Status: Archived – No Longer Maintained
SMART (Stock Market Analysis and Research Tools) was an exploratory project aimed at developing tools for comprehensive stock market analysis. The goal was to integrate various analytical components to assist in investment research and decision-making.
The envisioned capabilities of SMART included:
- Balance Sheet Analysis: Calculating real expected growth and assessing how this growth is reflected in stock prices.
- Regression/Classification: Training neural networks on market data for trading/backtesting purposes.
- Hyperparameter Tuning: Bayesian optimization for selecting better hyperparams.
- Feature Importance Analysis: Finding the feature weights that mostly contribute to training.
Proposed scoring metrics:
- Growth Consistency
smart/– Core modules for analysis.tests/– Unit tests for validating functionalities.market_list.csv– Sample dataset of market listings.feat_imp_scores.pkl– Serialized feature importance scores.requirements.txt– Python dependencies.setup.py– Installation script.test.py– Script for testing modules.
This project is no longer maintained and may not function as intended. It is archived for reference purposes only. Users are welcome to fork and modify the codebase as needed.
This project is licensed under the MIT License. See the LICENSE file for details.
Author: Kaan Bertuğ Ersoy
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