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Quantitative Methods – Time Series Utility

Overview

This utility introduces core time-series techniques used in quantitative finance. It serves as a bridge between the intermediate and advanced curriculum (Documentation/Programs/level3_financial.py and level4_advanced.py) and gives you reusable helpers for analyzing historical price data.

Key Skills

  • Generating and cleaning time-series price data
  • Calculating rolling statistics (moving averages, volatility)
  • Computing autocorrelation and partial autocorrelation
  • Performing stationarity checks (Augmented Dickey-Fuller)
  • Building a simple AR(1) forecast as a baseline model

Files

  • time_series_tools.py: Guided walkthrough with annotated prints and helper functions

How to Run

python time_series_tools.py
Open the script while it runs to follow the inline commentary.

Dependencies

  • pandas
  • numpy
  • statsmodels (for the ADF test)

Install them with: 

pip install pandas numpy statsmodels

Practice Ideas

  • Swap the simulated data with real prices from yfinance
  • Experiment with different rolling window lengths for volatility
  • Extend the AR(1) section to ARIMA using statsmodels.tsa.arima.model
  • Documentation/Programs/level3_financial.py
  • Documentation/Programs/level4_advanced.py
  • UTILS - Technical Indicators/