trading
Build a Polymarket Trading Bot in Python: 69.6% Win Rate
Quantitative Trading
System track
Production trading systems under real market constraints.
Prediction-market automation, postmortems, and the operational math behind live trading systems.
Guided track Machine-readable 7 posts
Why this cluster exists
Trading systems are only interesting if they survive production. This cluster is about execution, robustness, and market reality.
System object
signal matrix
Best for
quantitative builders and trading-system operators
Start here
The best first read in this track.
Core journey
Read these in order if you want the strongest mental model.
Foundation
I Sized My Polymarket Bets Wrong. Kelly Criterion Fixed It.
Sizing Polymarket bets wrong burns bankroll fast. This kelly criterion polymarket guide gives the exact formula: fee drag, half-Kelly, worked example on live win-rate data.
My Trading Bot Adjusts Its Own Bet Size. Here Are the 5 Rules.
How to build a self-tuning position sizing system that adjusts bet size based on recent performance, without overfitting or over-reacting to variance.
Applied / adjacent
Supporting angle
Not every important idea belongs in the main reading path.
Use the supporting pieces to deepen the model, test tradeoffs, and connect adjacent ideas without losing the main narrative.
Recommended next
I Sized My Polymarket Bets Wrong. Kelly Criterion Fixed It.
Sizing Polymarket bets wrong burns bankroll fast. This kelly criterion polymarket guide gives the exact formula: fee drag, half-Kelly, worked example on live win-rate data.