Manipulating Financial Data in Python

Module 1: Reading, Slicing and Plotting Stock Data

• 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 and plotting a dataframe with lots of stocks

• Overview of 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

• 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 2: Pandas DS- Series

• Working with index
• Operations
• Filtering
• Handling Incomplete Data

Module 3: Pandas DS- Data Frame

• Creating Data frame
• Operations
• Columns and rows
• Essential Function
• Reindexing
• Indexing and Filtering

Module 4: Data Analysis- Reading/Writing Data

• Importing Data using Pandas
• Importing data without pandas
• Saving and exporting data using pandas
• Saving and exporting data without pandas

Module 5

• Pre-processing Data
• Statistical Functions for Analysis

Module 6: Date And Time

• Creating Date and Time
• Date Mathematics
• Time Series Plotting