Polymarket Prediction Market Trading System

This repo now includes a complete end-to-end workflow for discovering Polymarket markets, collecting signals, modeling edge, backtesting, monitoring, and optionally executing bets.

Files Added

• polymarket_explorer.py — fetches top 100 active/open markets from Polymarket and saves to polymarket_markets.json.
• data_collector.py — enriches markets with polling, crypto, FRED, and NewsAPI sentiment signals into market_signals.json.
• probability_model.py — builds model probabilities, computes edge and Kelly sizing, and saves model_predictions.json.
• backtest_polymarket.py — simulates historical strategy performance on resolved markets and saves backtest_results.json.
• monitor_polymarket.py — Streamlit dashboard with top opportunities, backtest stats, positions, and PnL.
• bet_executor.py — auto-bet executor via CLOB endpoint with logging and daily-loss circuit breaker.

Setup

1. Install dependencies:

pip install -r requirements.txt

2. Create a .env file in the repo root:

NEWSAPI_KEY=your_newsapi_key
POLYMARKET_PRIVATE_KEY=your_wallet_private_key
POLYMARKET_WALLET_ADDRESS=your_wallet_address
PORTFOLIO_VALUE=1000

Run Order

Run the scripts in this order:

1. Fetch active markets

python polymarket_explorer.py

2. Collect market signals

python data_collector.py

3. Build model predictions

python probability_model.py

4. Backtest historical performance

python backtest_polymarket.py

5. Launch monitoring dashboard (auto-refresh every 5 minutes)

streamlit run monitor_polymarket.py

6. Execute high-edge bets (optional/live risk)

python bet_executor.py

Notes

• All scripts print progress updates and handle API retries.
• bet_executor.py stops placing bets if daily realized losses exceed 20% of configured portfolio value.
• News sentiment requires a valid NewsAPI key.
• Crypto signal in data_collector.py attempts to use checkpoints/gbm_model.pkl if available.