| """ |
| Multi-Agent RL Trading Environment — Entry Point |
| |
| Usage: |
| python app.py # Default: 100 episodes, dummy data |
| python app.py --episodes 50 --ticker AAPL # Custom config |
| python app.py --evaluate # Run evaluation with baseline comparison |
| """ |
|
|
| import argparse |
| import sys |
|
|
|
|
| def parse_args(): |
| parser = argparse.ArgumentParser( |
| description="Multi-Agent RL Trading Environment" |
| ) |
| parser.add_argument("--episodes", type=int, default=100, |
| help="Number of training episodes") |
| parser.add_argument("--ticker", type=str, default="AAPL", |
| help="Stock ticker symbol") |
| parser.add_argument("--start", type=str, default="2023-01-01", |
| help="Data start date") |
| parser.add_argument("--end", type=str, default="2024-12-31", |
| help="Data end date") |
| parser.add_argument("--cash", type=float, default=100_000.0, |
| help="Initial cash") |
| parser.add_argument("--evaluate", action="store_true", |
| help="Run evaluation with random baseline comparison") |
| parser.add_argument("--fetch-data", action="store_true", |
| help="Fetch real market data before training") |
| parser.add_argument("--seed", type=int, default=42, |
| help="Random seed") |
| parser.add_argument("--log-every", type=int, default=10, |
| help="Log metrics every N episodes") |
| parser.add_argument("--max-steps", type=int, default=None, |
| help="Max steps per episode") |
| parser.add_argument("--gbm", action="store_true", |
| help="Use synthetic GBM data instead of real data") |
| parser.add_argument("--fast", action="store_true", |
| help="Run a quick test with fewer episodes/steps and no LLM") |
| parser.add_argument("--demo", action="store_true", |
| help="Launch the Demo UI backend API server") |
| return parser.parse_args() |
|
|
|
|
| def main(): |
| args = parse_args() |
|
|
| if args.demo: |
| print("Starting Demo API Server on port 7860...") |
| import uvicorn |
| from api.server import app |
| uvicorn.run(app, host="0.0.0.0", port=7860) |
| return |
|
|
| config = TrainingConfig( |
| tickers=[args.ticker], |
| start_date=args.start, |
| end_date=args.end, |
| initial_cash=args.cash, |
| num_episodes=2 if args.fast else args.episodes, |
| seed=args.seed, |
| log_every=args.log_every, |
| max_steps=50 if args.fast else args.max_steps, |
| fast_mode=args.fast, |
| ) |
|
|
| config_cls = None |
| if not args.demo: |
| from training.config import TrainingConfig |
| config_cls = TrainingConfig |
|
|
| |
| df = None |
| if args.gbm: |
| from env.trading_env import TradingEnv |
| print("Generating synthetic GBM data (mu=0.1, sigma=0.2)...") |
| env_gen = TradingEnv() |
| df = env_gen._make_dummy_data_from_profile(n=500, mu=0.1, sigma=0.2) |
| print(f"Generated {len(df)} rows of GBM data.\n") |
| elif args.fetch_data: |
| from data.fetch_data import fetch_yfinance |
| print(f"Fetching data for {args.ticker} from {args.start} to {args.end}...") |
| df = fetch_yfinance(args.ticker, args.start, args.end) |
| print(f"Loaded {len(df)} rows of market data.\n") |
|
|
| if args.demo: |
| return |
|
|
| config = config_cls( |
| tickers=[args.ticker], |
| start_date=args.start, |
| end_date=args.end, |
| initial_cash=args.cash, |
| num_episodes=2 if args.fast else args.episodes, |
| seed=args.seed, |
| log_every=args.log_every, |
| max_steps=50 if args.fast else args.max_steps, |
| fast_mode=args.fast, |
| ) |
|
|
| if args.evaluate: |
| from utils.evaluate import evaluate |
| results = evaluate(config, df=df) |
| print(f"\nGrade improvement: {results['grade_improvement']:+.4f}") |
| else: |
| from training.train import train |
| metrics = train(config, df=df) |
| print(f"\nDone! {len(metrics)} episodes completed.") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
