mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 23:47:46 +00:00
feat(strategy): Continuous optimization with Optuna parameter injection
- Optuna now runs for ALL strategies (accepted AND rejected) - Fix critical bug: Optuna parameters are now injected into LLM-generated code via regex patching (entry_thresh, exit_thresh, window, signal_window) Previously all 30 trials executed identical code producing the same Sharpe - Add continuous optimization loop (--max-iterations) for repeated strategy generation and optimization cycles - Improve prompt v5 with better IC-inversion examples and realistic code templates - Expand Optuna search space: zscore_window, signal_bias, max_hold_bars - CLI: add --continuous, --max-iterations, --optuna-trials flags - Show best strategy with optimized parameters in summary output
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TPTBusiness
@@ -22,3 +22,5 @@ skips:
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- 'B608'
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# B609: linux_commands_wildcard_injection - intentional usage
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- 'B609'
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# B102: exec_used - required for sandboxed strategy code evaluation
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- 'B102'
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+86
-70
@@ -572,18 +572,23 @@ def generate_strategies_cli(
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optuna: bool = typer.Option(True, "--optuna/--no-optuna", help="Enable Optuna optimization"),
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optuna_trials: int = typer.Option(30, "--optuna-trials", help="Number of Optuna trials per strategy"),
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top_factors: int = typer.Option(20, "--top-factors", help="Number of top factors to consider"),
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continuous: bool = typer.Option(True, "--continuous/--single-pass", help="Optimize ALL strategies including rejected ones"),
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max_iterations: int = typer.Option(1, "--max-iterations", "-i", help="Number of generation-optimization cycles (1 = single pass, >1 = continuous)"),
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):
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"""
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Generate trading strategies from evaluated factors.
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Uses LLM to combine top factors into trading strategies,
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then evaluates each with real OHLCV backtest data.
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Optuna optimizes hyperparameters (thresholds, windows, etc.)
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Examples:
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rdagent generate_strategies # 10 strategies, swing
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rdagent generate_strategies # 10 strategies, swing, Optuna
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rdagent generate_strategies -n 20 -w 8 # 20 strategies, 8 workers
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rdagent generate_strategies -s daytrading # Day trading style
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rdagent generate_strategies --no-optuna # Skip optimization
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rdagent generate_strategies -i 5 # 5 continuous iterations
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rdagent generate_strategies -n 3 -i 10 --optuna-trials 50 # Deep optimization
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"""
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from rich.console import Console
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from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TimeRemainingColumn
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@@ -613,88 +618,80 @@ def generate_strategies_cli(
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console.print(f" Optuna: {'[green]Enabled[/green]' if optuna else '[yellow]Disabled[/yellow]'}")
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if optuna:
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console.print(f" Trials: [cyan]{optuna_trials}[/cyan]")
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console.print(f" Continuous: {'[green]Yes[/green]' if continuous else '[yellow]No[/yellow]'}")
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console.print(f" Iterations: [cyan]{max_iterations}[/cyan]")
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console.print(f" Top Factors: [cyan]{top_factors}[/cyan]")
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console.print(f"[bold blue]{'='*60}[/bold blue]\n")
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try:
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from rdagent.components.coder.strategy_orchestrator import StrategyOrchestrator
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import pandas as pd
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# Initialize orchestrator
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orchestrator = StrategyOrchestrator(
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top_factors=top_factors,
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trading_style=style,
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)
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all_results = []
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best_strategy = None
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best_sharpe = float('-inf')
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# Progress tracking
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progress_data = {"generated": 0, "accepted": 0, "rejected": 0, "errors": []}
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# CONTINUOUS OPTIMIZATION LOOP
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for iteration in range(1, max_iterations + 1):
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if max_iterations > 1:
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console.print(f"\n[bold cyan]{'='*60}[/bold cyan]")
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console.print(f"[bold cyan] ITERATION {iteration}/{max_iterations}[/bold cyan]")
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console.print(f"[bold cyan]{'='*60}[/bold cyan]\n")
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def progress_callback(current, total, result):
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progress_data["generated"] = current
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if result.get("status") == "accepted":
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progress_data["accepted"] += 1
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else:
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progress_data["rejected"] += 1
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# Generate strategies
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with Progress(
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SpinnerColumn(),
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TextColumn("[progress.description]{task.description}"),
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BarColumn(),
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TextColumn("[bold]{task.completed}/{task.total}[/bold]"),
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TimeRemainingColumn(),
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console=console,
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) as progress:
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task = progress.add_task(f"Generating {count} strategies...", total=count)
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results = orchestrator.generate_strategies(
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count=count,
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workers=workers,
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progress_callback=lambda c, t, r: (progress.update(task, completed=c), progress_callback(c, t, r)),
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# Initialize orchestrator
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orchestrator = StrategyOrchestrator(
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top_factors=top_factors,
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trading_style=style,
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use_optuna=optuna,
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optuna_trials=optuna_trials,
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continuous_optimization=continuous,
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)
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# Run Optuna optimization if enabled
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if optuna and results:
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console.print(f"\n[yellow]Running Optuna optimization ({optuna_trials} trials)...[/yellow]")
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try:
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from rdagent.components.coder.optuna_optimizer import OptunaOptimizer
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from rdagent.components.coder.strategy_orchestrator import StrategyOrchestrator
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# Progress tracking
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progress_data = {"generated": 0, "accepted": 0, "rejected": 0, "errors": []}
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optimizer = OptunaOptimizer(n_trials=optuna_trials)
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# Load factor values for optimization
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orchestrator2 = StrategyOrchestrator(top_factors=top_factors, trading_style=style)
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factors = orchestrator2.load_top_factors()
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factor_values_dict = {}
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for f in factors:
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series = orchestrator2.load_factor_values(f["factor_name"])
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if series is not None:
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factor_values_dict[f["factor_name"]] = series
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if factor_values_dict:
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factor_df = pd.DataFrame(factor_values_dict).dropna()
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accepted = [r for r in results if r.get("status") == "accepted"]
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if accepted:
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opt_results = optimizer.optimize_batch(
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accepted, factor_df, progress_callback=None
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)
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console.print(f"[green]Optimization complete for {len(opt_results)} strategies.[/green]")
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# Update results with optimized metrics
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for opt_r in opt_results:
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for i, r in enumerate(results):
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if r.get("strategy_name") == opt_r.get("strategy_name"):
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results[i] = opt_r
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break
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else:
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console.print("[yellow]No accepted strategies to optimize.[/yellow]")
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def progress_callback(current, total, result):
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progress_data["generated"] = current
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if result.get("status") == "accepted":
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progress_data["accepted"] += 1
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else:
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console.print("[yellow]No factor values available for optimization.[/yellow]")
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progress_data["rejected"] += 1
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except ImportError:
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console.print("[yellow]Optuna not installed. Skipping optimization.[/yellow]")
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except Exception as e:
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console.print(f"[yellow]Optimization failed: {e}[/yellow]")
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# Generate strategies
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with Progress(
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SpinnerColumn(),
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TextColumn("[progress.description]{task.description}"),
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BarColumn(),
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TextColumn("[bold]{task.completed}/{task.total}[/bold]"),
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TimeRemainingColumn(),
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console=console,
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) as progress:
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task = progress.add_task(f"Generating {count} strategies (iter {iteration})...", total=count)
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results = orchestrator.generate_strategies(
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count=count,
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workers=workers,
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progress_callback=lambda c, t, r: (progress.update(task, completed=c), progress_callback(c, t, r)),
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)
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all_results.extend(results)
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# Track best strategy
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for r in results:
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sharpe = r.get("sharpe_ratio", float('-inf'))
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if sharpe > best_sharpe:
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best_sharpe = sharpe
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best_strategy = r
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# Summary for this iteration
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accepted = [r for r in results if r.get("status") == "accepted"]
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console.print(f"\n[bold green]Iteration {iteration} complete: {len(accepted)}/{len(results)} accepted[/bold green]")
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if accepted:
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best_in_iter = max(accepted, key=lambda x: x.get("sharpe_ratio", 0))
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console.print(f" Best: [green]{best_in_iter['strategy_name']}[/green] | Sharpe={best_in_iter.get('sharpe_ratio', 0):.4f}")
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# Use all_results for final summary
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results = all_results
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# Print summary table
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accepted = [r for r in results if r.get("status") == "accepted"]
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@@ -727,6 +724,22 @@ def generate_strategies_cli(
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console.print(table)
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# Show best strategy details
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if best_strategy:
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console.print(f"\n[bold gold1]{'='*60}[/bold gold1]")
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console.print(f"[bold gold1] BEST STRATEGY[/bold gold1]")
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console.print(f"[bold gold1]{'='*60}[/bold gold1]")
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console.print(f" Name: [cyan]{best_strategy.get('strategy_name', 'Unknown')}[/cyan]")
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console.print(f" Sharpe: [green]{best_strategy.get('sharpe_ratio', 0):.4f}[/green]")
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console.print(f" Ann.Return: [green]{best_strategy.get('annualized_return', 0):.4f}[/green]")
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console.print(f" Max DD: [yellow]{best_strategy.get('max_drawdown', 0):.2%}[/yellow]")
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console.print(f" Win Rate: [cyan]{best_strategy.get('win_rate', 0):.2%}[/cyan]")
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if best_strategy.get("best_params"):
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console.print(f"\n [bold]Optimized Parameters:[/bold]")
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for param, val in best_strategy["best_params"].items():
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console.print(f" {param}: [cyan]{val}[/cyan]")
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console.print(f"[bold gold1]{'='*60}[/bold gold1]")
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if accepted:
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console.print(f"\n[bold]Accepted Strategies:[/bold]")
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acc_table = Table(show_header=True, header_style="bold cyan")
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@@ -736,8 +749,10 @@ def generate_strategies_cli(
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acc_table.add_column("Ann. Return", justify="right", width=12)
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acc_table.add_column("Max DD", justify="right", width=10)
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acc_table.add_column("Win Rate", justify="right", width=10)
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acc_table.add_column("Optuna", justify="right", width=8)
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for i, strat in enumerate(sorted(accepted, key=lambda x: x.get("sharpe_ratio", 0), reverse=True), 1):
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optuna_status = "[green]Yes[/green]" if strat.get("best_params") else "[dim]No[/dim]"
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acc_table.add_row(
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str(i),
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strat.get("strategy_name", "Unknown")[:30],
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@@ -745,6 +760,7 @@ def generate_strategies_cli(
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f"{strat.get('annualized_return', 0):.4f}",
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f"{strat.get('max_drawdown', 0):.2%}",
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f"{strat.get('win_rate', 0):.2%}",
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optuna_status,
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)
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console.print(acc_table)
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