0DTE Backtesting

Whether you're new to zero-days-to-expiration or scaling an existing approach, understanding the fundamentals of our complete 0DTE trading guide is essential before you trade live capital. This guide covers Before you risk real capital in the high-stakes environment of same-day expirations, you must validate your edge. 0DTE backtesting is the process of testing your trading rules against historical data to ensure your strategy is mathematically viable.

What Is 0DTE Backtesting?

Backtesting involves coding your entry, exit, and risk management rules and running them through years of historical options data. The goal is to calculate the expected value, win rate, max drawdown, and profit factor of your strategy to see if it survives different market regimes.

Data Sources for 0DTE Historical Data

The hardest part of options backtesting is acquiring clean data. Because options have hundreds of strikes expiring every day, the data sets are massive. You cannot use daily end-of-day data for 0DTE; you need at least 1-minute bar data, or preferably tick data. Reliable sources include the CBOE DataShop, ORATS, and Polygon.io.

Backtesting Tools (Python, TradingView, ThinkScript)

How you backtest depends on your technical ability:

• Python: The industry standard. Frameworks like VectorBT, Backtrader, and Zipline allow for robust testing if you have the data. Highly recommended before moving to 0DTE algo trading.
• TradingView: Great for testing the underlying asset's signals (e.g., SPX price action) using Pine Script, but it does not support native options backtesting.
• ThinkScript: Thinkorswim's OnDemand feature allows manual backtesting by replaying historical trading days, but programmatic testing is limited.

Common Backtesting Mistakes

Backtesting 0DTE is notoriously prone to errors. The biggest mistake is ignoring slippage and commissions. In a real market, you will rarely get filled at the mid-price of an option. Another fatal error is curve-fitting—tweaking your rules so perfectly to historical data that the strategy fails the moment it encounters live, unseen market conditions.

Walk-Forward Testing for 0DTE

To avoid curve-fitting, use walk-forward testing. Train your strategy on data from 2020-2022, and then test it "blind" on data from 2023. If the results hold up, you may have a real edge.

Frequently Asked Questions