Anomaly Detection Engine

Our system aims to sift through massive data sets to uncover hidden relationships, which we sometimes refer to as “alpha.” While correlation doesn’t imply causation, advanced analytics can highlight statistically significant patterns worthy of further investigation.

Statistical Approaches

• Cross-Correlation: Identifying lagged relationships between two time series (e.g., a spike in Reddit mentions precedes a price move by 10 minutes).

• Cointegration Tests: Checking if two assets maintain a stable long-term relationship, enabling pair-trading signals.

• Granger Causality: Providing insight into whether one series helps predict another. (Still not ironclad proof of “causation,” but a more refined approach to sequence prediction.)

Machine Learning for Pattern Recognition

• Unsupervised Anomaly Detection: Using methods like isolation forests, one-class SVMs, or deep autoencoders to detect unusual price-volume behaviors or outliers in fundamental data.

• Topic Modeling on Social Data: Extracting emerging discussion themes (like new meme stocks or upcoming crypto forks) using LDA or neural topic models.

From Insights to Actionable Signals

Raw anomalies become “human-readable” insights once we apply domain logic. For instance, if an anomaly is detected in options flow (e.g., an unusual ratio of calls to puts), the system can generate a contextual note: “High call volume on XYZ suggests bullish speculation. Correlated with positive chatter on Reddit. Potential short-term upswing but watch out for earnings volatility.”