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)

Convolutional neural networks for time series forecasting

Convolutional neural networks (CNN) were developed and remained very popular in the image classification domain. However, they can also be applied to 1-dimensional problems, such as predicting the next value in the sequence, be it a time series or the next word in a sentence.

In the following diagram, we present a simplified schema of a 1D CNN:

Based on the preceding diagram, we briefly describe the elements of a typical CNN architecture:

  • Convolutional layer: The goal of this layer is to apply convolutional filtering to extract potential features.
  • Pooling layer: This layer reduces the size of the image or series while preserving the important characteristics identified by the convolutional layer.
  • Fully connected layer: Usually, there are a few fully connected layers at the end of the network to map the features extracted...