Background
You have been investing in algorithmic trading strategies. Some of the investment managers love them, some hate them, but they all think their way is best.
You just learned these quantitative analysis techniques with Python and Pandas, and you want to determine which portfolio is performing the best across multiple areas: volatility, returns, risk, and Sharpe ratios.
What You’re Creating
You need to create a tool (an analysis notebook) that analyses and visualises the major metrics of the portfolios across all of these areas, and determine which portfolio outperformed the others. You will be given the historical daily returns of several portfolios: some from the firm's algorithmic portfolios, some that represent the portfolios of famous "whale" investors like Warren Buffett, and some from the big hedge and mutual funds. You will then use this analysis to create a custom portfolio of stocks and compare its performance to that of the other portfolios, as well as the larger market (S&P TSX 60 IndexLinks to an external site.).
Determine whether the algorithmic strategies outperform both the market (S&P TSX 60) and the whales portfolios.
The Algorithmic strategies, particularily portfolio 1, significantly outperforms both the S&P TSX 60 and all Whale portfolios based on visuals seen in analysis performed above including the cumulative returns and sharpe ratio metric graphics. Algorithmic portfolio 2, also shows strong performance versus the S&P TSX 60 and the majority of of whale portfolios, except for the Berkshire Hathaway portfolio which suggests it could be a better investment option based on its slightly higher sharpe ratio that can be viewed in the graph. The Algorithmic portfolios sharpe ratios of ~1.49 and ~0.39 respectivley, in comparison to the other portfolios tells us that the return-to-risk ratio of the Algo portfolios is much greater than that of the comparison portfolios - and that is a key indicator all investors must keep in mind when analyzing potiential investments.
Based on the shape ratio analysis metrics, my custom portfolio has the second highest sharpe ratio of the group of portfolios which suggests it has one of the highest risk-to-return ratios of the group. Algorithmic portfolio 1 in particular still has the highest sharpe ratio and indicates it is still the strongest performing portfolio of the group even after the inclusion of my custom portfolio. However, the sharpe ratio analysis also suggests that my custom portfolio does out perform all other portfolios, inlcuding out performing the S&P TSX. I believe the custom is a portfolio that would be worth investing in based on the metrics and analysis conducted above.
When computing the rolling beta for my custom portfolio over the S&P TSX, it could also be seen that there is significant amount of volitility in the movement of my custom portfolio with the overall larger market. This is the same case for the risk of my custom portfolio based on the standard deviation metric fluctuations that can be view in the graph above. With this said, with greater risk comes greater potiential reward and the investment attractiveness of my custom portfolio would be more directed to risk the loving investors who are looking for a large return.