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Friday 19 June 2026 15:18:26 GMT
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Improve your trading Bot with AI, Part 2. Here is the prompt I'm using (make sure to change pair and timeframe): You are an expert MQL5 developer and quantitative trading researcher. Your task: analyze and improve my MT5 Expert Advisor (EA) using (1) the full MQL5 code and (2) backtest results (images + metrics) for the past year. ## Objectives - Understand how the EA trades (entry, exit, risk, filters, sessions). - Analyze backtest data to find performance patterns, weak spots, and regime dependencies. - Suggest concrete, testable improvements that enhance risk-adjusted returns without curve fitting. ## What I’ll Provide - EA code (MQL5) - Backtest screenshots (equity curve, trade summary, returns, drawdown, etc.) ## Process 1. **Summarize EA logic** — map entry/exit rules, trade management, and safety features. 2. **Analyze performance** — report key metrics (CAGR, PF, Sharpe, Win%, DD, Expectancy). Identify where profits/losses cluster (by time, volatility, session, direction, etc.). 3. **Diagnose issues** — reveal common causes of large losses, inconsistent periods, or inefficiency. 4. **Propose improvements** — for each idea, explain rationale, expected impact, and overfit risk. Provide clear MQL5 snippets or pseudocode showing how to implement. Focus on:    - Smarter exits (dynamic TP/SL, trailing logic, partials)    - Regime/time filters (volatility, session, spread)    - Position sizing & risk control    - Entry quality (confirmation filters, cooldowns) 5. **Validation plan** — outline simple robustness tests (walk-forward, Monte Carlo, parameter sensitivity) and define pass/fail criteria. ## Deliverables - Concise summary of current EA behaviour - Diagnostic insights from backtest - Top 3–5 improvement suggestions with code or pseudocode - Quick test checklist for MT5 ## Context Platform: MetaTrader 5   Markets: [e.g., XAUUSD, FX majors]   Timeframe: [e.g., M15 entries, H1 context]   Goal: reduce large drawdowns and increase consistency. Be detailed, data-driven, and practical. Base every suggestion on observed backtest behaviour and provide reasoning for how it improves the EA’s edge.
Improve your trading Bot with AI, Part 2. Here is the prompt I'm using (make sure to change pair and timeframe): You are an expert MQL5 developer and quantitative trading researcher. Your task: analyze and improve my MT5 Expert Advisor (EA) using (1) the full MQL5 code and (2) backtest results (images + metrics) for the past year. ## Objectives - Understand how the EA trades (entry, exit, risk, filters, sessions). - Analyze backtest data to find performance patterns, weak spots, and regime dependencies. - Suggest concrete, testable improvements that enhance risk-adjusted returns without curve fitting. ## What I’ll Provide - EA code (MQL5) - Backtest screenshots (equity curve, trade summary, returns, drawdown, etc.) ## Process 1. **Summarize EA logic** — map entry/exit rules, trade management, and safety features. 2. **Analyze performance** — report key metrics (CAGR, PF, Sharpe, Win%, DD, Expectancy). Identify where profits/losses cluster (by time, volatility, session, direction, etc.). 3. **Diagnose issues** — reveal common causes of large losses, inconsistent periods, or inefficiency. 4. **Propose improvements** — for each idea, explain rationale, expected impact, and overfit risk. Provide clear MQL5 snippets or pseudocode showing how to implement. Focus on: - Smarter exits (dynamic TP/SL, trailing logic, partials) - Regime/time filters (volatility, session, spread) - Position sizing & risk control - Entry quality (confirmation filters, cooldowns) 5. **Validation plan** — outline simple robustness tests (walk-forward, Monte Carlo, parameter sensitivity) and define pass/fail criteria. ## Deliverables - Concise summary of current EA behaviour - Diagnostic insights from backtest - Top 3–5 improvement suggestions with code or pseudocode - Quick test checklist for MT5 ## Context Platform: MetaTrader 5 Markets: [e.g., XAUUSD, FX majors] Timeframe: [e.g., M15 entries, H1 context] Goal: reduce large drawdowns and increase consistency. Be detailed, data-driven, and practical. Base every suggestion on observed backtest behaviour and provide reasoning for how it improves the EA’s edge.

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