In this project, I used Python and several key libraries - including pandas, numpy, matplotlib, and mplfinance - to perform a detailed analysis of stock data from Tesla, Ford, and GM motors.
I conducted my analysis using Jupyter Notebook, a powerful tool for data manipulation and visualization. Jupyter's interactive nature allows for quick experimentation and iteration, making it an ideal platform for exploring and understanding data.
First, I imported the stock data and used pandas and numpy to manipulate and clean it. I then used matplotlib and mplfinance to visualize the data, including the volatility of the stocks as judged by the variance in their daily returns. I also calculated and compared the cumulative returns of the stocks, and used scatter plots to explore any potential correlations between them.
With this information in hand, I plan to use it to develop an investment portfolio optimization algorithm. This algorithm will allow me to distribute my total available capital across the three stocks in a way that maximizes potential gain.
Overall, this project allowed me to develop a range of skills in data manipulation, analysis, and visualization using Python and Jupyter Notebook. The insights I gained from my analysis will be valuable not only in understanding the performance of these individual stocks, but also in making informed investment decisions.
This Github Repository contains:
- The Jupyter Notebook file.
- A .pdf conversion of the notebook file.
- the original stock data .csv's