If you train an AI agent on 25 years of market data, can you realistically make $20k/month from it?
Spoiler: by the end you'll understand why 10x on prediction markets is possible if you catch the right moment
Before we start, bookmark this and drop a follow
Posting daily alpha on Polymarket and more
The human brain сan tracks max 19 things at once. Prediction markets move on hundreds of variables: news, volume, momentum, whales, sentiment - all at the same time.
Manually impossible. But code can.
This bot will open 100+ trades already knowing their scenario. Even if we hit 64% win rate - that's already profit
Phase 1 - Time Horizons
On Prediction markets there are many time horizons:
- 1 Day
- 7 Days
- 30 Days
The model can predict across different horizons, but in this piece, the focus is 1 Day. Will this prediction market contract resolve YES tomorrow?
Starting simple - just one day. If you can consistently call the direction 1 day ahead, that's already a working tool for daily trading
Phase 2 - What the model predicts (predict classification)
Target1 = 1 -> tomorrow higher -> BUY
Target1 = 0 -> tomorrow lower -> DONT BUY
Simple - the model doesn't try to predict the exact price. It answers one question only: up or down?
Same logic as Polymarket contracts - you're betting on an event, not an exact number
Phase 3 - 38 indicators that lead us to the result
The model doesn't just look at price - it analyzes 38 different signals across 30 prediction market contracts over the last 60 days
Imagine manually analyzing 38 indicators across 30 contracts every day
Phase 4 - How we split the data by time
Important - model learns on the past, gets tested on the future. No data leakage
This is the most common mistake people make building these systems - they test on the same data they trained on. We don't do that. The model never saw 2021-2025 during training - that's a clean honest test
Phase 5 - Neural network architecture
Conv1D - finds local patterns in the time series
LSTM - remembers long-term dependencies
MCDropout - measures prediction uncertainty
Sigmoid - outputs a number from 0 to 1 (probability)
The model outputs just one number. Say 0.85 - means the model is 85% confident price goes up tomorrow. You decide whether to trust it
Phase 6 - Monte Carlo Dropout (key part)
Instead of a single prediction, we run the model 50 times with different dropout rates. We get the mean (confidence) and std (uncertainty).
If the model disagrees with itself, we don't buy
Think of it like asking 50 analysts at once. If all 50 say BUY, we enter. If half say BUY and half say HOLD - we skip.
> We only trade when there's consensus
Phase 7 - BUY/HOLD signal
Confidence threshold is 70%. If the model says BUY with less than 70% confidence - we ignore the signal and keep looking
We don't trade every day on every market. We wait for strong signals only. 3 confident trades a week beats 20 uncertain ones
BLOCK 8 - Training
~59% accuracy sounds modest - but on prediction markets, even 59% win rate gives consistent profit
Casinos make money with just a 2-3% edge. We have 59%. With 100+ trades a month, the math works in our favor
Phase 9 - Final result
Strategy is simple - every day we buy the top 3 positions with the highest probability of going up.
The grey lines are 10 people opening random prediction market positions every day . The green line is our bot. Difference is obvious!
Result consistently beats random strategies and already gives more than 59% win rate - sometimes hitting 78%
Every signal, every indicator, every pattern across 30 markets - processed in seconds. Not by you. By a model that runs 24/7
We took 25 years of market data, compressed it into 38 indicators, ran it through an LSTM neural network 50 times per prediction - and the result consistently beats random strategies.
Full code on GitHub. Every one of you can try running it on your old laptop and increase your chances of successful trading
You build your own life - so choose the right path
/ if this helped you don't forget to follow /
and i'm already working on an improved version of this bot
