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It trades the KXBTC15M market, checking every ten seconds whether to enter. It buys yes or no contracts of size 500 when Bitcoin momentum and price conditions align, and exits by tiered profit/loss thresholds relative to position size, up to a 2001 position limit.
Over the May 14 to Jun 11 window, this custom strategy on Kalshi turned in +$519,067 of simulated profit (+25940.4% on its configured risk capital), at a 2.15 Sharpe. It placed 5738 simulated trades and won 67.8% of them — a high hit rate — against shallow worst peak-to-trough drawdown of -$2,051.
Under the hood it simulated 1653 Bitcoin (BTC) markets, closing 1143 winning and 544 losing positions after $33,701 in modeled fees, an average of 191.3 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 (May 14 to Jun 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: