Polymarket Trader

Maintain a profitable BTC 1h Up/Down strategy by anchoring decisions to Binance BTCUSDT (the resolution source) and enforcing anti-churn/risk rules.

Workflow (use this order)

1) Confirm the market type - This skill is optimized for bitcoin-up-or-down-* 1h markets (Binance 1H open vs close).

2) Compute the anchor signal (Binance) - Fetch 1m closes + the 1h open for the relevant hour. - Compute volatility (sigma) and time-to-expiry. - Convert to fair probability for Up/Down.

3) Trade only when there is measurable edge - Enter only if edge = fair_prob - market_price exceeds a threshold. - Add a directional guardrail: do not bet against the sign of the move when |z| is non-trivial.

4) Exit using the right logic for the entry mode - Model entries: exit on edge decay / model flip; hold to preclose when confidence is extreme. - Mean-reversion entries: exit on reversion targets (not model-tp), with strict churn limits.

5) Validate with logs - Every suspected “nonsense trade” must be explained via: - reason / entry_mode - Binance-derived fair probability + z - whether the correct exit block fired

Bundled scripts

All scripts are designed to be run from the OpenClaw workspace.

1) Fetch Binance klines

• {baseDir}/scripts/binance_klines.py
• Pulls klines and prints JSON.

2) Dump/stabilization and regime metrics

• {baseDir}/scripts/binance_regime.py
• Computes ret5/ret15/slope10 + simple “stabilized” boolean.

3) Explain fills (events.jsonl) with Binance context

• {baseDir}/scripts/explain_fills.py
• Reads paperbot events.jsonl and prints a concise table for the last N fills:
  • side/outcome/px/reason
  • estimated fair_up + z
  • “against trend?” flag

References

• {baseDir}/references/strategy.md — the math model, parameters, and tuning checklist.