Kalshi ETH 15m Trend-Momentum Stack with Tiered Exits

What this backtest shows

Over the May 14 to Jun 11 window, this custom strategy on Kalshi turned in +$5,764 of simulated profit (+28820.5% on its configured risk capital), at a 2.27 Sharpe. It placed 4816 simulated trades and won 58.9% of them — a balanced hit rate — against shallow worst peak-to-trough drawdown of -$63.

Under the hood it simulated 1653 Ethereum (ETH) markets, closing 1123 winning and 784 losing positions after $590 in modeled fees, an average of 160.5 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.

Market and data window

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.

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