Alex

@de1lymoon • 1,713 subscribers

Researcher & Contributor @Polymarket - your maximum, is someone’s minimum | dm open

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How to build your own weather trading bot Every weather trader making money on Polymarket follows the same 7 steps. Free data. Simple formula. Repeat daily Traders making $20k-$180k all started here free APIs, basic math the crowd ignores, and patience Here's the playbook: Step 1: Pick 2-3 cities Don't try to trade everything. Start with NYC, Chicago, and one international city. Learn how the forecast behaves for those exact locations. Fewer cities = faster feedback loop Start copy-trading any trader even with $10 → Step 2: Pull free forecast data Open-Meteo API free, no API key, updates every 6 hours. NOAA official US government forecasts, 85-90% accurate at 1-2 days. Windy / Ventusky, visual model viewers to sanity-check your data These are the same data sources the top weather traders use. All free. All public Step 3: Turn forecasts into probabilities The market shows 8-12 temperature buckets (25°C, 26°C, 27°C). Your job: calculate the real probability for each bucket based on the forecast Simple method: > take the forecast daily max > assume ±1.5°C error range > simulate 50,000 scenarios > count how many land in each bucket > that's your probability per bucket

How to build your own weather trading bot Every weather trader making money on Polymarket follows the same 7 steps. Free data. Simple formula. Repeat daily Traders making $20k-$180k all started here free APIs, basic math the crowd ignores, and patience Here's the playbook: Step 1: Pick 2-3 cities Don't try to trade everything. Start with NYC, Chicago, and one international city. Learn how the forecast behaves for those exact locations. Fewer cities = faster feedback loop Start copy-trading any trader even with $10 → Step 2: Pull free forecast data Open-Meteo API free, no API key, updates every 6 hours. NOAA official US government forecasts, 85-90% accurate at 1-2 days. Windy / Ventusky, visual model viewers to sanity-check your data These are the same data sources the top weather traders use. All free. All public Step 3: Turn forecasts into probabilities The market shows 8-12 temperature buckets (25°C, 26°C, 27°C). Your job: calculate the real probability for each bucket based on the forecast Simple method: > take the forecast daily max > assume ±1.5°C error range > simulate 50,000 scenarios > count how many land in each bucket > that's your probability per bucket

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Stop Guessing the Market. Start Running Simulations Most models try to predict the future. MiroFish does it differently: it simulates it p ≈ 320 / 1000 → EV = pW − (1 − p)L Instead of giving you one forecast, MiroFish builds a digital world from news, policy drafts, earnings reports, and market signals then fills it with thousands of AI agents and lets them react > They argue > They set up camps > They amplify narratives > They change their beliefs under pressure And after hundreds or thousands of runs, you don’t get a prophecy you get scenario frequencies. > If one outcome happens 320 times out of 1000, then p ≈ 0.32 From there, it becomes a decision problem: - calculate Expected Value - compare your probability to the market’s - use Kelly to size the bet correct

Stop Guessing the Market. Start Running Simulations Most models try to predict the future. MiroFish does it differently: it simulates it p ≈ 320 / 1000 → EV = pW − (1 − p)L Instead of giving you one forecast, MiroFish builds a digital world from news, policy drafts, earnings reports, and market signals then fills it with thousands of AI agents and lets them react > They argue > They set up camps > They amplify narratives > They change their beliefs under pressure And after hundreds or thousands of runs, you don’t get a prophecy you get scenario frequencies. > If one outcome happens 320 times out of 1000, then p ≈ 0.32 From there, it becomes a decision problem: - calculate Expected Value - compare your probability to the market’s - use Kelly to size the bet correct

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