SPY Options Put/Call Ratio Analytics

Pipeline using Charles Schwab API, MySQL (stored procedures), and Python on Linux (crontab) to compute and visualize SPY put/call ratios with Jupyter, Plotly, and pandas; exploring ML with scikit-learn.

FinanceOptionsPut/CallSchwab APIMySQLStored ProceduresPythonLinuxcrontabJupyterPlotlypandasscikit-learnData ScienceMachine Learning
October 2025

Quantitative Analysis of SPY Option Activity (coming soon)

This project builds a data pipeline to collect, curate, and analyze SPY options chain data, in specific, the infamous "zero day" option chain. The analysis computes aggregated put and call volumes across expirations to derive a rolling daily put/call ratio, including minute to minute. The results are visualized and interpreted, providing insight into derivatives activity in the S&P 500.

Tech stack

  • Data: Charles Schwab API (options chain, historical/intraday series)
  • Storage: MySQL with stored procedures (aggregation, rollups, retention)
  • Compute: Python on Linux, scheduled via crontab
  • Notebooks/Visualization: Jupyter + Plotly
  • Data wrangling: pandas (with optional parquet cache)
  • Modeling: scikit-learn for exploratory ML
  • Ops: simple ETL scripts and daily jobs, logs retained locally