After months of iteration, I built a profitable BTC-focused trading engine for Polymarket. Here’s the core technical architecture that actually works.
System Architecture
1. Real-time Data Layer
- Polymarket CLOB WebSocket for live order book and price updates
- Binance WebSocket (
@aggTrade+@bookTicker) for spot momentum - Maintain full local order book reconstruction (not just top-of-book)
2. Probability & Edge Engine
- Ensemble model: XGBoost + Bayesian smoothing + time-decay function
- Heavy weighting on remaining time (log scale, especially last 60 seconds)
- Features: short-term momentum (15s/30s basis points), order book imbalance, cross-market correlations
- Trade only when
model_prob - market_prob > 0.07after fees
3. Execution Layer
- Direct interaction via viem on Polygon (Conditional Tokens contract)
- IOC (Immediate-Or-Cancel) orders with pre-execution best-ask revalidation
- Dynamic fractional Kelly sizing with volatility adjustment
4. Risk & Production Layer
- Strict per-trade risk (max 1%)
- Daily drawdown circuit breaker (-6%)
- Auto-pause after consecutive losses
- Full audit logging + Redis-backed position tracking
Tech Stack
- Language: TypeScript (Node.js) + Python (ML)
- Blockchain: viem + Polygon RPC
- Real-time: WebSocket + Redis
- Deployment: Docker on low-latency VPS
Key Lesson: The edge wasn’t in fancy ML — it came from low-latency infrastructure, accurate time-decay modeling, and ruthless risk discipline. Most bots fail because they overtrade low-quality signals.
Consistent profitability requires treating Polymarket like a serious HFT-style information market.
If you have more questions, please feel free to contact me at any time: https://t.me/NevoSayNev0
Tags: #Polymarket #TradingBots #BTC #PredictionMarkets #DeFi #Web3 #AlgorithmicTrading #QuantitativeTrading #Fintech #TypeScript
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