Python Backtest Mastery for Risk Parity Portfolios

Python Backtest Mastery for Risk Parity Portfolios, available at $19.99, has an average rating of 5, with 42 lectures, based on 1 reviews, and has 25 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone curious about testing Modern Portfolio theories to understand if they are worth implementing It is particularly useful for Anyone curious about testing Modern Portfolio theories to understand if they are worth implementing.

Enroll now: Python Backtest Mastery for Risk Parity Portfolios

Summary

Title: Python Backtest Mastery for Risk Parity Portfolios

Price: $19.99

Average Rating: 5

Number of Lectures: 42

Number of Published Lectures: 42

Number of Curriculum Items: 42

Number of Published Curriculum Objects: 42

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Feature Set & What Youll Learn

Lecture 2: Welcome Introduction

Lecture 3: Welcome Video (Overview with compilation)

Lecture 4: End Product, The Main Juypter Notebook file

Lecture 5: Overview and reasons to do this backtest

Chapter 2: Setup

Lecture 1: Setup Notes

Lecture 2: Installing Python and Visual Studio Code (VSCode) on Windows: A Short Guide

Lecture 3: IDE Environment

Lecture 4: Opening with vscode enviroment

Lecture 5: Explore Contents of Zip File

Lecture 6: Install virtual environment (venv) and install libraries (pip install)

Lecture 7: Initialize Repository – Connect Project to Github

Chapter 3: Code Frameworks

Lecture 1: Next Steps

Lecture 2: Framework Overview

Lecture 3: Walkthrough of Project Architecture

Lecture 4: Walkthrough of Project Architecture p2

Lecture 5: Overview of Database Connection

Lecture 6: Browsing the Database

Lecture 7: The Riskfolio Library

Lecture 8: Riskfolio Code Walkthrough

Chapter 4: Code the Backtest

Lecture 1: Check Code Runs – Run after setup

Lecture 2: Python self variable

Lecture 3: On bar of data function

Lecture 4: Rebalancing

Lecture 5: Liquid Portfolio Worth

Lecture 6: Storing Portfolio Data and Allocations

Lecture 7: Generating Allocation Weights

Lecture 8: Validating Data

Lecture 9: Add Test Mode

Lecture 10: Completing the function

Lecture 11: Finding Optimal Allocation

Lecture 12: Finding Optimal Allocation part 2

Lecture 13: Recording the Portfolio

Lecture 14: Recording the Portfolio part 2

Lecture 15: Setting up the Database and Database Dlass

Chapter 5: Simulation

Lecture 1: Working out of the Juypter Notebook and setting up a backtest

Lecture 2: Backtest Results and Reviewing Pandas DataFrames

Lecture 3: Two Different Methods of running Analytics

Lecture 4: Backtest Analytics Class

Lecture 5: Analytics Plot Results vs. Benchmark

Lecture 6: Analytics with Empyrical

Lecture 7: Analytics of a Backtest

Instructors

Paul Carter
Engineer & Creator

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Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!