siva-chidambaram12/kalshi-trading-bot — explained in plain English
Analysis updated 2026-05-18
Research how multiple debating AI agents can reach a trading consensus.
Paper trade on Kalshi prediction markets without risking real money.
Study Kelly Criterion position sizing applied to prediction market bets.
Track which market categories an automated strategy performs well in.
| siva-chidambaram12/kalshi-trading-bot | neuralinverse/neuralinverse | yangshun/reclassify | |
|---|---|---|---|
| Stars | 82 | 82 | 82 |
| Language | TypeScript | TypeScript | TypeScript |
| Setup difficulty | hard | moderate | easy |
| Complexity | 4/5 | 4/5 | 3/5 |
| Audience | researcher | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires OpenRouter API access and a Kalshi account, real trading involves financial risk.
Kalshi AI Trading Bot is an automated trading system that places bets on Kalshi, a regulated prediction market platform where users trade on the outcomes of real-world events like elections, economic data releases, and sports results. The bot is described as educational and research-oriented. The system's distinguishing feature is its multi-agent architecture. Rather than a single AI model making decisions, it runs five specialized AI agents in parallel: a forecaster that assesses probability, a news analyst that scores incoming headlines for relevance, a bull researcher arguing for a trade, a bear researcher arguing against it, and a risk manager with veto power. The agents debate and the system aggregates their outputs into a consensus probability, penalizing confidence when the agents strongly disagree. Position sizing uses the Kelly Criterion, a mathematically-derived method for betting a fraction proportional to your estimated edge, with a default cap at 25 percent of the theoretical optimal to reduce variance. The bot includes a hard daily AI spending cap, paper trading mode for testing without real money, WebSocket streaming for real-time market prices, RSS news aggregation from major sources, and a category scoring system that tracks which types of markets the bot has historically performed well in. A developer interested in prediction market trading systems or AI-driven decision making research would use this. The tech stack is TypeScript on Node.js 22.5, using OpenRouter to access multiple language models and SQLite for trade logging.
An educational multi-agent AI trading bot that debates and places automated bets on Kalshi's regulated prediction markets.
Mainly TypeScript. The stack also includes TypeScript, Node.js, OpenRouter.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
Verify against the repo before relying on details.