elliott-wave

To detect Elliott Wave patterns in pandas OHLCV data, the engine identifies zigzag swing points and matches 5-wave impulse or 3-wave ABC structures. It validates these wave counts using Fibonacci relationships and strict impulse rules to generate trend-top and correction-complete signals.

Fibonacci validation for Elliott Wave impulse structures works by applying strict ABC rules to verify retracement levels between detected zigzag swing points. This mathematical validation ensures the identified 5-wave patterns adhere to standard wave theory before generating trading signals.

You can use this Elliott Wave signal engine for backtesting single-asset price streams. It processes OHLCV data directly through pandas and numpy to output long or short action signals aligned with trend phases, suitable for both backtesting and live trading integration.

You do not need live data requests to generate Elliott Wave trading signals. The implementation runs purely on pandas and numpy for historical data processing, while the requests library is only an optional dependency for fetching live OHLCV data streams.

The best way to automate waveform interpretation for technical analysis is using a self-contained Python engine that detects turning points and matches wave structures. This reduces manual chart analysis by validating impulse counts and outputting actionable trend-timing signals.

Elliott Wave detection requires strict impulse and ABC rules to prevent false pattern identification in price data. By enforcing these structural validations alongside Fibonacci relationships, the engine ensures generated signals accurately reflect valid trend phases rather than market noise.