Book Image

Python for Finance Cookbook

By : Eryk Lewinson

Book Image

Python for Finance Cookbook

By: Eryk Lewinson

Overview of this book

Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach.

Preface

Who this book is for

What this book covers

To get the most out of this book

Financial Data and Preprocessing

Financial Data and Preprocessing

Getting data from Yahoo Finance

Getting data from Quandl

Getting data from Intrinio

Converting prices to returns

Changing frequency

Visualizing time series data

Identifying outliers

Investigating stylized facts of asset returns

Free Chapter

Technical Analysis in Python

Technical Analysis in Python

Creating a candlestick chart

Backtesting a strategy based on simple moving average

Calculating Bollinger Bands and testing a buy/sell strategy

Calculating the relative strength index and testing a long/short strategy

Building an interactive dashboard for TA

Time Series Modeling

Time Series Modeling

Decomposing time series

Decomposing time series using Facebook's Prophet

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

Forecasting using ARIMA class models

Multi-Factor Models

Multi-Factor Models

Implementing the CAPM in Python

Implementing the Fama-French three-factor model in Python

Implementing the rolling three-factor model on a portfolio of assets

Implementing the four- and five-factor models in Python

Modeling Volatility with GARCH Class Models

Modeling Volatility with GARCH Class Models

Explaining stock returns' volatility with ARCH models

Explaining stock returns' volatility with GARCH models

Implementing a CCC-GARCH model for multivariate volatility forecasting

Forecasting the conditional covariance matrix using DCC-GARCH

Monte Carlo Simulations in Finance

Monte Carlo Simulations in Finance

Simulating stock price dynamics using Geometric Brownian Motion

Pricing European options using simulations

Pricing American options with Least Squares Monte Carlo

Pricing American options using Quantlib

Estimating value-at-risk using Monte Carlo

Asset Allocation in Python

Asset Allocation in Python

Evaluating the performance of a basic 1/n portfolio

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

Identifying Credit Default with Machine Learning

Identifying Credit Default with Machine Learning

Loading data and managing data types

Exploratory data analysis

Splitting data into training and test sets

Dealing with missing values

Encoding categorical variables

Fitting a decision tree classifier

Implementing scikit-learn's pipelines

Tuning hyperparameters using grid searches and cross-validation

Advanced Machine Learning Models in Finance

Advanced Machine Learning Models in Finance

Investigating advanced classifiers

Using stacking for improved performance

Investigating the feature importance

Investigating different approaches to handling imbalanced data

Bayesian hyperparameter optimization

Deep Learning in Finance

Deep Learning in Finance

Deep learning for tabular data

Multilayer perceptrons for time series forecasting

Convolutional neural networks for time series forecasting

Recurrent neural networks for time series forecasting

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Exploratory data analysis

The second step, after loading the data, is to carry out Exploratory Data Analysis (EDA). By doing this, we get to know the data we are supposed to work with. Some insights we try to gather are:

What kind of data do we actually have, and how should we treat different types?
What is the distribution of the variables?
- Are there outliers in the data, and how can we treat them?
- Are any transformations required? For example, some models work better with (or require) normally distributed variables, so we might want to use techniques such as log transformation.
- Does the distribution vary per group (for example, gender or education level)?
Do we have cases of missing data? How frequent are these, and in which variables?
Is there a linear relationship between some variables (correlation)?
Can we create new features using the existing set of variables? An example...