Python for Finance Cookbook: Over 80 powerful recipes for effective financial data analysis, 2nd Edition

Preface
    Who this book is for
    What this book covers
    To get the most out of this book
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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
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