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 data. We discuss potential origins of these signals (e.g. algorithmic coordination or hidden liquidity mechanisms) and outline steps for regulators and researchers to independently verify and further investigate Power Tracks using the provided framework.
Introduction
Modern equity markets generate enormous volumes of data at high frequency across dozens of trading venues. While the National Market System consolidates trade and quote information (the “consolidated tape”) for transparency [investor.gov], a significant portion of activity occurs in non-displayed venues or hidden order types. Recent studies estimate that hidden or off-exchange trades provide liquidity for roughly 40% of U.S. equity volume (and up to 75% for high-priced stocks) [papers.ssrn.com]. This fragmented, complex landscape raises the possibility that subtle patterns or “footprints” of algorithmic trading may be embedded in the data stream, escaping casual observation.
Hypothesis: We posit that certain market participants might be inserting encoded signals into trading data – intentionally or as a byproduct of algorithmic strategies – which carry information about future price direction or targets. We term these hypothesized signals “Power Tracks.” They are expected to manifest as brief bursts of trading activity with a non-random structure, possibly serving as instructions or forecasts when decoded. If such signals exist, uncovering them could have profound implications: it would suggest that some traders have knowledge of, or control over, future price movements, undermining market fairness and transparency. Regulators would have a strong interest in detecting and understanding these phenomena.
Research Questions: This study addresses several key questions: (1) Existence: Do Power-Track signals exist in consolidated market data, and how can we reliably identify them against the noisy background of normal trading? (2) Structure: If found, what is the format or encoding scheme of these bursts? Are they machine-readable sequences rather than random noise? (3) Decoding: Can we develop a method to decode the bursts into meaningful information (e.g. predicted prices or timestamps)? (4) Predictive Power: How well do decoded signals align with subsequent market movements – do they truly predict future price paths, and over what horizon? (5) Robustness: Are these tracks reproducible and statistically distinguishable from chance patterns? (6) Multiplicity: How do multiple overlapping signals interact if more than one is present? (7) Practical Detection: Can we detect new Power Tracks in real time, enabling potential regulatory monitoring or trading strategy adjustments?
We approach these questions by conducting a deep analysis of high-resolution trade data, focusing primarily on the volatile stock GameStop (GME) during periods of unusual market activity. GameStop’s trading in 2021–2024, amid meme-stock rallies and elevated retail participation, provides a rich dataset with many anomalies. However, our framework is generalizable to other symbols. We use data from Polygon.io – an aggregator providing tick-level and minute-bar data across all U.S. equity exchanges and dark pools/OTC [polygon.io] – supplemented by direct exchange feeds (e.g. CBOE’s EDGX). GME’s full tick data (including off-exchange trades via the include_otc=true flag) was collected and examined for the presence of Power Tracks.
Contributions: We present a complete pipeline for Power-Track discovery and analysis, including: a real-time detection algorithm for flagging candidate bursts; a rigorous extraction and decoding procedure that converts raw burst data into structured price/time outputs; and an evaluation of the decoded signals against subsequent ground-truth market data. We document specific case studies where a Power-Track correctly anticipated the stock’s trading range minutes, days, or even weeks ahead. We also provide quantitative aggregate results demonstrating that these signals have statistically significant predictive value. To our knowledge, this is the first documentation of an embedded “signal within the signal” in equity market data. By detailing our methodology and providing references to data sources and standard encoding schemes, we enable independent verification.
The remainder of this paper is organized as follows: Section 2 describes the data sources and our real-time detection strategy for isolating Power-Track events. Section 3 details how we capture the raw bursts and outlines the decoding pipeline, including bitstream processing, varint/zigzag decoding, and reconstruction of price sequences. Section 4 presents example decoded tracks and interprets their content as future price corridors, including a discussion of multi-timescale payloads. Section 5 examines cases of overlapping signals and their combined effect. Section 6 provides statistical validation of the signals’ predictive efficacy. Section 7 discusses implementation aspects of a real-time Power-Track monitoring system and potential regulatory applications. We conclude in Section 8 with implications, open questions, and recommendations for further research.
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