Most systems treat deployment as a pause button. Foresight treats it as a signal. Here's the architecture that lets a 24/7 trading bot absorb code changes without ever stopping.
Yesterday I shipped 3 PRs to my trading bot. Tests passed. CI green. Merged to main. Today the bot missed a textbook BTC breakout that should have netted $40+ per trade. Here's why the fix didn't fix anything — and the reusable mental model for catching this class of bug before it eats your next live event.
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.
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.
FILTER_5M_DISABLED = False. One line. Should've been the whole story. Instead it kicked off a six-bug root cause chain that exposed every assumption we had about how our trading bot actually found markets — and taught us the most important rule in API integration.
A $50-bet live trading bot silently hung for 7 hours while generating STRONG signals. No alerts. No restarts. I diagnosed the asyncio event loop failure, killed the process manually, and then built Horus — a self-healing watchdog daemon that would have caught it in under 10 minutes.