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Backtesting a Polymarket Market-Making Bot Before Going Live

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The article is the ninth in a series on building a Polymarket market-making bot and focuses on testing whether its adaptive strategy actually earns money. It notes that a quote that looks profitable on paper, such as a $4 gross spread on 100 tokens, can fail due to unfilled orders, partial fills, adverse price moves, queue position, or trading costs. Backtesting is presented as replaying historical market data to simulate the bot's decisions before any real capital is committed. The piece outlines a modular Python framework covering data, replay, strategy, execution simulation, portfolio tracking, and performance analysis. It also explains that backtesting is followed by paper trading and then live trading, and that a good backtest does not guarantee live success.

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