Alex

@de1lymoon • 1,713 subscribers

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

Shorts

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

13,953 views

Videos

Anya Rossi

sweetdream.ai

SweetDream.ai•Sponsored•Livecam

Watch Anya Live

Anya is streaming live right now! Join her private show and enjoy exclusive content.

Exclusive private shows

1.2k viewers online

Private Show

Join now for exclusive access

Free preview available • Premium content

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

187,683 views • 2 months ago

No more content to load