37 public examples that work with Kalshi, each with its source.
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New to the Kalshi API? Read how to build a Kalshi bot.
Framework for collecting and analyzing prediction-market data, including a large public Kalshi prediction market dataset.
Dataset stat Becker Kalshi dataset: 7.68M markets; 23.9% of settled markets resolved YES. Method on detail page.
Open-source AI agent that operates across Polymarket, Kalshi and other venues.
Unified API for trading on Polymarket, Kalshi and more.
Trades daily high-temperature markets on Kalshi (KXHIGH series) and Polymarket using weather data.
Curated list of agents, analytics, APIs, dashboards, copy-trading and alerting tools.
Toolkit for building AI-automated trading strategies on Kalshi.
Rust bots for Polymarket, Kalshi and Limitless. The README says copy trading is production-ready.
Low-latency cross-platform arbitrage written in Rust.
Deploys basic two-sided quoting strategies on Kalshi markets.
Runs deep fundamental research and produces independent probability estimates for Kalshi markets.
Watches 10,000+ Polymarket and Kalshi markets for mispriced pairs.
Real-time detection of price gaps in Bitcoin 1-hour price markets between Polymarket and Kalshi.
Educational bot that watches one event on Polymarket and Kalshi and buys YES on one and NO on the other when the combined price is under $1. Built on pmxt; dry-run by default.
Rust bot that compares sports markets across Polymarket and Kalshi (README in Chinese).
A university quant club project (QuantSC, Spring 2023) exploring market-making models for Kalshi.
Combines the Kalshi API with Perplexity Sonar search to research markets.
Trading system for Kalshi's S&P 500 daily range markets.
Performance tracker for Kalshi traders.
MCP server that lets AI assistants query and interact with Kalshi.
Multi-strategy bot for Kalshi's 15-minute Bitcoin up/down markets (KXBTC15M) that compares Binance spot and Polymarket prices, with a live dashboard.
WebSocket client that streams and charts tick-by-tick data from Kalshi and Polymarket.
Research repo on pricing Kalshi's MLB contracts: a data pipeline rebuilt from public MLB and Kalshi APIs, a market-making simulator, a paper-trading bot and contract-math helpers. Its README reports mostly negative findings for the game-winner contract (author's own backtests, unverified). Places no real orders.
Turns split-conformal price forecasts on public EIA WTI crude data into probabilities for threshold questions like 'will WTI close above $X by date Y?', with backtests and a dated forward track record. Analysis only; it never touches a Kalshi account.
Event-driven backtesting framework for binary markets such as Kalshi, Polymarket and PredictIt, with pluggable strategies, a simulated broker, settlement handling and a Streamlit UI showing equity curve, trade log and metrics.
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.
Polls Kalshi's free public market API and sends a Telegram message when a new market is listed, one alert per event. Each user picks categories, instant or digest frequency, and filters for minimum Yes bid, maximum spread and size at the bid. No Kalshi account or API key needed.
Unofficial Go library generated from the API specs Kalshi publishes: a REST trading client, market-data and perps WebSocket clients, and a hand-written request signer for Kalshi's RSA-PSS or Ed25519 key auth. Regenerated from the specs rather than edited by hand, with CI and tests. MIT licensed.
Dependency-free JavaScript functions that compute taker fees with the shared rate x contracts x p x (1 - p) formula, using each venue's rate, plus an all-in cost per contract. The README explains maker vs. taker fees, Kalshi's round-up to the cent, and per-market Polymarket rates. The author says the tests were checked against real order tickets (self-reported).
Low-overhead Rust app with a local browser UI for Kalshi's 15-minute BTC, ETH, SOL, XRP, DOGE and BNB markets. It builds candlesticks from Kalshi's public trades feed, adds Coinbase spot and RSI panes, and lets you drag entry, take-profit and stop-loss brackets onto paper or live positions. Starts in paper mode with no Kalshi account or API key.
Python research engine and paper on Kalshi daily temperature markets that tests whether quoted basket gaps survive fees, order-book depth, payoff constraints and chronological validation, using depth sweeps, fee rounding and linear programming. Includes executed notebooks and interactive graphs; its findings are the author's own (self-reported).
Columbia APMA seminar project that quotes Kalshi's daily New York high-temperature contracts: post-only bids and asks around rescaled mids, with inventory priced jointly across temperature bins, quiet periods around weather station reports, and a replay against Kalshi's public trade tape plus Iowa Environmental Mesonet data. Replay results are the author's own (self-reported).
Manual cross-venue arbitrage scanner that pulls public Kalshi and Polymarket market data, matches equivalent yes/no contracts with confidence scores and close-time checks, and ranks spreads on a review dashboard where quotes can be refreshed before a manual decision. Live order submission is disabled in this build.
Open research repo with a short paper comparing Kalshi and Polymarket Fed-decision markets with S&P 500 futures, USD/JPY and Treasury bills around CPI and payrolls releases (2023 to 2026). Measures whether both venues resolved correctly, how closely they agree, and how fast each absorbs macro news. Findings are self-reported by the author.
Open Python research project that checks whether Kalshi and Polymarket prices match how often outcomes actually happen. It measures prices at fixed horizons before close, tests for the favourite-longshot bias, and asks whether any mispricing survives trading costs. Includes CI and an interactive results page. Findings are the author's own and not independently verified.
Standard-library Python backtester for Kalshi's 15-minute Bitcoin up/down markets. It replays public one-minute candles and trade prints, charges Kalshi taker fees, runs parameter sweeps, and ships unit tests. The simulator has no network access; a separate fetcher caches public data. Backtest results are self-reported.
Student research notebook (Rennes School of Business graduating project) that compares the probability distribution implied by SPXW and NDX option prices, using a fitted SVI smile, with the distribution quoted by Kalshi's S&P 500 and Nasdaq-100 range contracts for the same expiry. Uses Yahoo Finance option data and the Kalshi public API, with a saved snapshot in the repo. Findings are self-reported and based on a single snapshot.
Open-source Python research system for the weekly EIA natural gas storage report. On release it polls EIA's JSON and CSV feeds, reads the Lower 48 net change, maps it onto bracket contracts from a YAML spec, and either records a simulated paper fill or places fee-aware limit orders on Kalshi (and optionally Polymarket). Paper mode is the default; the author warns that live mode can send real orders and lose money. No track record is claimed.
New public Kalshi & Polymarket builds, each with a source. No spam.
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