Model 2

 Creating a complete Python program for an ALGO model to scan stocks for long-term investment involves several steps, from data collection to implementing algorithms that filter and rank stocks based on predefined criteria. Here’s a simplified example using Python:

Explanation:

1. Data Setup: We define dummy data representing stocks and their respective attributes like Revenue Growth, ROE (Return on Equity), Debt-to-Equity Ratio (D/E Ratio), and Dividend Yield.
2. Criteria Definition: We set thresholds for each criterion (`revenue_growth_threshold`, `roe_threshold`, `de_ratio_threshold`, `dividend_yield_threshold`). These thresholds define what constitutes a favorable attribute for long-term investment.
3. Filtering: Using Pandas DataFrame operations, we filter out stocks that meet all the defined criteria.
4. Scoring and Ranking: We calculate a score for each stock based on how well it meets the criteria. In this example, a simple scoring mechanism adds up normalized values of each criterion. Then, stocks are ranked based on these scores in descending order to prioritize the best candidates for long-term investment.
5. Output: Finally, we print out the ranked list of stocks along with their attributes and scores.

This Python program provides a basic framework for an ALGO model to scan stocks for long-term investment based on predefined criteria. Depending on your specific needs and additional criteria, you can expand and customize this program further or you can contact us to write the code for you.