PortfolioIQ is an end-to-end data-driven system designed to analyze stock market performance, optimize portfolio allocation, and generate intelligent investment insights. The project leverages historical market data to compute key financial metrics such as return, volatility, Sharpe ratio, beta, and maximum drawdown, enabling a comprehensive risk-return analysis of selected stocks.
It integrates technical indicators like moving averages to generate trading signals and constructs optimized portfolios based on risk-adjusted performance. Additionally, machine learning models—including Random Forest and Linear Regression—are used to predict future stock returns and identify influential market drivers.
To enhance real-world applicability, the system is connected with an automated workflow that generates actionable alerts based on model predictions. Overall, PortfolioIQ bridges financial analytics, machine learning, and automation to support smarter and more informed investment decisions.
Built With
- csv
- jupyter-notebook
- linear-regression
- matplotlib
- n8n
- numpy
- pandas
- python
- random-forest
- scikit-learn
- seaborn
- yfinance-api
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