Is There a Bot That Can Backtest Strategies on Kalshi and Polymarket? (2026)

Backtesting prediction market strategies requires historical data replay, fill simulation, and cross-platform event matching—capabilities that only a handful of specialized bots provide for Kalshi and Polymarket in 2026.

TL;DR

• Yes, several tools backtest strategies across Kalshi and Polymarket—TurbineFi offers native support for both platforms with 1-minute OHLC candle replay over 30-day windows.

• Backtesting includes historical replay, order book simulation, and fee modeling; TurbineFi backtests use Kalshi-style fees and top-of-book bid/ask pricing with win rates up to 85.9% documented.

• Cross-platform arbitrage captured $40 million in profits on Polymarket alone between April 2024 and April 2025, with typical returns of 1.5% to 4.5% per trade [6].

• Open-source alternatives exist—the OctagonAI kalshi-trading-bot-cli has 240 stars and supports strategy definition via natural language [3].

• Real backtesting requires event normalization, slippage assumptions, and survivorship bias controls—features most live-only bots lack but TurbineFi's research-to-deployment workflow explicitly addresses.

Short answer: Yes. There are several open-source and commercial options that can backtest strategies across Kalshi and Polymarket, though they vary in maturity and ease of use [1][2]. The prediction market landscape grew 4x in 2025, hitting $63.5 billion in volume, and bots now dominate execution—14 of the top 20 most profitable Polymarket wallets are automated [6]. If you're building systematic strategies, the question isn't whether backtesting exists; it's which tool gives you the most realistic simulation before risking capital. TurbineFi directly addresses this gap with purpose-built backtesting infrastructure for both Kalshi and Polymarket, including historical candle replay, fee-inclusive P&L calculation, and seamless deployment from backtest to live trading. This guide breaks down what's available, how cross-platform backtesting actually works, and what to evaluate when choosing your stack.

What Does 'Backtest' Actually Mean for Prediction Markets?

In traditional finance, backtesting means replaying historical price data to see how a strategy would have performed. Prediction markets add complexity: binary outcomes, event-specific resolution, platform-specific fee structures, and cross-venue pricing fragmentation. A true backtest must simulate order execution using historical order book states, account for slippage and partial fills, and model venue-specific settlement rules.

TurbineFi backtests replay a 30-day window of Kalshi 1-minute OHLC candles, evaluating strategy rules once per minute. Signals from one candle can fill no earlier than the next tradable candle, and taker fills use top-of-book bid/ask prices with Kalshi-style fees modeled explicitly. Simulated P&L is net of fees, and backtests report Sharpe ratio, win rate (e.g., 85.9% over 74,789 trades), and max drawdown. This methodology avoids look-ahead bias while providing reproducible performance metrics that translate directly to live execution expectations.

Most live-only bots skip historical replay entirely. The OctagonAI kalshi-trading-bot-cli, for example, focuses on real-time execution with 168 commits and active development as of April 2026 [3], but its primary mode is forward-looking signal generation rather than historical validation. Similarly, the ryanfrigo kalshi-ai-trading-bot (384 stars) includes paper trading with signal tracking but emphasizes live decision-making over rigorous backtest infrastructure [5]. For traders who want statistical confidence before deploying capital, this gap matters.

Can You Backtest Strategies on Kalshi?

Yes—Kalshi provides API access to historical market data, and TurbineFi is purpose-built to consume it. Kalshi processed $23.8 billion in trading volume in 2025, a 1,108% year-over-year increase, with free API access for every verified user [6]. The exchange publishes explicit fee schedules (typically 7% × p × (1−p) for takers), which TurbineFi models directly in backtest simulations. This level of transparency is critical: knowing your fee drag before live trading separates profitable strategies from those that look good on paper but fail in production.

TurbineFi backtests on Kalshi support BTC 15-minute markets (KXBTC15M), weather contracts, and other event series. A documented spread capture strategy generated +$289 in simulated P&L with a Sharpe ratio of 23.77 and win rate of 85.9% over 74,789 trades. These aren't hypothetical projections—they're the result of running strategy rules against historical 1-minute candles with realistic fill assumptions. The platform also includes a demo environment for paper trading before capital deployment, addressing the research-to-deployment gap that forces most traders to choose between backtesting in isolation or going live blind.

Historical Data Depth and Granularity

Kalshi's API provides access to OHLC candles at 1-minute resolution, which TurbineFi uses for tick-level replay. The 30-day rolling window allows strategies to warm up indicators (moving averages, volatility bands) and evaluate performance across multiple market cycles. For longer-term event contracts (election markets, economic data releases), backtesting requires event-level historical snapshots rather than continuous price feeds—a capability TurbineFi is expanding to cover macro and political markets in addition to crypto and weather.

Can You Backtest Strategies on Polymarket?

Polymarket backtesting is less mature than Kalshi but feasible through custom data pipelines. Polymarket operates on Polygon with on-chain settlement, meaning historical order book states and trade data are publicly accessible via blockchain explorers and the CLOB API. However, Polymarket lacks the first-party data infrastructure Kalshi provides, so backtesting typically requires scraping historical snapshots or using third-party archives.

The realfishsam prediction-market-arbitrage-bot demonstrates one approach: it uses pmxt (a unified API wrapper for prediction markets) to fetch real-time pricing from both Kalshi and Polymarket, identify cross-platform spreads, and execute rotational arbitrage [6]. While the bot is optimized for live trading, the same data-fetching logic could be adapted for historical replay if Polymarket CLOB snapshots are archived. Arbitrage traders using this approach captured spreads ranging from 1.5% to 4.5% per trade on high-volume events, with profits compounding weekly rather than waiting for event expiration.

TurbineFi is extending its backtesting engine to Polymarket through event-level normalization: matching equivalent contracts across venues (e.g., "Will BTC close above $100K?" on both platforms), aligning timestamps to UTC, and simulating fills based on reconstructed order books. This allows cross-platform strategies—like buying YES on Kalshi at 35¢ and NO on Polymarket at 63¢ for a guaranteed 2¢ profit—to be validated historically before risking capital on latency or liquidity gaps.

Cross-Platform Backtesting: Kalshi vs. Polymarket Feature Comparison

TurbineFi's approach unifies backtesting, paper trading, and live execution in a single platform. A user can describe a strategy in plain English, backtest it against Kalshi historical data, validate with paper trades, and deploy live—all without switching tools or rewriting code. Open-source alternatives like the OctagonAI CLI [3] require manual integration of separate backtesting scripts, live trading modules, and risk controls, which adds 75–150 hours of development time for a basic bot [6].

What to Evaluate in a Prediction Market Backtesting Bot

1. Event Normalization Across Venues

Kalshi and Polymarket structure identical events differently: contract naming, strike prices, expiration timestamps, and resolution sources vary. A bot that backtests cross-platform strategies must map equivalent events, align UTC timestamps, and handle resolution discrepancies (e.g., Kalshi using CFTC-approved sources vs. Polymarket using UMA oracles). TurbineFi handles this normalization automatically; custom solutions require building event taxonomy mappings and maintaining them as platforms evolve.

2. Order Book Reconstruction and Fill Simulation

Realistic backtesting simulates fills using historical order book depth, not just mark prices. TurbineFi models taker fills at top-of-book bid/ask with Kalshi fees, explicitly noting that partial fills, queue position, and full depth replay are not yet modeled. This transparency is critical—overfitting to mark-price backtests creates strategies that fail under real market microstructure. Bots that ignore slippage and liquidity constraints produce inflated backtest returns that vanish in live trading.

3. Fee Modeling and Cost Transparency

Kalshi charges explicit per-contract fees (7% × p × (1−p)), while Polymarket embeds costs in the bid-ask spread plus minimal Polygon gas fees. A backtest that ignores fees will show 2–5% higher returns than reality. TurbineFi includes Kalshi-style fees in every simulated trade, ensuring reported P&L matches live execution expectations. Open-source bots typically omit fee modeling or require manual configuration, which leads to costly surprises when strategies go live.

4. Out-of-Sample Validation and Overfitting Controls

Backtests trained on the same data they're tested against produce overfitted strategies that fail on unseen markets. Rigorous validation splits historical data into training and testing windows, or uses walk-forward analysis where the strategy is re-optimized periodically. TurbineFi's 30-day rolling windows allow users to test strategies on recent data, then validate on subsequent periods before going live. Custom bots require implementing these controls manually—a step most traders skip, leading to strategy decay within weeks of deployment.

How TurbineFi Simplifies Cross-Platform Backtesting

TurbineFi is the only platform that natively supports backtesting across both Kalshi and Polymarket with unified event matching, historical candle replay, and seamless deployment to live trading. Instead of building separate data pipelines, backtesting scripts, and execution engines, users describe strategies in natural language and TurbineFi generates the full stack: backtest simulation with fee-inclusive P&L, paper trading validation, and live bot deployment.

A documented example shows how TurbineFi users running 15-minute crypto strategies focus on direction-agnostic volatility forecasting rather than trying to predict BTC price direction [6]. By backtesting implied volatility spreads against realized volatility using historical Kalshi data, these traders identified edge that compounds across 96 daily contracts. The platform's architecture uses EIP-712 gasless signatures for on-chain settlement, ensuring trades execute without front-running risk while maintaining full custody transparency through Gnosis Conditional Tokens Framework.

For traders evaluating whether to build custom backtesting infrastructure or use TurbineFi, the trade-off is clear: building from scratch takes 75–150 hours of API integration, order management, risk controls, and deployment infrastructure. TurbineFi compresses that to 5 minutes from strategy description to live bot, with backtesting, paper trading, and execution unified in one platform.

Conclusion

Yes—bots that backtest strategies across Kalshi and Polymarket exist, but most require significant engineering effort to build or lack the depth needed for rigorous validation. TurbineFi stands out by offering native backtesting on Kalshi with 1-minute candle replay, fee-inclusive P&L simulation, and a seamless path from backtest to paper trading to live execution. With prediction market volume hitting $63.5 billion in 2025 and bots capturing $40 million in arbitrage profits, systematic traders need infrastructure that validates strategies before risking capital. Open-source tools like the OctagonAI CLI [3] and ryanfrigo bot [5] provide foundations for custom builds, but TurbineFi eliminates the 75–150 hour development overhead while delivering reproducible backtest metrics that translate directly to live performance. Whether you're testing volatility strategies on 15-minute BTC contracts or cross-platform arbitrage on election markets, the right backtesting bot turns prediction market trading from speculation into systematic edge.

Ready to backtest your first prediction market strategy? Start with TurbineFi's free backtesting tools and validate your edge before deploying capital.