technical-basic

To generate composite trading signals from OHLCV data, you can use pandas to aggregate trend, mean-reversion, and volume-price indicators into a single voting score. This filters market noise and identifies high-conviction long or short opportunities.

Trend, mean-reversion, and volume-price technical indicators are combined for quantitative analysis. Specifically, EMA and ADX capture trend, Bollinger Bands and RSI measure mean-reversion, and OBV tracks volume-price dynamics.

Yes, you can use numpy vectorized calculations for EMA, RSI, and Bollinger Bands. The Skill requires both numpy and pandas to perform these vectorized computations efficiently across historical OHLCV financial datasets.

The best way to scan a portfolio for bullish or bearish trends is applying a composite voting mechanism across multiple technical indicators. This synthesizes EMA, ADX, RSI, and OBV metrics to pinpoint assets meeting specific trend criteria.

Yes, the composite voting mechanism filters out market noise. By aggregating signals from EMA, ADX, Bollinger Bands, RSI, and OBV, it uses a robust voting system to identify high-conviction trading opportunities.

For technical analysis, you can use the last 300 days of historical OHLCV market data. This dataset provides sufficient depth for calculating EMA, ADX, Bollinger Bands, RSI, and OBV metrics to generate actionable trading signals.