Introduction
Each week, the GUQFXP engine performs a full risk‐management refresh—ingesting new volatility data, recalculating stop & break‐even levels, and re‐tuning all Nine-Laws couplings. This “Weekly Pulse” ensures your 28-pair FX portfolio remains adaptive, self-calibrating, and aligned with current market regimes.
1. Automated DAATS Ingestion & Population σ Calculations
Objective: Retrieve the latest M1440 DAATS for all 28 pairs, then compute universe‐wide statistics for noise budgeting.
// 1. Fetch DAATS values
pairs = [“EURUSD”, “GBPUSD”, …, “CHFJPY”]
for pair in pairs:
DAATS[pair] = fetchDAATS(pair, timeframe="M1440")
// 2. Compute universe metrics
totalDAATS = sum(DAATS.values())
meanDAATS = totalDAATS / len(pairs)
popVariance = sum((DAATS[p] - meanDAATS)**2 for p in pairs) / len(pairs)
popSigma = sqrt(popVariance)
noiseFloor = popSigma / len(pairs) // Law 4 noise envelope
- Total DAATS: ∑ₖ DAATSₖ
- Mean DAATS (μ): TotalDAATS / 28
- Population σₚₒₚ: √[ (1/28) Σ(DAATSₖ–μ)² ]
- NoiseFloor: σₚₒₚ / 28
2. Portfolio Death-Stop, Break-Even & Profit-Target from Aggregated ATR
Objective: Derive portfolio-level stop, BE and PT from the aggregated ATR (Law 4–6).
// 1. Aggregated ATR (Daily scale)
aggATR = totalDAATS / 16 // since 16 = √256
// 2. Portfolio-level parameters
portDeathStop = 16 * aggATR // 16×ATR₍₂₅₆₎ (Law 4)
portBreakEven = 1 * aggATR // 1×ATR₍₂₅₆₎ (Law 6)
portProfitTarget = 5 * aggATR // 5×ATR₍₂₅₆₎
// 3. Log or store for trade gating
storePortfolioMetrics({
“DeathStop”: portDeathStop,
“BreakEven”: portBreakEven,
“ProfitTarget”: portProfitTarget
})
- Aggregated ATR: TotalDAATS ÷ 16
- Portfolio Death-Stop: 16×aggATR
- Portfolio Break-Even: 1×aggATR
- Portfolio Profit-Target: 5×aggATR
3. Re-coupling γₖ via Beta-Function Updates (Law 9)
Objective: Perform weekly “renormalization” of all nine couplings (γ₁…γ₉) based on recent performance metrics.
// 1. Backtest metrics collection
metrics = backtestLastWeek()
// returns { “stopHits”: n1, “BEHits”: n2, “PTHits”: n3, “drawdown”: dd, … }
// 2. Compute beta-functions βₖ = dγₖ / dln(s)
for k in 1..9:
beta[k] = estimateBetaFunction(k, metrics)
// 3. Update couplings
deltaS = 1.0 // step in ln(scale), e.g. 1 week
for k in 1..9:
gamma[k] = gamma[k] + deltaS * beta[k]
// 4. Persist new γₖ
saveCouplings(gamma)
- Backtest Metrics: Stop-hits vs. BE/PT-hits, P/L, drawdown contributions.
- Beta-Function (βₖ): Sensitivity of γₖ to performance drift: βₖ ≈ Δ(metricₖ) / Δ(ln period).
- Coupling Update: γₖ ← γₖ + Δs·βₖ ensures your laws remain tuned to current market behavior.
Conclusion
The **Weekly GUQFXP Pulse**—automated DAATS refresh, portfolio stop/BE/PT recalculation, and Law 9 coupling updates—forms the heartbeat of your 28-pair, multi-timeframe engine. By executing these steps each week, GUQFXP maintains its quantum‐inspired adaptivity and robust risk management.
About the Author
Dr. Glen Brown is President & CEO of Global Accountancy Institute, Inc. and Global Financial Engineering, Inc., and the architect of GATS and the Nine-Laws quantum risk engine. With over 25 years in proprietary trading, he combines cutting‐edge theory with practical execution.
Closed Business Model Disclaimer
All methodologies described are proprietary to Global Accountancy Institute, Inc. and Global Financial Engineering, Inc. for internal use only. No external services or licensing are offered.
Risk Disclaimer
Trading involves significant risk of loss. The examples herein are illustrative and not investment advice. Past performance does not guarantee future results.