标签：#mean

Build a Polymarket mean reversion bot in Python using price history, z-scores, order-book data, execution controls, and production risk management. Introduction A Polymarket mean reversion strategy is based on a simple quantitative hypothesis: when an outcome-token price moves unusually far from its recent statistical range, it may eventually move back toward its local mean. The difficult part is not calculating a moving average. The difficult part is determining whether the deviation is actually temporary—or whether new information has permanently changed the market's fair probability. For a trading bot, that means the strategy needs three separate components: A statistical signal. An executable market-data layer. An execution and risk-management system. Polymarket's current API exposes order-book snapshots and historical price data, making it possible to build the research layer directly from market data. ([Polymarket Documentation][1]) About the Author Bo$onaX I write about Polymarket trading bots, prediction-market infrastructure, algorithmic trading, Python automation, Web3 development, and quantitative strategies. Contact: Github: github.com/n9xdev/poly-alpha-lab Telegram: t.me/bosonax Youtube: youtube.com/@bosonax X: x.com/xxniiinxx Gmail: mailto: dylandevera91928@gmail.com What You'll Learn How mean reversion applies to prediction-market prices How to calculate a rolling z-score How to filter false signals How to incorporate the CLOB order book How to structure a Python bot Why fees, spread, liquidity and adverse selection matter How to backtest and paper trade the strategy 1. The Mean-Reversion Model Let (P_t) represent the price of an outcome token. Calculate a rolling mean: { mu_t = SMA(P_{t-n+1},...,P_t) } and rolling standard deviation: { sigma_t = Std(P_{t-n+1},...,P_t) } Then calculate: { Z_t = \frac{P_t-\mu_t}{\sigma_t} } A basic strategy might define: Z < -2: unusually low price → investigate a long Z > +2: unusually high price → investigate an exit