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This strategy trades Kalshi's KXZEC15M series and re-evaluates every 10 seconds, capped at 100 contracts and restricted to YES or NO prices between 0.85 and 0.99. Rules run top to bottom and the first match ends the tick: it cancels all orders when orders rest with no position (rule 1); sells the YES position if held and the YES best bid is at or below 0.50 (rule 2); sells the NO position if held and the NO best bid is at or below 0.50 (rule 3); and cancels all when time to expiry is at or below 0 seconds (rule 8). With no position and 60 seconds or less to expiry, it buys 100 YES contracts when the spread is at most 0.03, the YES ask is within price bounds, and Coinbase ZEC price exceeds its 1-hour VWAP, the 12-period 1-minute EMA exceeds the 20-period 1-minute SMA, and the 1-hour, 15-minute, 5-minute, and 1-minute changes and 1-minute velocity are all positive (rule 4); it buys 100 NO contracts under the mirror conditions with all those measures negative (rule 5). It also buys 100 YES or NO when no position exists, expiry is between 0 and 60 seconds, the spread is at most 0.01, the ask is within bounds, and 1-minute velocity is respectively positive or negative (rules 6 and 7).
Over the Sep 24 to Oct 24 window, this custom strategy on Kalshi turned in +$528 of simulated profit (+527.9% on its configured risk capital), at a 1.21 Sharpe. It placed 144 simulated trades and won 100.0% of them — a high hit rate — against shallow worst peak-to-trough drawdown of -$33.
Under the hood it simulated 2665 prediction-market markets, closing 72 winning and 0 losing positions after $40 in modeled fees, an average of 4.8 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 prediction-market markets on Kalshi's short-horizon series across 30 days (Sep 24 to Oct 24). These are short-horizon contracts that open and settle on a fixed short-horizon cadence, so the strategy is measured across many independent events rather than one long trend. Rules are evaluated once per short-horizon 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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