## Inspiration

As a mathematics student thats passionate in finance, I have no computer science background and am often intimated by technical programs when I see them. I saw this project as a chance to build a very simple program that would be easily understood by the layman, with an easy to use interface.

## What it does

Input a stock, the program calculates the relative performance of a stock to an index (Nasdaq was selected due to it being the only free resource on Quandl, I was not able to find more). The program then outputs the expected relative performance of the stock to the same index after a given time period. It does this by analysing every day's relative performance since the start of the dataset and taking the mean of each value of relative performance after the given time period.

With the mean and standard deviation, I am able to fit a normal distribution curve to each value of current relative performance. This can be used to predict the exact probability of the stock performing more or less than x% after the same time period. I assumed a normal distribution, due to the law of large numbers, but I am aware it might not necessarily follow as such.

The project can be expanded to measure the same stock against multiple other stocks and indexes in different geographies. Currency and other risks need to be properly measured and quantified but I am currently not knowledgeable enough in programming to implement these measures. I would also ideally like to fit multiple distributions across the datasets and use the one with the least mean squared error but I am not able to implement these ideas thus my usage of only the normal distribution.

## How I built it

I built it with Python.

## Challenges I ran into

As a non-computer science student, I am unfamiliar with programming. I had to learn many techniques from scratch. I had many more ideas of expanding the project but was unable to due to practicality reasons. This was a solo project for me and I had to learn a lot on my own.

## Accomplishments that I'm proud of

I built this project by myself. I started learning Python only a few months ago and it was very intimidating but I am extremely proud of my achievement. This was definitely a great experience and an asset that would put me in good stead to further pursue this interest of mine.

## What I learned

I learned that I can definitely program and that it isnt necessarily as tough as I think it is. Baby steps for me, work. All I need to do is keep trying. I am in no way expecting my project to produce the most alpha or win an award but I am thankful for BlackRock for giving me this chance to personally develop myself.

## What's next for Hudson Yeo - Simple Relative Performance Measurement

Will probably continue working on and improving my project on my own.