- Quantitative Stock Market Analysis and Strategy Backtesting
Fetch historical stock data (OHLCV) and financial indicators from APIs like Alpha Vantage, Yahoo Finance, or Quandl. Gather macroeconomic indicators (GDP, interest rates, etc.) from sources like FRED (Federal Reserve Economic Data). Transform:
Clean the data, handle missing values, and normalize the data. Calculate quantitative metrics like Sharpe ratio, Sortino ratio, beta, alpha, and volatility. Implement technical indicators (e.g., Bollinger Bands, MACD, RSI). Load:
Store the cleaned and processed data in a relational database like PostgreSQL or SQLite. Quantitative Analysis & Backtesting Quantitative Models:
Develop trading strategies using quantitative methods like mean reversion, momentum strategies, and pairs trading. Backtest the strategies using historical data to evaluate performance metrics (e.g., cumulative returns, drawdowns, win/loss ratio). Risk Management:
Implement risk management techniques like stop-loss, take-profit levels, and portfolio diversification. Use VaR (Value at Risk) and CVaR (Conditional Value at Risk) to assess potential losses. Streamlit Visualization Interactive Dashboards: Visualize historical stock data, trading signals, and strategy performance using interactive plots (e.g., candlestick charts, moving averages). Include backtesting results with metrics like Sharpe ratio, drawdowns, and cumulative returns. Allow users to adjust strategy parameters (e.g., lookback periods, thresholds) and see updated performance metrics in real-time.
Frontend requires data for X analysis (backtrading/ BlackSholes of a stock etc) sends request to backend
Backend accepts json which converts tto pydantic for validation.
if not in db go to yf finance get data
save to db, do backend process send response back to frontend.