In this project I want to show my python coding skills in Google Colaboratory Environment. Beside this, I will try to show all steps of technical analysis of investment portfolio using next python libraries:
- Pandas(pandas data reader)
- yfinance
- NumPy(linAlg)
- SciPy(optimize and stats)
- Plotly(graph objects and subplots)
- Statsmodels.api
I will do it in 7 steps:
- Portfolio Optimization
- Efficient Frontiers
- CAPM Model
- Sharpe's Ratio
- Testing Portfolio Performances
- Robustness portfolio
- Black-Litterman model
After I did this I will compare it with non-technical analysis portfolio (Decision comes from gut and inspiration).
According to non-tech analysis starting portfolio, we use 6 companies:
- Starbucks - SBUX - 0.13 weight
- Coca Cola - KO - 0.22 weight
- Microsoft - MSFT 0.04 weight
- Pfizer - PFE 0.37 weight
- Nike - NKE 0.12 weight
- Exxon Mobil - 0.12 weight
Later Non Technical portfolio had 83 trades and a lot buying-selling companies, so for this analysis I will use only this 6 stocks
According to TA starting portfolio, we use 6 companies:
- Astra Zeneca - AZN
- Nvidia - NVD
- Under Armour
- Rivian - RIVN
- Carnival Corporation - CCL
- McDonalds - MCD