Quantitative copy-trading bot for Polymarket — follows the top 1% of profitable wallets using Bayesian credibility updates + Kelly position sizing + Favorite-Longshot bias correction.

What's Different from Project A (Whale Watcher)

Aspect	Project A (whale-watcher)	Project B (copy-trader)
Goal	Detect "insider" trades via LLM	Mirror statistically profitable wallets
LLM cost	$0.5-2 per signal	$0 (all math, no LLM)
Win rate evidence	63.5% on 250 signals	Expected ≥55% from top-1% PnL data
Latency	Asynchronous	Real-time WebSocket

Phase Plan

✅ Phase 0: foundation, pool builder, Telegram notifier
✅ Phase 1: Trade stream (poll-based, 30s interval, restart-safe)
✅ Phase 2: Signal aggregator (Bayesian update + Kelly sizer + 5-layer filter)
✅ Phase 3: CLOB trader (optional, off by default — manual first)
✅ Phase 4: Dashboard (FastAPI for P&L tracking)
✅ Phase 5: Strategy analytics (signal outcome backfill + 6-dimension hit-rate dashboard for parameter tuning)

Architecture (target)

Data API ──┐
           ├─→ Wallet Pool Builder ──→ SQLite (top 100 wallets)
           │
           └─→ Health score = f(PnL, trades, category diversity)
                    ↓
           User WebSocket (User channel) per wallet
                    ↓
           On each trade → check eligibility (size, price)
                    ↓
           Bayesian credibility update
                    ↓
           Softmax aggregation across wallets
                    ↓
           Kelly sizing + Favorite-Longshot correction
                    ↓
           Telegram notification + optional CLOB execution

Running

# Main bot (6 asyncio loops: pool/stream/credibility/cleanup/health/outcome-resolve)
python -m src.main run

# Strategy analytics dashboard (http://localhost:8518/analytics)
python -m src.main dashboard

# CLI stats — hit rate / PnL
python -m src.main stats

# Manually backfill resolved-market outcomes onto historical signals
python -m src.main backfill --batch 500

# Docker (cloud)
docker compose build --no-cache && docker compose up -d

Configuration

Copy .env.example to .env and fill in:

Polymarket Gamma + Data API (no auth needed for reads)
Telegram bot token + chat ID for notifications
CLOB credentials (only if enabling live execution)
Wallet pool criteria (PnL minimum, trade count, etc.)

Why this will (probably) make money

Polymarket data: top 1% users capture 84% of all profits
Their edge = identifying mispriced contracts, NOT insider info
Following their trades = systematic exposure to that edge
Bayesian updating = self-correcting when a wallet's skill degrades
Favorite-Longshot correction = avoid 70.8% user loss pattern

Disclaimer

Backtesting not yet performed. Start with ENABLE_EXECUTION=false and observe signals via Telegram for 2-4 weeks before any live trading.