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

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

Lesson 4: Statistical analysis of time series

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

script

• 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]
• Show time series plot of daily rets
• Show histogram of daily rets
• 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

Lesson 5: Incomplete data

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

script

• [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

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

Lesson 7: Computing statistics on a portfolio

• Average daily return
• Volatility: stddev of daily return (don't count first day)
• Cumulative return
• Relationship between cumulative and daily

script

• 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

Lesson 7: Optimizers: Building a parameterized model

• Problem statement for an optimizer (inputs, outputs, assumptions)
• How to build a parameterized model from real data using an optimizer

script

• 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)]

Lesson 8: Optimizers: How to optimize a portfolio

• Framing the portfolio problem for an optimizer
• Constraints for an optimizer
• Optimizing a portfolio

script

• 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]