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Marketlens

Query historical Polymarket L2 books, trades and candles, then replay a Python strategy with explicit fill and latency assumptions.

4.0/ 5Editorial rating0 user reviews
Visit official sitemarketlens.trade
Available on
API, Python
Pricing
Free: $0 · Pro: $39/month · Scale: $199/month

Screenshots and interface

Product preview1 / 2

Historical-book and Python-backtesting product overview.

What is Marketlens?

Marketlens combines a historical data API with a Python backtesting engine. It is useful when a price chart is too coarse: order-book data lets you investigate executable depth, queue assumptions and price impact. The archive begins on March 1, 2026 for covered markets, with coverage checked per market.

Who it is for

  • Quant researchers replaying execution against historical books
  • Developers building datasets from trades and candles
  • Teams budgeting large historical exports

Features and coverage

Available on
API, Python
Markets & venues
Polymarket
Research topics
Backtesting, Historical data
  • Historical L2 data

    Retrieve book depth, trades and candles for covered markets.

  • Python strategy replay

    Handle book updates, trades, fills, rejects and market boundaries in strategy callbacks.

  • Simulated account state

    Access balances, positions, order controls and split/merge operations.

  • Export budgeting

    Run a dry-run quote before requesting a large export; repeated downloads of the same completed export do not consume new row charges.

  • Pagination

    List endpoints use opaque cursors; the SDK can paginate automatically.

  • Execution assumptions

    Control latency, queue and fill settings instead of assuming every displayed price is executable.

Workflows with Marketlens

Backtest one market

Find a covered market, obtain its history and run a small strategy with stated latency and fill assumptions. Inspect fills and positions before scaling to many markets.

Build a research export

Set the market and time range, request a dry-run estimate and compare it with your row allowance. Save the completed export for repeatable local analysis.

Pricing and total cost

Free includes 2 million rows once and a recent seven-day market-access window. Pro is $39/month; Scale is $199/month.

Free

$0

  • 2 million rows once
  • Recent seven-day access window

Pro

$39/month

  • 5 billion rows/month
  • 3,600 requests/minute
  • 5 API keys
  • Full archive

Scale

$199/month

  • 50 billion rows/month
  • 10,800 requests/minute
  • 25 API keys
  • Full archive

Fees and usage costs

  • Paid plans include one-month rollover of unused rows.
  • A separate daily request-unit budget also applies. Limits aggregate across a user’s keys.

Getting started

Before you start: An archive start date does not mean every market has continuous data from that date.

  1. Create an API key in the Marketlens console.
  2. Install the documented marketlens Python package or use REST.
  3. Set MARKETLENS_API_KEY and request a small market list.
  4. Check coverage and obtain a dry-run quote before exporting history.
  5. Run a one-market replay and inspect the resulting fills and account state.

Open documentation

Useful links

    What to check

    • An archive start date does not mean every market has continuous data from that date.
    • Cursors expire after 24 hours.
    • A history-store failure can cause a backtest market to be skipped; inspect effective coverage.
    • Queue, latency and fill assumptions materially affect a replay.

    A closer look

    Ratings & reviews

    Write a review
    Editorial rating
    4.0/ 5
    0 user reviews
    Polytoolhub editorial

    Our take on Marketlens

    A clear data allowance and explicit fill assumptions make historical research comparatively easy to scope. Archive gaps and skipped backtest markets still need inspection in every result.

    Based on documented features, setup and access terms. How we rate

    User reviews 0

    The main score switches to user ratings after 5 published reviews.

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    Frequently asked questions about Marketlens

    How is this different from downloading a price chart?

    L2 history contains the book depth needed to study fills and liquidity, while a chart typically summarizes prices.

    Can I estimate an export before paying in rows?

    Yes. The documented dry-run quote is designed for export budgeting.

    Can I use my own Python strategy?

    Yes. The replay engine exposes strategy callbacks and simulated account controls.