One-Shot Discount Clock Exit

Starter is $19/month with 10 deploys per week. Upgrade as you grow.

This Kalshi strategy trades the KXBTC15M series and re-evaluates every 10 seconds, with a position limit of 2 contracts and a YES price range of 0.10 to 0.90. Rules are checked top to bottom and the first match ends that tick: Rule 1 sells everything when position size is above 0 and time to expiry is 2 minutes or less; Rule 2 sells the YES position when YES size is above 0 and the YES best bid is at least 0.75; Rule 3 buys 2 YES contracts when position size is 0, more than 8 minutes remain, and the YES best ask is at most 0.35. Entry orders are capped at one per market, and a $5 max loss can halt and liquidate only this strategy's attributed Kalshi inventory.

Kalshi·Sep 19 to Oct 19 UTC·Created 3d ago

Net P&L

-$126

Return -6319.0% on Risk Capital

Sharpe

-1.30

Return Sharpe

Win rate

33.1%

2456 trades

Max drawdown

-$130

Peak to Trough

A few notes on these numbers

Methodology: Backtests replay supported historical market data, usually a 30-day Studio window, and evaluate strategy rules on the available cadence. Signals can fill no earlier than the next tradable step. Taker fills use top-of-book prices or available L2 depth, with Kalshi-style fees where modeled. Queue position, latency, and future liquidity are not fully modeled. Simulated P&L is net of modeled fees over the window.
Starting Capital: Kalshi backtests use the strategy's configured max position as the risk-capital denominator. Return is computed from that configured capital, while total P&L is still shown in dollars.
Past Performance: Backtested results are hypothetical. They are derived from historical data and do not reflect live execution risks like slippage beyond the orderbook, partial fills, API latency, or venue outages. Past performance does not guarantee future results.
Not Financial Advice: Nothing on this page is investment, legal, or tax advice. Prediction markets carry real risk of loss. Only trade with money you can afford to lose, and do your own research before deploying any strategy live.