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GL Market Phase Transition Model

RTSG × Ginzburg-Landau · S&P 500 · Live


Current State (2026-03-23)

Parameter Value Interpretation
SPY Close $655.38
Phase DISORDERED Mean-reverting regime
α (stability) 1.30 Strong mean reversion
Volatility 11.9% Low
Trend (W) -3.76% Below 20-day MA
Susceptibility 0.77 Low (market robust)
Hurst exponent 0.71 Persistent (trending)

Regime: 🔵 MEAN REVERSION — market is stabilizing after recent selloff


The Model

The GL action on market state \(W\):

\[S[W] = \int \left( |\partial_t W|^2 + \alpha |W|^2 + \frac{\beta}{2} |W|^4 \right) dt\]

Where: - \(W\) = trend strength (deviation from moving average) - \(\alpha\) = stability parameter (estimated from return autocorrelation) - \(\beta\) = nonlinear stabilization (estimated from excess kurtosis)

Phase Interpretation

α value Phase Market behavior Physics analogy
α > 0 DISORDERED Mean-reverting, prices return to fair value Paramagnet (spins random)
α ≈ 0 CRITICAL Phase transition, susceptibility diverges, fragile Critical point (Curie temperature)
α < 0 ORDERED Trend-following, momentum, bubbles/crashes Ferromagnet (spins aligned)

Key Observables

  • Susceptibility \(\chi = 1/|\alpha|\): Diverges at the critical point. High susceptibility = market responds disproportionately to small shocks.
  • Relaxation time \(\tau = 1/\sqrt{|\alpha|}\): How long disturbances take to decay. Diverges at critical point.
  • Condensate \(|W_0| = \sqrt{-\alpha/\beta}\): Equilibrium trend strength in the ordered phase.
  • Hurst exponent \(H\): \(H > 0.5\) = persistent (trends continue), \(H < 0.5\) = anti-persistent (trends reverse), \(H = 0.5\) = random walk.

Recent Phase Transitions

Date Direction What happened
2025-04-10 → DISORDERED Market stabilized after Q1 momentum
2025-07-17 → ORDERED Brief momentum spike
2025-07-18 → DISORDERED Immediate reversion
2026-01-07 → ORDERED New Year momentum regime
2026-02-23 → DISORDERED Current mean-reverting regime

Connection to RTSG

The Boltzmann-McFadden isomorphism maps:

Physics Markets
Energy Negative utility
Temperature \(T\) Inverse risk aversion \(1/\beta\)
Order parameter \(W\) Trend strength
Phase transition Crash / bubble
Landauer floor \(kT\ln 2\) Minimum cost per trading decision
Susceptibility \(\chi\) Market fragility

The complexification functor says markets spiral — same seasonal patterns at different altitudes. Monday isn't last Monday.

Implementation

  • Data source: yfinance (S&P 500 SPY ETF)
  • Window: 60-day rolling estimation
  • α estimation: Negative autocorrelation × 10 (positive autocorr = momentum → negative α)
  • β estimation: Excess kurtosis / 5 (heavy tails = strong nonlinear stabilization)
  • Dashboard: React + Recharts, live at artifacts

Literature

  • Bouchaud, Bonamy, "Financial markets and the phase transition between water and steam" (ScienceDirect 2022) — directly measures GL coefficients
  • Fry, "Endogenous and Endogenous Crashes as Phase Transitions" (2012) — market crashes as first-order transitions
  • Sornette, "Why Stock Markets Crash" (2003) — log-periodic power law

Research tool only. Not investment advice. RTSG BuildNet · Jean-Paul Niko · smarthub.my