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Introduction Implementing Machine Learning Finance Book Details
Title: Implementing Machine Learning Finance
Author: Tshepo Chris Nokeri
No. of pages: 192
Format: PDF, EPUB
Implementing Machine Learning Finance
Kindly welcome to Implementing Machine Learning for Finance. This book is your guide to mastering machine and deep learning applied to practical, real-world investment strategy problems using Python programming. In this Implementing Machine Learning Finance book, you will learn how to properly build and evaluate supervised and unsupervised machine learning and deep learning models adequate for partial algorithmic trading and investment portfolio and risk analysis.
To begin with, it prudently introduces pattern recognition and
future price forecasting exerting time-series analysis models, like the autoregressive integrated moving average (ARIMA) model, seasonal ARIMA (SARIMA) model, and additive model, and then it carefully
covers the least squares model and the long-short term memory (LSTM) model. Also, it covers hidden pattern recognition and market regime prediction applying the Gaussian hidden Markov model. Third, it presents the practical application of the k-means model in stock clustering.
Fourth, it establishes the practical application of the prevalent variance- covariance method and empirical simulation method (using Monte
Carlo simulation) for value-at-risk estimation. Fifth, it encloses market direction classification using both the logistic classifier and the multilayer perceptron classifier. Lastly, it promptly presents performance and risk analysis for investment portfolios.
I used Anaconda (an open source distribution of Python programming) to prepare the examples. The libraries covered in this Implementing Machine Learning Finance book include, but are not limited to, the following:
- Auto ARIMA for time-series analysis
- Prophet for time-series analysis
- HMM Learn for hidden Markov models
- Yahoo Finance for web data scraping
- Pyfolio for investment portfolio and risk analysis
- Pandas for data structures and tools
- Statsmodels for basic statistical computation and modeling
- SciKit-Learn for building and validating key machine learning algorithms
- Keras for high-level frameworks for deep learning
- Pandas MonteCarlo for Monte Carlo simulation
- NumPy for arrays and matrices
- SciPy for integrals, solving differential equations, and optimization
- Matplotlib and Seaborn for popular plots and graphs This Implementing Machine Learning Finance book targets data scientists, machine learning engineers, and business and finance professionals, including retail investors who want
to develop systematic approaches to investment portfolio management, risk analysis, and performance analysis, as well as predictive analytics using data science procedures and tools. Prior to exploring the contents of this book, ensure that you understand the basics of statistics, investment strategy, Python programming, and probability theories. Also, install the packages mentioned in the previous list in your environment.