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Python for Finance Cookbook – Second Edition

You're reading from   Python for Finance Cookbook – Second Edition Over 80 powerful recipes for effective financial data analysis

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781803243191
Length 740 pages
Edition 2nd Edition
Languages
Tools
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Author (1):
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Eryk Lewinson Eryk Lewinson
Author Profile Icon Eryk Lewinson
Eryk Lewinson
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Table of Contents (18) Chapters Close

Preface 1. Acquiring Financial Data FREE CHAPTER 2. Data Preprocessing 3. Visualizing Financial Time Series 4. Exploring Financial Time Series Data 5. Technical Analysis and Building Interactive Dashboards 6. Time Series Analysis and Forecasting 7. Machine Learning-Based Approaches to Time Series Forecasting 8. Multi-Factor Models 9. Modeling Volatility with GARCH Class Models 10. Monte Carlo Simulations in Finance 11. Asset Allocation 12. Backtesting Trading Strategies 13. Applied Machine Learning: Identifying Credit Default 14. Advanced Concepts for Machine Learning Projects 15. Deep Learning in Finance 16. Other Books You May Enjoy
17. Index

Finding the Efficient Frontier using Monte Carlo simulations

According to the Modern Portfolio Theory, the Efficient Frontier is a set of optimal portfolios in the risk-return spectrum. This means that the portfolios on the frontier:

  • Offer the highest expected return for a given level of risk,
  • Offer the lowest level of risk for a given level of expected returns.

All portfolios located under the Efficient Frontier curve are considered sub-optimal, so it is always better to choose the ones on the frontier instead.

In this recipe, we show how to find the Efficient Frontier using Monte Carlo simulations. We build thousands of portfolios, using randomly assigned weights, and visualize the results. To do so, we use the returns of the four US tech companies from 202

How to do it...

Execute the following steps to find the Efficient Frontier using Monte Carlo simulations.

  1. Import the libraries:
import yfinance as yf
import numpy as np
import pandas as pd
  1. Set up the parameters:
N_PORTFOLIOS...
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