Financial prediction projects often stop at model training and ignore evaluation rigor, deployment considerations, and real-world constraints. This project explores end-to-end stock prediction and algorithmic trading logic using machine learning.
Market Data
→ Feature Engineering
→ Prediction Model
→ Strategy Logic
→ Trade Simulation
The system separates prediction from trading decisions to allow strategy experimentation.
- Clear separation between prediction and execution
- Emphasis on reproducibility
- Focus on interpretable features
- Offline simulation before live usage
- Time-series stock prediction
- Feature engineering pipeline
- Trading strategy simulation
- Performance evaluation
pip install -r requirements.txt
python main.py