├── .gitignore
└── README.md


/.gitignore:
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1 | .env
2 | __pycache__/
3 | .ipynb_checkpoints
4 | */.ipynb_checkpoints/*
5 | .DS_store


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/README.md:
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 1 | # Cluster_Portfolio_Analysis
 2 | ***
 3 | ## This project examines a 35 stock varying weight portfolio loosely based on a colleague's portfolio with similar composition.
 4 | ***
 5 | ## Quantitative analytics performed on the portfolio included component correlation and interaction, a benchmark comparison, distribution of returns analysis and sharpe ratio analysis. 
 6 | ***
 7 | ## Furthermore, a "deep dive" into the components of the portfolio was undertaken and numerous metrics including valuation & performance were examined.
 8 | ***
 9 | ## Primary tools used in the project were:
10 | * OpenBB SDK - quick & easy access to stock data on all components of the portfolio.  Further "deep dive" into certain metrics fo the portfolio was also achieved through application of just a few of the hundreds of available functions.  
11 |     * https://docs.openbb.co/
12 | * Riskfolio-Lib - portfolio optimization library that was used to explore how the components of the portfolio "interacted" via correlation & clustering analysis
13 |     *  https://riskfolio-lib.readthedocs.io/
14 | ***
15 | ## Summary
16 | * This project was an excellent case used to explore two very powerful analysis tool kits; OpenBB SDK & Riskfolio-Lib
17 | * Through use of the OpenBB SDK stock data of portfolio components was quickly and easily obtained ready for further analysis.
18 | * The use of Riskfolio-lib allow for the examination of correlation and interaction of portfolio components. A minimum spanning tree analysis and asset cluster map gave excellent insight to the interactions of the stocks in the portfolio.
19 | * The OpenBB SDK allowed for the analysis to dive deep into the components of the portfolio and start to examine multiple valuation and performance metrics.
20 | ***
21 | ## Follow On
22 | * At this point in the project, the analysis could take a number of different directions depending on the end user/client objectives.  Some possible avenues to explore are:
23 |     * Portfolio rebalancing/reduction based on cluster analysis
24 |     * Portfolio optimization around risk parameters (Sharpe optimization, risk parity ...)
25 |     * Predictive analytics utilizing current data frames of performance and valuation data.
26 |     * Portfolio hedging


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