Cross Market State Fusion

Cross-Market State Fusion

RL agents that exploit information lag between fast markets (Binance futures) and slow markets (Polymarket prediction markets) through real-time multi-source state fusion.

**[View the presentation (PDF)](cross-market-state-fusion.pdf)** | **[LACUNA visual writeup](https://humanplane.com/lacuna)**

What This Is

A PPO (Proximal Policy Optimization) agent that paper trades Polymarket's 15-minute binary crypto markets. The agent observes live data from Binance futures and Polymarket's orderbook, then learns to predict short-term price direction.

**Current status**: Paper trading only. The agent trains and makes decisions on live market data, but doesn't execute real orders.

Setup

• Markets: 4 concurrent 15-min binary crypto markets (BTC, ETH, SOL, XRP) on Polymarket
• Position size: $5–$500 per trade (configurable via --size)
• Max exposure: Position size × 4 markets
• Data sources: Binance futures (order flow, returns) + Polymarket CLOB (orderbook)
• Training: Online PPO with MLX on Apple Silicon, learns from live market data
• Reward: Share-based PnL on position close (sparse signal)

What This Proves

1. **RL can learn from sparse PnL signals** - The agent only gets reward when positions close. No intermediate feedback during the 15-minute window. Despite this sparsity, it learns profitable patterns (~$50K PnL, 2,500% ROI in Phase 5 with temporal architecture).

2. **Multi-source data fusion works** - Combining Binance futures order flow and Polymarket orderbook state into a single 18-dim observation gives the agent useful signal.

3. **Low win rate can be profitable** - The agent wins only 23% of trades but profits because binary markets have asymmetric payoffs. Buy at 0.40, win pays 0.60; lose costs 0.40.

4. **On-device training is viable** - MLX on Apple Silicon handles real-time PPO updates during live market hours without cloud GPU costs.

5. **Temporal context helps** - Processing the last 5