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This strategy trades Kalshi's KXZEC15M series, checking conditions every 10 seconds with a 100-contract maximum position and YES prices bounded between 0.85 and 0.99. Rules run top to bottom, first match wins: Rule 1 cancels all resting orders when any exist and no position is held; Rule 2 sells the YES position when it's held and the YES best bid is at or below 0.50; Rule 3 does the same for a NO position at a NO best bid of 0.50 or below. Rules 4 and 5 enter when flat, 60 seconds or less remain, spread is at or below 0.03, and the ask is within 0.85-0.99: Rule 4 buys 100 YES if ZEC price is above its 1-hour VWAP, the 12-period 1-minute EMA is above the 20-period 1-minute SMA, and 1-hour, 15-minute, 5-minute changes and 1-minute velocity are all positive; Rule 5 buys 100 NO on the mirrored negative conditions. Rules 6 and 7 are late entries requiring 0-60 seconds to expiry, spread at or below 0.01, ask within 0.85-0.99, and only the sign of 1-minute velocity, buying 100 YES if positive or 100 NO if negative. Rule 8 cancels all orders once time to expiry reaches zero.
Over the Sep 22 to Oct 22 window, this custom strategy on Kalshi turned in +$510 of simulated profit (+509.7% on its configured risk capital), at a 1.18 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 $36 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 22 to Oct 22). 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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