Python for Finance Cookbook: Over 80 powerful recipes for effective financial data analysis. 2 Ed

Table of Contents....2 Preface....3 Acquiring Financial Data....14 Getting data from Yahoo Finance....16 Getting data from Nasdaq Data Link....21 Getting data from Intrinio....26 Getting data from Alpha Vantage....35 Getting data from CoinGecko....42 Summary....46 Data Preprocessing....48 Converting prices to returns....48 Adjusting the returns for inflation....52 Changing the frequency of time series data....58 Different ways of imputing missing data....62 Converting currencies....69 Different ways of aggregating trade data....73 Summary....82 Visualizing Financial Time Series....83 Basic visualization of time series data....84 Visualizing seasonal patterns....92 Creating interactive visualizations....99 Creating a candlestick chart....106 Summary....113 Exploring Financial Time Series Data....114 Outlier detection using rolling statistics....115 Outlier detection with the Hampel filter....120 Detecting changepoints in time series....126 Detecting trends in time series....133 Detecting patterns in a time series using the Hurst exponent....136 Investigating stylized facts of asset returns....142 Summary....157 Technical Analysis and Building Interactive Dashboards....159 Calculating the most popular technical indicators....160 Downloading the technical indicators....167 Recognizing candlestick patterns....172 Building an interactive web app for technical analysis using Streamlit....179 Deploying the technical analysis app....193 Summary....197 Time Series Analysis and Forecasting....198 Time series decomposition....199 Testing for stationarity in time series....211 Correcting for stationarity in time series....219 Modeling time series with exponential smoothing methods....228 Modeling time series with ARIMA class models....241 Finding the best-fitting ARIMA model with auto-ARIMA....258 Summary....274 Machine Learning-Based Approaches to Time Series Forecasting....275 Validation methods for time series....276 Feature engineering for time series....293 Time series forecasting as reduced regression....312 Forecasting with Meta’s Prophet....329 AutoML for time series forecasting with PyCaret....347 Summary....361 Multi-Factor Models....364 Estimating the CAPM....365 Estimating the Fama-French three-factor model....374 Estimating the rolling three-factor model on a portfolio of assets....382 Estimating the four- and five-factor models....386 Estimating cross-sectional factor models using the Fama-MacBeth regression....393 Summary....401 Modeling Volatility with GARCH Class Models....402 Modeling stock returns’ volatility with ARCH models....403 Modeling stock returns’ volatility with GARCH models....412 Forecasting volatility using GARCH models....420 Multivariate volatility forecasting with the CCC-GARCH model....430 Forecasting the conditional covariance matrix using DCC-GARCH....436 Summary....447 Monte Carlo Simulations in Finance....448 Simulating stock price dynamics using a geometric Brownian motion....449 Pricing European options using simulations....459 Pricing American options with Least Squares Monte Carlo....467 Pricing American options using QuantLib....473 Pricing barrier options....478 Estimating Value-at-Risk using Monte Carlo....482 Summary....490 Asset Allocation....491 Evaluating an equally-weighted portfolio’s performance....493 Finding the efficient frontier using Monte Carlo simulations....505 Finding the efficient frontier using optimization with SciPy....515 Finding the efficient frontier using convex optimization with CVXPY....525 Finding the optimal portfolio with Hierarchical Risk Parity....536 Summary....546 Backtesting Trading Strategies....547 Vectorized backtesting with pandas....550 Event-driven backtesting with backtrader....557 Backtesting a long/short strategy based on the RSI....569 Backtesting a buy/sell strategy based on Bollinger bands....579 Backtesting a moving average crossover strategy using crypto data....588 Backtesting a mean-variance portfolio optimization....596 Summary....603 Applied Machine Learning: Identifying Credit Default....605 Loading data and managing data types....606 Exploratory data analysis....617 Splitting data into training and test sets....637 Identifying and dealing with missing values....643 Encoding categorical variables....653 Fitting a decision tree classifier....663 Organizing the project with pipelines....682 Tuning hyperparameters using grid searches and cross-validation....694 Summary....711 Advanced Concepts for Machine Learning Projects....713 Exploring ensemble classifiers....715 Exploring alternative approaches to encoding categorical features....728 Investigating different approaches to handling imbalanced data....740 Leveraging the wisdom of the crowds with stacked ensembles....755 Bayesian hyperparameter optimization....764 Investigating feature importance....784 Exploring feature selection techniques....800 Exploring explainable AI techniques....819 Summary....846 Deep Learning in Finance....849 Exploring fastai’s Tabular Learner....850 Exploring Google’s TabNet....866 Time series forecasting with Amazon’s DeepAR....880 Time series forecasting with NeuralProphet....896 Summary....915 Other Books You May Enjoy....920 Index....923