Book Image

Python for Finance Cookbook - Second Edition

By : Eryk Lewinson
5 (1)
Book Image

Python for Finance Cookbook - Second Edition

5 (1)
By: Eryk Lewinson

Overview of this book

Python is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions. You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses. Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.
Table of Contents (18 chapters)
16
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17
Index

Asset Allocation

Asset allocation is the most important decision that any investor needs to face, and there is no one-size-fits-all solution that can work for each and every investor. By asset allocation, we mean spreading the investor’s total investment amount over certain assets (be it stocks, options, bonds, or any other financial instruments). When considering the allocation, the investor wants to balance the risk and the potential reward. At the same time, the allocation is dependent on factors such as the individual goals (expected return), risk tolerance (how much risk the investor is willing to accept), or the investment horizon (short-or long-term investment).

The key framework in asset allocation is the modern portfolio theory (MPT, also known as mean-variance analysis). It was introduced by the Nobel recipient Harry Markowitz and describes how risk-averse investors can construct portfolios to maximize their expected returns (profits) for a given level of risk...