Data and code for a 2026 preprint that measures Kalshi calibration while stepping from the displayed price to the order-book midpoint, the executable quote and the fee-adjusted taker return. Includes per-market JSONL logs, frozen outputs and a script that runs deterministic checks against the paper's tables.
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Data and code for a 2026 preprint that measures Kalshi calibration while stepping from the displayed price to the order-book midpoint, the executable quote and the fee-adjusted taker return. Includes per-market JSONL logs, frozen outputs and a script that runs deterministic checks against the paper's tables. See the source for setup, usage and current status.
Tools: PythonNumPyJSONL
Difficulty: advanced
Platforms: Kalshi
Data and code for a 2026 preprint that measures Kalshi calibration while stepping from the displayed price to the order-book midpoint, the executable quote and the fee-adjusted taker return. Includes per-market JSONL logs, frozen outputs and a script that runs deterministic checks against the paper's tables. It is listed under Data & analytics in this directory.
According to its source, it works with Kalshi.
The source lists: Python, NumPy, JSONL.
It was published by ken10677. The public source is at github.com/ken10677/mwta-kalshi-replication.
This directory rates it advanced (beginner, intermediate or advanced).
It had 0 GitHub stars as of Oct 16, 2026.
The repository was last updated on Oct 16, 2026, as checked on Oct 23, 2026. Check the source for current status.
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