Supported Kalshi markets.
Turbine can replay candle history where Kalshi data is available for the markets or series your strategy targets. Coverage varies by market and window.
Product · Backtesting
30 days of Kalshi. Replayed in seconds.
Turbine can replay supported Kalshi and Polymarket market history with Kalshi fee modeling and explicit fill assumptions. The same strategy spec is used for replay and live deployment, but live fills can differ.
Window30 days
Granularity1-min candles
SpeedSeconds
Turbine pulls Kalshi's public candlestick endpoints across the markets your strategy targets and replays them inside the Studio session.
One-minute candles.
Turbine can use OHLC plus best bid and ask snapshots, sourced from Kalshi's public candlestick endpoints.
Default 30-day window.
Studio runs a 30-day backtest by default. Actual replay coverage depends on market history and available archive data.
No CSV downloads.
No notebook setup. Turbine uses the same DSL spec for the live runner and the replay.
Most backtests finish in seconds.
Turbine uses a Go engine that runs in process and skips DataFrame round-trips and database hops. Exact time depends on how many markets the strategy touches and how dense the candle history is for those markets.
One screen. Equity curve, drawdown, trade log. Headline metrics in the strip below.
Headline numbers, fill quality, trade log.
PnL in dollars and as percent. Sharpe, max drawdown, win rate, total trades. Per-fill maker / taker classification, slippage versus signal price, fees applied. Trade log exports to CSV.
Turbine simulates next-bar or depth-aware fills, applies Kalshi's fee schedule where modeled, and reports model slippage so you can judge how much the result depends on execution assumptions.
Strategy fills.
Turbine simulates next-bar fills using top-of-book prices or available L2 depth, with maker / taker classification per fill where supported.
Marketable orders.
Turbine models marketable orders against the next bar or available depth. Reported slippage is model-based, not a promise of live execution.
Kalshi fees.
Turbine can apply Kalshi's fee schedule per fill. Reported PnL is net of fees, not gross.
Forced exits at close.
Turbine can flatten open positions at market close so PnL is not left ambiguous.
turbinefi
Notebook + cron
Generic AI wrapper
CSV downloads
Default 30-day supported history
Yes
Partial
No
Partial
Backtest in seconds
Yes
No
No
No
Same strategy spec live and replay
Yes
No
No
No
No infra to run
Yes
No
Partial
No
Kalshi fee model included
Yes
Partial
No
No
Starter
$19/ month
Start at $19/month with 10 deploys per week. Backtests are included, subject to normal abuse-prevention rate limits.
- Starter · BasicBacktests included, cloud deployments, every supported venue.
- $19 · $99/mo10 · 100 deploys/week
- Pro400 deploys per week with concierge support.
- $199/mo
Where does the historical Kalshi data come from?
Turbine pulls Kalshi public candlestick data and PMXT archive data where configured for the markets and series your strategy targets. The default Studio window is 30 days, but actual coverage varies by venue, market, and available archive history.
How fast is the turbinefi backtester?
Most 30-day backtests finish in seconds. Turbine uses a Go engine that runs in process and skips DataFrame round-trips and database hops. The exact time depends on how many markets and how many candles the strategy touches.
Can I export backtest results?
Yes. The backtest panel exports the trade log to CSV. The full equity curve and per-trade metrics are visible in the Studio.
What is included in a turbinefi backtest fill model?
Turbine includes one-minute Kalshi candles where available, PMXT L2 archive replay where covered, next-bar/top-of-book or depth-aware fill assumptions, Kalshi fee modeling, and close/settlement handling. Reported Kalshi PnL is net of modeled fees.
Does turbinefi support custom data feeds in a backtest?
Turbine can use Coinbase prices and NWS forecasts in the backtester today as edge series. Other feeds are on the roadmap. See the Edge Data Feeds page for what is live.