technical-basic

To generate trading signals from OHLCV data, this skill computes EMA crossovers, ADX strength, Bollinger Bands, RSI, and OBV within a pandas DataFrame. It returns composite buy, sell, or neutral signals by blending trend, mean-reversion, and volume-price analysis.

Composite signal generation uses EMA for trend direction, ADX for trend strength, Bollinger Bands and RSI for mean-reversion, and OBV with volume ratio for price confirmation. These indicators combine into a single rule-based trading signal.

Yes, you can use this skill for algorithmic trading on any financial instrument where open, high, low, close, and volume series are available. It processes standard OHLCV DataFrames to output signals for assets like BTC-USDT or equities.

You need pandas and numpy installed to compute the technical indicators, as they handle the mathematical operations for EMA, ADX, and RSI. The requests dependency is listed, but indicator calculation itself relies on pandas and numpy arrays.

The best way to combine RSI and Bollinger Bands for mean-reversion signals is to use them together within a pandas DataFrame to detect overbought and oversold conditions. This skill rule-basedly merges those outputs with trend and volume metrics.

You should not use a composite indicator approach when you need isolated indicator outputs or trade purely on single metrics like EMA crossover. This skill blends multiple analyses into one signal, which may mask individual indicator behaviors.