What Are My Risk Tolerance And Portfolio Distribution? You Can Find Out With This Python Script

A blog about the customization of the python script

A very small percentage of people are risk takers. They dive head first into anything that sounds interesting and they’re thrilled with the thrill of it all. It’s a risky strategy, but if you’re one of those people, you might enjoy the process itself more than the outcome.

Billionaire investor Warren Buffett said, “I could improve your ultimate financial welfare by giving you a ticket with only twenty slots in it so that you had twenty punches – representing all the investments that you got to make in a lifetime. And once you’d punched through the card, you couldn’t make any more investments at all. Under those rules, you’d really think carefully about what you did, and you’d be forced to load up on what you’d really thought about. So you’d do so much better.”

For most people, it is wiser to take an early buyout or retirement package rather than trying for a bigger payout later on or waiting for something better to come along. While waiting is risky, most people don’t have enough time left in their careers to outwait changes in their industry or economy.

It can be hard to tell which risk tolerance is right for your portfolio. There are many factors involved, but this Python script will help you figure it out.

Investors should find their risk tolerance in order to invest wisely. Most investors have trouble finding their personal risk level and portfolio distribution.

This article will show you how to use the Python script in order to find your risk tolerance and portfolio allocation.

The script computes the efficient frontier so as to determine the best return-per-risk portfolio distribution. It also computes the optimal portfolio close to a given one, i.e., with a minimum possible variation of asset weights, so that it matches an investor’s risk profile (risk tolerance).

The script is in python, and it runs in Jupyter notebooks. You can find the Jupyter notebook file here. The script is written so you can run it in Google Colab, but if you want to run it in a local environment, you will need to download some libraries. I’ve listed them as comments at the beginning of the script.

A word of warning: if you are not familiar with python, you may have problems running the script. The code needs to be customized a bit before running it, which means that unless you know python, this script may not be for you.

If you are interested in using this script, please note that I am not a quant or a data scientist. I am only a blogger who likes programming and investing; so please take my code with a grain of salt.

This is an educational project, and I am sharing this code because I think it can be useful for DIY investors like myself; however, if you do use this code for your own purposes, you must do it at your own risk.

There are many different ways to tackle this question, and they largely depend on how you want to invest your money. In this article, I will present a simple approach, but first, let’s talk about our investment goals.

It would be very helpful if we could answer the following questions:

1. How much risk am I willing to take?

2. What distribution should my portfolio be?

3. How can I build my portfolio based on the results of 1 and 2?

We should know our risk tolerance before deciding how to invest our money. If we are too conservative (i.e., we take less risk), we won’t make as much money as possible; if we are too aggressive (i.e., we take more risk), we can lose everything. So, the first step is to find out how much risk we are willing to take with our portfolio, so that we don’t make any rash moves.

One of the biggest challenges facing new investors is figuring out what kind of portfolio they should build. There are several factors to consider, including risk tolerance and time horizon. In this article, I will show you how to build a simple Python script which will help you to assess the answers to these questions.


Everyone has their own unique set of circumstances and investment goals. The first step in building a portfolio is determining how much risk you can tolerate. There are many different ways you can go about this, including quizzes and questionnaires that are available online, but I am going to discuss one method using Python.

The way that I think about risk tolerance is in terms of my retirement goals. If I have $1 million in savings at age 65 and I plan on retiring and living off of my savings income for 30 years, then I would like to have as large a nest egg as possible at age 95 so that I have enough money to live off of until the end of my life.

The first time I encountered risk tolerance was in my freshman year of college. I was thinking about transferring to a different university and considering an exchange program abroad. When I discussed the idea with my parents, they told me that they would support whatever I decided but that I needed to be ready for a lower quality of life when I returned: A new house, new friends, and a new school would mean starting over. They thought I should be prepared to live in a dorm instead of an apartment and that I would need to work harder to make friends than if I had stayed in the same place. All of these were things that made me uncomfortable, but I knew that if I wanted this opportunity I would need to take the risk.

In this post we will look at how we can use machine learning techniques to understand our risk profile and see whether or not we are making good investment decisions based on our personality type. The goal is not necessarily to get rich quick but rather develop a deeper understanding of ourselves so we can make better long term choices.

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