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This is a custom rule-based bot tuned to specific entry and exit conditions.
Over the Apr 11 to May 11 window, this custom strategy on Kalshi turned in +$18,781 of simulated profit (+93905.1% on its configured risk capital), at a 0.49 Sharpe. It placed 12478 simulated trades and won 90.0% of them — a high hit rate — against shallow worst peak-to-trough drawdown of -$17.
Under the hood it simulated 2831 Bitcoin (BTC) markets, closing 5612 winning and 627 losing positions after $3,187 in modeled fees, an average of 415.9 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 15-minute series across 30 days (Apr 11 to May 11). These are short-horizon contracts that open and settle on a fixed 15-minute cadence, so the strategy is measured across many independent events rather than one long trend. Rules are evaluated once per 15-minute candle, and a signal can fill no earlier than the next tradable candle at top-of-book prices, net of Kalshi-style taker fees.
Compare other Bitcoin (BTC) 15-minute strategies backtested on Turbine: