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"""
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Parameter Optimization for ONNX Strategy
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This script optimizes strategy parameters (prediction_threshold, min_confidence,
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stop_loss_pips, take_profit_pips, lot_size) using grid search or random search.
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"""
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import os
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import sys
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from datetime import datetime, timedelta
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import MetaTrader5 as mt5
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import pandas as pd
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import numpy as np
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from itertools import product
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import json
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# Add paths
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current_dir = os.path.dirname(os.path.abspath(__file__))
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backtest_dir = os.path.join(os.path.dirname(current_dir), 'backtesting', 'MT5')
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sys.path.insert(0, backtest_dir)
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from backtest_engine import BacktestEngine
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from onnx_backtest_strategy import ONNXBacktestStrategy
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from performance_analyzer import PerformanceAnalyzer
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class ONNXParameterOptimizer:
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"""Optimize ONNX strategy parameters."""
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def __init__(self, symbol: str, timeframe: int, model_path: str, scaler_path: str,
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start_date: datetime, end_date: datetime, initial_balance: float = 10000.0):
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"""
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Initialize optimizer.
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Args:
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symbol: Trading symbol
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timeframe: MT5 timeframe
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model_path: Path to ONNX model
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scaler_path: Path to scaler
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start_date: Backtest start date
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end_date: Backtest end date
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initial_balance: Starting balance
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"""
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self.symbol = symbol
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self.timeframe = timeframe
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self.model_path = model_path
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self.scaler_path = scaler_path
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self.start_date = start_date
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self.end_date = end_date
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self.initial_balance = initial_balance
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def grid_search(self, param_grid: dict, metric: str = 'sharpe_ratio') -> pd.DataFrame:
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"""
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Perform grid search optimization.
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Args:
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param_grid: Dictionary of parameter ranges
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Example: {
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'prediction_threshold': [0.0001, 0.0002, 0.0005],
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'min_confidence': [0.2, 0.3, 0.4],
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'stop_loss_pips': [30, 50, 70],
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'take_profit_pips': [60, 100, 150],
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'lot_size': [0.1, 0.2]
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}
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metric: Metric to optimize ('sharpe_ratio', 'total_return', 'max_drawdown', 'profit_factor')
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Returns:
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DataFrame with results sorted by metric
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"""
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print("="*60)
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print("Grid Search Parameter Optimization")
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print("="*60)
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# Generate all parameter combinations
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param_names = list(param_grid.keys())
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param_values = list(param_grid.values())
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combinations = list(product(*param_values))
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total_combinations = len(combinations)
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print(f"\nTotal parameter combinations: {total_combinations}")
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print(f"Optimizing for: {metric}\n")
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results = []
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for i, combo in enumerate(combinations, 1):
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params = dict(zip(param_names, combo))
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print(f"[{i}/{total_combinations}] Testing: {params}")
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try:
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# Create strategy with these parameters
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strategy = ONNXBacktestStrategy(
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symbol=self.symbol,
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timeframe=self.timeframe,
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model_path=self.model_path,
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scaler_path=self.scaler_path,
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initial_balance=self.initial_balance,
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**params
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)
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# Run backtest
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engine = BacktestEngine(strategy, self.start_date, self.end_date)
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backtest_results = engine.run()
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# Calculate metrics
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analyzer = PerformanceAnalyzer(backtest_results)
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metrics = analyzer.metrics
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# Store results
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result = params.copy()
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# Map metric names to match what we're looking for
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result['total_return'] = metrics.get('total_return_pct', 0) / 100.0
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result['max_drawdown'] = metrics.get('max_drawdown_pct', 0) / 100.0
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result['sharpe_ratio'] = metrics.get('sharpe_ratio', 0.0) if 'sharpe_ratio' in metrics else 0.0
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result['profit_factor'] = metrics.get('profit_factor', 0.0)
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result['win_rate'] = metrics.get('win_rate_pct', 0) / 100.0
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result['total_trades'] = metrics.get('total_trades', 0)
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result['final_balance'] = metrics.get('final_balance', self.initial_balance)
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results.append(result)
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metric_value = result.get(metric, 0)
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print(f" -> {metric}: {metric_value:.4f} | Trades: {result['total_trades']}")
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except Exception as e:
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print(f" X Error: {e}")
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continue
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# Convert to DataFrame
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df_results = pd.DataFrame(results)
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if len(df_results) == 0:
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raise ValueError("No successful backtests!")
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# Sort by metric (descending for most metrics, ascending for max_drawdown)
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if metric == 'max_drawdown':
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df_results = df_results.sort_values(metric, ascending=True)
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else:
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df_results = df_results.sort_values(metric, ascending=False)
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return df_results
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def random_search(self, param_ranges: dict, n_iter: int = 50,
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metric: str = 'sharpe_ratio') -> pd.DataFrame:
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"""
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Perform random search optimization.
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Args:
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param_ranges: Dictionary of parameter ranges
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Example: {
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'prediction_threshold': (0.0001, 0.001),
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'min_confidence': (0.1, 0.5),
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'stop_loss_pips': (20, 100),
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'take_profit_pips': (40, 200),
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'lot_size': (0.1, 0.5)
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}
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n_iter: Number of random combinations to test
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metric: Metric to optimize
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Returns:
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DataFrame with results sorted by metric
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"""
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print("="*60)
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print("Random Search Parameter Optimization")
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print("="*60)
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print(f"\nTesting {n_iter} random parameter combinations")
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print(f"Optimizing for: {metric}\n")
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results = []
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np.random.seed(42) # For reproducibility
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for i in range(1, n_iter + 1):
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# Generate random parameters
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params = {}
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for param_name, (min_val, max_val) in param_ranges.items():
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if isinstance(min_val, int) and isinstance(max_val, int):
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params[param_name] = np.random.randint(min_val, max_val + 1)
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else:
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params[param_name] = np.random.uniform(min_val, max_val)
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print(f"[{i}/{n_iter}] Testing: {params}")
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try:
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# Create strategy
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strategy = ONNXBacktestStrategy(
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symbol=self.symbol,
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timeframe=self.timeframe,
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model_path=self.model_path,
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scaler_path=self.scaler_path,
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initial_balance=self.initial_balance,
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**params
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)
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# Run backtest
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engine = BacktestEngine(strategy, self.start_date, self.end_date)
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backtest_results = engine.run()
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# Calculate metrics
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analyzer = PerformanceAnalyzer(backtest_results)
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metrics = analyzer.metrics
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# Store results
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result = params.copy()
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# Map metric names to match what we're looking for
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result['total_return'] = metrics.get('total_return_pct', 0) / 100.0
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result['max_drawdown'] = metrics.get('max_drawdown_pct', 0) / 100.0
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result['sharpe_ratio'] = metrics.get('sharpe_ratio', 0.0) if 'sharpe_ratio' in metrics else 0.0
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result['profit_factor'] = metrics.get('profit_factor', 0.0)
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result['win_rate'] = metrics.get('win_rate_pct', 0) / 100.0
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result['total_trades'] = metrics.get('total_trades', 0)
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result['final_balance'] = metrics.get('final_balance', self.initial_balance)
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results.append(result)
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metric_value = result.get(metric, 0)
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print(f" -> {metric}: {metric_value:.4f} | Trades: {result['total_trades']}")
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except Exception as e:
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print(f" X Error: {e}")
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continue
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# Convert to DataFrame
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df_results = pd.DataFrame(results)
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if len(df_results) == 0:
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raise ValueError("No successful backtests!")
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# Sort by metric
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if metric == 'max_drawdown':
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df_results = df_results.sort_values(metric, ascending=True)
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else:
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df_results = df_results.sort_values(metric, ascending=False)
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return df_results
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def save_results(self, df_results: pd.DataFrame, output_file: str = 'optimization_results.csv'):
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"""Save optimization results to CSV."""
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df_results.to_csv(output_file, index=False)
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print(f"\nResults saved to: {output_file}")
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# Also save top 10 as JSON
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top_10 = df_results.head(10).to_dict('records')
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json_file = output_file.replace('.csv', '_top10.json')
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with open(json_file, 'w') as f:
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json.dump(top_10, f, indent=2, default=str)
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print(f"Top 10 results saved to: {json_file}")
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def main():
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"""Main optimization function."""
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# Configuration
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symbol = 'XAUUSD'
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timeframe = mt5.TIMEFRAME_H1
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model_path = 'models/XAUUSD_H1_model.onnx'
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scaler_path = 'models/XAUUSD_H1_scaler.pkl'
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initial_balance = 10000.0
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# Backtest date range (use last 6 months for optimization)
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end_date = datetime.now()
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start_date = end_date - timedelta(days=180)
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# Check if model exists
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if not os.path.exists(model_path):
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print(f"ERROR: Model not found: {model_path}")
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print("Please train the model first using train_onnx_model.py")
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return
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# Initialize MT5
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if not mt5.initialize():
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print("ERROR: Failed to initialize MT5")
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return
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try:
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# Create optimizer
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optimizer = ONNXParameterOptimizer(
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symbol=symbol,
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timeframe=timeframe,
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model_path=model_path,
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scaler_path=scaler_path,
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start_date=start_date,
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end_date=end_date,
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initial_balance=initial_balance
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)
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# Choose optimization method (use command line args or defaults)
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import sys
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choice = "2" # Default to random search
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n_iter = 30 # Default iterations
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if len(sys.argv) > 1:
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choice = sys.argv[1]
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if len(sys.argv) > 2:
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n_iter = int(sys.argv[2])
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print("\nOptimization Configuration:")
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print(f"Method: {'Grid Search' if choice == '1' else 'Random Search'}")
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if choice != "1":
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print(f"Iterations: {n_iter}")
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print()
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if choice == "1":
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# Grid search parameters
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param_grid = {
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'prediction_threshold': [0.0001, 0.0002, 0.0005, 0.001],
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'min_confidence': [0.1, 0.2, 0.3, 0.4],
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'stop_loss_pips': [30, 50, 70, 100],
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'take_profit_pips': [60, 100, 150, 200],
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'lot_size': [0.1, 0.2]
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}
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results = optimizer.grid_search(param_grid, metric='sharpe_ratio')
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else:
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# Random search parameters
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param_ranges = {
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'prediction_threshold': (0.00005, 0.002), # Lower threshold to get more trades
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'min_confidence': (0.05, 0.5), # Lower confidence requirement
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'stop_loss_pips': (20, 150),
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'take_profit_pips': (40, 300),
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'lot_size': (0.05, 0.3)
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}
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results = optimizer.random_search(param_ranges, n_iter=n_iter, metric='sharpe_ratio')
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# Display top results
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print("\n" + "="*60)
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print("Top 10 Results")
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print("="*60)
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print(results.head(10).to_string(index=False))
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# Save results
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optimizer.save_results(results, 'onnx_optimization_results.csv')
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# Display best parameters
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best = results.iloc[0]
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print("\n" + "="*60)
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print("Best Parameters")
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print("="*60)
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print(f"Prediction Threshold: {best['prediction_threshold']:.6f}")
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print(f"Min Confidence: {best['min_confidence']:.2f}")
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print(f"Stop Loss (pips): {best['stop_loss_pips']}")
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print(f"Take Profit (pips): {best['take_profit_pips']}")
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print(f"Lot Size: {best['lot_size']:.2f}")
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print(f"\nPerformance Metrics:")
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print(f" Sharpe Ratio: {best.get('sharpe_ratio', 'N/A'):.4f}")
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print(f" Total Return: {best.get('total_return', 'N/A'):.2%}")
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print(f" Max Drawdown: {best.get('max_drawdown', 'N/A'):.2%}")
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print(f" Profit Factor: {best.get('profit_factor', 'N/A'):.2f}")
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except KeyboardInterrupt:
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print("\n\nOptimization interrupted by user")
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except Exception as e:
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print(f"\nERROR: {e}")
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import traceback
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traceback.print_exc()
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finally:
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mt5.shutdown()
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if __name__ == '__main__':
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main()
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