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prediction-market-analysis

Download market and trade datasets, resume collection and run extensible Python analyses that export figures and structured results.

3.5/ 5Editorial rating0 user reviews
View repositorygithub.com
Available on
Python, Parquet, CLI
Pricing
MIT-licensed code and a public downloadable dataset

Screenshots and interface

Product preview1 / 2

Dataset, indexer and analysis framework overview.

What is prediction-market-analysis?

Prediction Market Analysis combines pre-collected Polymarket and Kalshi data with indexers and a framework for research scripts. Its Parquet organization is suited to repeatable empirical work: collect metadata and trades, apply an analysis and save the output as charts or tables.

Who it is for

  • Researchers studying market behavior and trader outcomes
  • Analysts who want a downloadable trade dataset
  • Python developers extending existing collection and analysis scripts

Features and coverage

Available on
Python, Parquet, CLI
Markets & venues
Polymarket, Kalshi
  • Pre-collected data

    Start from the downloadable market and trade archive.

  • Two-venue organization

    Keep Polymarket and Kalshi datasets in separate directories.

  • Collection indexers

    Gather market metadata and trade history from supported APIs and blockchain sources.

  • Resumable progress

    Interrupt and resume collection through saved state.

  • Parquet storage

    Query columnar market, trade and block files.

  • Extensible analyses

    Add scripts within the analysis framework.

  • Multiple outputs

    Generate PNG, PDF, CSV and JSON results.

Workflows with prediction-market-analysis

Reproduce a research figure

Download the dataset, inspect the schema and run the corresponding analysis. Save the input version and filters with the output.

Extend an empirical question

Add a custom analysis using the shared interfaces and output structured intermediate tables alongside the figure.

Refresh a dataset

Select the relevant indexer and resume from saved progress, then record the new coverage boundary.

Pricing and total cost

MIT-licensed code and a public downloadable dataset. Local storage, compute and any collection-provider access are your operating costs.

Getting started

Before you start: The documented compressed archive is 36 GiB; extraction needs additional disk space.

  1. Use Python 3.9 or newer and install with uv.
  2. Allocate disk space for the compressed archive and extracted data.
  3. Read the market and trade schemas.
  4. Download the pre-collected dataset or run a selected indexer.
  5. Run a bounded analysis before expanding the workload.
  6. Keep derived outputs separate from source data.

Useful links

    What to check

    • The documented compressed archive is 36 GiB; extraction needs additional disk space.
    • A trade-history dataset does not provide a full historical resting order book.
    • The packaging command removes the data directory after creating the archive.
    • Dataset cutoffs and coverage differ from a live service.
    • Research results depend on how transfers, open positions and resolved trades are treated.

    A closer look

    Ratings & reviews

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    Editorial rating
    3.5/ 5
    0 user reviews
    Polytoolhub editorial

    Our take on prediction-market-analysis

    Public data and resumable collectors support reproducible analysis. Large storage needs, dataset cutoffs and a destructive packaging command make careful local setup necessary.

    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 prediction-market-analysis

    Can I start without collecting everything myself?

    Yes. A pre-collected archive is provided.

    Can collection resume after interruption?

    The indexers save progress.

    Where do charts and tables go?

    Analyses write their outputs under the output directory.

    Does the archive support exact L2 replay?

    Its core contents are market and trade data; use a dedicated book archive for resting-depth reconstruction.