Upcoming works

1. To build time series forecaster onto Streamlit next
2. Consider use of other databases (DuckDB, PostgresSQL) for learning

Part 1 Time Series analysis and forecasting:

This script aims to perform SARIMA to analyse stock prices and forecast into the future with the following purposes:

1. Identify near-term price movement trends
2. Determine optimal actions for maximum gain

Part 2 Web scrapper:

The web scrapper utilises BeautifulSoup 4 to scrap info from the yahoo finance statistics page.

Two sets of data are utilized: S&P constituents and a personal portfolio.

Part 3 S&P and personal portfolio segment analysis with recommendations:

This script performs a K-means clustering on the S&P constituents to determine different groups of companies. The following features are used in K means clustering:

1. Market cap
2. Revenue
3. Profit Margin
4. 52 Week Change
5. Quarterly Earnings Growth (yoy)

The identified clusters are compared with one's portfolio for comparison.

Improvement logs:

20240110:

1. Added RMSE or AIC into TS validation
2. Optimized for SARIMA parameters, using auto_arima and further analysis

20240108:

1. Modularising Forecaster on notebook
2. Added functions to
   • forecast
   • validate forecast
   • plot forecast
   • determine single buy and sell for maximum profit within forecast

20240107:

1. Completed modularisation of YfScrapper
2. Completed learning on time series (ARIMA, SARIMA)

20240103:

1. Refactored code and directory to modularise scrapper
2. Renamed project