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This bot posts two-sided limit orders on your chosen market to capture the bid-ask spread plus Kalshi's maker rebate.
Over the Apr 11 to May 11 window, this spread capture strategy on Kalshi turned in +$939 of simulated profit (+3130.4% on its configured risk capital), at a 0.66 Sharpe. It placed 28640 simulated trades and won 100.0% of them — a high hit rate — against shallow worst peak-to-trough drawdown of -$25.
Under the hood it simulated 705 Bitcoin (BTC) markets, closing 470 winning and 0 losing positions after $227 in modeled fees, an average of 954.7 trades a day. That trade-by-trade detail, the equity curve above, and the full rule set below are what separate this page from a one-line leaderboard entry.
Net PnL is the headline here; the Sharpe is unannualized over this short window, so read it as a within-sample texture of the equity curve rather than an industry-standard risk score. Because every figure comes from a single 30-day historical replay, it is best treated as a hypothesis to pressure-test rather than a forecast — the same rules can behave very differently once live fills, API latency, and shifting volatility enter the picture.
This backtest runs against Bitcoin (BTC) markets on Kalshi's hourly series across 30 days (Apr 11 to May 11). These are short-horizon contracts that open and settle on a fixed hourly cadence, so the strategy is measured across many independent events rather than one long trend. Rules are evaluated once per hour, and a signal can fill no earlier than the next tradable candle at top-of-book prices, net of Kalshi-style taker fees.
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