Difference between revisions of "Manipulating Financial Data in Python"

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*Rolling statistics on dataframes
 
*Rolling statistics on dataframes
 
*Plotting a technical indicator (Bollinger Bands)
 
*Plotting a technical indicator (Bollinger Bands)
*Memoizing
+
 
 +
Reading: "Python for Finance", Chapter 6: Financial time series
  
 
==Lesson 5: Incomplete data==
 
==Lesson 5: Incomplete data==
Line 45: Line 46:
 
* Compare SPY vs XOM, with SPY vs GLD scatter plots
 
* Compare SPY vs XOM, with SPY vs GLD scatter plots
  
==Lesson 7: Computing statistics on a portfolio==
+
Reading: "Python for Finance", Chapter 5: Data Visualization
 +
 
 +
==Lesson 7: Sharpe ratio & other portfolio statistics==
 +
*Speed up reading data by memoizing
 
*Average daily return
 
*Average daily return
 
*Volatility: stddev of daily return (don't count first day)
 
*Volatility: stddev of daily return (don't count first day)
 
*Cumulative return
 
*Cumulative return
 
*Relationship between cumulative and daily
 
*Relationship between cumulative and daily
 
+
*Sharpe Ratio
<b>script</b>
+
*How to model a buy and hold 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
 
  
 
==Lesson 8: Optimizers: Building a parameterized model==
 
==Lesson 8: Optimizers: Building a parameterized model==
 +
*What does an optimizer do?
 +
*Syntax of optimizer use
 
*Problem statement for an optimizer (inputs, outputs, assumptions)
 
*Problem statement for an optimizer (inputs, outputs, assumptions)
*How to build a parameterized model from real data using an optimizer
+
*How to find X that minimizes f(X) with a minimizer
 
+
*How to build a parameterized polynomial model from real data using an optimizer
<b>script</b>
 
*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 9: Optimizers: How to optimize a portfolio==
 
==Lesson 9: Optimizers: How to optimize a portfolio==
*Framing the portfolio problem for an optimizer
+
*What does it mean to "optimize" a portfolio
*Constraints for an optimizer
+
*Framing the problem for an optimizer
*Optimizing a portfolio
+
*Constraints on X for an optimizer
 +
*Ranges on X for an optimizer
  
<b>script</b>
+
Reading: "Python for Finance", Chapter 11: Statistics-Portfolio Optimization
*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]
 

Latest revision as of 19:10, 24 August 2016

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