QuantConnect Research: Exploring SPY Returns, Seasonality, and Autocorrelation

Summary

This article presents a practical workflow for exploratory research on SPY using QuantConnect. It examines daily return distributions, compares them with a normal distribution, looks for possible calendar and intraday seasonal patterns, and measures return autocorrelation as a possible sign of trend behavior. The examples use historical price data and basic statistical plots, including rolling lag-one autocorrelation and rolling annualized volatility.

The reported observations are that SPY returns have heavier tails than a normal distribution would imply and that daily autocorrelation may show potentially interesting behavior related to volatility. These are exploratory findings rather than a complete trading strategy: the article does not provide detailed significance tests, corrected comparisons for trying many patterns, or out-of-sample trading results. The author emphasizes checking assumptions and avoiding a search for a perfect backtest. The displayed analyses can help generate hypotheses, but any apparent seasonal or autocorrelation effects need further validation before being treated as persistent edges.

Key ideas

• The workflow uses historical SPY data to examine return distributions and possible patterns.
• The article reports that SPY returns have larger tail moves than a normal model suggests.
• Hourly and calendar-day groupings are used to explore possible seasonal behavior.
• Rolling lag-one autocorrelation is compared with annualized volatility to investigate trend effects.
• The findings are exploratory and require further statistical and out-of-sample validation.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.