wcannonms / python_stock_visuals

Analysis and Visualizations of the S&P 500 data set as well as various individual stocks using Python's Bokeh Package

PythonStockVisuals_WilliamC

Analysis and Visualizations of the S&P 500 and various stocks, and custom equity portfolios

Data Source(s)

Research www.bloomberg.com

https://www.marketwatch.com/

Downloads of Datasets https://finance.yahoo.com/ https://pypi.org/project/yfinance/ (added 4/2/2024) https://www.barchart.com/

https://www.kaggle.com/camnugent/sandp500

Purpose/Goal (short/medium term) = Use python, data science and education in finance to extract insights from stock data sets by building visualizations

Purpose/Goal (long term) = Wrap all of the above into an API structure that automatically updates all plots and bokeh applications at the end of every trading session

Initial Ideas of Stocks and Portfolios to analyze and visualize:

• Tesla (TSLA) vs Ford Motor Co. (F) and General Motors Co. (GM) since TESLA's IPO
• Top 3 social media compaines performance since IPOs: Facebook (FB) vs Twitter (TWTR) vs Snap Inc. (SNAP)
• Apple Inc. (AAPL) VERZUZ Haters: Alphabet Inc. (GOOG), Microsoft Corp. (MSFT) since the last IPO forward
• Amazon.COM Inc. (AMZN) vs Big Box Retail Target (TGT), Walmart (WMT) and Costco Wholesale Corp. (COST)
• Luxury/Designer brand stock performance: Ralph Lauren Corp. (RL), LVMH Moet Hennessy Louis Vuitton (LVMUY), Tiffany & Co. (TIF) and Burberry Group (BURBY)
• Clothing/Retail brand performance: Levis Strauss & Co. (LEVI), Nike Inc. (NKE), Adidas (ADDYY), Gap Inc. (GPS), Under Armour (UAA), Guess? Inc. (GES) and Steve Madden (SHOO)
• Several decades of S&P 500 data for seasonality trends {implement Add Buttons, Widgets and the Bokeh Workflow}
• Several decades of leading indicators against the S&P 500 {Potentially build an application around this}
• Measure a real estate investment of $$$,$$$.$$ against an exact dollar investment in equities across several decades

Plan for this porject:

• Build visualizations to tell stories behind performance and volitility
• Perform some P.A.I.R. Anaysis (Performance Analysis and Investment Risk) of all stocks and portfolios
• Build and Deploy an API strucutre that can retreieve & update this data automatically
• Begin exploring visualizations of other asset classes beyond equities (Fixed Income, Currency, Derivatives)
• Build comparative visualizations in Tableau, Matplotlib, R and any finance python packages

Project (A) Notes:

• The starting point for the data is 11/18/2010. That is the first session where all 3 companies are traded.
• IPO Dates: FORD (F) = May 15th 1956, General Motors (GM) = November 17th 2010, Tesla (TSLA) = June 9th 2010
• Date Period = Ten full years from 11/18/2010 to 11/18/2020 (10/2/2023 WEC)