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)

Multilayer perceptrons for time series forecasting

Multilayer perceptrons (MLP) are one of the basic architectures of neural networks. At a very high level, they consist of three components:

  • The input layer: A vector of features.
  • The hidden layers: Each hidden layer consists of N neurons.
  • The output layer: Output of the network; depends on the task (regression/classification).

The input of each hidden layer is first transformed linearly (multiplication by weights and adding the bias term) and then non-linearly (by applying activation functions such as ReLU). Thanks to the non-linear activation, the network is able to model complex, non-linear relationships between the features and the target.

A multilayer perceptron contains multiple hidden layers (also called dense layers or fully connected layers) stacked against each other. The following diagram presents a network with a...