diff --git a/.bandit.yml b/.bandit.yml index a1394edc..e51a9464 100644 --- a/.bandit.yml +++ b/.bandit.yml @@ -22,3 +22,5 @@ skips: - 'B608' # B609: linux_commands_wildcard_injection - intentional usage - 'B609' + # B102: exec_used - required for sandboxed strategy code evaluation + - 'B102' diff --git a/rdagent/app/cli.py b/rdagent/app/cli.py index 172777f3..ff638e98 100644 --- a/rdagent/app/cli.py +++ b/rdagent/app/cli.py @@ -572,18 +572,23 @@ def generate_strategies_cli( optuna: bool = typer.Option(True, "--optuna/--no-optuna", help="Enable Optuna optimization"), optuna_trials: int = typer.Option(30, "--optuna-trials", help="Number of Optuna trials per strategy"), top_factors: int = typer.Option(20, "--top-factors", help="Number of top factors to consider"), + continuous: bool = typer.Option(True, "--continuous/--single-pass", help="Optimize ALL strategies including rejected ones"), + max_iterations: int = typer.Option(1, "--max-iterations", "-i", help="Number of generation-optimization cycles (1 = single pass, >1 = continuous)"), ): """ Generate trading strategies from evaluated factors. Uses LLM to combine top factors into trading strategies, then evaluates each with real OHLCV backtest data. + Optuna optimizes hyperparameters (thresholds, windows, etc.) Examples: - rdagent generate_strategies # 10 strategies, swing + rdagent generate_strategies # 10 strategies, swing, Optuna rdagent generate_strategies -n 20 -w 8 # 20 strategies, 8 workers rdagent generate_strategies -s daytrading # Day trading style rdagent generate_strategies --no-optuna # Skip optimization + rdagent generate_strategies -i 5 # 5 continuous iterations + rdagent generate_strategies -n 3 -i 10 --optuna-trials 50 # Deep optimization """ from rich.console import Console from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TimeRemainingColumn @@ -613,88 +618,80 @@ def generate_strategies_cli( console.print(f" Optuna: {'[green]Enabled[/green]' if optuna else '[yellow]Disabled[/yellow]'}") if optuna: console.print(f" Trials: [cyan]{optuna_trials}[/cyan]") + console.print(f" Continuous: {'[green]Yes[/green]' if continuous else '[yellow]No[/yellow]'}") + console.print(f" Iterations: [cyan]{max_iterations}[/cyan]") console.print(f" Top Factors: [cyan]{top_factors}[/cyan]") console.print(f"[bold blue]{'='*60}[/bold blue]\n") try: from rdagent.components.coder.strategy_orchestrator import StrategyOrchestrator + import pandas as pd - # Initialize orchestrator - orchestrator = StrategyOrchestrator( - top_factors=top_factors, - trading_style=style, - ) + all_results = [] + best_strategy = None + best_sharpe = float('-inf') - # Progress tracking - progress_data = {"generated": 0, "accepted": 0, "rejected": 0, "errors": []} + # CONTINUOUS OPTIMIZATION LOOP + for iteration in range(1, max_iterations + 1): + if max_iterations > 1: + console.print(f"\n[bold cyan]{'='*60}[/bold cyan]") + console.print(f"[bold cyan] ITERATION {iteration}/{max_iterations}[/bold cyan]") + console.print(f"[bold cyan]{'='*60}[/bold cyan]\n") - def progress_callback(current, total, result): - progress_data["generated"] = current - if result.get("status") == "accepted": - progress_data["accepted"] += 1 - else: - progress_data["rejected"] += 1 - - # Generate strategies - with Progress( - SpinnerColumn(), - TextColumn("[progress.description]{task.description}"), - BarColumn(), - TextColumn("[bold]{task.completed}/{task.total}[/bold]"), - TimeRemainingColumn(), - console=console, - ) as progress: - task = progress.add_task(f"Generating {count} strategies...", total=count) - - results = orchestrator.generate_strategies( - count=count, - workers=workers, - progress_callback=lambda c, t, r: (progress.update(task, completed=c), progress_callback(c, t, r)), + # Initialize orchestrator + orchestrator = StrategyOrchestrator( + top_factors=top_factors, + trading_style=style, + use_optuna=optuna, + optuna_trials=optuna_trials, + continuous_optimization=continuous, ) - # Run Optuna optimization if enabled - if optuna and results: - console.print(f"\n[yellow]Running Optuna optimization ({optuna_trials} trials)...[/yellow]") - try: - from rdagent.components.coder.optuna_optimizer import OptunaOptimizer - from rdagent.components.coder.strategy_orchestrator import StrategyOrchestrator + # Progress tracking + progress_data = {"generated": 0, "accepted": 0, "rejected": 0, "errors": []} - optimizer = OptunaOptimizer(n_trials=optuna_trials) - - # Load factor values for optimization - orchestrator2 = StrategyOrchestrator(top_factors=top_factors, trading_style=style) - factors = orchestrator2.load_top_factors() - factor_values_dict = {} - for f in factors: - series = orchestrator2.load_factor_values(f["factor_name"]) - if series is not None: - factor_values_dict[f["factor_name"]] = series - - if factor_values_dict: - factor_df = pd.DataFrame(factor_values_dict).dropna() - accepted = [r for r in results if r.get("status") == "accepted"] - - if accepted: - opt_results = optimizer.optimize_batch( - accepted, factor_df, progress_callback=None - ) - console.print(f"[green]Optimization complete for {len(opt_results)} strategies.[/green]") - - # Update results with optimized metrics - for opt_r in opt_results: - for i, r in enumerate(results): - if r.get("strategy_name") == opt_r.get("strategy_name"): - results[i] = opt_r - break - else: - console.print("[yellow]No accepted strategies to optimize.[/yellow]") + def progress_callback(current, total, result): + progress_data["generated"] = current + if result.get("status") == "accepted": + progress_data["accepted"] += 1 else: - console.print("[yellow]No factor values available for optimization.[/yellow]") + progress_data["rejected"] += 1 - except ImportError: - console.print("[yellow]Optuna not installed. Skipping optimization.[/yellow]") - except Exception as e: - console.print(f"[yellow]Optimization failed: {e}[/yellow]") + # Generate strategies + with Progress( + SpinnerColumn(), + TextColumn("[progress.description]{task.description}"), + BarColumn(), + TextColumn("[bold]{task.completed}/{task.total}[/bold]"), + TimeRemainingColumn(), + console=console, + ) as progress: + task = progress.add_task(f"Generating {count} strategies (iter {iteration})...", total=count) + + results = orchestrator.generate_strategies( + count=count, + workers=workers, + progress_callback=lambda c, t, r: (progress.update(task, completed=c), progress_callback(c, t, r)), + ) + + all_results.extend(results) + + # Track best strategy + for r in results: + sharpe = r.get("sharpe_ratio", float('-inf')) + if sharpe > best_sharpe: + best_sharpe = sharpe + best_strategy = r + + # Summary for this iteration + accepted = [r for r in results if r.get("status") == "accepted"] + console.print(f"\n[bold green]Iteration {iteration} complete: {len(accepted)}/{len(results)} accepted[/bold green]") + if accepted: + best_in_iter = max(accepted, key=lambda x: x.get("sharpe_ratio", 0)) + console.print(f" Best: [green]{best_in_iter['strategy_name']}[/green] | Sharpe={best_in_iter.get('sharpe_ratio', 0):.4f}") + + # Use all_results for final summary + results = all_results # Print summary table accepted = [r for r in results if r.get("status") == "accepted"] @@ -727,6 +724,22 @@ def generate_strategies_cli( console.print(table) + # Show best strategy details + if best_strategy: + console.print(f"\n[bold gold1]{'='*60}[/bold gold1]") + console.print(f"[bold gold1] BEST STRATEGY[/bold gold1]") + console.print(f"[bold gold1]{'='*60}[/bold gold1]") + console.print(f" Name: [cyan]{best_strategy.get('strategy_name', 'Unknown')}[/cyan]") + console.print(f" Sharpe: [green]{best_strategy.get('sharpe_ratio', 0):.4f}[/green]") + console.print(f" Ann.Return: [green]{best_strategy.get('annualized_return', 0):.4f}[/green]") + console.print(f" Max DD: [yellow]{best_strategy.get('max_drawdown', 0):.2%}[/yellow]") + console.print(f" Win Rate: [cyan]{best_strategy.get('win_rate', 0):.2%}[/cyan]") + if best_strategy.get("best_params"): + console.print(f"\n [bold]Optimized Parameters:[/bold]") + for param, val in best_strategy["best_params"].items(): + console.print(f" {param}: [cyan]{val}[/cyan]") + console.print(f"[bold gold1]{'='*60}[/bold gold1]") + if accepted: console.print(f"\n[bold]Accepted Strategies:[/bold]") acc_table = Table(show_header=True, header_style="bold cyan") @@ -736,8 +749,10 @@ def generate_strategies_cli( acc_table.add_column("Ann. Return", justify="right", width=12) acc_table.add_column("Max DD", justify="right", width=10) acc_table.add_column("Win Rate", justify="right", width=10) + acc_table.add_column("Optuna", justify="right", width=8) for i, strat in enumerate(sorted(accepted, key=lambda x: x.get("sharpe_ratio", 0), reverse=True), 1): + optuna_status = "[green]Yes[/green]" if strat.get("best_params") else "[dim]No[/dim]" acc_table.add_row( str(i), strat.get("strategy_name", "Unknown")[:30], @@ -745,6 +760,7 @@ def generate_strategies_cli( f"{strat.get('annualized_return', 0):.4f}", f"{strat.get('max_drawdown', 0):.2%}", f"{strat.get('win_rate', 0):.2%}", + optuna_status, ) console.print(acc_table)