I Reverse-Engineered a Trader's Strategy From Public Polymarket Data

Polymarket is a prediction market platform where every trade is public - you can see exactly what any wallet bought, at what price, and when. I built a tool that takes that public history and works out what strategy a trader is actually using, then turns it into rules a bot can run on its own.
Here's how it works, and the one finding that made this actually interesting instead of just a data-collection exercise. The pipeline collect → enrich → signals → analyse → report Collect pulls full trade history for a wallet from Polymarket's Data API (handles 100k+ fills via cursor pagination), plus market metadata for everything they traded. Enrich joins external context onto every single trade at the moment it happened: Binance and Coinbase OHLCV, the Chainlink ETH/USD oracle price on Polygon, order book spread and imbalance from the Polymarket CLOB, and how close the trade was to market close (I bucket this into early, mid, late, urgent phases). Signals computes candidate features on every row - momentum z-scores, oracle-vs-price agreement, spread percentage, timing phase. Analyse runs statistical analysers across all of it - win rate, Sharpe, Brier score, sizing patterns, and the core piece: strategy discovery. The interesting part: one rule doesn't work My first assumption was that a trader has a strategy - pick a direction rule, measure how well it holds. That assumption was wrong, and testing it wrong is what made the real pattern visible. Running this on a real wallet (~65k trades in ETH 5-minute Up/Down markets), a single global rule like "always follow the Chainlink oracle" doesn't hold. But segmenting by phase and price bucket reveals two genuinely different behaviors: Mode 1 - cheap lottery. In the late/urgent phase, when a token is priced 0–35¢, the trader buys the cheaper side. Win rate is only ~20% - but the payoff structure makes it positive EV. This mode barely correlates with the Chainlink oracle at all (~19% agreement). Mode 2 - oracle follow. At any phase, once a token is priced 50¢+, the trader follows Chainlink's price vs. the window-open price. Win rate here is ~78%, with a smaller payoff per win. Two completely different behaviors, cleanly separated by price and timing, both consistently profitable in their own regime - but only visible once you stop assuming one global rule and start segmenting. There's a gap between 35-50¢ in mid-phase where no rule reaches significance, and the bot is built to skip that gap rather than force a signal that isn't there. That's the actual deliverable: a strategy_config.json with a direction.strategies[] array, each entry a phase/price-scoped rule with its own logic, no hardcoded strategy in the bot itself. Re-run the analysis, restart the bot, get new behavior - nothing to redeploy. The bot side Phase 2 loads that config and runs a ~5-second scan loop: find active markets, match phase+price to a strategy mode, pull the mode-specific signal (oracle diff or cheap-side book price), run an EV/sizing check, execute paper or live. Daily loss limits and max-position caps sit underneath all of it, independent of which strategy mode is active. What this isn't Worth being upfront about: this isn't a guaranteed-profit system, it's not real-time copy trading (it extracts rules, it doesn't mirror the wallet), and it's not a general ML platform - it's segmented statistical rule discovery. Fill history also isn't full intent - you see what someone traded, not what they considered and skipped. Historical validity isn't future validity either; paper trading before live is the whole point of the default config. I build tooling like this for clients working on Polymarket - custom strategy extraction, market-making bots, or ongoing maintenance on an existing system. Full source, no black-box logic, paper-trading validation before anything goes live.