Manipulating Financial Data in Python

Lesson 1: Reading, slicing and plotting stock data

  • Overview of the data we’ll be working with (from Yahoo!)
  • Introduction to our primary library: Pandas
  • Reading CSV data into Pandas
  • Filtering to specific dates
  • Plotting

Reading: “Python for Finance”, Chapter 6: Financial time series

Lesson 2: Working with many stocks at once

  • Our target data frame structure
  • Address reverse order issue
  • Reading data for multiple stocks into the structure
  • Date slicing
  • Symbol slicing
  • Plotting
  • Normalizing

Lesson 3: The power of Numpy

  • What is Numpy and how it relates to Pandas
  • Why is Numpy powerful/important?
  • Creating Numpy arrays
  • Indexing and slicing Numpy arrays
  • Important data processing on Numpy arrays
  • Example use with pandas too

Reading: “Python for Finance”, Chapter 4: Data types and structures

Lesson 4: Statistical analysis of time series

  • Gross statistics on dataframes
  • Rolling statistics on dataframes
  • Plotting a technical indicator (Bollinger Bands)

Reading: “Python for Finance”, Chapter 6: Financial time series

Lesson 5: Incomplete data

  • How incomplete data arises in financial data
  • Different approaches to dealing with it

Lesson 6: Histograms and scatter plots

  • Histogram of daily returns
  • Compare SPY with XOM
  • Scatter plots
  • Correlation is not slope!
  • Compare SPY vs XOM, with SPY vs GLD scatter plots

Reading: “Python for Finance”, Chapter 5: Data Visualization

Lesson 7: Sharpe ratio & other portfolio statistics

  • Speed up reading data by memoizing
  • Average daily return
  • Volatility: stddev of daily return (don’t count first day)
  • Cumulative return
  • Relationship between cumulative and daily
  • Sharpe Ratio
  • How to model a buy and hold portfolio

Lesson 8: Optimizers: Building a parameterized model

  • What does an optimizer do?
  • Syntax of optimizer use
  • Problem statement for an optimizer (inputs, outputs, assumptions)
  • How to find X that minimizes f(X) with a minimizer
  • How to build a parameterized polynomial model from real data using an optimizer

Lesson 9: Optimizers: How to optimize a portfolio

  • What does it mean to “optimize” a portfolio
  • Framing the problem for an optimizer
  • Constraints on X for an optimizer
  • Ranges on X for an optimizer

Reading: “Python for Finance”, Chapter 11: Statistics-Portfolio Optimization