English | 2020 | ISBN: 978-1789618518 | 410 Pages | PDF, EPUB, MOBI | 312 MB
Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas
Python is one of the most popular languages used with a huge set of libraries in the financial industry.
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, and 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 Fama-French’s 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 finance domain. You’ll also learn how to solve 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 models and handle class imbalance. Finally, you’ll focus on solving problems in finance with deep learning using PyTorch.
By the end of this book, you’ll have learned how to effectively analyze financial time series using a recipe-based approach.
What you will learn
- Download and preprocess financial data from different sources
- Backtest the performance of automatic trading strategies in a real-world setting
- Create financial econometrics models in Python and interpret their results
- Use Monte Carlo simulations for a variety of tasks
- Improve the performance of financial models with the latest Python libraries
- Apply machine learning and deep learning techniques to solve different financial problems
- Understand the different approaches used to model financial time series data