Algorithmic Trading Frameworks - Python-based framework for building and backtesting quantitative trading strategies using financial data and portfolio optimization.
Value-Based Stock Selection - Implements a quantitative value strategy that selects stocks based on fundamental metrics and builds weighted portfolios.
Value Strategy Implementations - Identifies undervalued stocks using fundamental metrics and builds portfolios weighted by value scores.
Momentum Trading Strategies - Selects stocks with strongest recent price performance and rebalances portfolio based on momentum signals.
Fundamental Value Scorers - Ranks stocks by fundamental ratios like P/E and P/B to identify undervalued companies for portfolio weighting.
Portfolio Rebalancing - Runs periodic rebalancing of stock portfolios based on momentum or value signals, exporting updated allocations.
Trading Notebook Execution - Runs trading strategies as sequential cells in Jupyter notebooks, combining code, output, and documentation.
Equal-Weight Fund Builders - Constructs an equal-weight S&P 500 index fund by pulling constituent data and calculating portfolio allocations.
Jupyter Trading Notebooks - Interactive Python notebook environment for developing, testing, and executing algorithmic trading strategies.
Value Factor Portfolio Builders - Identifies undervalued stocks using fundamental metrics like P/E and P/B ratios, then constructs a weighted portfolio.
