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VWAP Aligned Momentum Rider

Name: VWAP Aligned Momentum Rider
Creator: Turbine

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

The strategy trades Bitcoin 15-minute binary contracts on Kalshi, evaluating every 10 seconds. It enters buy-yes or buy-no positions of 500 contracts when BTC price data from Coinbase meets trend, momentum, spread, and expiration conditions across several rules. It exits all positions via tiered take-profit and stop-loss rules that sell when unrealized profit or loss thresholds are met, with the overall position capped at 2,000 contracts.

Kalshi·May 14 to Jun 11·Created 1mo ago

Net P&L

+$512,552

Return +25627.6% on risk capital

Sharpe

2.11

Return Sharpe

Win rate

68.0%

5546 trades

Max drawdown

-$2,626

Peak to trough

What this backtest shows

Over the May 14 to Jun 11 window, this custom strategy on Kalshi turned in +$512,552 of simulated profit (+25627.6% on its configured risk capital), at a 2.11 Sharpe. It placed 5546 simulated trades and won 68.0% of them — a high hit rate — against shallow worst peak-to-trough drawdown of -$2,626.

Under the hood it simulated 1653 Bitcoin (BTC) markets, closing 1169 winning and 550 losing positions after $34,411 in modeled fees, an average of 184.9 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 Bitcoin (BTC) 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 Bitcoin (BTC) 15-minute strategies backtested on Turbine:

The Best One — 2.15 Sharpe, +$519,067 simulated PnL
The Best One — 2.15 Sharpe, +$518,758 simulated PnL
The Best One — 2.12 Sharpe, +$513,111 simulated PnL
KXBTC15M 10-second spread capture — 3.90 Sharpe, +$3,086 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.