khashabri / stockanalyser

This repository contains a Python script designed to evaluate and rank a list of companies based on their stock performance. It utilizes various metrics to assess each company's stock growth and performance over a specified period.

Stock Analysis and Evaluation Tool

This repository contains a Python script designed to evaluate and rank a list of companies based on their stock performance. It utilizes various metrics to assess each company's stock growth and performance over a specified period.

Key Features

• Data Retrieval: Uses yfinance to download historical stock data for a list of companies.
• Data Processing: Calculates key metrics such as the number of positive days, net growth, mean relative daily growth, and total relative growth.
• Evaluation: Computes an overall evaluation score for each company based on the calculated metrics.
• Output: Displays and saves a formatted table of evaluated companies, ranked by their performance.

Key Performance Metrics

1. Ratio of Positive Days (rel_num_of_pos)

   • Definition: The ratio of days the stock price increased compared to the total number of days.
   • Calculation:

     rel_num_of_pos = num_of_pos / len(diffs)

   • Interpretation: A higher ratio indicates a more consistently bullish stock.

2. Mean Relative Daily Growth (mean_rel_growth)

   • Definition: The average daily percentage change in the stock price.
   • Calculation:

     mean_rel_growth = np.mean(diffs[1:] / x[:-1])

   • Interpretation: A higher mean relative growth indicates more significant average daily gains.

3. Total Growth (total_rel_growth)

   • Definition: The overall percentage change in stock price over the entire period.
   • Calculation:

     total_rel_growth = nettoGrowth / x[0]

   • Interpretation: A higher total growth indicates a more significant overall increase in stock value.

4. Evaluation Score

   • Definition: A composite score evaluating the overall performance of the stock.
   • Calculation:

     evaluation = ((1/3) * rel_num_of_pos + (1/3) * mean_rel_growth + (1/3) * total_rel_growth) * 10

   • Interpretation: Combines the above metrics to provide a balanced evaluation score. A higher score indicates better performance.

Dependencies

• yfinance: For downloading historical stock data.
• numpy: For numerical operations and data processing.
• tabulate: For formatting the output table.
• progress: For displaying a progress bar during the data processing.
• psutil: For monitoring CPU usage.
• Custom modules:
  • Timing: Contains a timer utility.
  • finanz_proj_database: Assumed to be used for additional data handling or storage.

Usage

Setup: Ensure all dependencies are installed. You can install the required packages using pip:

pip install yfinance numpy tabulate progress psutil

Run: python main.py