This algorithm combines statistics, trend theory, and noise reduction to identify high-probability trading opportunities. It first filters out market “noise” using smoothing techniques to reveal the underlying trend. Statistical measures such as volatility, skewness, and return distributions are then used to characterize market regimes, while trend indicators confirm directional bias. Patterns and motifs in the data are evaluated probabilistically to estimate win likelihood. Trades are only taken when trend, probability, and volatility filters align, with stops and targets sized statistically. The result is a disciplined framework that adapts to changing market conditions and focuses on trading only when the odds are favorable.