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  • Book Overview & Buying Python for Finance Cookbook – Second Edition
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Python for Finance Cookbook – Second Edition

Python for Finance Cookbook – Second Edition - Second Edition

By : Eryk Lewinson
4.9 (38)
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Python for Finance Cookbook – Second Edition

Python for Finance Cookbook – Second Edition

4.9 (38)
By: Eryk Lewinson
Buy this Book

Overview of this book

Python is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions. You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses. Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.
Table of Contents (18 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Get in touch
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1
Acquiring Financial Data
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Acquiring Financial Data
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Getting data from Yahoo Finance
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Getting data from Nasdaq Data Link
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Getting data from Intrinio
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Getting data from Alpha Vantage
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Getting data from CoinGecko
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Summary
2
Data Preprocessing
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Data Preprocessing
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Converting prices to returns
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Adjusting the returns for inflation
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Changing the frequency of time series data
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Different ways of imputing missing data
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Converting currencies
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Different ways of aggregating trade data
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Summary
3
Visualizing Financial Time Series
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Visualizing Financial Time Series
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Basic visualization of time series data
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Visualizing seasonal patterns
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Creating interactive visualizations
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Creating a candlestick chart
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Summary
4
Exploring Financial Time Series Data
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Exploring Financial Time Series Data
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Outlier detection using rolling statistics
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Outlier detection with the Hampel filter
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Detecting changepoints in time series
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Detecting trends in time series
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Detecting patterns in a time series using the Hurst exponent
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Investigating stylized facts of asset returns
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Summary
5
Technical Analysis and Building Interactive Dashboards
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Technical Analysis and Building Interactive Dashboards
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Calculating the most popular technical indicators
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Downloading the technical indicators
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Recognizing candlestick patterns
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Building an interactive web app for technical analysis using Streamlit
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Deploying the technical analysis app
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Summary
6
Time Series Analysis and Forecasting
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Time Series Analysis and Forecasting
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Time series decomposition
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Testing for stationarity in time series
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Correcting for stationarity in time series
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Modeling time series with exponential smoothing methods
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Modeling time series with ARIMA class models
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Finding the best-fitting ARIMA model with auto-ARIMA
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Summary
7
Machine Learning-Based Approaches to Time Series Forecasting
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Machine Learning-Based Approaches to Time Series Forecasting
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Validation methods for time series
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Feature engineering for time series
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Time series forecasting as reduced regression
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Forecasting with Meta’s Prophet
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AutoML for time series forecasting with PyCaret
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Summary
8
Multi-Factor Models
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Multi-Factor Models
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Estimating the CAPM
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Estimating the Fama-French three-factor model
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Estimating the rolling three-factor model on a portfolio of assets
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Estimating the four- and five-factor models
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Estimating cross-sectional factor models using the Fama-MacBeth regression
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Summary
9
Modeling Volatility with GARCH Class Models
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Modeling Volatility with GARCH Class Models
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Modeling stock returns’ volatility with ARCH models
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Modeling stock returns’ volatility with GARCH models
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Forecasting volatility using GARCH models
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Multivariate volatility forecasting with the CCC-GARCH model
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Forecasting the conditional covariance matrix using DCC-GARCH
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Summary
10
Monte Carlo Simulations in Finance
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Monte Carlo Simulations in Finance
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Simulating stock price dynamics using a geometric Brownian motion
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Pricing European options using simulations
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Pricing American options with Least Squares Monte Carlo
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Pricing American options using QuantLib
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Pricing barrier options
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Estimating Value-at-Risk using Monte Carlo
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Summary
11
Asset Allocation
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Asset Allocation
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Evaluating an equally-weighted portfolio’s performance
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Finding the efficient frontier using Monte Carlo simulations
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Finding the efficient frontier using optimization with SciPy
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Finding the efficient frontier using convex optimization with CVXPY
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Finding the optimal portfolio with Hierarchical Risk Parity
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Summary
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Backtesting Trading Strategies
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Backtesting Trading Strategies
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Vectorized backtesting with pandas
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Event-driven backtesting with backtrader
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Backtesting a long/short strategy based on the RSI
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Backtesting a buy/sell strategy based on Bollinger bands
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Backtesting a moving average crossover strategy using crypto data
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Backtesting a mean-variance portfolio optimization
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Summary
13
Applied Machine Learning: Identifying Credit Default
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Applied Machine Learning: Identifying Credit Default
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Loading data and managing data types
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Exploratory data analysis
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Splitting data into training and test sets
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Identifying and dealing with missing values
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Encoding categorical variables
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Fitting a decision tree classifier
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Organizing the project with pipelines
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Tuning hyperparameters using grid searches and cross-validation
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Summary
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Advanced Concepts for Machine Learning Projects
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Advanced Concepts for Machine Learning Projects
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Exploring ensemble classifiers
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Exploring alternative approaches to encoding categorical features
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Investigating different approaches to handling imbalanced data
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Leveraging the wisdom of the crowds with stacked ensembles
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Bayesian hyperparameter optimization
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Investigating feature importance
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Exploring feature selection techniques
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Exploring explainable AI techniques
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Summary
15
Deep Learning in Finance
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Deep Learning in Finance
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Exploring fastai’s Tabular Learner
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Exploring Google’s TabNet
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Time series forecasting with Amazon’s DeepAR
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Time series forecasting with NeuralProphet
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Summary
16
Other Books You May Enjoy
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Other Books You May Enjoy
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Index
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Index

Summary

In this chapter, we have covered a few of the most popular sources of financial data. However, this is just the tip of the iceberg. Below, you can find a list of other interesting data sources that might suit your needs even better.

Additional data sources are:

  • IEX Cloud (https://iexcloud.io/)—a platform providing a vast trove of different financial data. A notable feature that is unique to the platform is a daily and minutely sentiment score based on the activity on Stocktwits—an online community for investors and traders. However, that API is only available in the paid plan. You can access the IEX Cloud data using pyex, the official Python library.
  • Tiingo (https://www.tiingo.com/) and the tiingo library.
  • CryptoCompare (https://www.cryptocompare.com/)—the platform offers a wide range of crypto-related data via their API. What stands out about this data vendor is that they provide order book data.
  • Twelve Data (https://twelvedata.com/).
  • polygon.io (https://polygon.io/)—a trusted data vendor for real-time and historical data (stocks, forex, and crypto). Trusted by companies such as Google, Robinhood, and Revolut.
  • Shrimpy (https://www.shrimpy.io/) and shrimpy-python—the official Python library for the Shrimpy Developer API.

In the next chapter, we will learn how to preprocess the downloaded data for further analysis.

Join us on Discord!

To join the Discord community for this book – where you can share feedback, ask questions to the author, and learn about new releases – follow the QR code below:

https://packt.link/ips2H

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