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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
# Optionally fetch real data or generate GBM
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()
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