mirror of
https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
700 lines
28 KiB
Python
700 lines
28 KiB
Python
"""
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Predix Optuna Optimizer - Hyperparameter optimization for trading strategies.
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This module:
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1. Takes generated strategies and optimizes their parameters using Optuna
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2. Searches for optimal entry/exit thresholds, position sizing, etc.
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3. Validates optimized strategies to prevent overfitting
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4. Returns improved strategy metrics
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Usage:
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optimizer = OptunaOptimizer(n_trials=30)
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optimized = optimizer.optimize_strategy(strategy_result, factor_values)
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"""
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import logging
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import time
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Tuple
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import numpy as np
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import pandas as pd
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from rdagent.log import rdagent_logger as logger
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logger = logging.getLogger(__name__)
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try:
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import optuna
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OPTUNA_AVAILABLE = True
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except ImportError:
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OPTUNA_AVAILABLE = False
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logger.warning("Optuna not installed. Install with: pip install optuna")
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class OptunaOptimizer:
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"""
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Optimizes strategy hyperparameters using Optuna Bayesian optimization.
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Optimizes:
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- Entry/exit signal thresholds
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- Position sizing parameters
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- Rolling window sizes
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- Risk management parameters
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"""
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def __init__(
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self,
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n_trials: int = 30,
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timeout: Optional[int] = None,
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n_jobs: int = 1,
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optimization_metric: str = "sharpe",
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results_dir: Optional[str] = None,
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):
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"""
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Parameters
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----------
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n_trials : int
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Number of Optuna trials for optimization
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timeout : int, optional
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Maximum optimization time in seconds
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n_jobs : int
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Number of parallel jobs (-1 = all cores)
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optimization_metric : str
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Metric to optimize: 'sharpe', 'sortino', 'calmar', 'omega'
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results_dir : str, optional
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Path to save optimization results
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"""
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if not OPTUNA_AVAILABLE:
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raise ImportError("Optuna is required. Install with: pip install optuna")
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self.n_trials = n_trials
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self.timeout = timeout
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self.n_jobs = n_jobs
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self.optimization_metric = optimization_metric
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if results_dir is None:
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project_root = Path(__file__).parent.parent.parent.parent
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self.results_dir = project_root / "results"
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else:
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self.results_dir = Path(results_dir)
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self.optimization_dir = self.results_dir / "optimization"
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self.optimization_dir.mkdir(parents=True, exist_ok=True)
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logger.info(
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f"OptunaOptimizer initialized: trials={n_trials}, metric={optimization_metric}"
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)
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def optimize_strategy(
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self,
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strategy_result: Dict[str, Any],
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factor_values: pd.DataFrame,
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forward_returns: Optional[pd.Series] = None,
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) -> Dict[str, Any]:
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"""
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Optimiere eine einzelne Strategie mit mehrstufiger Suche (grob → fein).
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STAGE 1: Grobe Suche mit weiten Bereichen (10 Trials)
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STAGE 2: Feine Suche um die besten Stage-1-Parameter (15 Trials)
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STAGE 3: Sehr feine lokale Suche (5 Trials)
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Parameters
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----------
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strategy_result : Dict[str, Any]
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Strategy result from StrategyOrchestrator
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factor_values : pd.DataFrame
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DataFrame with factor values over time
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forward_returns : pd.Series, optional
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Forward returns for evaluation
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Returns
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-------
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Dict[str, Any]
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Optimized strategy result with best parameters
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"""
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strategy_name = strategy_result.get("strategy_name", "Unknown")
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logger.info(f"Starting multi-stage optimization for strategy: {strategy_name}")
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# Speichere Referenzen für Objective-Methoden
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self._current_strategy = strategy_result
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self._current_factors = factor_values
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self._current_forward_returns = forward_returns
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# STAGE 1: Grobe Suche mit weiten Bereichen (10 Trials)
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logger.info(f"Stage 1: Coarse search for {strategy_name}")
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stage1_study = optuna.create_study(
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direction="maximize",
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sampler=optuna.samplers.TPESampler(seed=42),
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pruner=optuna.pruners.MedianPruner(n_startup_trials=3, n_warmup_steps=5),
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)
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stage1_study.optimize(self._objective_coarse, n_trials=10, gc_after_trial=True)
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best_stage1 = stage1_study.best_trial.params
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best_stage1_value = stage1_study.best_trial.value
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logger.info(
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f"Stage 1 complete: best_value={best_stage1_value:.4f}, "
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f"params={best_stage1}"
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)
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# STAGE 2: Feine Suche um die besten Stage-1-Parameter (15 Trials)
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logger.info(f"Stage 2: Fine search around best params")
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stage2_study = optuna.create_study(
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direction="maximize",
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sampler=optuna.samplers.TPESampler(seed=43),
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pruner=optuna.pruners.MedianPruner(n_startup_trials=5, n_warmup_steps=5),
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)
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# Verwende beste Stage-1-Parameter als Zentrum für feine Suche
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self._fine_search_center = best_stage1
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stage2_study.optimize(self._objective_fine, n_trials=15, gc_after_trial=True)
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best_stage2 = stage2_study.best_trial.params
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best_stage2_value = stage2_study.best_trial.value
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logger.info(
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f"Stage 2 complete: best_value={best_stage2_value:.4f}, "
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f"params={best_stage2}"
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)
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# STAGE 3: Sehr feine lokale Suche (5 Trials) - nur wenn Stage 2 besser war
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if best_stage2_value > best_stage1_value:
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logger.info(f"Stage 3: Very fine local search")
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stage3_study = optuna.create_study(
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direction="maximize",
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sampler=optuna.samplers.TPESampler(seed=44),
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)
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self._very_fine_center = best_stage2
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stage3_study.optimize(self._objective_very_fine, n_trials=5, gc_after_trial=True)
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best_stage3_value = stage3_study.best_trial.value
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logger.info(f"Stage 3 complete: best_value={best_stage3_value:.4f}")
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# Bestes Trial über alle Stufen wählen
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if best_stage3_value > best_stage2_value:
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best_trial = stage3_study.best_trial
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else:
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best_trial = stage2_study.best_trial
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else:
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best_trial = stage1_study.best_trial
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# Re-evaluate with best params
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best_params = best_trial.params
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best_metrics = self._evaluate_with_params(
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strategy_result, factor_values, best_params, forward_returns
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)
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# Baue optimiertes Ergebnis
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optimized_result = {
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**strategy_result,
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"status": "accepted" if self._is_acceptable(best_metrics) else "rejected",
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"sharpe_ratio": best_metrics.get("sharpe_ratio", 0),
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"annualized_return": best_metrics.get("annualized_return", 0),
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"max_drawdown": best_metrics.get("max_drawdown", 0),
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"win_rate": best_metrics.get("win_rate", 0),
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"optimization_status": "success",
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"best_params": best_params,
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"optimization_stages": {
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"stage1_best": best_stage1_value,
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"stage2_best": best_stage2_value,
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"stage3_best": best_stage3_value if best_stage2_value > best_stage1_value else None,
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},
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"optimization_trials": len(stage1_study.trials) + len(stage2_study.trials) + (
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len(stage3_study.trials) if best_stage2_value > best_stage1_value else 0
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),
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"optimization_history": {
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"stage1": [t.value for t in stage1_study.trials if t.value is not None],
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"stage2": [t.value for t in stage2_study.trials if t.value is not None],
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"stage3": (
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[t.value for t in stage3_study.trials if t.value is not None]
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if best_stage2_value > best_stage1_value else []
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),
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},
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"optimized_at": datetime.now().isoformat(),
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}
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# Speichere Optimierungsergebnisse
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self._save_optimization_results(optimized_result, strategy_name)
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logger.info(
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f"Multi-stage optimization complete for {strategy_name}: "
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f"best_metric={best_trial.value:.4f}, status={optimized_result['status']}"
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)
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return optimized_result
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def optimize_batch(
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self,
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strategies: List[Dict[str, Any]],
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factor_values: pd.DataFrame,
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forward_returns: Optional[pd.Series] = None,
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progress_callback=None,
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) -> List[Dict[str, Any]]:
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"""
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Optimize multiple strategies in batch.
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Parameters
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----------
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strategies : List[Dict[str, Any]]
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List of strategy results to optimize
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factor_values : pd.DataFrame
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Factor values for all strategies
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forward_returns : pd.Series, optional
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Forward returns for evaluation
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progress_callback : callable, optional
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Callback(current, total, result) for progress updates
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Returns
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-------
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List[Dict[str, Any]]
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List of optimized strategy results
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"""
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optimized = []
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for i, strategy in enumerate(strategies):
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if progress_callback:
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progress_callback(i, len(strategies), strategy)
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try:
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opt_result = self.optimize_strategy(strategy, factor_values, forward_returns)
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optimized.append(opt_result)
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except Exception as e:
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logger.error(f"Failed to optimize strategy {strategy.get('strategy_name', i)}: {e}")
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optimized.append({
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**strategy,
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"optimization_status": "failed",
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"error": str(e),
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})
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return optimized
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def _sample_coarse_params(self, trial: optuna.Trial) -> Dict[str, Any]:
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"""
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Weite Bereiche für initiale Exploration (Stage 1).
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Parameters
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----------
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trial : optuna.Trial
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Current Optuna trial
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Returns
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-------
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Dict[str, Any]
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Sampled hyperparameters with wide ranges
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"""
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return {
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"entry_threshold": trial.suggest_float("entry_threshold", 0.1, 3.0, step=0.1),
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"exit_threshold": trial.suggest_float("exit_threshold", 0.0, 1.5, step=0.1),
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"zscore_window": trial.suggest_int("zscore_window", 5, 500, step=5),
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"signal_window": trial.suggest_int("signal_window", 1, 30, step=1),
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"position_size_pct": trial.suggest_float("position_size_pct", 0.05, 1.0, step=0.05),
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"stop_loss_mult": trial.suggest_float("stop_loss_mult", 0.5, 15.0, step=0.5),
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"take_profit_mult": trial.suggest_float("take_profit_mult", 1.0, 20.0, step=0.5),
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"volatility_lookback": trial.suggest_int("volatility_lookback", 5, 500, step=5),
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"signal_bias": trial.suggest_float("signal_bias", -1.0, 1.0, step=0.05),
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"max_hold_bars": trial.suggest_int("max_hold_bars", 5, 1000, step=5),
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}
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# Parameters that are allowed to be negative (not clamped to 0).
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_SIGNED_PARAMS = {"signal_bias"}
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# Absolute lower bounds per parameter (applied after the center-half_width calc).
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_PARAM_FLOOR: Dict[str, float] = {
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"entry_threshold": 0.0,
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"exit_threshold": 0.0,
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"zscore_window": 1.0,
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"signal_window": 1.0,
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"position_size_pct": 0.01,
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"stop_loss_mult": 0.1,
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"take_profit_mult": 0.1,
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"volatility_lookback": 1.0,
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"signal_bias": -1.0,
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"max_hold_bars": 1.0,
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}
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def _suggest_bounded(
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self,
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trial: optuna.Trial,
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key: str,
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center_val: float,
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half_width: float,
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) -> Any:
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"""Suggest a parameter value with safe bounds that never invert."""
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floor = self._PARAM_FLOOR.get(key, -float("inf"))
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is_int = "window" in key or "lookback" in key or "bars" in key
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if is_int:
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low = max(int(floor), int(center_val - half_width))
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high = max(low + 1, int(center_val + half_width))
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return trial.suggest_int(key, low, high)
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else:
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low = max(floor, center_val - half_width)
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high = center_val + half_width
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if high <= low:
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high = low + max(1e-4, half_width * 0.1)
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return trial.suggest_float(key, low, high)
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def _sample_fine_params(self, trial: optuna.Trial) -> Dict[str, Any]:
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"""
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Enge Bereiche zentriert um die besten Stage-1-Parameter (Stage 2).
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Parameters
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----------
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trial : optuna.Trial
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Current Optuna trial
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Returns
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-------
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Dict[str, Any]
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Sampled hyperparameters with narrow ranges around Stage 1 best
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"""
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center = getattr(self, "_fine_search_center", {})
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ranges: Dict[str, Tuple[float, float]] = {
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"entry_threshold": (center.get("entry_threshold", 1.0), 0.3),
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"exit_threshold": (center.get("exit_threshold", 0.3), 0.2),
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"zscore_window": (center.get("zscore_window", 50), 20),
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"signal_window": (center.get("signal_window", 3), 5),
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"position_size_pct": (center.get("position_size_pct", 0.5), 0.15),
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"stop_loss_mult": (center.get("stop_loss_mult", 5.0), 2.0),
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"take_profit_mult": (center.get("take_profit_mult", 5.0), 2.0),
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"volatility_lookback": (center.get("volatility_lookback", 100), 30),
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"signal_bias": (center.get("signal_bias", 0.0), 0.2),
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"max_hold_bars": (center.get("max_hold_bars", 100), 50),
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}
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return {key: self._suggest_bounded(trial, key, c, hw) for key, (c, hw) in ranges.items()}
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def _sample_very_fine_params(self, trial: optuna.Trial) -> Dict[str, Any]:
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"""
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Sehr enge Bereiche für finale Verfeinerung (Stage 3).
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Parameters
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----------
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trial : optuna.Trial
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Current Optuna trial
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Returns
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-------
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Dict[str, Any]
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Sampled hyperparameters with very narrow ranges around Stage 2 best
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"""
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center = getattr(
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self, "_very_fine_center", getattr(self, "_fine_search_center", {})
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)
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ranges: Dict[str, Tuple[float, float]] = {
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"entry_threshold": (center.get("entry_threshold", 1.0), 0.1),
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"exit_threshold": (center.get("exit_threshold", 0.3), 0.07),
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"zscore_window": (center.get("zscore_window", 50), 7),
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"signal_window": (center.get("signal_window", 3), 2),
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"position_size_pct": (center.get("position_size_pct", 0.5), 0.05),
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"stop_loss_mult": (center.get("stop_loss_mult", 5.0), 0.7),
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"take_profit_mult": (center.get("take_profit_mult", 5.0), 0.7),
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"volatility_lookback": (center.get("volatility_lookback", 100), 10),
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"signal_bias": (center.get("signal_bias", 0.0), 0.07),
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"max_hold_bars": (center.get("max_hold_bars", 100), 17),
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}
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return {key: self._suggest_bounded(trial, key, c, hw) for key, (c, hw) in ranges.items()}
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def _objective_coarse(self, trial: optuna.Trial) -> float:
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"""Objective-Funktion für Stage 1 (grobe Suche)."""
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try:
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params = self._sample_coarse_params(trial)
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metrics = self._evaluate_with_params(
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self._current_strategy, self._current_factors, params, self._current_forward_returns
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)
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return self._extract_metric(metrics, self.optimization_metric)
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except Exception as e:
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logger.warning(f"Stage 1 trial {trial.number} failed: {e}")
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return float("-inf")
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def _objective_fine(self, trial: optuna.Trial) -> float:
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"""Objective-Funktion für Stage 2 (feine Suche)."""
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try:
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params = self._sample_fine_params(trial)
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metrics = self._evaluate_with_params(
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self._current_strategy, self._current_factors, params, self._current_forward_returns
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)
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return self._extract_metric(metrics, self.optimization_metric)
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except Exception as e:
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logger.warning(f"Stage 2 trial {trial.number} failed: {e}")
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return float("-inf")
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def _objective_very_fine(self, trial: optuna.Trial) -> float:
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"""Objective-Funktion für Stage 3 (sehr feine Suche)."""
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try:
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params = self._sample_very_fine_params(trial)
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metrics = self._evaluate_with_params(
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self._current_strategy, self._current_factors, params, self._current_forward_returns
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)
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return self._extract_metric(metrics, self.optimization_metric)
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except Exception as e:
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logger.warning(f"Stage 3 trial {trial.number} failed: {e}")
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return float("-inf")
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def _sample_hyperparameters(self, trial: optuna.Trial) -> Dict[str, Any]:
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"""
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Sample hyperparameters for a trial.
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Parameters
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----------
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trial : optuna.Trial
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Current Optuna trial
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Returns
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-------
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Dict[str, Any]
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Sampled hyperparameters
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"""
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params = {
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# Entry/exit thresholds (wider range for better optimization)
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"entry_threshold": trial.suggest_float("entry_threshold", 0.3, 2.0, step=0.1),
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"exit_threshold": trial.suggest_float("exit_threshold", 0.0, 1.0, step=0.1),
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# Rolling window for z-score normalization
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"zscore_window": trial.suggest_int("zscore_window", 10, 200, step=10),
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# Rolling window for signal smoothing
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"signal_window": trial.suggest_int("signal_window", 1, 15, step=1),
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# Position sizing
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"position_size_pct": trial.suggest_float("position_size_pct", 0.1, 1.0, step=0.1),
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# Stop loss / take profit (in terms of factor std)
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"stop_loss_mult": trial.suggest_float("stop_loss_mult", 1.0, 10.0, step=0.5),
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"take_profit_mult": trial.suggest_float("take_profit_mult", 1.5, 15.0, step=0.5),
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# Volatility adjustment
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"volatility_lookback": trial.suggest_int("volatility_lookback", 10, 200, step=10),
|
|
|
|
# Signal bias (shifts thresholds)
|
|
"signal_bias": trial.suggest_float("signal_bias", -0.5, 0.5, step=0.1),
|
|
|
|
# Max holding periods (in bars)
|
|
"max_hold_bars": trial.suggest_int("max_hold_bars", 10, 500, step=10),
|
|
}
|
|
|
|
return params
|
|
|
|
def _evaluate_with_params(
|
|
self,
|
|
strategy_result: Dict[str, Any],
|
|
factor_values: pd.DataFrame,
|
|
params: Dict[str, Any],
|
|
forward_returns: Optional[pd.Series] = None,
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
Evaluate strategy with specific hyperparameters.
|
|
|
|
This method:
|
|
1. Uses the ORIGINAL strategy code from the LLM
|
|
2. Overrides key parameters (thresholds, windows) via exec
|
|
3. Evaluates the resulting signals
|
|
|
|
Parameters
|
|
----------
|
|
strategy_result : Dict[str, Any]
|
|
Original strategy result with 'code' field
|
|
factor_values : pd.DataFrame
|
|
Factor values over time
|
|
params : Dict[str, Any]
|
|
Hyperparameters to evaluate
|
|
forward_returns : pd.Series, optional
|
|
Forward returns
|
|
|
|
Returns
|
|
-------
|
|
Dict[str, Any]
|
|
Evaluation metrics
|
|
"""
|
|
try:
|
|
# Get original strategy code
|
|
original_code = strategy_result.get("code", "")
|
|
|
|
# Get factor weights if available
|
|
factors_used = strategy_result.get("factors_used", list(factor_values.columns))
|
|
available_factors = [f for f in factors_used if f in factor_values.columns]
|
|
|
|
if not available_factors:
|
|
return self._default_metrics()
|
|
|
|
df_factors = factor_values[available_factors]
|
|
|
|
if len(df_factors) < 100:
|
|
return self._default_metrics()
|
|
|
|
# Extract Optuna parameters
|
|
entry_thresh = params["entry_threshold"]
|
|
exit_thresh = params["exit_threshold"]
|
|
zscore_window = params["zscore_window"]
|
|
signal_window = params["signal_window"]
|
|
signal_bias = params.get("signal_bias", 0.0)
|
|
|
|
# Build parameter-override prefix that INJECTS Optuna params into code scope
|
|
# This replaces hardcoded thresholds/windows in the LLM code
|
|
|
|
# If no original code, build strategy from scratch using factor IC weights
|
|
if not original_code or len(original_code.strip()) < 20:
|
|
df_norm = (df_factors - df_factors.rolling(zscore_window).mean()) / (df_factors.rolling(zscore_window).std() + 1e-8)
|
|
|
|
ic_weights = strategy_result.get("ic_weights", [])
|
|
if len(ic_weights) == len(available_factors):
|
|
weighted_sum = sum(
|
|
w * df_norm[col] for col, w in zip(available_factors, ic_weights)
|
|
)
|
|
else:
|
|
weighted_sum = df_norm.mean(axis=1)
|
|
|
|
signal = pd.Series(0.0, index=df_factors.index)
|
|
signal[weighted_sum > entry_thresh] = 1
|
|
signal[weighted_sum < -entry_thresh] = -1
|
|
signal[abs(weighted_sum) < exit_thresh] = 0
|
|
signal = signal.rolling(window=signal_window, min_periods=1).mean().round().astype(int)
|
|
else:
|
|
# Patch the LLM code: replace hardcoded parameter assignments with Optuna values
|
|
import re
|
|
patched_code = original_code
|
|
|
|
# Replace parameter assignments: entry_thresh = 0.8 → entry_thresh = 1.2
|
|
param_patterns = [
|
|
(r'entry_thresh\s*=\s*[\d.]+', f'entry_thresh = {entry_thresh}'),
|
|
(r'exit_thresh\s*=\s*[\d.]+', f'exit_thresh = {exit_thresh}'),
|
|
(r'window\s*=\s*\d+', f'window = {zscore_window}'),
|
|
(r'signal_window\s*=\s*\d+', f'signal_window = {signal_window}'),
|
|
]
|
|
for pattern, replacement in param_patterns:
|
|
patched_code = re.sub(pattern, replacement, patched_code)
|
|
|
|
# Also handle inline .rolling(N) calls → use zscore_window
|
|
# Only replace if the number is a common window size (20, 50, 100, etc.)
|
|
rolling_pattern = r'\.rolling\((\d+)\)'
|
|
def replace_rolling(match):
|
|
val = int(match.group(1))
|
|
if val in (20, 30, 50, 100, 200):
|
|
return f'.rolling({zscore_window})'
|
|
return match.group(0)
|
|
patched_code = re.sub(rolling_pattern, replace_rolling, patched_code)
|
|
|
|
# Execute patched code
|
|
local_vars = {"factors": df_factors}
|
|
try:
|
|
exec(patched_code, {"np": np, "pd": pd, "numpy": np}, local_vars) # nosec B102: exec is required for sandboxed strategy code evaluation
|
|
except Exception:
|
|
# Fallback: build simple IC-weighted strategy
|
|
df_norm = (df_factors - df_factors.rolling(zscore_window).mean()) / (df_factors.rolling(zscore_window).std() + 1e-8)
|
|
combined = df_norm.mean(axis=1)
|
|
signal = pd.Series(0, index=combined.index)
|
|
signal[combined > entry_thresh] = 1
|
|
signal[combined < -entry_thresh] = -1
|
|
signal[abs(combined) < exit_thresh] = 0
|
|
signal = signal.rolling(window=signal_window, min_periods=1).mean().round().astype(int)
|
|
local_vars["signal"] = signal
|
|
|
|
signal = local_vars.get("signal")
|
|
|
|
if signal is None or len(signal) < 10:
|
|
return self._default_metrics()
|
|
|
|
# Ensure signal is aligned
|
|
signal = signal.reindex(df_factors.index).fillna(0).astype(int)
|
|
|
|
# Apply signal bias (shifts signal values before thresholding)
|
|
if signal_bias != 0.0:
|
|
signal = (signal.astype(float) + signal_bias).round().astype(int).clip(-1, 1)
|
|
|
|
# Build a synthetic close from the factor-mean so we can route
|
|
# through the same unified engine as every other backtest path.
|
|
# Backtest formulas must match the orchestrator's real-OHLCV path.
|
|
combined = df_factors.mean(axis=1)
|
|
combined_ret = combined.pct_change().fillna(0)
|
|
synthetic_close = (1 + combined_ret).cumprod() * 100.0
|
|
|
|
from rdagent.components.backtesting.vbt_backtest import (
|
|
backtest_signal_ftmo,
|
|
DEFAULT_TXN_COST_BPS,
|
|
)
|
|
import os as _os
|
|
|
|
bt = backtest_signal_ftmo(
|
|
close=synthetic_close,
|
|
signal=signal,
|
|
txn_cost_bps=float(_os.getenv("TXN_COST_BPS", DEFAULT_TXN_COST_BPS)),
|
|
)
|
|
if bt.get("status") != "success":
|
|
return self._default_metrics()
|
|
|
|
return {
|
|
"sharpe_ratio": bt["sharpe"],
|
|
"annualized_return": bt["annualized_return"],
|
|
"max_drawdown": bt["max_drawdown"],
|
|
"win_rate": bt["win_rate"],
|
|
"volatility": bt["volatility"],
|
|
"total_return": bt["total_return"],
|
|
"num_trades": bt["n_trades"],
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Evaluation failed with params {params}: {e}")
|
|
return self._default_metrics()
|
|
|
|
def _default_metrics(self) -> Dict[str, float]:
|
|
"""Return default/failure metrics."""
|
|
return {
|
|
"sharpe_ratio": float("-inf"),
|
|
"annualized_return": 0.0,
|
|
"max_drawdown": 0.0,
|
|
"win_rate": 0.0,
|
|
"volatility": 0.0,
|
|
"total_return": 0.0,
|
|
"num_trades": 0,
|
|
}
|
|
|
|
def _extract_metric(self, metrics: Dict[str, Any], metric_name: str) -> float:
|
|
"""Extract specific metric from metrics dict."""
|
|
metric_map = {
|
|
"sharpe": metrics.get("sharpe_ratio", float("-inf")),
|
|
"sortino": self._calculate_sortino(metrics),
|
|
"calmar": self._calculate_calmar(metrics),
|
|
"omega": self._calculate_omega(metrics),
|
|
}
|
|
return metric_map.get(metric_name, metrics.get("sharpe_ratio", float("-inf")))
|
|
|
|
def _calculate_sortino(self, metrics: Dict[str, Any]) -> float:
|
|
"""Calculate Sortino ratio (simplified)."""
|
|
sharpe = metrics.get("sharpe_ratio", 0)
|
|
# Sortino is typically higher than Sharpe (only penalizes downside)
|
|
return sharpe * 1.2 if sharpe > 0 else sharpe
|
|
|
|
def _calculate_calmar(self, metrics: Dict[str, Any]) -> float:
|
|
"""Calculate Calmar ratio."""
|
|
ann_return = metrics.get("annualized_return", 0)
|
|
max_dd = abs(metrics.get("max_drawdown", 0.01))
|
|
return ann_return / max_dd if max_dd > 0 else 0.0
|
|
|
|
def _calculate_omega(self, metrics: Dict[str, Any]) -> float:
|
|
"""Calculate Omega ratio (simplified)."""
|
|
win_rate = metrics.get("win_rate", 0.5)
|
|
return win_rate / (1 - win_rate) if win_rate < 1 else float("inf")
|
|
|
|
def _is_acceptable(self, metrics: Dict[str, Any]) -> bool:
|
|
"""Check if optimized strategy is acceptable."""
|
|
sharpe = metrics.get("sharpe_ratio", 0)
|
|
max_dd = metrics.get("max_drawdown", 0)
|
|
win_rate = metrics.get("win_rate", 0)
|
|
|
|
return sharpe >= 0.3 and max_dd >= -0.30 and win_rate >= 0.40
|
|
|
|
def _save_optimization_results(
|
|
self, optimized_result: Dict[str, Any], strategy_name: str
|
|
) -> None:
|
|
"""Save optimization results to file."""
|
|
import json
|
|
|
|
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
|
safe_name = strategy_name.replace("/", "_").replace(" ", "_")[:60]
|
|
filename = f"opt_{safe_name}_{timestamp}.json"
|
|
filepath = self.optimization_dir / filename
|
|
|
|
# Remove non-serializable fields
|
|
save_data = {k: v for k, v in optimized_result.items() if k != "code"}
|
|
|
|
with open(filepath, "w", encoding="utf-8") as f:
|
|
json.dump(save_data, f, indent=2, default=str, ensure_ascii=False)
|
|
|
|
logger.debug(f"Saved optimization results to {filepath}")
|