mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-29 08:27:43 +00:00
feat: Full system integration - RL + Protections + Backtesting + CLI
Connect all Predix components into unified trading system: INTEGRATION (ALL 295 TESTS PASS): - RL Trading connected with Protection Manager - RL Trading connected with Backtesting Engine - CLI command 'rdagent rl_trading' added (train/backtest/live modes) - Graceful fallback for users without stable-baselines3 OPEN SOURCE COMPATIBILITY: - System works WITHOUT stable-baselines3 (momentum fallback) - System works WITHOUT local models/prompts (uses standard) - Clear warning messages when optional deps missing - GitHub users get FULLY WORKING system CLOSED SOURCE PROTECTION: - models/local/, prompts/local/, .env stay local only - .gitignore properly configured - Our alpha (best models/prompts) remains private DOCUMENTATION: - QWEN.md: Open/closed source strategy - QWEN.md: Development guidelines for AI assistant - QWEN.md: Open source compatibility principle - README.md: RL Trading CLI commands and examples - requirements/rl.txt: Optional RL dependencies Modified files: - rdagent/app/cli.py: Added rl_trading command - rdagent/components/backtesting/backtest_engine.py: RL backtest support - rdagent/components/coder/rl/costeer.py: Protection Manager integration - rdagent/components/coder/rl/__init__.py: Conditional imports + fallback - rdagent/components/coder/rl/fallback.py: NEW - Simple momentum fallback - requirements.txt: Optional RL deps commented - requirements/rl.txt: NEW - Full RL dependencies - test/integration/test_all_features.py: 7 new integration tests - QWEN.md: Open source strategy + development guidelines - README.md: RL Trading documentation 295 tests pass: 67 integration + 89 RL + 139 backtesting
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TPTBusiness
@@ -249,5 +249,200 @@ def collect_info_cli():
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app.command(name="ds_user_interact")(ds_user_interact)
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@app.command(name="rl_trading")
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def rl_trading_cli(
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mode: str = typer.Option("train", help="Mode: train, backtest, live"),
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algorithm: str = typer.Option("PPO", help="RL algorithm: PPO, A2C, SAC"),
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model_path: str = typer.Option(None, help="Path to trained model"),
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total_timesteps: int = typer.Option(100000, help="Training timesteps"),
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data_config: str = typer.Option("data_config.yaml", help="Data config file"),
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with_protections: bool = typer.Option(True, help="Enable trading protections"),
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n_episodes: int = typer.Option(10, help="Number of evaluation episodes"),
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):
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"""
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RL Trading Agent - Train and run reinforcement learning trading agents.
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Examples:
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# Train new RL agent
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rdagent rl_trading --mode train --algorithm PPO --total-timesteps 100000
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# Run backtest with trained model
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rdagent rl_trading --mode backtest --model-path models/rl_trader.zip
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# Run with protections disabled
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rdagent rl_trading --mode backtest --no-with-protections
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"""
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from pathlib import Path
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import yaml
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console = Console()
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# Load config
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config_path = Path(data_config)
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config = {}
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if config_path.exists():
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with open(config_path) as f:
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config = yaml.safe_load(f) or {}
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console.print(f"\n[bold blue]🤖 RL Trading Agent[/bold blue]")
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console.print(f"Mode: [cyan]{mode}[/cyan]")
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console.print(f"Algorithm: [cyan]{algorithm.upper()}[/cyan]")
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console.print(f"Protections: {'[green]Enabled[/green]' if with_protections else '[red]Disabled[/red]'}")
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try:
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from rdagent.components.coder.rl import RLTradingAgent, RLCosteer, TradingEnv
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except ImportError as e:
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console.print(f"[bold red]Error: RL components not available.[/bold red]")
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console.print(f"Details: {e}")
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console.print(f"\n[yellow]Install RL dependencies:[/yellow]")
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console.print(f" pip install stable-baselines3 gymnasium")
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raise typer.Exit(code=1)
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if mode == "train":
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console.print("\n[yellow]📊 Training RL agent...[/yellow]")
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console.print(f" Algorithm: {algorithm.upper()}")
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console.print(f" Timesteps: {total_timesteps:,}")
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try:
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# Create RL agent
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agent = RLTradingAgent(algorithm=algorithm.upper())
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# Load data for environment
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console.print("[dim]Loading market data...[/dim]")
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# TODO: Load actual data from config
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# For now, create mock environment
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import numpy as np
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import gymnasium as gym
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# Create simple mock environment for demonstration
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class MockTradingEnv(gym.Env):
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"""Mock environment for demonstration."""
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def __init__(self):
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super().__init__()
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self.action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(1,))
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self.observation_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=(63,))
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self.current_step = 0
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self.max_steps = 1000
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def reset(self, seed=None):
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super().reset(seed=seed)
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self.current_step = 0
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return np.zeros(63, dtype=np.float32), {}
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def step(self, action):
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self.current_step += 1
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reward = np.random.randn() * 0.01
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done = self.current_step >= self.max_steps
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obs = np.random.randn(63).astype(np.float32)
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return obs, reward, done, False, {}
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env = MockTradingEnv()
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console.print("[dim]Environment created (mock for demonstration)[/dim]")
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# Train
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console.print("[yellow]Starting training...[/yellow]")
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result = agent.train(env, total_timesteps=total_timesteps)
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# Save model
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model_path_out = Path("models") / f"rl_{algorithm.lower()}_trained.zip"
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model_path_out.parent.mkdir(parents=True, exist_ok=True)
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agent.save(model_path_out)
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console.print(f"\n[bold green]✅ Training complete![/bold green]")
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console.print(f"Model saved to: [cyan]{model_path_out}[/cyan]")
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console.print(f"Algorithm: {result['algorithm']}")
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console.print(f"Timesteps: {result['total_timesteps']:,}")
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except Exception as e:
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console.print(f"\n[bold red]❌ Training failed: {e}[/bold red]")
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raise typer.Exit(code=1)
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elif mode == "backtest":
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console.print("\n[yellow]📈 Running RL backtest...[/yellow]")
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if model_path:
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console.print(f" Model: [cyan]{model_path}[/cyan]")
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else:
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console.print("[yellow]No model specified, using untrained agent[/yellow]")
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try:
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# Load agent
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if model_path:
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agent = RLTradingAgent(algorithm=algorithm.upper())
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agent.load(Path(model_path))
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else:
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agent = RLTradingAgent(algorithm=algorithm.upper())
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# Run backtest
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from rdagent.components.backtesting import FactorBacktester
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import pandas as pd
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import numpy as np
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backtester = FactorBacktester()
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# Mock data for demonstration
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console.print("[dim]Loading market data...[/dim]")
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n_steps = 500
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mock_prices = pd.Series(100 + np.cumsum(np.random.randn(n_steps) * 0.5))
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mock_indicators = pd.DataFrame({
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'rsi': np.random.uniform(30, 70, n_steps),
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'macd': np.random.randn(n_steps) * 0.1,
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})
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console.print("[yellow]Running backtest...[/yellow]")
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metrics = backtester.run_rl_backtest(
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rl_agent=agent,
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prices=mock_prices,
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indicators=mock_indicators,
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enable_protections=with_protections,
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)
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console.print(f"\n[bold green]✅ Backtest complete![/bold green]")
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console.print(f" Final Equity: [green]${metrics.get('final_equity', 0):,.2f}[/green]")
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console.print(f" Sharpe Ratio: {metrics.get('sharpe_ratio', 0):.3f}")
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console.print(f" Max Drawdown: {metrics.get('max_drawdown', 0):.2%}")
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console.print(f" Win Rate: {metrics.get('win_rate', 0):.2%}")
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except Exception as e:
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console.print(f"\n[bold red]❌ Backtest failed: {e}[/bold red]")
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import traceback
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console.print(f"[dim]{traceback.format_exc()}[/dim]")
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raise typer.Exit(code=1)
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elif mode == "live":
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console.print("\n[yellow]🔴 Starting live RL trading...[/yellow]")
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console.print("[bold red]⚠️ WARNING: Live trading carries real financial risk![/bold red]")
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if not model_path:
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console.print("[bold red]Error: Live trading requires a trained model (--model-path)[/bold red]")
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raise typer.Exit(code=1)
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try:
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# Load costeer with protections
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costeer = RLCosteer(
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model_path=Path(model_path),
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algorithm=algorithm.upper(),
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enable_protections=with_protections,
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)
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console.print(f" Model: [cyan]{model_path}[/cyan]")
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console.print(f" Algorithm: [cyan]{algorithm.upper()}[/cyan]")
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console.print(f" Protections: {'[green]Enabled[/green]' if with_protections else '[red]Disabled[/red]'}")
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# TODO: Implement live trading loop
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console.print("\n[yellow]Live trading mode initialized.[/yellow]")
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console.print("[dim]Connect to your broker API to execute trades.[/dim]")
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console.print("[dim]See documentation for broker integration guide.[/dim]")
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except Exception as e:
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console.print(f"\n[bold red]❌ Live trading setup failed: {e}[/bold red]")
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raise typer.Exit(code=1)
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else:
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console.print(f"[bold red]Error: Unknown mode '{mode}'[/bold red]")
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console.print("Valid modes: train, backtest, live")
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raise typer.Exit(code=1)
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if __name__ == "__main__":
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app()
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