roboteagle0x

roboteagle0x / kalshi-backtest

Public

Strategy backtesting engine for Kalshi event contracts. Test your entry and exit rules against historical market data and get P&L curves, win rate, Sharpe ratio, and drawdown metrics before risking real money.

kalshi-backtest kalshi-backtest-config kalshi-backtest-engine kalshi-backtest-python kalshi-backtest-results
48
0
69% credibility
Found Mar 30, 2026 at 48 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
AI Summary

Kalshi Backtest simulates trading strategies on historical Kalshi prediction market data to evaluate performance metrics like P&L, win rate, Sharpe ratio, and drawdowns before risking real money.

How It Works

1
🔍 Discover the Tool

You learn about Kalshi Backtest, a simple way to test your prediction market trading ideas on past results without using real money.

2
📥 Download Your Choice

Pick the easy Windows app or the flexible Python version and get it on your computer.

3
Choose Your Path
🪟
Windows App

Double-click to start and everything opens nicely.

🐍
Python Way

Follow a few steps to set it up on any computer.

4
📈 Gather Past Data

Let the tool pull in years of historical market info so you can replay real events.

5
⚙️ Define Your Strategy

Set rules for when to buy, sell, how much to bet, and which markets to focus on.

6
▶️ Run the Simulation

Hit go and watch it replay your rules against history to see what would have happened.

✅ Review Your Insights

Get clear reports with profits, win rates, risks, and charts to confidently improve your live trading plan.

Sign up to see the full architecture

5 more

Sign Up Free

Star Growth

See how this repo grew from 48 to 48 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is kalshi-backtest?

Kalshi-backtest is a Python-based engine for simulating trading strategies on Kalshi's event contracts using historical market data from their API. You define entry rules like price ranges or categories, exit rules such as take-profit or stop-loss, and sizing modes including Kelly criterion, then get backtested results with P&L curves, win rate, Sharpe ratio, and drawdown metrics. It's a free strategy backtesting tool that lets you validate ideas offline via a Windows executable or pip-installable CLI before deploying live bots.

Why is it gaining traction?

Unlike generic strategy backtesting software, this Kalshi backtesting GitHub project handles prediction market specifics like time-to-resolution filters, category breakdowns, and settlement fees natively, with multi-strategy comparison for quick parameter sweeps akin to a GitHub strategy matrix. Developers dig the fail-fast workflow: auto-download data, tweak TOML configs, and export JSON/CSV results or equity charts in minutes. The shared config with companion live traders seals the deal for seamless strategy-to-production handoff.

Who should use this?

Kalshi API users building automated bots for politics, crypto, or economics markets. Quant traders testing probability dip-buying or time-window entries on event contracts. Prediction market hobbyists analyzing win rates by category without TradingView limitations.

Verdict

Solid starter for Kalshi strategy backtesting trading at 48 stars and thorough docs, but the 0.699999988079071% credibility score flags it as early-stage—test thoroughly before production reliance. Grab it if you're in prediction markets; skip for broader forex or stock backtesting needs.

(198 words)

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.