Skip to content

Statistical Analysis of Equities and Notebooks about Financial Engineering

Notifications You must be signed in to change notification settings

dBCooper2/pythonic-finance

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

58 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Pythonic Finance - Financial Engineering/Analysis in Python

Statistical Analysis of Equities using Time Series Models, Regression, the Capital Asset Pricing Model(CAPM), and the Fama French 3-Factor and 5-Factor Models in Python and iPython Notebooks.

I will be taking notes on the Textbook Statistics and Data Analysis for Financial Engineering with R examples, and use it as a reference to learn about financial engineering and translate it into Python code and iPython notebooks.

Jupyter/iPython Notebooks

Textbook Notes: Calculating Returns

amd_log

This notebook examines the calculation of Net, Gross and Log Returns of an asset, along with how and why they are used. Included is a comparison of 3 semiconductor equities, ADI, AMAT and AMD, as well as a simple time series for each stock's logarithmic returns over a 5-year period.

Textbook Notes: Examining Distributions

dists_compared

This notebook covers histograms and kernel density estimates for examining sample distributions. Included are histograms and Gaussian KDE's of the semiconductor equity APH, implemented using matplotlib and KDEPy.

normprobplots

This notebook covers testing for normality with samples of asset prices: Sample Quantiles, and Tests of Normality including the Shapiro-Wilk Test are some of the functions in this notebook; The plots covered are Box Plots, Normal Probability Plots, Half-Normal Plots, and Quantile-Quantile Plots.

Textbook Notes: Time Series Modeling: Basics

Textbook Notes: Time Series Modeling: Further Topics

Textbook Notes: Regression Models

This notebook contains notes on Univariate and Multivariate Linear Regression and evaulation of these models, this notebook is a work-in-progress and does not cover all the topics in the chapter yet. This will be completed when the Python Regression Model Scripts are finished.

Textbook Notes: Modeling Univariate and Multivariate Statistical Models

Textbook Notes: GARCH Models

Textbook Notes: Portfolio Selection

Textbook Notes: Capital Asset Pricing Model

Fama-French 3-Factor Model

Fama-French 5-Factor Model

Portfolio Evaluation and Comparison Measures

Custom Regression Models for CAPM and FF3/FF5 Models

Python Scripts

Classes

Equity.py

Equities are a class that creates a Yahoo Finance Ticker to store Stock Data for Analysis as PKL Files.

Portfolio.py

Portfolios are a class that implements a Collection of Equity Objects to run CAPM and Fama-French Portfolio Analysis.

OLS_SimpleLinearRegression.py

OLS_SimpleLinearRegression is a superclass for the CAPM and Fama-French Models that contains the multiple linear regression functions and a simple ANOVA table generator.

MultipleLinearRegression is a superclass for the Fama-French Models and estimates the betas using the MLR Model's Normal Equations. ANOVA Tables will be added once the subclasses and Simple Linear Regression Classes are done.

CAPM_Regression.py

CAPM_Regression is a subclass of OLS_SimpleLinearRegression that overrides the linear regression functions to perform a single-variable linear regression of an asset, and overrides the ANOVA table generator to add CAPM-specific metrics for the stock like Jensen's Alpha.

FF_Regression.py

FF_Regression is a subclass of OLS_SimpleLinearRegression that inherits the multiple linear regression functions of its superclass to run either a 3-factor or 5-factor Fama-French analysis on a Stock, and overrides the ANOVA table generator to add finance-specific metrics for the stock.

Visualizations.py

Visualizations is a class that handles the visualization of the analysis performed in the regression scripts, and will implement PyGWalker to handle Dashboard creations for a portfolio.

Scripts will be written as/after I finish sections of the textbook, and will focus on Object-Oriented representations of the finance topics covered in the Jupyter/iPython Notebooks

If you are interested in previous projects I have written that cover similar topics, please take a look at:

CAPM and Fama-French Beta Estimation in Jupyter Notebooks

Scraping Data from Yahoo Finance

Visualizing Candle and Financial Statement Data

Resources

Ruppert, David and David S. Matteson. Statistics and Data Analysis for Financial Engineering with R examples. $2^{nd}$ ed., Springer, 2015.

Quantivity. "Why Log Returns?" Quantivity, 21 Feb. 2011, https://quantivity.wordpress.com/2011/02/21/why-log-returns/.

Costan, Julian. "Linear vs. Log Returns." Decentralized Meta-Learning, 14 Sept. 2021, https://blog.costan.ro/post/2021-09-14-linear-log-returns/.

Mathison, Jake. "Kernel Density Estimation." Kernel Density Estimation, n.d., mathisonian.github.io/kde/.

Tommy Odland. Tommyod/kdepy: Kernel Density Estimation in Python. v0.9.10, Zenodo, 18 Dec. 2018, doi:10.5281/zenodo.2392268.

Hudson, Robert and Gregoriou, Andros, Calculating and Comparing Security Returns is Harder than you Think: A Comparison between Logarithmic and Simple Returns (February 7, 2010). Available at SSRN: https://ssrn.com/abstract=1549328

Meucci, Attilio, Quant Nugget 2: Linear vs. Compounded Returns – Common Pitfalls in Portfolio Management (May 1, 2010). GARP Risk Professional, pp. 49-51, April 2010 , Available at SSRN: https://ssrn.com/abstract=1586656

About

Statistical Analysis of Equities and Notebooks about Financial Engineering

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published