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.