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Dual-Tier Momentum Expiry Entry

Starter is $49/month with 30 deploys per week. Upgrade as you grow.

This strategy trades Kalshi's KXZEC15M series, checking conditions every 10 seconds and capping positions at 100 contracts, with YES prices restricted to 0.83–0.98. Rules run top to bottom and the first match ends that tick. Rule 1 cancels all resting orders when orders exist but position size is zero. Rules 2 and 3 sell the held YES or NO position if its best bid falls to 0.50 or below. Rules 4 and 5 enter only when flat, with 60 seconds or less to expiry, a spread of 0.03 or less, and an ask between 0.83 and 0.99: buy YES if ZEC price exceeds its 1-hour VWAP, the 12-period 1-minute EMA exceeds the 20-period 1-minute SMA, and 1-hour, 15-minute, 5-minute changes and 1-minute velocity are all positive; buy NO on the mirror-image negatives. Rules 6 and 7 enter only when flat, with 0 to 60 seconds to expiry, a spread of 0.01 or less, an ask between 0.83 and 0.99, and a positive (YES) or negative (NO) 1-minute velocity. Rule 8 cancels all orders once time to expiry reaches zero or below.

Kalshi·Sep 22 to Oct 22 UTC·Created 5d ago
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Net P&L
+$504
Return +503.9% on Risk Capital
Sharpe
0.94
Return Sharpe
Win rate
98.5%
132 trades
Max drawdown
-$78
Peak to Trough

What This Backtest Shows

Over the Sep 22 to Oct 22 window, this custom strategy on Kalshi turned in +$504 of simulated profit (+503.9% on its configured risk capital), at a 0.94 Sharpe. It placed 132 simulated trades and won 98.5% of them — a high hit rate — against shallow worst peak-to-trough drawdown of -$78.

Under the hood it simulated 2665 prediction-market markets, closing 65 winning and 1 losing positions after $41 in modeled fees, an average of 4.4 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.

Market and Data Window

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.

Related Backtests

Compare other prediction-market short-horizon strategies backtested on Turbine:

  • POD — 1.21 Sharpe, +$528 simulated PnL
  • POD — 0.98 Sharpe, +$517 simulated PnL
  • POD — 1.18 Sharpe, +$510 simulated PnL

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.