Difference between revisions of "Manipulating Financial Data in Python"
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| − | == | + | ==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 | |
| − | How does | + | *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 | ||
Latest revision as of 18:10, 24 August 2016
Contents
- 1 Lesson 1: Reading, slicing and plotting stock data
- 2 Lesson 2: Working with many stocks at once
- 3 Lesson 3: The power of Numpy
- 4 Lesson 4: Statistical analysis of time series
- 5 Lesson 5: Incomplete data
- 6 Lesson 6: Histograms and scatter plots
- 7 Lesson 7: Sharpe ratio & other portfolio statistics
- 8 Lesson 8: Optimizers: Building a parameterized model
- 9 Lesson 9: Optimizers: How to optimize a portfolio
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
