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KXETH15M Multi-Condition Trend & Expiry Momentum with Tiered PnL

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Trades the KXETH15M market on Kalshi, evaluating every 10 seconds. It enters buy-yes or buy-no positions of 50 contracts when Ethereum price and momentum signals align with VWAP, moving averages, and tight spreads, also considering time remaining. It exits entirely at tiered take-profit or stop-loss levels based on position size, and limits total position to 100 contracts.

Kalshi·May 14 to Jun 11·Created 8d ago
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Net P&L
+$20,231
Return +20230.7% on risk capital
Sharpe
2.23
Return Sharpe
Win rate
69.5%
3565 trades
Max drawdown
-$182
Peak to trough

What this backtest shows

Over the May 14 to Jun 11 window, this custom strategy on Kalshi turned in +$20,231 of simulated profit (+20230.7% on its configured risk capital), at a 2.23 Sharpe. It placed 3565 simulated trades and won 69.5% of them — a high hit rate — against shallow worst peak-to-trough drawdown of -$182.

Under the hood it simulated 1653 Ethereum (ETH) markets, closing 988 winning and 433 losing positions after $1,747 in modeled fees, an average of 118.8 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.

Market and data window

This backtest runs against Ethereum (ETH) markets on Kalshi's 15-minute series across 30 days (May 14 to Jun 11). These are short-horizon contracts that open and settle on a fixed 15-minute cadence, so the strategy is measured across many independent events rather than one long trend. Rules are evaluated once per 15-minute 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 Ethereum (ETH) 15-minute strategies backtested on Turbine:

  • The Best One - ETH — 2.19 Sharpe, +$20,184 simulated PnL
  • The Best One - ETH — 2.17 Sharpe, +$20,033 simulated PnL
  • The Best One - ETH — 2.18 Sharpe, +$14,212 simulated PnL
  • ETH 15m Tight Spread Capture 05 — 2.53 Sharpe, +$1,308 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.