The bot was right. The timing was wrong. v4.x had a fundamental reactive architecture problem — by the time signals scored, the CLOB asks were too expensive. v5.0 solved it with event-driven candle boundaries and predictive early-window scoring.
Political prediction markets don't move on charts — they move on information. Hermes is a Python bot that scores political markets using Grok sentiment, Perplexity probability estimation, and calibration consensus from Metaculus and Manifold. Here's how it works.
The signal stack I built for prediction markets turns out to work on perpetual futures — with modifications. Here's how a 9-factor scoring engine, conviction-scaled leverage, and six independent risk gates become a perps trading system.
Sports prediction markets are inefficient because the crowd prices on narrative, not data. Shiva is an automated bot that estimates true probabilities from team stats, injuries, and media sentiment — then executes when the edge is real.
We built a 666-line candle engine with full TD Sequential integration into PolyEdge v3.0. Four days live, 97 closed trades, 91.3% win rate, and +$694.25 on $1,092 wagered. Here's what predictive signal layers do that reactive TA cannot.