Technical and AI Stock-Selection Indicators: Formulas and Uses
Summary
This educational overview introduces common inputs for quantitative stock selection, beginning with simple and exponential moving averages, RSI, Bollinger Bands, MACD, moving-average crossovers, VWAP, and historical volatility. It describes their usual interpretations: trend direction, momentum extremes, price dispersion, volume-weighted benchmarks, and changes in volatility. It also outlines factor ranking, event-driven analysis, text-based sentiment extraction, machine-learning models, and high-frequency trading as broader approaches that may feed into AI-based selection.
The article includes Python examples that calculate and chart several indicators using a synthetic price series and randomly generated volume. This demonstrates mechanics rather than investment performance; it gives no live-market evidence, validation, or trading results. Some formula descriptions are incomplete or imprecise, and the example VWAP is cumulative rather than a session-specific benchmark. The text presents indicators as data sources to combine with fundamentals and other information, while emphasizing that advanced models require substantial financial and technical expertise. It does not provide a complete, tested stock-selection system or guidance on controlling model overfitting.
Key ideas
- Moving averages and MACD describe trend and momentum using smoothed price data.
- RSI and Bollinger Bands summarize momentum extremes and price dispersion.
- VWAP weights prices by volume, while volatility measures the degree of price variation.
- Factor ranking, event analysis, sentiment extraction, and machine learning are described as broader selection methods.
- The examples use synthetic data and do not establish predictive performance.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.