Investment Commentary: Quantum-Inspired Turning Point in USDCHF — April 2025
Date: 24 April 2025
Instrument: USD/CHF Spot and Implied Volatility (1M ATM)
Framework: Modified Schrödinger Equation (MSE) Analysis
Source: Claremont Partners – Quantum Predictive Systems Unit
Overview
USDCHF recently exhibited one of its most volatile episodes in recent memory, with a sharp collapse followed by signs of systemic reorganisation. Drawing from our Modified Schrödinger Equation (MSE) toolkit — a quantum-inspired framework — we monitored 7 proprietary indicators to diagnose turning points.
These indicators do not just chase trends. They interpret the shape, slope, energy, and synchrony of price and volatility waves. What emerges is a more wave-sensitive and interference-aware approach to trading — where sudden alignment or collapse in these fields may precede actual market turning points.
Summary of Indicators and What They Reveal
Chart 1–7: Full Panel of MSE-Based Indicators on USDCHF (Feb–Apr 2025)
From top to bottom:
(1) Price Slope — tracks the direction of price momentum,
(2) Momentum — shows acceleration or deceleration in movement,
(3) Curvature — detects inflection points and trend reversals,
(4) Attention Collapse Index (ACI) — identifies panic-driven disorder,
(5) Convexity × Curvature Index (CCI) — highlights nonlinear gamma squeezes,
(6) Amplitude Attention Index (AAI) — captures volatility outpacing price movement,
(7) Phase Interference Index (PII) — reveals systemic coherence between volatility and spot.
Together, these wave-sensitive indicators offer a probabilistic lens for identifying turning points in market structure.
1. Ψₚ(t): Price Slope
What it tells us: Measures how fast price is moving.
Current status: Recovered from sharp downward slope (early April crash) to mild upward slope as of April 23 — a directional shift underway.
Implication: The crash has bottomed, and upward pressure is building.
2. Ψₚ′(t): Momentum
What it tells us: Is the market accelerating or slowing?
Current status: Momentum has flipped positive post-April 20.
Implication: Bullish pressure is increasing — not just a pause, but a drive higher.
3. Ψₚ″(t): Curvature
What it tells us: Detects turning points — a spike means “U-turn”.
Current status: Strong curvature spike mid-April.
Implication: A rebound zone formed. Market is in early-stage recovery arc.
4. ACI(t): Attention Collapse Index
What it tells us: When fear and vol explode together — crowd panic.
Current status: Peaked on April 9 — now in decline.
Implication: Market fear has climaxed. Historically a contrarian buy signal.
5. CCI(t): Convexity × Curvature
What it tells us: Interaction between hedging flows and trend shape.
Current status: Spiked on April 22 — gamma effects visible.
Implication: Dealers likely short gamma — squeeze pressure active. Rally extension likely.
6. AAI(t): Amplitude Attention Index
What it tells us: Are traders nervous — and buying volatility?
Current status: Elevated around April 11–15, but not extreme.
Implication: Tension was proactive, not reactive. Reversal priced in early.
7. PII(t): Phase Interference Index
What it tells us: Is price and vol behaviour aligned or clashing?
Current status: Tight synchronisation near +1.0 resumed post mid-April.
Implication: Stability is returning. Price and risk are moving in phase — signalling coherence.
Investment View (Short-Term)
With 6 out of 7 indicators showing stabilisation or rebound conditions, we view this as a high-conviction Signal *B/D convergence zone — suggesting upside skew repricing and gamma dynamics are in play. Traders caught short upside may now be hedging through spot and call options.
Strategy Preference:
Buy 3-month call option near 0.8220–0.8250 strike.
Add put spread as hedge if vols compress.
Avoid full short gamma; instead, scale hedges on rallies.
What Makes MSE Different
Traditional indicators react to price. MSE-based tools anticipate structure — they measure:
When price waves curve (like ripples turning),
When volatility interferes with direction,
When attention collapses market coherence,
And when synchronisation returns.
This gives us a field-theoretic lens into markets — wave-based, nonlinear, and tuned to crowd psychology.
Final Word
This USDCHF episode is a compelling live example of how MSE analysis can pre-empt turning points by observing where and how wave structures align — often before price visibly reacts. As of 24 April 2025, the data suggests that positioning for a continuation of this recovery is both reasonable and empirically grounded.
Limitations and Strengths of MSE in the Face of External Shocks
While the Modified Schrödinger Equation (MSE) framework does not forecast the precise timing of external shocks — such as unexpected policy changes by the Swiss National Bank (SNB), geopolitical ruptures, or systemic news catalysts — it excels at identifying the precursors and aftermath of such events through wave-based behavioural patterns.
Unlike conventional indicators that respond reactively to price movement, the MSE model reveals underlying structural fragility: volatility imbalances, curvature inflections, and phase dissonance across price and implied volatility. These wave disturbances frequently emerge before markets visibly react, serving as early warnings of systemic vulnerability.
Specifically:
ACI (Attention Collapse Index) often spikes ahead of collective capitulation, flagging panic before it peaks.
AAI (Amplitude Attention Index) rises when implied volatility accelerates faster than spot — a hallmark of building stress.
PII (Phase Interference Index) captures loss of synchrony in the lead-up to chaos, and its restoration in the recovery phase.
Ψₚ″(t) (Curvature) and CCI (Convexity × Curvature Index) highlight nonlinear feedback loops — including gamma squeezes — that amplify external shocks.
While MSE cannot predict the content of a news event, it measures how close the system is to a state where any news can matter disproportionately.
Strategic Summary
MSE does not predict catalysts — it anticipates fragility.
It highlights moments when market structure is brittle, crowd positioning is unstable, and minor events may cause disproportionate reactions due to wave interference.
As such, the MSE system is not simply a predictive model — it functions as a quantum-informed market sensor, guiding risk management through awareness of systemic coherence, phase instability, and collapse potential.
Footnote
*Here’s the formal definition of Signals A–D used in the MSE (Modified Schrödinger Equation) framework,
In the context of quantum-inspired financial modelling, the following four signal types represent distinct behavioural regimes in price-volatility dynamics. Each signal reflects an interference-induced collapse or rebound, interpreted through wave-based indices.
Signal A: Hedging Panic (Collapse Trigger)
Definition: A sharp spike in Attention Collapse Index (ACI), usually coupled with falling price and rising implied volatility.
Interpretation: Traders are overwhelmed by downside moves, triggering disorderly hedging or liquidation.
Wave Interpretation: Observer-induced collapse where price wavefunction localises downward due to amplified attention.
Indicators:
ACI(t) spikes
Ψₚ(t) sharply negative
Volatility rises ahead of or alongside price drop.
Signal B: Gamma Squeeze / Nonlinear Rebound
Definition: Strong positive feedback in Convexity × Curvature Index (CCI), often with rising spot and declining skew.
Interpretation: Short-vol traders or option desks are forced to buy back due to upward price acceleration — creating a feedback loop.
Wave Interpretation: Recoherence of wave amplitude in higher price states due to external compression (gamma).
Indicators:
CCI(t) spikes
Ψₚ′(t) and Ψₚ″(t) both positive
Implied volatility may start to compress.
Signal C: Volatility Catch-Up
Definition: A delayed surge in Amplitude Attention Index (AAI) after price has already moved significantly.
Interpretation: Option markets “wake up” to a move they initially missed — a lagging fear or repricing effect.
Wave Interpretation: Delayed amplitude phase interference; observer correction after initial localisation.
Indicators:
AAI(t) spikes after price slope (Ψₚ) or momentum (Ψₚ′) has already moved.
Volatility lags spot.
Signal D: Skew Repricing / Volatility Realignment
Definition: Strong recovery in Phase Interference Index (PII) toward +1, often with declining implied vol but steady spot.
Interpretation: Market transitions into a more stable, synchronised state; implied volatility normalises.
Wave Interpretation: Constructive interference between price and vol waveforms; post-collapse coherence.
Indicators:
PII(t) rising steadily to near +1
AAI(t) flat or declining
Spot stable or trending gradually
Footnote: Patent Filings, Disclosures, and Inventor Information
The research presented here — including the Modified Schrödinger Equation (MSE) model, its financial waveform indices (ACI, CCI, PII, AAI, etc.), and quantum interference analogues applied to price dynamics — is protected under the following filings with the United States Patent and Trademark Office (USPTO):
• Provisional Patent Application
U.S. Provisional Application No. 63/780,503
Title: “Wave-Induced Collapse of Quantum and Probabilistic Systems via Observer Interference”
• Non-Provisional Utility Patent Application
U.S. Application No. 19/172,805
• Continuation-in-Part 1 (CIP1)
U.S. Application No. 19/173,849
Title: “Wave-Induced Collapse of Quantum and Probabilistic Systems via Observer Interference”
(extended to quantum entanglement, tunnelling, and control systems)
• Continuation-in-Part 2 (CIP2)
U.S. Application No. 19/178,991
Title: “Wave-Induced Collapse Systems and Observer Interference Framework for Resolving Foundational Quantum Paradoxes”
• Continuation-in-Part 3 (CIP3)
U.S. Application No. 19/181,360
Title: “Observer Collapse Control Systems for Quantum Memory, Biofeedback, and AI-Guided Interference Applications”
Public Disclosures:
• Zenodo: https://doi.org/10.5281/zenodo.15101452
• SSRN: https://ssrn.com/abstract=5203175
Inventor: Larry Lim Kheng Cheong, founder of Claremont Partners Pte Ltd (Singapore), is the sole inventor and conceptual originator of this quantum-finance framework.
We do collaboration across disciplines including:
Quantitative finance and investment signal generation
Quantum computing and wave-based algorithm design
Artificial intelligence and machine learning for portfolio management
Macroeconomics and systemic risk analysis
Advanced predictive modelling grounded in wave theory
Quantum mechanics and observer-induced collapse theory
Theoretical and experimental physics exploring interference and localisation
Contact for collaboration, licensing, or publication: kc@cppl.com.sg