Application of Dynamic time warping (DTW) pattern recognition in Financial Trading Financial time series display intricate, erratic, and non-stationary characteristics, rendering short-term price forecasting especially difficult in highly efficient markets. Dynamic Time Warping (DTW) has been suggested in previous literature as a method for detecting recurring temporal patterns; however, its practical utility within realistic trading constraints is still ambiguous. This thesis examines whether DTW-based pattern similarity can yield additional predictive and economic value beyond conventional technical indicators when assessed within a rigorously causal and validation-restricted framework. We built a full experimental pipeline that uses DTW to make pattern-outcome features by comparing recent market windows to similar historical segments and summarising their returns after that.