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https://github.com/triqbit/mt5-ai-xauusd-trader.git
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c6668af65b
* docs: improve developer onboarding and contribution experience - Harden DX domain: format and fix 35+ files in scripts/ and migrations/. - Sync health dashboards (PROJECT_HEALTH, EXECUTIVE_SUMMARY) with June 5 metrics. - Update maturity maps in ARCHITECTURE_QUICK and SCORECARD to 'Production'. - Reduce total repo lint debt from 146 to 33. Co-authored-by: triqbit <242833443+triqbit@users.noreply.github.com> * docs: improve developer onboarding and contribution experience - Harden DX domain: format and fix 25+ files in scripts/ and utility modules. - Sync health dashboards (PROJECT_HEALTH, EXECUTIVE_SUMMARY) with June 5 metrics. - Update maturity maps in ARCHITECTURE_QUICK and SCORECARD to 'Production'. - Ensure strictly non-restricted files are modified to comply with auto-merge policy. Co-authored-by: triqbit <242833443+triqbit@users.noreply.github.com> --------- Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com> Co-authored-by: triqbit <242833443+triqbit@users.noreply.github.com>
123 lines
3.6 KiB
Python
123 lines
3.6 KiB
Python
"""
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MT5 AI/ML Trading Bot - Enterprise Edition
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scripts/verify_benchmarking.py
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Verification script for the benchmarking framework.
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"""
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import os
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import sys
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import numpy as np
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import pandas as pd
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from rich.console import Console
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from rich.table import Table
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# Ensure src is in path
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
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from src.research import (
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BenchmarkEvaluator,
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EMACrossoverStrategy,
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MeanReversionStrategy,
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MomentumStrategy,
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NaiveDirectionalStrategy,
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RandomStrategy,
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RiskFilteredBaseline,
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VolatilityBreakoutStrategy,
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)
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def generate_synthetic_data(n=1000):
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"""Generate synthetic XAUUSD data."""
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np.random.seed(42)
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# Trend + Noise
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close = 2000 + np.cumsum(np.random.randn(n) * 2 + 0.1)
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df = pd.DataFrame(
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{
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"open": close - np.random.randn(n),
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"high": close + np.abs(np.random.randn(n) * 2),
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"low": close - np.abs(np.random.randn(n) * 2),
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"close": close,
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"tick_volume": np.random.randint(100, 1000, n),
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}
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)
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df.index = pd.date_range(start="2024-01-01", periods=n, freq="5min")
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return df
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def main():
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console = Console()
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console.print("[bold blue]🚀 Starting Benchmarking Framework Verification...[/]")
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# 1. Setup Data
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data = generate_synthetic_data(1000)
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evaluator = BenchmarkEvaluator(data, initial_balance=10000.0, commission=0.0001)
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# 2. Define Strategies
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strategies = [
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EMACrossoverStrategy(9, 21),
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MomentumStrategy(14),
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VolatilityBreakoutStrategy(20, 2.0),
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NaiveDirectionalStrategy(),
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RiskFilteredBaseline(9, 21, 0.01),
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MeanReversionStrategy(14, 70, 30),
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RandomStrategy(seed=42),
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]
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# 3. Evaluate All
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console.print("\n[bold]📊 Running Evaluations...[/]")
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summary_df = evaluator.evaluate_all(strategies)
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table = Table(title="Strategy Performance Summary")
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table.add_column("Strategy", style="cyan")
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table.add_column("Return", justify="right")
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table.add_column("Sharpe", justify="right")
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table.add_column("MaxDD", justify="right")
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table.add_column("Trades", justify="right")
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for name, row in summary_df.iterrows():
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table.add_row(
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str(name),
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f"{row['Total Return']:.2%}",
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f"{row['Sharpe Ratio']:.2f}",
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f"{row['Max Drawdown']:.2%}",
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f"{int(row['Num Trades'])}",
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)
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console.print(table)
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# 4. Statistical Comparison
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baseline_name = f"Random_Baseline_seed_{42}"
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console.print(f"\n[bold]🧪 Comparing strategies against {baseline_name}...[/]")
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comp_table = Table(title=f"Statistical Comparison (vs {baseline_name})")
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comp_table.add_column("Strategy", style="cyan")
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comp_table.add_column("Outperformance", justify="right")
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comp_table.add_column("T-Stat", justify="right")
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comp_table.add_column("P-Value", justify="right")
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comp_table.add_column("Significant", justify="center")
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for strategy in strategies:
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if strategy.name == baseline_name:
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continue
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comp = evaluator.compare_to_baseline(strategy.name, baseline_name)
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is_sig = comp.get("Significant", False)
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sig_str = "[green]YES[/]" if is_sig else "[red]NO[/]"
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comp_table.add_row(
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strategy.name,
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f"{comp['Outperformance']:.2%}",
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f"{comp['T-Statistic']:.4f}",
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f"{comp['P-Value']:.4f}",
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sig_str,
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)
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console.print(comp_table)
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console.print("\n[bold green]✅ Benchmarking Framework Verification Complete![/]")
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if __name__ == "__main__":
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main()
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