kalshieye

Python client for Kalshi Eye flat files. Kalshi: daily Parquet for the order book (book_l2), 1-second top-10 book snapshots (book_snap_1s), top of book (bbo), one-minute bars (bbo_1m), trades, crypto spot (spot), and the reference tables (markets, settlements, series, incidents). Polymarket: polymarket_l2, polymarket_trades, polymarket_fills, polymarket_markets. Limitless: limitless_l2, limitless_markets. Chainlink prices: chainlink. replay_book rebuilds Kalshi books from book_l2.

pip install "kalshieye[pandas]"      # or kalshieye[polars], or plain kalshieye
export KALSHIEYE_API_KEY=clab_...     # create one under API keys

from kalshieye import Client, replay_book

kx = Client()
kx.datasets()                                             # schemas, coverage, required plan
kx.estimate(["bbo_1m", "trades"], "2026-10-12", "2026-10-18", series=["KXBTC*"])

kx.download(["bbo_1m", "trades"], "2026-10-12", "2026-10-18",
            series=["KXBTC*"], dest="data", progress=True)

df = kx.load("bbo_1m", "2026-10-12", "2026-10-18", series="KXBTC15M")
fills = kx.load("trades", "2026-10-12", "2026-10-12", tickers=["KXBTC15M-26OCT121545-45"])

# Rebuild the book from book_l2 rows
book = kx.load("book_l2", "2026-10-12", "2026-10-12", tickers=["KXBTC15M-26OCT121545-45"])
for ts, ticker, bids, asks in replay_book(book):
    ...

# One event in one zip: book, snapshots, trades, spot, metadata.json, settlement.json
kx.bundle("KXBTC15M-26OCT121545")

# CSV.gz copy of a single file
f = next(kx.files("trades", "2026-10-12", "2026-10-12", series="KXBTC15M"))
kx.csv(f["key"])

# S3 access (paid plans): a read-only key for aws s3 sync, rclone, DuckDB, boto3
key = kx.create_s3_key("laptop")
print(key["snippets"]["aws_cli"])

• Files land under dest in the lake layout (bbo_1m/date=2026-10-12/series=KXBTC15M/...parquet), so DuckDB, Polars, and Spark can read the folder as a hive-partitioned dataset. spot is partitioned by asset= instead of series=.
• Every file is checked against its sha256. Files already on disk are skipped and do not count toward your monthly download allowance.
• series accepts wildcards (KXBTC*). categories filters by archive category (crypto, politics, ...). tickers picks the series files holding those markets, and load keeps only their rows.
• bundle, csv, and replay are built on the server the first time; the client waits for them.
• Errors raise kalshieye.ApiError. Use err.code to tell plan limits (plan_required, history_limited, download_quota) apart.

Without an account

replay_book works on any book_l2 rows, including the free one-day sample on Hugging Face (KalshiEye/kalshi-order-book-sample), which DuckDB reads in place:

# pip install "kalshieye[pandas]" duckdb
import duckdb
from kalshieye import replay_book

# Bills vs Rams, YES if Buffalo wins: every book change up to 11 PM Eastern
book = duckdb.sql("""
  SELECT * FROM 'hf://datasets/KalshiEye/kalshi-order-book-sample/kalshieye-data/book_l2/*/series=KXNFLGAME/*.parquet'
  WHERE ticker = 'KXNFLGAME-26OCT12BUFLAR-BUF'
    AND ts_recv::TIMESTAMPTZ <= '2026-10-13 03:00:00+00'::TIMESTAMPTZ
  ORDER BY ts_recv, seq
""").df()

*_, (ts, ticker, bids, asks) = replay_book(book, levels=3)
print(bids)  # [(0.51, 155253.51), (0.5, 405328.0), (0.49, 15126.0)]
print(asks)  # [(0.52, 960851.07), (0.53, 862817.41), (0.54, 113351.03)]

More runnable examples (a backtest, spreads and depth, calibration) are in KalshiEye/kalshieye-examples. Every column is described in the data dictionary.