---

name: prediction-market-analysis description: "Route analysis, indexing, and data-ops guidance for the prediction-market-analysis repo." metadata: disco-role: operating disable-model-invocation: true license: MIT

prediction-market-analysis

This skill covers the repo that analyzes and collects Kalshi and Polymarket market data. The repo is centered on three user-facing workflows:

• analyze for calibration, returns, volume, category, and comparison studies.
• index for Kalshi and Polymarket market/trade/block backfills.
• setup / package for dataset download, archive management, and local data packaging.

Environment

• Preferred setup: uv sync --frozen --group dev from the repo root.
• Minimal import smoke: uv run python -c "import src; print(src.__file__)".
• The bundled helper scripts assume the repo runtime dependencies are available.

First stop

• Read references/cli-reference.md for command names and command-line behavior.
• Read references/data-layout.md for directory layout, parquet inputs, and env vars.
• Read references/api-reference.md for the public Python classes and helper functions.
• Read references/troubleshooting.md when a workflow fails, returns empty data, or cannot find files.

When to use each sub-skill

sub-skills/analysis/

Use when the task is about:

• Running or interpreting an existing analysis.
• Comparing Kalshi vs Polymarket calibration, returns, or volume.
• Understanding Analysis, AnalysisOutput, ChartConfig, or Analysis.load() behavior.
• Adding a new analysis class under src/analysis/.

Typical signals: win_rate_by_price, mispricing, maker_taker, volume_over_time, meta_stats, polymarket, calibration, chart, output/.

sub-skills/indexing/

Use when the task is about:

• Backfilling Kalshi or Polymarket markets or trades.
• Resuming interrupted cursor-based collection jobs.
• Fetching Polygon block timestamps or legacy FPMM trades.
• Understanding KalshiClient, PolymarketClient, PolygonClient, or Indexer.load() behavior.
• Adding a new indexer class under src/indexers/.

Typical signals: kalshi_markets, kalshi_trades, polymarket_markets, polymarket_trades, polymarket_blocks, polymarket_fpmm_trades, cursor, backfill, POLYGON_RPC, resume.

sub-skills/data-ops/

Use when the task is about:

• Downloading the prebuilt dataset or restoring the data/ directory.
• Packaging the dataset into data.tar.zst.
• Installing or diagnosing local host tools such as zstd, aria2c, curl, or wget.
• Understanding the repo's make setup and make package workflows.

Typical signals: make setup, make package, data.tar.zst, download.sh, install-tools.sh, zstd, aria2c, curl, wget.

Shared facts

• The package distribution is prediction-market-data; the source package tree is rooted at src/.
• Analysis.load() and Indexer.load() default to src/analysis and src/indexers relative to the current working directory.
• The repo uses Parquet data under data/kalshi/ and data/polymarket/, plus CSV/JSON/PNG/PDF/GIF outputs under output/.
• main.py dispatches analyze, index, and package.
• make setup is a convenience wrapper for host-tool installation plus dataset download.
• main.py package / make package create data.tar.zst; the code does not delete the source data/ directory.

Bundled helpers

• scripts/catalog.py lists available analyses and indexers from the repo checkout when run through the repo environment.
• scripts/run_analysis.sh runs the analysis CLI from the repo root.
• scripts/run_index.sh runs the indexing CLI from the repo root.
• scripts/package_data.sh runs dataset packaging from the repo root.

Shared references

• references/analysis-catalog.md for the full analysis catalog.
• references/indexing-catalog.md for the full indexer catalog and cursor files.
• references/data-ops.md for download, extraction, and packaging details.
• references/troubleshooting.md for cross-cutting failures.

If a request names missing data, a cursor problem, a Polygon RPC issue, a download failure, or a headless plotting problem, read the matching troubleshooting section before guessing.