technical-basic

To generate composite trading signals from OHLCV data, use a pandas-based engine that fuses EMA/ADX trend, RSI/Bollinger Bands mean reversion, and OBV volume indicators into a single long/short/neutral output via a three-dimensional voting rule.

Combine RSI, ADX, and Bollinger Bands into a single buy or sell signal by applying a voting logic that evaluates trend strength, mean-reversion extremes, and volume-price confirmation together, returning deterministic values of 1, -1, or 0.

Yes, you can use pandas to calculate Wilder EWM smoothing for technical indicators. This approach uses pure pandas computations for RSI and ADX calculations, making it deterministic and suitable for repeatable backtesting without external libraries.

The best way to backtest trading strategies across multiple symbols is to pass a mapping of symbols to OHLCV DataFrames, letting a composite signal engine process trend, mean-reversion, and volume indicators to return deterministic signal Series for each dataset.

Yes, the composite signal engine works with numpy and requests for backtesting. It relies on pandas and numpy for computing Wilder-style EWM indicators and can use requests to fetch OHLCV time series data for processing.

Inconsistent buy and sell signals often occur when indicators are calculated independently. Using a deterministic pandas implementation with Wilder EWM smoothing and a unified voting rule ensures repeatable 1, -1, or 0 outputs across backtests.