Building an Automated Volatility Trading System with Python & Tastytrade API: Implementing and Backtesting an IV Rank-Based Strategy

Volatility trading is a complex and time-consuming task. This article introduces how to build an automated IV Rank-based volatility trading system using Python and the Tastytrade API, and how to backtest it with historical data. This will help individual investors make more efficient and objective investment decisions.

1. The Challenge / Context

Volatility trading is a strategy that predicts market volatility and uses it to generate profits. Especially in options trading, volatility is a crucial factor, and IV Rank (Implied Volatility Rank) is an indicator that shows where current volatility stands within its historical range. A high IV Rank means high volatility, which can be favorable for option selling strategies. However, volatility trading requires a lot of time and effort, including real-time data analysis, complex calculations, and rapid order execution. Manually handling these processes is inefficient and highly likely to lead to emotional judgments. Therefore, an automated system solves these problems and enables more objective and efficient volatility trading.

2. Deep Dive: IV Rank (Implied Volatility Rank)

IV Rank is an indicator that expresses, as a percentage, where a specific asset's current implied volatility stands within its implied volatility range over the past 52 weeks. For example, if the IV Rank is 80%, it means that the current implied volatility is in the top 20%