Difference between revisions of "Manipulating Financial Data in Python"
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*Reading CSV data into Pandas | *Reading CSV data into Pandas | ||
*Filtering to specific dates | *Filtering to specific dates | ||
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*Plotting | *Plotting | ||
Reading: "Python for Finance", Chapter 6: Financial time series | Reading: "Python for Finance", Chapter 6: Financial time series | ||
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==Lesson 2: Working with many stocks at once== | ==Lesson 2: Working with many stocks at once== | ||
| − | *Our target | + | *Our target data frame structure |
| + | *Address reverse order issue | ||
*Reading data for multiple stocks into the structure | *Reading data for multiple stocks into the structure | ||
| + | *Date slicing | ||
| + | *Symbol slicing | ||
*Plotting | *Plotting | ||
*Normalizing | *Normalizing | ||
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==Lesson 3: The power of Numpy== | ==Lesson 3: The power of Numpy== | ||
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*Example use with pandas too | *Example use with pandas too | ||
| − | + | Reading: "Python for Finance", Chapter 4: Data types and structures | |
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==Lesson 4: Statistical analysis of time series== | ==Lesson 4: Statistical analysis of time series== | ||
| + | *Gross statistics on dataframes | ||
*Rolling statistics on dataframes | *Rolling statistics on dataframes | ||
| − | * | + | *Plotting a technical indicator (Bollinger Bands) |
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| − | + | Reading: "Python for Finance", Chapter 6: Financial time series | |
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==Lesson 5: Incomplete data== | ==Lesson 5: Incomplete data== | ||
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*Different approaches to dealing with it | *Different approaches to dealing with it | ||
| − | + | ==Lesson 6: Histograms and scatter plots== | |
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| − | ==Lesson | + | * Histogram of daily returns |
| + | * Compare SPY with XOM | ||
| + | * Scatter plots | ||
| + | * Correlation is not slope! | ||
| + | * Compare SPY vs XOM, with SPY vs GLD scatter plots | ||
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| + | Reading: "Python for Finance", Chapter 5: Data Visualization | ||
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| + | ==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 | ||
| + | *How to model a buy and hold portfolio | ||
| − | + | ==Lesson 8: Optimizers: Building a parameterized model== | |
| − | + | *What does an optimizer do? | |
| − | + | *Syntax of optimizer use | |
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| − | ==Lesson | ||
*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 | |
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| − | ==Lesson | + | ==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 | |
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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
