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It trades the KXETH15M market on Kalshi, evaluating every 10 seconds. It buys YES when ETH's price is above its hourly VWAP and with positive short-term momentum, and NO when below; near expiry, it uses tighter price extremes. Exits are triggered by tiered unrealized gain/loss levels depending on position size, and the strategy never holds more than 70 contracts.
Over the May 14 to Jun 11 window, this custom strategy on Kalshi turned in +$14,212 of simulated profit (+20303.5% on its configured risk capital), at a 2.18 Sharpe. It placed 3132 simulated trades and won 66.1% of them — a high hit rate — against shallow worst peak-to-trough drawdown of -$126.
Under the hood it simulated 1653 Ethereum (ETH) markets, closing 842 winning and 432 losing positions after $1,205 in modeled fees, an average of 104.4 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 Ethereum (ETH) 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 Ethereum (ETH) 15-minute strategies backtested on Turbine: