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It trades the Kalshi 15‑minute Ethereum market, checking every 10 seconds. It enters by buying Yes or No contracts when Coinbase ETH price data meets conditions like crossing the volume‑weighted average price or acceleration, with a maximum total position of 20 contracts. Exits happen through tiered take‑profit and stop‑loss rules that depend on unrealized profit or loss and current position size.
Over the May 14 to Jun 11 window, this custom strategy on Kalshi turned in +$6,272 of simulated profit (+31360.3% on its configured risk capital), at a 2.30 Sharpe. It placed 4348 simulated trades and won 62.6% of them — a high hit rate — against shallow worst peak-to-trough drawdown of -$29.
Under the hood it simulated 1653 Ethereum (ETH) markets, closing 1087 winning and 649 losing positions after $520 in modeled fees, an average of 144.9 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 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: