prediction-market-weather-trading

name	Prediction Market Weather Trading
description	Production weather market trading system extracted from alteregoeth-ai/weatherbot — airport station resolution matching eliminates 3-8F systematic error, multi-source forecast selection (HRRR/ECMWF/METAR) weighted by geography and time horizon, self-calibrating per-city sigma replaces static probability assumptions after 30+ samples, quarter-Kelly with hard cap sizes every position defensively, and a three-exit framework (stop-loss + trailing + forecast-change) manages risk on price, profit, and information dimensions simultaneously.
version	1.0
format	completion-engine
workflows	3
source	MES 3.0 Deep Extraction — alteregoeth-ai/weatherbot (2,009 lines production Python)

Prediction Market Weather Trading

The bot wins because it solves a coordination problem most traders ignore — weather markets resolve on specific airport stations, not cities. Using KLGA coordinates instead of "New York City" coordinates eliminates 3-8 degrees F of systematic error on markets with 1-2 degree F buckets. Combined with multi-source forecast cross-validation and learned per-city sigma calibration, this creates persistent informational edge against traders using generic weather data.

Core Principle: More accurate data at the resolution point beats better trading at the wrong location.

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Available Workflows

#	Workflow	Produces	Use When
forecast	Market Forecast & Edge Detection	Ranked opportunity table with probability, EV, Kelly sizing, and top-3 actionable edges	Scanning active Polymarket weather markets for mispriced positions
execution	Trade Execution Plan	Complete trade ticket with entry, position size, all five exit scenarios, and decision tree	You have an identified edge and need the full lifecycle plan before placing a trade
calibration	Self-Calibration Review	Per-city sigma report, parameter optimization recommendations, and updated config.json	After 30+ resolved markets, reviewing model accuracy and tuning parameters

Quick Reference

• Genius Context: genius.md — load before any workflow
• Source Extraction: weatherbot-extraction.md
• Reference Implementation: alteregoeth-ai/weatherbot (GitHub) — bot_v1.py (450 lines) + bot_v2.py (1,050 lines)
• Key Constraint: ALWAYS map markets to airport ICAO codes before forecasting. Never use city-center coordinates.
• Architecture: LLM reasons, deterministic code executes. Separation is absolute and non-negotiable.

Execution Prompts (structure-pure v2)

3 deterministic practitioner prompts — each carries an Output Contract, Output Skeleton, and Quality Gate. When a deliverable matches one, Read it and honor its contract instead of improvising the output shape.

• Weatherbot System — Market Scan & Edge Detection — skills/prediction-market-weather-trading/references/prompts-v2/market-scan-edge-detection.md
• Weatherbot System — Self-Calibration Review — skills/prediction-market-weather-trading/references/prompts-v2/self-calibration-review.md
• Weatherbot System — Trade Execution Blueprint — skills/prediction-market-weather-trading/references/prompts-v2/trade-execution-blueprint.md