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Python for Finance Cookbook - Second Edition

Name: Python for Finance Cookbook - Second Edition
Author: Eryk Lewinson
ISBN: 9781803243191

by Eryk Lewinson

December 2022

Beginner to intermediate

740 pages

18h 11m

English

Packt Publishing

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Preface
Who this book is forWhat this book coversTo get the most out of this bookGet in touch
Acquiring Financial Data
Getting data from Yahoo FinanceGetting data from Nasdaq Data LinkGetting data from IntrinioGetting data from Alpha VantageGetting data from CoinGeckoSummary
Data Preprocessing
Converting prices to returnsAdjusting the returns for inflationChanging the frequency of time series dataDifferent ways of imputing missing dataConverting currenciesDifferent ways of aggregating trade dataSummary
Visualizing Financial Time Series
Basic visualization of time series dataVisualizing seasonal patternsCreating interactive visualizations Creating a candlestick chartSummary
Exploring Financial Time Series Data
Outlier detection using rolling statisticsOutlier detection with the Hampel filterDetecting changepoints in time seriesDetecting trends in time seriesDetecting patterns in a time series using the Hurst exponentInvestigating stylized facts of asset returnsSummary
Technical Analysis and Building Interactive Dashboards
Calculating the most popular technical indicatorsDownloading the technical indicatorsRecognizing candlestick patternsBuilding an interactive web app for technical analysis using StreamlitDeploying the technical analysis appSummary
Time Series Analysis and Forecasting
Time series decompositionTesting for stationarity in time seriesCorrecting for stationarity in time series Modeling time series with exponential smoothing methodsModeling time series with ARIMA class modelsFinding the best-fitting ARIMA model with auto-ARIMASummary
Machine Learning-Based Approaches to Time Series Forecasting
Validation methods for time seriesFeature engineering for time seriesTime series forecasting as reduced regressionForecasting with Meta’s ProphetAutoML for time series forecasting with PyCaretSummary
Multi-Factor Models
Estimating the CAPMEstimating the Fama-French three-factor modelEstimating the rolling three-factor model on a portfolio of assets Estimating the four- and five-factor modelsEstimating cross-sectional factor models using the Fama-MacBeth regressionSummary
Modeling Volatility with GARCH Class Models
Modeling stock returns’ volatility with ARCH models Modeling stock returns’ volatility with GARCH models Forecasting volatility using GARCH modelsMultivariate volatility forecasting with the CCC-GARCH modelForecasting the conditional covariance matrix using DCC-GARCHSummary

Monte Carlo Simulations in Finance
Simulating stock price dynamics using a geometric Brownian motionPricing European options using simulationsPricing American options with Least Squares Monte CarloPricing American options using QuantLib Pricing barrier optionsEstimating Value-at-Risk using Monte CarloSummary
Asset Allocation
Evaluating an equally-weighted portfolio’s performanceFinding the efficient frontier using Monte Carlo simulationsFinding the efficient frontier using optimization with SciPy Finding the efficient frontier using convex optimization with CVXPY Finding the optimal portfolio with Hierarchical Risk ParitySummary
Backtesting Trading Strategies
Vectorized backtesting with pandasEvent-driven backtesting with backtraderBacktesting a long/short strategy based on the RSI Backtesting a buy/sell strategy based on Bollinger bandsBacktesting a moving average crossover strategy using crypto dataBacktesting a mean-variance portfolio optimizationSummary
Applied Machine Learning: Identifying Credit Default
Loading data and managing data typesExploratory data analysisSplitting data into training and test sets Identifying and dealing with missing valuesEncoding categorical variablesFitting a decision tree classifierOrganizing the project with pipelinesTuning hyperparameters using grid searches and cross-validation Summary
Advanced Concepts for Machine Learning Projects
Exploring ensemble classifiersExploring alternative approaches to encoding categorical featuresInvestigating different approaches to handling imbalanced dataLeveraging the wisdom of the crowds with stacked ensemblesBayesian hyperparameter optimizationInvestigating feature importanceExploring feature selection techniquesExploring explainable AI techniquesSummary
Deep Learning in Finance
Exploring fastai’s Tabular LearnerExploring Google’s TabNetTime series forecasting with Amazon’s DeepARTime series forecasting with NeuralProphetSummary
Other Books You May Enjoy
Index

Content preview from Python for Finance Cookbook - Second Edition

9 Modeling Volatility with GARCH Class Models

In Chapter 6, Time Series Analysis and Forecasting, we looked at various approaches to modeling time series. However, models such as ARIMA (Autoregressive Integrated Moving Average) cannot account for volatility that is not constant over time (heteroskedastic). We have already explained that some transformations (such as log or Box-Cox transformations) can be used to adjust for modest changes in volatility, but we would like to go a step further and model it.

In this chapter, we focus on conditional heteroskedasticity, which is a phenomenon caused when an increase in volatility is correlated with a further increase in volatility. An example might help to understand this concept. Imagine the price ...

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Python for Finance Cookbook - Second Edition

by Eryk Lewinson

9

Modeling Volatility with GARCH Class Models

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.