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Polymarket self-calibrating weather-trading Python bot (Full Guide) run the smartest weather-trading bot with 0 coding knowledge after reading this article: what's inside: > github with full Python code > trading 20 cities & 4 continents > 3 forecast sources {ECMWF, HRRR, METAR} > EV+ automatic calculation for each trade...

93,175 Aufrufe • vor 7 Monaten •via X (Twitter)

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I rebuilt my Polymarket weather bot Launched a simulation with $1,000 An hour later, it was already $1,250. Another hour later - $680 The problem was simple: the forecast changed, but the bot didn't know about it and continued to hold its position That's exactly what I'm fixing in version v2 What I'm adding: 1. Expected Value EV = (p · b) − (1 − p) Before every entry the bot calculates expected value. Negative EV - no trade. But EV is useless if p comes from nowhere 2. Kelly Criterion f* = (p · b − q) / b Determines exactly how much to bet based on real edge Small edge → small position. Large edge → larger. Fixed sizing can't do this 3. Brier Score BS = (1/n) · Σ(fₜ − oₜ)² Measures how accurate our probabilities actually are. If the bot assigned 80% but the outcome won 40% of the time - Brier Score catches that 4. Calibration on own data After each trade the bot saves city, month, bucket, forecast at entry, final result History accumulates → bot calculates Brier Score on its own trades → if Chicago in March is consistently off - p for that city and month gets adjusted automatically 5. Adding new data sources Instead of relying on a single source, the bot will draw on several sources at once, and if the forecasts match, it will enter into a trade Every cycle of v2: - scan markets - check station forecast - apply calibration - calculate EV - apply Kelly - enter or skip - monitor every minute - close if forecast changes Btw, here's a short video clip showing the terminal in action A major update is coming soon

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Claude and a free weather API will earn you $100k+. Success rate for beginners: 80%. Complete guide and algorithm for building Polymarket weather trading bot. Simple logic, a low entry budget and high ROI -that’s why weather bots are so clean. Onchain proof these bots exist: 1st bot: 2nd bot: I verified their profitability by myself copying every trade - each bot's win rate over time ranges from 80 to 90%. I grew my starting capital by +40% in just one week. You can copy their trades and see for yourself in two clicks through this bot: The alpha is simple: you're not trading weather. You're trading other people's ignorance. Gap between what the crowd prices and what 51 ensemble models say. Polymarket asks: "Will Atlanta hit 95°F tomorrow?" Normies bet on vibes. You bet on math. The core tool: Open-Meteo API. Free. No key needed. 51-model ensemble. Clean JSON. Cooked and ready. Update every 30 min. Hardcode your city coordinates - don't waste time on geocoding at runtime. This single endpoint beats most paid tools for what Polymarket actually needs. The edge in one sentence: Market is heavy on 16°C. Your 51-model ensemble points at 19°C. That's your trade. Find that gap systematically across every city market, every day - and you have a scanner. That's what separates consistent traders from gamblers. How to start: - Week 1: Open-Meteo + tropicaltidbits. Pick one city market. Track model vs market price daily. Don't trade yet — just watch where you'd have been right. - Weeks 2–3: Automate the pull. Log ensemble divergences. Build the scanner. - Week 4: Now you have an edge. Trade it. Most people want to skip to week 4. That's exactly why most people lose. Now you have the algorithm framework plus a complete guide to get started. All that's left is to actually do it. Bookmark this post so you can come back to it when you start building the bot.

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