technical-basic

You can generate trading signals from OHLCV data using pandas by passing a mapping of symbols to OHLCV DataFrames into a SignalEngine, which computes EMA, ADX, Bollinger Bands, RSI, and OBV indicators to return 1, -1, or 0 signal values per asset.

A composite technical signal combines trend, mean-reversion, and volume-price indicators into a single directional output. Three-dimensional voting aggregates EMA cross, Bollinger Bands plus RSI, and OBV signals to decide a final long, short, or neutral position.

Yes, you can apply technical analysis indicators to multiple assets simultaneously by passing a dictionary mapping each symbol to its respective OHLCV DataFrame, which returns a corresponding signal series for each individual asset.

Besides pandas, you need to install numpy to compute ADX and RSI. The calculation engine relies purely on pandas and numpy to implement Wilder-smoothed ADX, EMA cross, and other volume-price indicator logic.

The best way to combine trend strength and mean reversion for backtesting is using a voting rule that integrates Wilder-smoothed ADX for directional momentum with Bollinger Bands and RSI for overbought or oversold conditions.

Your volume-price momentum signal returns 0 when the three-dimensional voting rule evaluates trend, mean reversion, and volume indicators without a clear majority, resulting in a neutral consensus rather than a definitive long or short signal.