--- url: "https://predictiontoolkit.com/tools/marketlens/" publisher: "PredictionToolkit" dateModified: "2026-11-02" --- [← Backtesting]() ![Marketlens logo]() Data API # Marketlens: replay a strategy against historical order books Marketlens pairs a historical Polymarket API with a Python replay engine. It provides L2 order books, trades and candles for researchers who need to investigate liquidity and simulated execution beyond a price chart. [**4.0**/ 5Editorial rating]() [Editorial assessment]() [Open Marketlens and plans]() marketlens.trade **Available on:** API **Pricing:** [See plans and costs ↓]() [Screenshots]() [Explore alternatives]() [Backtesting]() [Historical data]() +1 more [Data APIs]() ## Screenshots and interface Product preview1 / 2 Historical-data and backtesting product overview. ## What is Marketlens? The archive starts on March 1, 2026 for covered markets; check each market’s actual coverage rather than treating that date as continuous history for everything. Strategy callbacks expose book updates, trades, fills, rejects and market boundaries, with a simulated account for balances, positions and order controls. Exports and replays need two kinds of budgeting: data allowance and execution assumptions. Request a dry-run quote before a large export, and specify latency, queue and fill behavior before judging a strategy. Inspect effective coverage because a history-store failure can skip a market during a backtest. ### Who it is for - Quant researchers studying execution and depth. - Developers building trade and candle datasets. - Teams planning large historical exports. ## Data and API methods - **Historical market data** Retrieve L2 book depth, trades and candles for covered markets. - **Python callbacks** React to book updates, trades, fills, rejects and market boundaries in your strategy. - **Simulated account controls** Inspect balances and positions, manage orders and use split/merge operations. - **Export estimates** Quote a large export before running it. Downloading the same completed export again does not incur new row charges. - **Cursor pagination** List endpoints use opaque cursors; the SDK can paginate automatically. Cursors expire after 24 hours. - **Explicit execution assumptions** Configure latency, queue and fills rather than assuming every displayed price is tradable. ## Integration tasks ### Start with one market Check coverage, fetch history and replay a small strategy with documented fill and latency settings. Inspect fills and positions before expanding the test. ### Budget a research dataset Choose markets and dates, request a dry-run estimate and compare it with your row allowance. Retain the finished export for repeatable local analysis. ## Pricing and total cost Free supplies 2 million rows once and a recent seven-day market-access window. Pro costs US$39/month; Scale costs US$199/month. ### Free US$0 - 2 million rows once. - Recent seven-day market-access window. ### Pro US$39/month - 5 billion rows/month. - 3,600 requests/minute. - 5 API keys. - Full archive. ### Scale US$199/month - 50 billion rows/month. - 10,800 requests/minute. - 25 API keys. - Full archive. ### Fees and usage costs - Unused paid-plan rows roll over for one month. - A separate daily request-unit budget applies; limits are shared across a user’s keys. ## API access and setup 1. Create an API key in the Marketlens console. 2. Install the marketlens Python package or use REST. 3. Set MARKETLENS\_API\_KEY and request a small market list. 4. Check coverage and quote an export before fetching history. 5. Replay one market and inspect fills and simulated account state. [Read Marketlens documentation]() ## Useful links ### What to check - Check actual coverage per market; the archive start date is not universal continuous history. - Cursors expire after 24 hours. - A history-store failure can skip a backtest market. - Queue, latency and fill settings materially affect replay results. A closer look ## Ratings & reviews Editorial rating **4.0**/ 5 0 user reviews PredictionToolkit editorial ### Our take on Marketlens Explicit allowances and execution assumptions make the research task easier to scope. Archive gaps and markets skipped during a backtest still require inspection in each result. **Assessment criteria:** Task usefulness, documented scope, setup clarity, and access and cost transparency. **Assessment date:** 2026-10-01 **Documentation reviewed:** 2026-10-01 Documentation-based assessment. [How we rate]() ### User reviews 0 The main score switches to user ratings after 5 published reviews. **Be the first to share your experience** A specific example helps the next person choose the right tool. ## Related tools [Browse category]() ![Marketlens Python installation and Strategy quickstart]() ![Marketlens Python SDK logo]()[Marketlens Python SDK]() SDK**4.0**Editorial Compare fast bar experiments with detailed L2 replay, reusing downloaded history between strategy changes. - Language Python Dedicated profile for the Python client and replay package. ![NautilusTrader Polymarket adapter scope and integration-guide entry]() ![NautilusTrader logo]()[NautilusTrader]() SDK**4.0**Editorial Receive native market events and manage signed orders, reconciliation and resolution in one trading framework. - Language Python · Rust A broader programmable research and execution engine. ![PolyOrderbooks historical L2 overview and provider book example]() ![PolyOrderbooks logo]()[PolyOrderbooks]() Dataset**3.5**Editorial Query historical books at 250 ms intervals or replay supported crypto strategies with depth-based fills. - Access Web · API · MCP Another historical-book provider with API, SDK and MCP access. ## Frequently asked questions about Marketlens ### Why use L2 history instead of a chart? Book depth helps study liquidity and fills; a price chart usually summarizes prices. ### Can I estimate row usage first? Yes. Use the documented dry-run quote before starting an export. ### Can I replay my own Python strategy? Yes. The engine supplies strategy callbacks and simulated account controls. In this profile