Correlation-Regime Detection and Crisis Attribution

Overview

The correlation-analysis skill answers "how correlated are these assets?", a snapshot. This skill answers the temporal questions a snapshot cannot:

1. When did the market fuse into one highly-correlated bloc, and when did it release? (Mode 1, regime detection)
2. What does a fused regime mean for position sizing? (Mode 2, risk context)
3. Who moved first when a crisis broke, is there a nameable trigger asset? (Mode 3, first-mover attribution)
4. Who quietly rewired their relationship to the rest of the market, even without a violent move? (Mode 4, rewiring leaderboard)

The methodology comes from an open-source streaming pipeline (see References) whose public repository pins the regime machinery's math and an eight-event historical replay regression (COVID, May-2021, China ban, Nov-2021 top, LUNA, FTX, SVB, yen-carry, 17 crypto symbols, 1-minute bars) that its CI reproduces bit-for-bit. The finer-grained numbers quoted in this skill, 13 fused/defused regime cycles on the continuous 2020, 2024 tape at a ~0.008/day calm false-alarm rate, and zero wrong culprit names across 10 labeled crises (2 held out-of-sample), including naming FTT roughly two days before the November 2022 collapse, are the author's unpublished internal replays on that same pipeline and are not independently verifiable. All of it is historical replay, never live results, and the method is market-agnostic even though the validation tape is crypto.

What this skill is NOT: a trade-timing signal. The same validation program tested regime-based exits head-to-head against a plain price stop and lost, correlation regimes cannot time tops, and the give-up cost of selling into a crash is a property of the tape, not of any signal. Use these modes for risk context, monitoring, and post-hoc attribution; never present them as buy/sell triggers.

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