High-Performance EMA with Scipy - Quant Researcher Interview Question

Problem Description

Exponential Moving Averages (EMA) are critical in quantitative finance for smoothing time-series data while prioritizing recent observations through exponentially decreasing weights. Implementing these filters using optimized signal processing primitives like scipy.signal.lfilter ensures high performance and scalability for large datasets or real-time pipelines compared to standard iterative approaches. Task Implement the function solution(data, alpha) to compute the Exponential Moving Average