technical-basic

To compute a composite trading signal in pandas, you can use a class-based engine to apply EMA, ADX, Bollinger Bands, RSI, and OBV to OHLCV DataFrames, combining them via three-dimensional voting for trend, mean reversion, and volume-price confirmation.

Three-dimensional voting is a mechanism that aggregates directional signals from trend indicators like EMA cross and ADX, mean-reversion tools like Bollinger Bands and RSI, and volume-price metrics like OBV into a single composite trading decision.

You can apply Wilder smoothing to RSI and ADX calculations by passing your OHLCV DataFrame to a pandas-based signal engine, which internally computes these technical indicators with the specified smoothing method to generate directional votes.

Yes, you can batch evaluate technical signals across multiple assets by instantiating a signal engine and passing it a mapping of asset codes to their respective OHLCV DataFrames, processing them all within a single pandas workflow.

Yes, the technical indicator engine requires numpy and requests in addition to pandas as core dependencies to compute multi-indicator trading signals and handle OHLCV time series data effectively.

You should use a multi-factor trading signal instead of a single indicator like RSI when you need volume-confirmation via OBV and trend validation via EMA cross and ADX to reduce false positives in automated decision-making and backtesting pipelines.