kalshi-dash: what it does, what it costs, where it stops

What it does

Beyond the PnL line, the README lists per-series performance, maker/taker split, yes/no direction, settlement versus market-exit outcomes, and risk-adjusted returns including variance and Sharpe.

Multi-CSV upload covers several years of trading in one session.

Settlement versus market-exit outcomes is the stat worth having. It separates contracts you held to resolution from positions you closed early, which is the first thing to check when a Kalshi P&L looks better or worse than expected.

• Stack: Next.js, TypeScript, Chart.js for plots, Papa Parse for CSVs
• 34 stars and 10 forks, created April 19, 2025, last push August 5, 2026
• No API key needed; nothing is sent to a server

Setup

Download your transaction CSVs from the Documents section of the Kalshi desktop site and upload them on the hosted page. To run locally: npm install, then npm run dev.

Because parsing happens in the browser, the hosted GitHub Pages copy and a local build behave the same way. There is no account, no login and nothing to configure beyond choosing the CSV files.

Venues and data

Kalshi only, and only your own account history. It reads the exchange's CSV format rather than the API.

Where it falls short

No market data, no live prices and no other traders. It is a personal performance tool.

No license file is stated in the repo metadata, so reuse terms are unclear.

It is a single-author side project; a Kalshi CSV format change would break parsing until someone patches it.

Charts are Chart.js line and bar plots. There is no export of the computed stats back to CSV, so the numbers stay on screen.

Who should use it

Kalshi traders who want a private, free equity curve and per-series breakdown in under a minute. Anyone wanting deeper journaling metrics or benchmarks should look at a paid journal.