Team Statistics

I built PredictFlow because I kept staring at Polymarket thinking that price looks wrong, but I had no way to prove it or decide how much to risk. PredictFlow pulls live data from Polymarket and Kalshi, runs markets through a three model ensemble, calculates the edge after fees, and automatically paper trades with guardrails. Users stay in control without babysitting. I trained models on 4,341 resolved markets using a strict temporal split. While building I found leakage in volume features with a 0.85 correlation to outcomes. I learned and removed them, and documented it. The cleaned system still beats baseline by 21.5%, especially in the 20 to 80 percent range where trades happen. Claude Opus 4.6 returns structured probability, drivers, and risks. Requests are SHA-256 cached, so repeats cost zero. Under the hood are PostgreSQL, many APIs, and a loop that collects, predicts, trades, and scores itself. I started with a signal page. I went above and beyond and built a small quant desk.