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.
Table of Contents (12 chapters)

Deep learning for tabular data

Deep learning is not often associated with tabular data, as this kind of data comes with some possible issues:

  • How to represent features in a way that can be understood by the neural networks? In tabular data, we often deal with numerical and categorical features, so we need to correctly represent both types of inputs.
  • How to use feature interactions – both between the features themselves and the target?
  • How to effectively sample the data? Tabular datasets tend to be smaller than typical datasets used for solving computer vision or NLP problems. There is no easy way to apply augmentation, such as random cropping or rotation in the case of images. Also, there is no general large dataset with some universal properties, based on which we could apply transfer learning.
  • How to interpret the neural network's decisions?

That is why practitioners...