type=strategy.commission.percent, commission_value=0.1)

//---------------------------------------------------
// User Inputs
//---------------------------------------------------
// sym = input.symbol("swap", "Symbol")
timeframe = input.timeframe("15", "Timeframe")

fastLength  = input.int(10, "Fast MA Length")
slowLength  = input.int(30, "Slow MA Length")

stopLossPerc = input.float(2.0, "Stop Loss %", step=0.1) // This is an example; adjust to achieve ~45% win rate
RR           = input.float(2.5, "Risk to Reward Ratio", step=0.1)

//---------------------------------------------------
// Data Sources
//---------------------------------------------------
price = request.security("swap", timeframe, close)

// Compute moving averages
fastMA = ta.sma(price, fastLength)
slowMA = ta.sma(price, slowLength)

// Entry Conditions
longCondition  = ta.crossover(fastMA, slowMA)
shortCondition = ta.crossunder(fastMA, slowMA)

//---------------------------------------------------
// Stop Loss and Take Profit Calculation
//---------------------------------------------------
var entryPrice = 0.0

if (strategy.position_size == 0) // not in a position
    if longCondition
        // Long entry
        entryPrice := price
        strategy.entry("Long", strategy.long)

    if shortCondition
        // Short entry
        entryPrice := price
        strategy.entry("Short", strategy.short)

if strategy.position_size > 0
    // We are in a long position
    if strategy.position_avg_price > 0 and strategy.position_size > 0
        longStop  = strategy.position_avg_price * (1 - stopLossPerc/100)
        longTarget = strategy.position_avg_price * (1 + (stopLossPerc/100)*RR)
        strategy.exit("Long Exit", "Long", stop=longStop, limit=longTarget)

if strategy.position_size < 0
    // We are in a short position
    if strategy.position_avg_price > 0 and strategy.position_size < 0
        shortStop  = strategy.position_avg_price * (1 + stopLossPerc/100)
        shortTarget = strategy.position_avg_price * (1 - (stopLossPerc/100)*RR)
        strategy.exit("Short Exit", "Short", stop=shortStop, limit=shortTarget)

//---------------------------------------------------
// Plotting
//---------------------------------------------------
plot(fastMA, color=color.new(color.teal, 0), title="Fast MA")
plot(slowMA, color=color.new(color.orange, 0), title="Slow MA")

Published by ChaoZhang on the FMZ strategy square · last modified 2024-12-27. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

Build this with AgenKit

A spec is not a trading system. Paste this into your coding agent and AgenKit runs a specialist team — architecture, test-first build, risk gates, review — with paper trading on by default.

in Claude Code, Cursor, Codex & 4 more

/agenkit build a production-grade trading bot with a backtest engine, risk controls and paper trading by default from strategies/Risk-Reward-Ratio-Optimized-Strategy-Based-on-Moving-Average-Crossover.md

npx agenkit install engineering-kit in your project
Paste the command into your agent and approve each phase
Get tests, backtest, risk controls and a paper-trading switch

Get AgenKit How /agenkit works

Understand it first

Before you run this, read the lesson behind the idea and how to backtest it honestly. Specs like this one are the survivors of an unknown number of trials; deflate their results accordingly.