Difference between revisions of "Manipulating Financial Data in Python"
Jump to navigation
Jump to search
| (15 intermediate revisions by 4 users not shown) | |||
| Line 7: | Line 7: | ||
Reading: "Python for Finance", Chapter 6: Financial time series | Reading: "Python for Finance", Chapter 6: Financial time series | ||
| − | |||
| − | |||
==Lesson 2: Working with many stocks at once== | ==Lesson 2: Working with many stocks at once== | ||
| Line 33: | Line 31: | ||
*Rolling statistics on dataframes | *Rolling statistics on dataframes | ||
*Plotting a technical indicator (Bollinger Bands) | *Plotting a technical indicator (Bollinger Bands) | ||
| − | + | ||
| + | Reading: "Python for Finance", Chapter 6: Financial time series | ||
==Lesson 5: Incomplete data== | ==Lesson 5: Incomplete data== | ||
*How incomplete data arises in financial data | *How incomplete data arises in financial data | ||
*Different approaches to dealing with it | *Different approaches to dealing with it | ||
| − | |||
| − | |||
==Lesson 6: Histograms and scatter plots== | ==Lesson 6: Histograms and scatter plots== | ||
| Line 49: | Line 46: | ||
* Compare SPY vs XOM, with SPY vs GLD scatter plots | * Compare SPY vs XOM, with SPY vs GLD scatter plots | ||
| − | ==Lesson 7: | + | Reading: "Python for Finance", Chapter 5: Data Visualization |
| + | |||
| + | ==Lesson 7: Sharpe ratio & other portfolio statistics== | ||
*Speed up reading data by memoizing | *Speed up reading data by memoizing | ||
*Average daily return | *Average daily return | ||
| Line 55: | Line 54: | ||
*Cumulative return | *Cumulative return | ||
*Relationship between cumulative and daily | *Relationship between cumulative and daily | ||
| − | + | *Sharpe Ratio | |
| − | + | *How to model a buy and hold portfolio | |
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | * | ||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
==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 | |
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
==Lesson 9: Optimizers: How to optimize a portfolio== | ==Lesson 9: Optimizers: How to optimize a portfolio== | ||
| − | *Framing the | + | *What does it mean to "optimize" a portfolio |
| − | *Constraints for an optimizer | + | *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
