Manipulating Financial Data in Python

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Module 1: Reading, slicing and plotting stock data

  • Overview of the data we'll be working with.
  • Introduction to our primary library: Pandas
  • Reading CSV data into Pandas
  • Filtering to specific dates
  • Sorting
  • Plotting

script

  • Overview of data we'll be working with: AAPL.csv, SPY.csv (note date order)
    • Meaning of various columns
  • The Pandas dataframe
  • Read CSV into a dataframe (AAPL example)
  • Slice according to dates
  • [quiz: read SPY.csv and slice against different dates]
  • Plot (note date order wrong)
  • Sort
  • Plot

Module 2: Building a dataframe with lots of stocks

  • Our target dataframe structure
  • Reading data for multiple stocks into the structure
  • Plotting
  • Normalizing

script

  • What we want to end up with: Rows: Dates, Columns: Symbols
  • Step by step how to build it
  • SPY.csv will be our reference -- it trades every day the market is open.
  • Read SPY.csv, slice to date range, sort
  • Read AAPL.csv, merge() into existing dataframe
  • Repeat with GLD, IBM, GOOG
  • Plot and display legend
  • Observe: Scale not good, let's normalize
  • Print some of the numbers
  • Plot after normalization
  • [quiz: normalize at a different date]

Module 3: Numpy fundamentals

  • What is Numpy and how it relates to Pandas
  • Creating Numpy arrays
  • Indexing and slicing Numpy arrays
  • Important data processing on Numpy arrays

script

  • Numpy relationship to Pandas
  • Creating Arrays
    • empty, zeros, ones
  • Basic Indexing and Slicing (start at 0 not 1)
  • [quiz: print 2nd & 3rd columns]
  • Index one array by another
  • Reshaping
  • Data Processing using Arrays
    • Sum rows, Sum columns
    • Statistics on columns: Mean, Median, stddev
  • See: http://wiki.quantsoftware.org/index.php?title=Numpy_Tutorial_1

Module 4: Statistical analysis of time series

  • Rolling statistics example
    • Read SPY
    • 20 day rolling average
    • +- 20 day rolling stdev * 2
    • Plot above as Bollinger bands
  • Discussion of daily returns, what they are, how to calculate
  • [quiz: compute and plot SPY and XOM daily returns]
  • Scatter plot (plot SPY vs XOM)
  • Compare SPY vs GLD. How can we quantify these differences?
  • Fit a line and plot it, print slope and corrcoef for SPY & XOM
  • Discussion of correlation not the same as slope

Module 5: Incomplete data

  • [for this lesson: need to create 4 assets: SPY no missing, X ends midway, Y begins midway, Z has periodic outages]
  • Read SPY
    • 20 day rolling average
    • Plot
  • Attempt above with X, what happens? (incomplete data)
  • Look at data; NaN!
  • What to do?
  • Discussion & drawing of the types of incomplete data characterized by 4 examples above.
  • What is the proper way to handle?
  • [quiz: implement and plot fill forward on X]
  • Show Pandas methods for forward fill and backward fill, plot rolling averages

Module 6: Computing statistics on a portfolio

  • Statistics we'll look at:
    • Average daily return
    • Stddev of daily return: volatility
    • Total return
    • Sharpe ratio
  • Buy and hold: SPY, GLD, GOOG, XOM, $100K each
  • Show how to read assets, normalize so each starts at $100K, goes forward.
  • [quiz: compute total daily portfolio value and daily rets for portfolio]
  • Show how to compute, avg daily rets, stdev, total ret, Sharpe ratio

Module 7: Optimizers: Building a parameterized model

  • What does an optimizer do?
  • Show syntax of optimizer use
  • Create fake data
  • Try to fit it using an optimizer
  • [quiz: add another type of curve to fit (e.g., sine)]

Module 8: Optimizers: How to optimize a portfolio

  • Frame the portfolio optimization problem for the optimizer
  • Add target return
  • Plug the parts together in code for 4 assets
  • [quiz: Add constraints on holdings]