Python for finance cookbook: over 80 powerful recipes for effective financial data analysis

Recommended By: Banikanta Mishra
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Chapter 1: 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

Chapter 2: 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

Chapter 3: Visualizing Financial Time Series

Basic visualization of time series data
Visualizing seasonal patterns
Creating interactive visualizations
Creating a candlestick chart
Summary

Chapter 4: 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

Chapter 5: 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

Chapter 6: 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

Chapter 7: 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

Chapter 8: 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

Chapter 9: 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

Chapter 10: 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

Chapter 11: 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

Chapter 12: 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

Chapter 13: 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

Chapter 14: 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

Chapter 15: 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

Index