This project explores real-world and sample data to understand market trends and inform trading strategies. With the goal of utilizing logistic regression models, new features will include forecasting financial instrument demand and identifying behavioral patterns to support client-aligned strategies.
- Notebooks to visualize key market metrics
- Logistic regression to forecast market demand
- Data analysis to identify patterns in market behavior
- Insights to align trading strategies with client preferences
- Python (Pandas, scikit-learn, Matplotlib/Seaborn)
- SQL
- Jupyter Notebooks / VS Code
- data -> Raw and cleaned datasets
- models -> Logistic regression scripts
- visualizations -> Plots, charts, and dashboard exports
- analysis -> Jupyter notebooks for data exploration
- Install requirements (
pip install -r requirements.txt) - Run analysis scripts in the
analysis/folder
This project is licensed under the MIT License.