Home Page Icon
Home Page
Table of Contents for
Python for Finance Cookbook - Second Edition
Close
Python for Finance Cookbook - Second Edition
by Eryk Lewinson
Python for Finance Cookbook - Second Edition
Preface
Acquiring Financial Data
Data Preprocessing
Visualizing Financial Time Series
Exploring Financial Time Series Data
Technical Analysis and Building Interactive Dashboards
Time Series Analysis and Forecasting
Machine Learning-Based Approaches to Time Series Forecasting
Multi-Factor Models
Modeling Volatility with GARCH Class Models
Monte Carlo Simulations in Finance
Asset Allocation
Backtesting Trading Strategies
Applied Machine Learning: Identifying Credit Default
Advanced Concepts for Machine Learning Projects
Deep Learning in Finance
Other Books You May Enjoy
Index
Search in book...
Toggle Font Controls
Playlists
Add To
Create new playlist
Name your new playlist
Playlist description (optional)
Cancel
Create playlist
Sign In
Email address
Password
Forgot Password?
Create account
Login
or
Continue with Facebook
Continue with Google
Sign Up
Full Name
Email address
Confirm Email Address
Password
Login
Create account
or
Continue with Facebook
Continue with Google
Prev
Previous Chapter
Python for Finance Cookbook - Second Edition
Next
Next Chapter
Preface
Contents
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Acquiring Financial Data
Getting data from Yahoo Finance
Getting data from Nasdaq Data Link
Getting data from Intrinio
Getting data from Alpha Vantage
Getting data from CoinGecko
Summary
Data Preprocessing
Converting prices to returns
Adjusting the returns for inflation
Changing the frequency of time series data
Different ways of imputing missing data
Converting currencies
Different ways of aggregating trade data
Summary
Visualizing Financial Time Series
Basic visualization of time series data
Visualizing seasonal patterns
Creating interactive visualizations
Creating a candlestick chart
Summary
Exploring Financial Time Series Data
Outlier detection using rolling statistics
Outlier detection with the Hampel filter
Detecting changepoints in time series
Detecting trends in time series
Detecting patterns in a time series using the Hurst exponent
Investigating stylized facts of asset returns
Summary
Technical Analysis and Building Interactive Dashboards
Calculating the most popular technical indicators
Downloading the technical indicators
Recognizing candlestick patterns
Building an interactive web app for technical analysis using Streamlit
Deploying the technical analysis app
Summary
Time Series Analysis and Forecasting
Time series decomposition
Testing for stationarity in time series
Correcting for stationarity in time series
Modeling time series with exponential smoothing methods
Modeling time series with ARIMA class models
Finding the best-fitting ARIMA model with auto-ARIMA
Summary
Machine Learning-Based Approaches to Time Series Forecasting
Validation methods for time series
Feature engineering for time series
Time series forecasting as reduced regression
Forecasting with Meta’s Prophet
AutoML for time series forecasting with PyCaret
Summary
Multi-Factor Models
Estimating the CAPM
Estimating the Fama-French three-factor model
Estimating the rolling three-factor model on a portfolio of assets
Estimating the four- and five-factor models
Estimating cross-sectional factor models using the Fama-MacBeth regression
Summary
Modeling Volatility with GARCH Class Models
Modeling stock returns’ volatility with ARCH models
Modeling stock returns’ volatility with GARCH models
Forecasting volatility using GARCH models
Multivariate volatility forecasting with the CCC-GARCH model
Forecasting the conditional covariance matrix using DCC-GARCH
Summary
Monte Carlo Simulations in Finance
Simulating stock price dynamics using a geometric Brownian motion
Pricing European options using simulations
Pricing American options with Least Squares Monte Carlo
Pricing American options using QuantLib
Pricing barrier options
Estimating Value-at-Risk using Monte Carlo
Summary
Asset Allocation
Evaluating an equally-weighted portfolio’s performance
Finding the efficient frontier using Monte Carlo simulations
Finding the efficient frontier using optimization with SciPy
Finding the efficient frontier using convex optimization with CVXPY
Finding the optimal portfolio with Hierarchical Risk Parity
Summary
Backtesting Trading Strategies
Vectorized backtesting with pandas
Event-driven backtesting with backtrader
Backtesting a long/short strategy based on the RSI
Backtesting a buy/sell strategy based on Bollinger bands
Backtesting a moving average crossover strategy using crypto data
Backtesting a mean-variance portfolio optimization
Summary
Applied Machine Learning: Identifying Credit Default
Loading data and managing data types
Exploratory data analysis
Splitting data into training and test sets
Identifying and dealing with missing values
Encoding categorical variables
Fitting a decision tree classifier
Organizing the project with pipelines
Tuning hyperparameters using grid searches and cross-validation
Summary
Advanced Concepts for Machine Learning Projects
Exploring ensemble classifiers
Exploring alternative approaches to encoding categorical features
Investigating different approaches to handling imbalanced data
Leveraging the wisdom of the crowds with stacked ensembles
Bayesian hyperparameter optimization
Investigating feature importance
Exploring feature selection techniques
Exploring explainable AI techniques
Summary
Deep Learning in Finance
Exploring fastai’s Tabular Learner
Exploring Google’s TabNet
Time series forecasting with Amazon’s DeepAR
Time series forecasting with NeuralProphet
Summary
Other Books You May Enjoy
Index
Landmarks
Cover
Index
Add Highlight
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click
here
login for view all page.
Day Mode
Cloud Mode
Night Mode
Reset