elliott-wave

Automate Elliott Wave pattern detection by processing OHLCV time-series data with pandas and numpy to identify swing points, validate Fibonacci ratios, and generate trend reversal signals. This removes subjective human bias from technical analysis.

Identify 5-wave impulse structures by applying Zigzag swing-point analysis to financial time-series data. The process mathematically validates wave counts against strict Fibonacci ratio rules to filter market noise and isolate valid structures.

Elliott Wave signal generation requires OHLCV time-series data to detect swing points and validate wave theory rules. High-performance numerical computation relies on pandas and numpy dependencies to process the market data efficiently.

You can use Fibonacci ratios in pandas to validate 3-wave corrective structures by checking swing points against mathematical wave count rules. This validation filters out invalid patterns and isolates potential correction-complete buy opportunities.

Automated technical analysis fails when strict Fibonacci ratio checks and wave count rules are not properly applied to swing points. Validating impulse and corrective structures correctly filters noise and accurately identifies trend-top reversal signals.

Limitations of automated wave theory include its reliance on accurate OHLCV time-series data and strict mathematical validation of Fibonacci relationships. Complex market noise can obscure swing points, leading to missed or false trend reversal signals.