We have already spent quite some time on creating the entire pipeline and tuning the models to achieve better performance. However, what is equally—or even more—important is the model's interpretability: so, not only giving an accurate prediction but also being able to explain the why. In the case of customer churn, an accurate model is important. However, knowing what are the actual predictors of the customers leaving might be helpful in improving the overall service and, potentially, making them stay longer. In a financial setting, banks often use machine learning in order to predict a customer's ability to repay credit. And, in many cases, they are obliged to justify their reasoning, in case they decline a credit application—why exactly this customer's application was not approved. In the case of very...
Python for Finance Cookbook
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Python for Finance Cookbook
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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.
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Current Title:
Python for Finance Cookbook
Table of Contents (12 chapters)
Preface
Financial Data and Preprocessing
Free Chapter
Technical Analysis in Python
Time Series Modeling
Multi-Factor Models
Modeling Volatility with GARCH Class Models
Monte Carlo Simulations in Finance
Asset Allocation in Python
Identifying Credit Default with Machine Learning
Advanced Machine Learning Models in Finance
Deep Learning in Finance
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