Python for Finance
About the Author
III. Financial Modeling
Supervised Learning
Autoregressive Models
Welcome
Why Python?
Python Development Tools
Google Colab Overview
Table of Contents
Accessing the Filesystem
Forms and Inputs
Notebook Secrets
Advanced Integrations
Installing Packages with Pip
I. Financial Applications
Python Language Overview
Basic Datatypes
Booleans
Numbers
Strings
Python Operators
Control Flow
Conditional Logic
Custom Functions
While Loops, Counters, and Accumulators
Container Datatypes
Lists
Dictionaries
Python Modules
The
math
Module
The
random
Module
The
statistics
Module
Dates and Times with the
datetime
Module
Data Processing
List Iteration and Looping
Sorting Lists
Mapping Lists
Filtering Lists
List Comprehensions
Data Visualization
Charts with Trendlines
Candlestick Charts
Fetching Data from the Internet
Fetching JSON Data
Fetching CSV Data
Fetching XML Data
Fetching HTML Data (i.e. “Web Scraping”)
II. Applied Data Science
Pandas Package Overview
Dataframes
Grouping and Pivoting
Shift based Methods
Moving Averages
Joining and Merging
Applied Statistics
Summary Statistics
Statistical Tests
Data Scaling
Correlation
III. Financial Modeling
Predictive Modeling
Machine Learning Foundations
Generalization
Data Encoding
Data Scaling
Supervised Learning
Regression
Linear Regression w/ sklearn
Linear Regression w/ statsmodels
Time Series Forecasting
Seasonality Analysis
Autoregressive Models
Classification
Binary Classification
Multi-class Classification
Model Management
Model Management
Model Deployment
Model Optimization
Unsupervised Learning
Dimensionality Reduction
Clustering
Appendices
Financial Data Sources
Pandas Datareader
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Autocorrelation and Auto-Regressive Models
Autocorrelation
Auto-Regressive Models
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Autocorrelation and Auto-Regressive Models
Autocorrelation
Auto-Regressive Models
Seasonality Analysis
Classification