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This project was a quick visualization on a dataset that I found through a weekly community known as Tiny Tuesday. Though the data was published over a year ago, it’s still a great dataset to practice some visualizations on. For this dataset I chose to use Tableau again!

The dataset today contains data from the Olympics between 1896 and 2016. The main story were looking at here is how has representation in the Olympics has changed. 

The first visual here takes a look at representation across the Olympic games, what a massive change over the last few years! It’s also important to note, many of the “dips” you’re seeing across the graph is Winter Games vs. Summer Games, as the Summer Olympics is a much bigger event. Also seems to show less representation within the winter Olympics.



![](/images/sex%20by%20year.png)

Next, it’d be interesting to see the top countries by both Sex representation. All 5 top countries by count of male participants, also make up the top 5 by count of female participants. This is fairly logical.



![](/images/sex%20by%20country.png)

Lastly, lets look at the top male and female sports by medal count. Interestingly, Swimming, Athletics and Rowing make up the top 3 for both Sex’s. 

 ![](/images/top%20sports.png)

Please interact with the dashboard yourself, available [here.](https://public.tableau.com/views/olympics-vis/RepresentationintheOlympics?:language=en-US\&publish=yes&:display_count=n&:origin=viz_share_link)
Thanks for reading!




Representation in the Olympics

Interactive DowJones Dashboard

Hi! This post will go over a project I completed to analyze a portfolio of stocks using python and various packages. For the purposes of this write up, I’ll be choosing a few stocks to create a portfolio.

For this analysis, were going to be creating portfolios of the following tickers: GSPC, AAPL, META, MFST, NFLX, GOOGL.

Before we do that, lets look at how these stocks are correlated. We can do this via a heatmap. As expected, stock prices among this group appear to be fairly highly correlated
![](/images/heatmap.png)

This comes as no surprise if you were at all familiar with these companies. This is certainly not the way to build a well-balanced portfolio, but this is for fun so why not!

Now let’s start creating some portfolios from this collection. To do this, we’ll generate semi-random weightings among the stocks, allowing for shorting (negative % holdings) but ensuring the total portfolio has a 100% allocation to the market. For this example, we’ve taken 10,000 samples.

The most likely question after this, how did the portfolios perform? For this, lets take a look at the annualized returns distribution for our portfolios.
![](/images/annual%20returns.png)

Alright, following that it might be interesting to see how our worst and best performing portfolios. The portfolio with the lowest return boasted a -1,358% loss with an annualize std. of 6,528% and an annualize Sharpe Ratio
of -0.208. The allocation of the portfolio was -7171% GSPC, 2152% AAPL, -3286% META.,-19,561% MSFT, 19,185% NFLX, 8,682% GOOGL. On the other side of things, our best performing portfolio boasted 4,834% annual returns with a standard
deviation of 15,863% and an annualize Sharpe Ratio of 0.304. The allocation of the portfolio was 24,384% GSPC, 59,757% AAPL, -46,927% META.,-55,220% MSFT, 76% NFLX, 17,929% GOOGL.

These numbers are are obviously not realistic, nor a feasible investment strategy so let’s look at one that might be. Let’s analyze the Tangency Portfolio, i.e., the portfolio with the highest Sharpe Ratio. This portfolio returns of 83% on annualized basis with 50% std and a Sharpe Ratio of 1.66. The portfolio had the following weightings: -130% GSPC, 130% AAPL, -70% META., 105% MSFT,  1% NFLX, 68% GOOGL. These seem a little more realistic.

Here's what a plot of all our sample portfolios.

![](/images/efficient%20frontier.png)

Lastly, lets dive just a little deeper into our strategy with the highest Sharpe Ratio and see if it would have performed well over time. Here’s a quick distribution of our strategy’s daily returns.

![](/images/portfoliodaily.png)

Next, let’s take a look at it’s rolling 6month Beta to the S\&P 500.

![](/images/portfoliobeta.png)
Lastly, lets look at our strategy’s rolling 6month Sharpe Ratio.

![](/images/rolling_sharpe.png)
Yeah…maybe generating random numbers in python isn’t the way to create a successful portfolio? Even though I might not have stumbled upon the most optimized portfolio, it was still a good exercise! Thanks for reading and be
sure to check out other articles below!


Interactive DowJones Dashboard

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Representation in the Olympics

Interactive DowJones Dashboard

Portfolio Optimization in Python