opencode-skill/skills/mql-developer/references/backtesting.md

Backtesting & Optimization

Table of Contents

• Strategy Tester Modes
• Minimum Requirements for Valid Backtest
• OnTester() - Custom Optimization Criterion
• TesterStatistics() Constants
• Key Performance Metrics
• Walk-Forward Analysis
• Avoiding Overfitting
• Monte Carlo Simulation
• Multi-Currency Testing (MQL5)
• Optimization Modes (MT5)
• Frame Functions (Inter-Pass Communication)
• Practical Backtesting Workflow

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Strategy Tester Modes

Mode	Speed	Accuracy	When to Use
Every tick based on real ticks	Slowest	Most realistic	Final validation with broker-specific ticks
Every tick	Slow	High	Final validation, scalpers, intra-bar logic
1-minute OHLC	Medium	Good	Most strategies (4 ticks per M1 bar)
Open prices only	Fast	Low	Bar-open strategies, rapid optimization
Math calculations	Instant	N/A	Pure computation without ticks

Best practice: Optimize with Open Prices first, validate winners with Every Tick.

Minimum Requirements for Valid Backtest

• 1,000+ trades minimum (ideally 2,000+)
• 2+ full market cycles (bull + bear + range)
• Multiple years of data
• Consistent spread settings (or variable spread)

OnTester() - Custom Optimization Criterion (MQL5)

double OnTester() {
    double profit = TesterStatistics(STAT_PROFIT);
    double profitFactor = TesterStatistics(STAT_PROFIT_FACTOR);
    double sharpeRatio = TesterStatistics(STAT_SHARPE_RATIO);
    double recoveryFactor = TesterStatistics(STAT_RECOVERY_FACTOR);
    double maxDrawdown = TesterStatistics(STAT_EQUITY_DD_RELATIVE);
    double totalTrades = TesterStatistics(STAT_TRADES);

    if(totalTrades < 100) return 0;     // Reject small samples
    if(maxDrawdown > 25) return 0;      // Reject excessive DD
    if(profitFactor < 1.3) return 0;    // Reject low PF

    return sharpeRatio * recoveryFactor * MathSqrt(totalTrades);
}

TesterStatistics() Constants

Complete table:

• STAT_PROFIT - Net profit
• STAT_GROSS_PROFIT - Gross profit
• STAT_GROSS_LOSS - Gross loss
• STAT_TRADES - Total trades
• STAT_PROFIT_TRADES - Winning trades
• STAT_LOSS_TRADES - Losing trades
• STAT_PROFIT_FACTOR - Gross profit / gross loss
• STAT_EXPECTED_PAYOFF - Expected payoff per trade
• STAT_SHARPE_RATIO - Sharpe ratio
• STAT_RECOVERY_FACTOR - Net profit / max drawdown
• STAT_EQUITY_DD - Max equity drawdown in money
• STAT_EQUITY_DD_PERCENT - Max equity drawdown %
• STAT_EQUITY_DD_RELATIVE - Relative equity drawdown %
• STAT_BALANCE_DD - Max balance drawdown
• STAT_BALANCE_DD_PERCENT - Max balance drawdown %
• STAT_MAX_PROFITTRADE - Largest profitable trade
• STAT_MAX_LOSSTRADE - Largest losing trade
• STAT_CONPROFITMAX - Max consecutive profit
• STAT_CONLOSSMAX - Max consecutive loss
• STAT_SHORT_TRADES - Short trades
• STAT_LONG_TRADES - Long trades
• STAT_WIN_SHORT_TRADES - Winning short trades
• STAT_WIN_LONG_TRADES - Winning long trades

Key Performance Metrics

Metric	Formula	Good Value	Excellent
Sharpe Ratio	(Mean Return - Rf) / StdDev	> 1.0	> 2.0
Profit Factor	Gross Profit / Gross Loss	> 1.5	> 2.0
Recovery Factor	Net Profit / Max DD	> 3.0	> 5.0
Max Drawdown	Peak-to-trough %	< 20%	< 10%
Expected Payoff	Net Profit / Total Trades	> 0	Context
Win Rate	Winning / Total	Context	Context

Walk-Forward Analysis

Methodology

Window 1: [===== IN-SAMPLE =====][= OOS =]
Window 2:    [===== IN-SAMPLE =====][= OOS =]
Window 3:       [===== IN-SAMPLE =====][= OOS =]
Window 4:          [===== IN-SAMPLE =====][= OOS =]

Steps

1. Split data into overlapping windows (e.g., 12-month IS, 3-month OOS)
2. Optimize on in-sample period
3. Test optimized params on out-of-sample period
4. Slide window forward, repeat
5. Accept if OOS performance >= 50-80% of IS
6. MT5 supports natively: Strategy Tester > Settings > Forward period

Walk-Forward Efficiency

WFE = (OOS annualized return) / (IS annualized return)

• WFE > 0.5 = acceptable
• WFE > 0.7 = good
• WFE < 0.3 = likely overfitted

Avoiding Overfitting

Principles

1. Parameter stability: Good params have good neighbors. If X=14 works but X=13 and X=15 don't, it's curve-fitted
2. Fewer parameters: Each extra parameter increases overfitting risk exponentially
3. Out-of-sample testing: Reserve 25-30% of data untouched
4. Cross-market validation: Test EURUSD strategy on GBPUSD
5. Regime awareness: Test across trending AND ranging markets

Signs of Overfitting

• Sharp performance drop in forward test
• Many parameters (>5 optimized)
• Strategy only works on specific date range
• Unrealistically high backtest metrics
• Parameter sensitivity (small changes cause large performance swings)

Monte Carlo Simulation

Purpose

Test if results are due to skill or luck by randomizing trade sequence.

Implementation

double OnTester() {
    // Collect all trade P&Ls
    double trades[];
    // ... populate from history ...

    int simulations = 1000;
    double worstDD95;

    for(int sim = 0; sim < simulations; sim++) {
        // Fisher-Yates shuffle
        // Calculate max drawdown for shuffled sequence
    }

    // Sort DDs, find 95th percentile
    // Reject if 95th percentile DD > 30%

    return profit / (1 + worstDD95);
}

What Monte Carlo Tells You

• Expected range of drawdowns (not just the one historical path)
• Probability of ruin at different risk levels
• Confidence interval for returns
• If strategy is fragile (high variance across simulations)

Multi-Currency Testing (MQL5)

MT5 Strategy Tester supports multi-symbol natively:

int OnInit() {
    // Reference other symbols to include them in test
    int handle_eur = iMA("EURUSD", PERIOD_H1, 14, 0, MODE_SMA, PRICE_CLOSE);
    int handle_gbp = iMA("GBPUSD", PERIOD_H1, 14, 0, MODE_SMA, PRICE_CLOSE);
    // Tester auto-synchronizes all referenced symbols
    return INIT_SUCCEEDED;
}

Optimization Modes (MT5)

Mode	Description
Slow (Complete)	Tests every combination (exhaustive)
Fast (Genetic)	Genetic algorithm, finds near-optimal efficiently
Custom max/min	Optimizes by OnTester() return value

Cloud Computing

MQL5 Cloud Network distributes optimization across thousands of agents worldwide.

Frame Functions (Inter-Pass Communication)

// In EA (agent): send data at end of each pass
double OnTester() {
    uchar data[];
    // serialize results into data
    FrameAdd("Results", 1, profit, data);
    return profit;
}

// In terminal: receive during optimization
void OnTesterPass() {
    ulong pass; string name; long id; double value; uchar data[];
    while(FrameNext(pass, name, id, value, data)) {
        PrintFormat("Pass #%d: profit=%.2f", pass, value);
    }
}

// Control functions
void OnTesterInit() { /* before optimization starts */ }
void OnTesterDeinit() { /* after optimization finishes, aggregate results */ }

Practical Backtesting Workflow

1. Quick scan: Open Prices Only, wide parameter ranges, genetic optimization
2. Narrow down: Reduce ranges around promising areas, complete optimization
3. Validate: Every Tick mode with best parameters
4. Walk-forward: Set forward period, verify OOS performance
5. Monte Carlo: Randomize trade sequence, check robustness
6. Multi-market: Test on correlated instruments
7. Demo forward test: Run on demo account for 1-3 months minimum