(책 요약) Quantitative Finance with Python (Chapter 6 - Working with Financial Datasets)

핵심 내용

  • Data sources
    • Yahoo Finance: stock price data + some futures data.
    • FRED: historical data on economic variables (GDP, employment, credit spreads) (fredapi in python)
    • Treasury.gov: Historical yield curve data for the US
    • Quandl: Equity market data + data on futures
    • HistData: Contains free data from FX markets, including intra-day data
    • OptionMetrics: Contains relatively clean options and futures data for equity and other markets (great, but costly)
    • CRSP: A broad, robust, historical database that doesn’t suffer from survivorship bias (e.g. Equity prices)
    • Ken French’s Website: Useful historical datasets of the returns for Fama-French factors
  • Missing data filling
    • Interpolation & Filling Forward
    • Filling via Regression
    • Filling via Bootstrapping
    • Filling via K-Nearest Neighbor
  • Outlier detection
    • Single vs. Multi-Variate Outlier Detection
    • Plotting
    • Standard Deviation
    • Density Analysis
    • Distance from K-Nearest Neighbor