Detection, Decoding of "Power Track" Predictive Signaling in Equity Market Data

Summary

Detection and Decoding of "Power Track" Signals in Equity Market Data Abstract We report the discovery of “Power Tracks” – brief, structured bursts in stock market trading data that carry encoded information predictive of future price movements. These signals were first observed in high-resolution consolidated tape data, which aggregates trades from all exchanges and off-exchange venues [investor.gov]. We develop a rigorous methodology to detect these anomalies in real time, extract their encoded content, and decode them into future price paths or corridors. Using 1-minute interval price data for GameStop Corp. (GME) as a case study (sourced via Polygon.io’s API, which covers all U.S. exchanges and dark pools/OTC [polygon.io]), we identified distinct millisecond-scale bursts exhibiting unusual spectral and rate-of-change signatures. Through a custom decoding pipeline – involving signal isolation, bitstream reconstruction, XOR-based de-obfuscation, and variable-length integer parsing with zigzag encoding – we converted these bursts into sequences of price and timestamp data. The decoded outputs consistently aligned with subsequent stock price movements, often predicting high-low price corridors minutes to months into the future. Statistical validation confirms that the likelihood of these alignments arising by chance (under a random-walk null hypothesis) is p < 0.001, indicating that Power Tracks convey genuine predictive information. We document multiple instances where overlapping Power Tracks (“layered” signals) jointly influence price trajectories, as well as successful real-time detection of new tracks within ~300 ms of their appearance. This paper presents our hypothesis, data sources, detection algorithms, decoding methodology, results, and implications. We provide extensive technical detail – including parameter choices, decoding logic, and example outcomes – to ensure reproducibility. Our findings reveal a previously unknown communication layer in market dat...