This Python framework provides a modular approach to portfolio optimization, risk management, and performance evaluation. It includes various optimization techniques, risk-adjusted metrics, and visualization tools for financial portfolio analysis.
- Portfolio Optimization: Mean-Variance Optimization, Risk Parity, Minimum Variance, and more.
- Risk Management: VaR, CVaR, Sharpe Ratio, and other risk-adjusted performance metrics.
- Backtesting: Evaluate historical performance with benchmark comparisons.
- Visualization Tools: Efficient frontier plotting, risk-return scatter plots, and asset allocation charts.
- Modular Design: Easily extendable for custom optimization strategies.
Command using csv: python main.py --data sample.csv --tickers AAPL NVDA MSFT --riskfree 0.01 --plots --backtest --sensitivity --llm
Command using tickers: python main.py --tickers AAPL NVDA MSFT --start 2020-04-12 --end 2024-04-12 --riskfree 0.01 --plots --backtest --sensitivity -llm
Command to run Frontend: python app.py