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Research·August 7, 2026·2,355 words·By Bryan Feinberg

How to Backtest Prediction Market Strategies on Kalshi Historical Data

To backtest prediction market strategies on Kalshi historical data, you need to pull event-contract data via Kalshi's API, build a friction-aware simulator, and measure performance with metrics suited to binary markets.

Backtesting Prediction Market Strategies on Kalshi Historical Data

By Infiniti Oracle at Infiniti Markets · Published August 2026 · Edited and reviewed by Infiniti Markets's editorial team

To backtest prediction market strategies on Kalshi historical data, you need to pull event-contract data via Kalshi's API, build a friction-aware simulator, and measure performance with metrics suited to binary markets. At Infiniti Markets, the quantitative analysis tools in Infiniti Terminal are built around exactly this workflow for Polymarket and Kalshi traders.

Why Kalshi's API and historical data matter more than the web interface for serious backtesting

Kalshi's web interface is a trading surface. It shows current prices and recent fills, but it hides everything a rigorous backtest actually needs: partial fill sequences, bid-ask spread history, order-book depth at the moment your signal fired, and the settlement timing of resolved contracts.

Kalshi partitions exchange data into live and historical tiers to keep the live API fast. Live endpoints return current and recent data, while older data sits behind a separate set of historical endpoints. That split is invisible in the UI. If you're clicking through the web interface to study a past election contract, you're seeing a summary, not the microstructure.

What the UI hides that the API exposes

Kalshi's public API handles live data well, but it doesn't expose historical order-book snapshots, only trades and candles. Third-party services continuously poll Kalshi's live order book and persist the bid/ask depth so you can query any past state of any market. That depth is what separates a real backtest from a toy one: without it, your slippage estimates are guesses.

Some historical information is retrievable through the API, but the data is fragmented across endpoints and requires stitching together trades, markets, and price history by hand, which becomes time-consuming fast. Most traders don't feel that friction until they've already lost a weekend to pagination loops.

Kalshi vs. native web interface: friction comparison

Dimension Kalshi Web Interface Kalshi API + Custom Tooling
Order-book depth history Not available Available via third-party capture
Partial fill visibility Hidden Exposed via /portfolio/fills
Batch contract queries Manual, one at a time Cursor-paginated bulk pulls
Settlement metadata Summary only Full event-contract resolution data
Slippage modelling Impossible Computable from bid-ask snapshots
Latency measurement Not visible Timestamp-level fill data

Kalshi secured CFTC approval in November 2020 and has grown rapidly, with event contracts totalling $52 billion as of March 2026. That scale means the data is rich, but the US regulatory compliance framework also means the API is the only legitimate, institutional-grade path to it.

How to set up a backtesting environment that mirrors real Kalshi execution conditions

The goal is a simulator that makes the same mistakes live trading would make, rather than an optimistic replay that ignores friction.

Step 1: Pull and structure the contract data

Kalshi's historical API covers markets, market_candlesticks, trades, orders, and market_positions. An event contract, the binary YES/NO instrument on a real-world outcome that settles to $1 or $0 at resolution, is the fundamental unit you're working with. Start with /events to pull event-level metadata, then nest into /markets for each contract under that event. Pagination is cursor-based: every list endpoint accepts cursor and limit, and signals completion by returning an empty cursor. Rate limiting uses a token-bucket model with five tiers, from Basic at 200 tokens/second up to Prime at 4,000 tokens/second.

For a concrete starting point, pull 3 to 6 months of a single event series (say, monthly CPI contracts), requesting candlestick data at 1-hour resolution and trade-level fills separately. Align timestamps to UTC, flag any gaps where the market was halted, and mark the settlement timestamp explicitly. Contract rollovers, where one monthly contract expires and the next opens, need a gap day in your time series or your returns will show a phantom spike.

Step 2: Build the friction layer

A backtest that ignores unresolved markets is subject to survivorship bias, where your historical universe skews toward contracts that resolved cleanly. Per TurbineFi's aggregation of Eurekahedge hedge-fund survivorship research, this bias can inflate returns by roughly 2% per year and Sharpe ratios by up to 0.5. Include every contract in your universe, resolved or not. Then model three friction sources explicitly:

  • Bid-ask spread: use the mid-price for signal generation but fill at the ask when buying and the bid when selling. On thinner Kalshi contracts, this spread can be 3 to 5 cents on a 50-cent contract.
  • Partial fills: Kalshi's order book is shallower than Polymarket's for many event categories. If your position size exceeds the resting depth, model a partial fill at the first price level and a worse fill for the remainder.
  • Submission latency: add a realistic delay (50 to 150 ms) between signal generation and order submission. Strategies that look profitable at zero latency often break at realistic latency on fast-moving contracts.

Step 3: Run the first backtest pass

With clean data and a friction model in place, run a single pass: generate your signal (a mean-reversion trigger when the contract price deviates more than 8 cents from a rolling 4-hour average, for instance), apply the friction layer, and record every simulated fill with its timestamp, price, and size. That first pass will almost certainly look better than live trading. The next section covers why, and what to do about it.

Platforms with an integrated backtesting engine, such as TurbineFi for Polymarket or Infiniti Terminal for both Kalshi and Polymarket, can run this pipeline without building the data infrastructure from scratch. That matters if data-pipeline engineering isn't where your edge lives.

Worked example: one simulated trade through the friction layer

Signal fires on a monthly CPI contract. The book shows 52¢ bid / 55¢ ask, with 120 contracts resting at the ask and 80 contracts available at the next level (57¢). You want 200 contracts.

  • Fill 1: 120 contracts at 55¢ = $66.00
  • Fill 2: 80 contracts at 57¢ = $45.60
  • Total cost: $111.60 for 200 contracts, average entry 55.8¢
  • Mid-price assumption would have priced the same position at 200 × 53.5¢ = $107.00
  • 100 ms latency consumed the price improvement you were targeting; the next level was already printing before your order landed
  • Round-trip cost vs. mid: $4.60 on entry, mirrored on exit, so roughly $9.20 total friction on a position where your signal expected $8.00 of mean-reversion profit

Net expectancy at mid-price: positive. Net expectancy after realistic fills: negative. That gap is why the friction layer has to be built before you run a single pass, not after.

What backtesting metrics actually predict live Kalshi performance, and which ones mislead?

Win rate is nearly useless here. A strategy that wins 70% of trades on a 55-cent contract but loses on the 30% that include election-night volatility spikes can still blow up. The shape of your losses matters more than their frequency.

The metrics that actually travel to live trading

Profit factor above 1.5, positive expectancy per trade, and a Sortino ratio above 1.0 are the metrics most correlated with live durability. Sharpe ratio matters, but because it penalises upside volatility equally with downside, the Sortino ratio gives a more honest read on asymmetric strategies. Weight it by contract liquidity: a strong Sortino on a high-volume contract carries more meaning than the same number on a thinly traded one.

Maximum drawdown under event-shock conditions is the metric most traders skip and most regret skipping. A drawdown that took 14 months to recover is a psychological and financial event, and most traders abandon the system before it comes back. Stress-test your drawdown by replaying your strategy through the 3 to 5 sharpest single-day price moves in your dataset.

The Polymarket-to-Kalshi trap

A strategy validated on Polymarket data does not transfer cleanly to Kalshi execution. Polymarket's order books are deeper for many political and macro contracts, so slippage estimates built on Polymarket fills will be optimistic when you deploy on Kalshi. Use Polymarket data for correlation logic only. Prediction markets have unique liquidity characteristics, and event-specific factors may not show up in historical data, so treat any cross-platform backtest as directional evidence rather than a deployment signal.

FAQs

How far back does Kalshi's historical API actually go?

Kalshi's historical trade data goes back to July 2021, covering 7.68 million unique markets and over 72 million historical trades. For events before that date, no official API data exists, and you'll need to rely on third-party archives or academic datasets, accepting that coverage will be incomplete.

Can I use Polymarket historical data as a proxy for Kalshi backtests?

Only for correlation logic. Polymarket and Kalshi often price similar events within a few cents of each other, so Polymarket data can help you validate whether a signal has predictive power. Fill assumptions built on Polymarket's deeper liquidity are too optimistic for Kalshi's thinner order books, and fee structures differ enough to shift your net return series materially.

How should I handle contract splits and settlement delays in my dataset?

Mark every settlement timestamp explicitly and add a one-period gap in your return series at rollover. Settlement delays on Kalshi, where a contract's outcome is known but the market hasn't officially resolved, create a window where prices can sit at 99 cents for hours. If your strategy holds through resolution, model that window as a zero-return period rather than a fill opportunity.

Why does my backtest look so much better than my live results?

The most common culprit is fill optimism. Backtests often assume you fill at the mid-price, but live trading fills you at the ask when buying. On a 3-cent spread, that's a 6% round-trip cost on a 50-cent contract. Model every fill at the aggressive side of the spread, add realistic submission latency, and include unresolved markets in your universe to correct survivorship bias.

Should I backtest on 6 months or 12 months of Kalshi data?

Six months is a reasonable minimum for event categories with frequent contract cycles (crypto, economic data). For political or one-off event contracts, 12 months gives you at least one full electoral or policy cycle to stress-test against. A 3-month backtest with realistic slippage will outperform a 2-year backtest run at mid-price, because the quality of your friction model matters more than the length of your window.

Is 2–4 weeks of paper trading on Kalshi worth doing before deploying real capital?

Yes, and it's the step most people skip. Run it on the actual contracts you plan to trade: a live paper-trading period exposes fill latency, rate-limit throttling, and order-book depth in real time, none of which a backtest fully replicates. Two to four weeks will surface execution problems that months of backtesting missed.

Does an integrated backtesting engine replace the need for custom API code?

For most traders, no. An integrated backtesting engine like the one in Infiniti Terminal handles the data pipeline and simulation layer, but you still need to define your signal logic and friction assumptions. What it removes is the infrastructure work: pagination loops, timestamp alignment, and database management, freeing you to focus on strategy rather than plumbing.

Key takeaways

  • The web interface hides order-book depth, partial fill data, and settlement timing; pulling that granularity requires the API.
  • Build your friction layer before running a single pass: bid-ask spread filled at the aggressive side, partial fills against realistic book depth, and 50 to 150 ms submission latency baked in.
  • Sortino ratio weighted by contract liquidity is a stronger predictor of live Kalshi performance than raw win rate or Sharpe, because binary markets reward asymmetric upside rather than smooth volatility.
  • Polymarket data can validate signal logic but will produce dangerously optimistic slippage estimates if you use it for Kalshi execution modelling.
  • Traders who want to skip the data-pipeline build and go straight to strategy testing should start with a prediction market trading terminal that combines an integrated backtesting engine with live execution on both Kalshi and Polymarket.

If you want to follow along without building the data pipeline yourself, Infiniti Terminal's free plan gives you access to the quantitative analysis workspace and live educational sessions on backtesting and risk management, a practical next step after reading this guide.

Sources

About the author

Infiniti Oracle at Infiniti Markets, who leads Infiniti Markets's work in prediction market trading terminal.

Sourcing: each external figure links back to its original study. Last reviewed August 2026.

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Disclaimer: Portions of this content may be generated with the assistance of AI and are provided for educational and informational purposes only. This content does not constitute financial, investment, legal, or tax advice. Fee schedules, market availability, and eligibility rules may change without notice. Always confirm current terms, fees, and trading requirements directly with each exchange, broker, or trading venue before making any trading or investment decisions.