Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Python Backtest Mastery for Risk Parity Portfolios
Rating: 2.3 out of 5(2 ratings)
43 students

Python Backtest Mastery for Risk Parity Portfolios

Optimize Investment Strategies with Python: Comprehensive Risk Parity Backtesting Techniques & Insights
Created byPaul Carter
Last updated 12/2023
English

What you'll learn

  • Implement Modern Risk Parity analysis to select a portfolio of stocks and weights.
  • Write a reusable backtesting class that iteratively implements your parameters
  • Analytical outputs such as Sharpe Ratio, CAGR, Drawdown, Benchmark Charts , etc.
  • Select optimal stock universes from S&P500 data
  • Integrate SQL databases for streamlined data retrieval
  • Generate key financial metrics for performance review
  • Craft a Python backtesting class for strategy analysis
  • Utilize Python for dynamic asset allocation
  • Backtest how your strategy would have done through time

Course content

5 sections • 42 lectures • 2h 12m total length

  • Feature Set & What You'll Learn1:18
  • Welcome Introduction0:11
  • Welcome Video (Overview with compilation)2:47

    Explore backtesting and modern portfolio theory with python, building a backtesting engine using riskfolio lib to optimize diversified s&p 500 portfolios and generate actionable insights.

  • End Product, The Main Juypter Notebook file1:06
  • Overview and reasons to do this backtest1:49

Requirements

  • Some exposure to python is preferred, although code will be given if student prefer to only execute the code

Description

Dive into the world of portfolio management with our comprehensive course that teaches you how to build an iterative Python backtester from scratch, specialized for Risk Parity strategies. This course is meticulously tailored to guide finance professionals, traders, and investment enthusiasts through the intricacies of constructing and analyzing risk parity portfolios using Python's powerful programming capabilities.


Throughout this course, you will:


  • Understand the foundational concepts of Risk Parity and why it is a preferred method for portfolio construction.

  • Learn how to code a backtesting environment in Python that can simulate trading strategies and evaluate their historical performance.

  • Gain hands-on experience with data retrieval, cleansing, and manipulation using Python's renowned libraries such as Pandas and NumPy.

  • Explore portfolio optimization techniques, including how to apply leverage and balance asset classes to achieve desired risk levels.

  • Master the art of visualizing complex financial data to make informed decisions, using libraries such as Matplotlib and Plotly

  • Discover advanced risk management concepts and learn to integrate them into your backtesting framework to develop robust investment strategies.

  • Engage with real-world case studies that will take you through the journey of backtesting and optimizing risk parity portfolios in a step-by-step process


By the end of this course, you will be equipped with the practical skills to implement risk parity strategies, the knowledge to enhance them with custom risk management techniques, and the confidence to apply Python's versatile tools to optimize your investment portfolio. Whether you're looking to manage your investments, advance your career, or simply gain a deeper understanding of portfolio management, this course is your gateway to success in the realm of Risk Parity Portfolio Management.


Join us on this educational adventure and transform the way you think about and manage risk in your investment portfolio.

Who this course is for:

  • Anyone curious about testing Modern Portfolio theories to understand if they are worth implementing

Report abuse