Size-Bucketed Exit Gates

Starter is $29/month with 30 deploys per week. Upgrade as you grow.

This strategy trades Kalshi's KXBTC15M Bitcoin markets and evaluates every 10 seconds, checking rules top to bottom so only the first match fires each tick. Six exits act on current position size and unrealized profit or loss: with 200 or fewer contracts it sells all if profit exceeds 12 (price between 0.10 and 0.90) or loss goes below -40; with 201 to 600 contracts it sells all if profit exceeds 18 (same price bounds) or loss below -60; with 601 or more it sells all if profit exceeds 50 or loss below -150. Eight entry rules buy 25 contracts, all requiring 7 minutes or less to expiry and a spread of at most 0.015 (0.03 for the last two). They buy yes or no depending on BTC price versus its 1-hour VWAP and 12-period 1-minute EMA, 5-minute change and 1-minute velocity thresholds, price bands, and in two cases EMA versus 20-period SMA or 15-minute high/low levels. Spend is capped at $5,000 per market, with sells not releasing spent budget, and a $100 max loss can halt and liquidate only this strategy's Kalshi inventory.

Kalshi·Sep 29 to Oct 29 UTC·Created 5d ago

Net P&L

+$8,404

Return +2010.5% on Risk Capital

Sharpe

0.79

Return Sharpe

Win rate

81.0%

15848 trades

Max drawdown

-$720

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.