Symplectic.Research

Updated preprint: Scale Invariant Dynamics in Market Price Momentum - with connections to information geometry and thermodynamic coupling. SSRN: papers.ssrn.com/sol3/papers.cf…

@JungleRockRes Looks amazing, totally smash!

smash or pass

@JungleRockRes Smash! Looks awesome

smash or pass

@riemannzeta Yes totally agree. Nice work!

@QuantSymplectic Love it. I've been looking for good data sets to test and you obviously put a lot of work into pulling yours (and your analysis together). Glad we're looking at things in a similar way.

Revised version now live: "Scale Invariant Dynamics in Market Price Momentum." Across six markets and five timescales, two dimensionless quantities remain stable: Dissipation rate and deterministic fraction: Ω ≈ 0.92 R² ≈ 0.57 SSRN: ssrn.com/abstract=59906…

@riemannzeta Less arb capital, thinner market-making, weaker institutional infrastructure, exactly where "maintenance cost" should fail. Illiquid/emerging markets are the right place to look.

@riemannzeta Your testable prediction is the strongest part. We already have partial evidence: crypto-equity data from another practitioner shows Ω ≈ 0 across all conditions, a clear break from the universal band.

A possible explanation for the Jegadeesh–Titman momentum anomaly? Analysis of 462 years of market data suggests the transition from mean-reversion to momentum may arise from scale-dependent dynamics in market phase space. New preprint: papers.ssrn.com/sol3/papers.cf…

@riemannzeta Your interpretation of Φ is exactly right. Updating a sharp distribution (storage constraints) costs more than updating a diffuse one (weak interest parity). That's the KL asym mapping onto Table 3's ordering. And the asymmetry connects to nonlinear extensions at longer horizons.

@riemannzeta The mapping is deeper than metaphor. The Fisher metric in Sec 6.2 IS the local KL divergence: D_KL(p_θ ∥ p_{θ+dθ}) = ½ g_ij dθⁱdθʲ. So "maintenance" (Ω) and "synchronization" (k) being Fisher-orthogonal means they represent independent channels of informational disagreement.

@JungleRockRes Smash!

smash or pass

@VolatilityQ Sorry to hear, I'm sure she was very proud of you!

@SeaChangeLabs Take a look at 2 of the papers I've posted (the first is pinned; the 2nd paper was posted a day or so ago. These describe how the phase-planes are constructed from momentum data. Check back if you get stuck!

@QuantSymplectic I am a theoretical physics phd, two words on how do you derive the streamlines?

Been working on a framework for classifying volatility regimes using only publicly available VIX closing data from Yahoo Finance. Here we see 2 complementary phase spaces: a vol-of-vol diagram that captures the second derivative of VIX dynamics, and a term structure diagram (VIX/VIX_MA63 vs VIX percentile rank) that captures contango/backwardation conditions. Still experimenting but love the regime classifier approach.

@JungleRockRes Totally smash - thats looks amazing!

smash or pass Paris 🇫🇷

@IndepBets Thats kind of you to say!

@QuantSymplectic I got to say I am pretty jealous 😁❤️⛷️🚶‍♂️

My office, love trading from the mountains.

@mytradesignals Yes it sounds crazy but the Bank of England UK equity data provides 308 years and 3,697 monthly observations, from the Bank of England’s “A millennium of macroeconomic data” compilation. And then combining with Schiller (154 years) and SPY data, we get 462 years.

@QuantSymplectic 462years?