Law 1 – Correlation Regime Transition Law (CRTL)

Statement

In a multi-instrument universe, systemic risk rises when cross-asset volatility (“DAATS”) spikes and pairwise correlations converge. Let:

V(t) = [ DAATS₁(t), DAATS₂(t), …, DAATS₂₈(t) ].

Define the volatility-vector norm:

‖V(t)‖ = sqrt( DAATS₁(t)² + DAATS₂(t)² + … + DAATS₂₈(t)² ).

Let its 30-day rolling average be:

‖V‖_hist(t) = (1/30) × Σ_k=1…30 [ ‖V(t – k)‖ ].

If both of the following hold:

1. ‖V(t)‖ ≥ [ 1 + δ × ( σ_‖V‖(t) / ‖V‖_hist(t) ) ] × ‖V‖_hist(t), where σ_‖V‖(t) is the 30-day standard deviation of ‖V(·)‖.
2. λ₁(t) – λ₁(t – 1) ≥ β(t), where λ₁(t) is the largest eigenvalue of the 28×28 correlation matrix C(t), and β(t) = 90th-percentile of { λ₁(k) – λ₁(k – 1) : k = t – 90 … t – 1 }.

Then a Stress Correlation Regime is triggered.

Action

1. Diagonalize C(t) and underweight instruments loading heavily on its top eigenvector.
2. Tighten break-even percentage (“BE %”) by +5 percentage points (e.g. 20 % → 25 % of each DAATS) until conditions normalize.

Explanation & Rationale

• Volatility-Vector Norm vs. Sum
  We use
  ‖V(t)‖ = sqrt( Σ_i=1…28 [ DAATS_i(t) ]² )
  so that large outliers dominate the measure rather than being averaged out.
• Normalized Z-Score
  Compute
  Z_‖V‖(t) = [ ‖V(t)‖ – ‖V‖_hist(t) ] / σ_‖V‖(t).
  Trigger if Z_‖V‖(t) ≥ δ (typically δ = 1).
• Eigenvalue Jump
  Under stress, correlations converge. If
  λ₁(t) – λ₁(t – 1) ≥ β(t),
  where β(t) = 90th-percentile of { λ₁(k) – λ₁(k – 1) } for k = t – 90 … t – 1,
  then correlation structure has shifted sharply.
• Portfolio Action
  
  • Rebalance along principal-component eigenvectors, reducing weights on highly correlated instruments.
  • Raise BE % from 20 % → 25 % of each DAATS until the regime passes.

Enhancements & Additional Adjustments

• Tail-Dependence Filter
  In addition to Pearson C(t), compute a tail-correlation matrix using Kendall’s τ on the bottom 5 % of returns. Trigger a Super-Stress Regime if both λ₁^Pearson and λ₁^Tail exceed their 95th percentiles.
• Intraday vs. Daily Norms
  Maintain two norms:
  • ‖V‖_M60 (computed on M 60 DAATS)
  • ‖V‖_Daily (computed on daily ATR)
  If ‖V‖_M60 > 1 σ above its 30-day mean and ‖V‖_Daily > 1 σ above its 30-day mean, reduce overall portfolio leverage by 20 %.
• Machine-Learning Calibration
  Monthly, run a logistic regression on 60 days of
  ( Z_‖V‖(t), Δλ₁(t) ) → 5-day drawdown indicator
  to re-estimate δ and β, targeting a false-positive rate < 10 %.

Law 2 – Weighted Decay of Historical DAATS (WDHDI)

Statement

For each instrument i, define an Effective DAATS that exponentially decays past values using an adaptive half-life:

DAATS_eff,i(t)
= Σ_k=0…Ki(t) [ λ_i(t) ]^k × DAATS_i(t – k),
where λ_i(t) = 0.5^( 1 / H_i(t) ).

And define:

H_i(t) = H₀,_i × [ ATR_P,i(t) / ATR̄_P,i ]^κ, 0.5 ≤ κ ≤ 1,
K_i(t) ≈ 4 × H_i(t).

Formulas & Definitions

1. Base Half-Life
   H₀,<sub>i</sub> is chosen per instrument (e.g. H₀,EURUSD = 12, H₀ for JPY-crosses = 10).
   ATR̄<sub>P,i</sub> = 30-day mean of ATR<sub>P,i</sub>.
2. Adaptive Half-Life
   H_i(t) = H₀,_i × [ ATR_P,i(t) / ATR̄_P,i ]^κ
   Cap H_i(t) to [ H₀,_i / 2, 2 × H₀,_i ].
3. Decay Factor
   λ_i(t) = 0.5^( 1 / H_i(t) ).
4. Effective DAATS
   DAATS_eff,i(t)
   = Σ_k=0…Ki(t) [ λ_i(t) ]^k × DAATS_i(t – k),
   where K_i(t) = ceil( 4 × H_i(t) ).

Explanation & Rationale

A fixed half-life ignores changing volatility regimes. By defining:

H_i(t) = H₀,_i × [ ATR_P,i(t) / ATR̄_P,i ]^κ,

we slow the decay when ATR is elevated (preventing overreaction) and accelerate the decay when ATR contracts, ensuring DAATS_eff remains responsive in calm markets.

Example (EUR USD)

• DAATS_EURUSD = 2899 ⇒ ATR_200,EURUSD(t) = 2899 / 15 ≈ 193.27 pips.
• Suppose ATR̄_200,EURUSD = 160 pips, H₀ = 12, κ = 0.75. Then
  H(t) ≈ 12 × (193.27 / 160)^0.75 ≈ 12 × 1.114 ≈ 13.37 days.
  λ(t) = 0.5^( 1 / 13.37 ) ≈ 0.949.
• Hence:
  DAATS_eff(t)
  ≈ DAATS(t) + 0.949 × DAATS(t – 1) + (0.949)^2 × DAATS(t – 2) + … (up to ~52 bars).

Enhancements & Additional Adjustments

• Regime-Switching κ
  • If Law 6 clustering labels today as “Strong,” set κ = 1.
  • If “Moderate,” κ = 0.75.
  • If “Choppy,” κ = 0.5.
• Piecewise Lookback Cap
  If ATR_P,i(t) > 2 × ATR̄_P,i, then set
  K_i(t) = 2 × H_i(t), so that in extreme volatility, we emphasize recent values.
• Quarterly Recalibration of H₀,_i
  Every quarter, sweep H ∈ [5, 20] days to minimize one-step-ahead forecast RMSE:
  RMSE(H) = sqrt( (1/N) × Σ_k=1…N [ DAATS_i(k+1) – DAATS_eff,i(k; H) ]² ).
  Set H₀,_i = argmin RMSE(H). If H₀,_i drifts by > ±10 %, update it.

Law 3 – Macro Shock Propagation Law (MSPL)

Statement

Whenever a macro-event m occurs at time t, with surprise magnitude:

S_m(t)
= [ Realized VIX(t) – Implied VIX(t–) ] / σ_VIX
or
[ ΔCreditSpread(t) – ΔCreditSpread(t–) ] / σ_ΔCS,

each instrument’s DAATS<sub>i</sub> is updated via:

ΔDAATS_i(t)
= φ_i(t) × | S_m(t) |^γ_i(t),
DAATS_i(t) ← max [ DAATS_i(t), DAATS_i(t–1) + ΔDAATS_i(t) ].

Formulas & Definitions

1. Shock Magnitude
   S_m(t)
   = [ Realized VIX(t) – Implied VIX(t–) ] / σ_VIX
   or
   [ ΔCreditSpread(t) – ΔCreditSpread(t–) ] / σ_ΔCS.
2. Sensitivity φ_i(t) & Exponent γ_i(t)
   Calibrate by nonlinear regression over a rolling 90-day window of shock dates:
   ΔDAATS_i(k) = φ_i × | S_m(k) |^γ_i + ε_i(k), 0.8 ≤ γ_i ≤ 1.3.
3. Update Rule
   DAATS_i(t)
   ← max [ DAATS_i(t), DAATS_i(t–1) + φ_i × | S_m(t) |^γ_i ].

Explanation & Rationale

Empirically, during major shocks, volatility often rises superlinearly (i.e. γ_i > 1). For example, GBPJPY and GBPNZD might exhibit γ_i ≈ 1.15, whereas EURUSD remains near γ_i ≈ 1.0.

Example (USD JPY on May 13, 2025)

• Suppose a Fed surprise drives ΔVIX = +4, Implied VIX = 20, σ_VIX = 2 ⇒ S_m = (24 – 20) / 2 = 2.
• If regression yields φ_USDJPY = 0.6, γ_USDJPY = 1.1, then:
  ΔDAATS_USDJPY(t)
  = 0.6 × 2^1.1 ≈ 0.6 × 2.14 ≈ 1.28 (DAATS units).
• If DAATS_USDJPY(t–1) = 4611, set:
  DAATS_USDJPY(t)
  = max [ 4611, 4611 + 1.28 ].

Enhancements & Additional Adjustments

• Hybrid Shock Measure
  Combine VIX surprise with real-time news sentiment from a BERT-based model. Define:
  S_m^hybrid(t)
  = w₁ × S_m^VIX(t) + w₂ × SentimentScore(t), w₁ + w₂ = 1.
  Re-fit φ_i, γ_i monthly on this hybrid measure.
• Tiered Exponent γ_i
  – If |S_m| > 3, set γ_i = 1.2 and increase φ_i by +10 %.
  – If |S_m| < 1, set γ_i = 0.9 to dampen overreaction.
• Shock-Decay Floor
  After updating, enforce:
  DAATS_i(t) ≥ DAATS_eff,i(t) (from Law 2),
  so that shock-inflated DAATS never falls below the weighted-decay average.

Law 4 – Exposure & Death-Stop Law (E & DS)

Statement

For any EMA zone whose top boundary is P, define:

X = ceil( sqrt(P) ).

Compute ATR<sub>P</sub> on your trading timeframe. Then the Death Stop is:

DeathStop = X × ATR_P.

In choppy regimes—if:

ADX(14, t) < 20
AND
ATR_P(t) < 0.75 × ATR̄_P,

then use:

X_adj = X – 1 (never below 1).

Zone-Exposure Table

Formulas & Definitions

1. Compute ATR<sub>P</sub> on the chosen timeframe (e.g. ATR₂₀₀ on M 60 for EUR USD).
2. Exposure Multiplier
   X = ceil( sqrt(P) ).
3. Adaptive Downshift
   If:
   ADX(14, t) < 20
   AND
   ATR_P(t) < 0.75 × ATR̄_P,
   then:
   X_adj = X – 1.
4. Death Stop
   DeathStop = X_adj × ATR_P.

Explanation & Rationale

The sublinear scaling X = ceil(sqrt(P)) ensures that larger EMA periods (e.g. P = 200) yield proportionally larger stops without scaling linearly. In choppy regimes (low ADX + low ATR), downshifting X by 1 tightens the stop to avoid leaving too much room for small swings.

Example (EUR USD, Momentum Zone)

• DAATS_EURUSD = 2899 ⇒ DeathStop = 2899. Since X = 3 for P = 8, we have:
  ATR₈ = 2899 / 3 ≈ 966.3 pips.
• ADX(14) = 22, ATR₈ = 966.3 vs. ATR̄₈ = 800 ⇒ no downshift, so X_adj = 3.
• Therefore:
  DeathStop = 3 × 966.3 ≈ 2898.9 pips.

Enhancements & Additional Adjustments

• Leverage Cap Enforcement
  Ensure that X × ATR_P ≤ 1.5 % of account equity at standard position sizing. If it exceeds, reduce X by 1 until it fits.
• Zone-Transition Smoothing
  If price is within ± 5 pips of two adjacent EMAs (e.g. EMA 15 & EMA 25), let:
  X = ceil( w₁ × sqrt(15) + w₂ × sqrt(25) ),
  where:
  w₁ = | Price – EMA₂₅ | / | EMA₂₅ – EMA₁₅ |,
  w₂ = 1 – w₁.
  This prevents abrupt jumps when price oscillates near a boundary.
• Time-of-Day Multiplier
  On M 60, if current UTC ∈ [21:00 … 02:00], add +10 % to X to account for thin liquidity at off-peak hours.

Law 5 – Exit Only on Death (EOD)

Statement

Once a valid trend entry occurs (price has definitively climbed through EMA 8), no exit may occur until one of two “death” conditions is met:

1. Price reverses from entry by the Full Death Stop:
   DeathStop = X_adj × ATR_P.
2. After achieving a Fractional Gain of:
   f(t) × DeathStop, price then reverses by that same amount (the Fractional Trail).

Define:

f(t)
= ( X_min / X_max ) × [ 1 + c × ( σ_DAATS(t) – σ̄_DAATS ) / σ̄_DAATS ],
where:
X_min = 3,
X_max = 15,
c = 0.25,
σ_DAATS(t) = 30-day σ of DAATS across all pairs,
σ̄_DAATS = 30-day mean of σ_DAATS.
Cap f(t) to [ 0.15, 0.30 ].

Formulas & Definitions

1. Full Death Stop
   DeathStop = X_adj × ATR_P.
   (Where X_adj is from Law 4.)
2. Fraction f(t)
   f(t)
   = ( 3 / 15 ) × [ 1 + 0.25 × ( σ_DAATS(t) – σ̄_DAATS ) / σ̄_DAATS ],
   then cap f(t) to [ 0.15, 0.30 ].
3. Fractional BE & Trail Distance
   BE/Trail = f(t) × DeathStop = f(t) × ( 15 × ATR_P ).
   If f(t) = 0.20, then BE/Trail = 3 × ATR_P.
   If f(t) = 0.25, then BE/Trail = 3.75 × ATR_P.
4. Exit Conditions
   
   1. Death Stop Hit: If price reverses from entry ≥ DeathStop → exit.
   2. Fractional Trail Hit:
      • Once price moves in favor by f(t) × DeathStop, move stop to breakeven.
      • Thereafter, trail by f(t) × DeathStop. If price reverses from its high-water mark by that amount, exit.
   3. No EMA 8 Exit: A close back under EMA 8 does not trigger exit unless one of the two death conditions is met.

Explanation & Rationale

By defining:

f(t)
= ( 3 / 15 ) × [ 1 + 0.25 × ( σ_DAATS(t) – σ̄_DAATS ) / σ̄_DAATS ],

we adapt the fractional break-even to current DAATS dispersion. If dispersion is high, f(t) rises (up to 0.30), allowing deeper intratrend retracements. If dispersion is low, f(t) tightens (down to 0.15), preserving capital in calm conditions.

Example (May 13, 2025)

• σ_DAATS(t) = 1184.21, σ̄_DAATS = 1000 ⇒
  f(t)
  = ( 3 / 15 ) × [ 1 + 0.25 × ( 1184.21 – 1000 ) / 1000 ]
  = 0.20 × [ 1 + 0.25 × 0.18421 ]
  = 0.20 × 1.04605 ≈ 0.2092 ≈ 0.21.
• So BE/Trail = 0.21 × ( 15 × ATR₂₀₀ ) = 3.15 × ATR₂₀₀.

Enhancements & Additional Adjustments

• Position-Size Scaling
  Let position size N_i(t) scale inversely with f(t):
  N_i(t)
  = [ RiskBudget × Equity ] / [ DeathStop_i × ( 1 – f(t) ) ].
  In volatile regimes ( f(t) large ), N_i(t) shrinks so max loss stays within the risk budget.
• EMA Breach Filter (Soft)
  If price dips below EMA 8 after BE is on, do not exit immediately. Require two consecutive closes below EMA 8 that exceed half the DeathStop distance to trigger exit. This filters momentary wobbles.
• Volatility-Adjusted Fraction Floor
  If ADX(14) ≥ 25 (strong trend), enforce f(t) ≥ 0.18 even if σ_DAATS(t) < σ̄_DAATS. Prevents narrow but strong trends from being cut off by an overly tight f(t).

Law 6 – Adaptive Break-Even Decision Law (ADBED)

Statement

Classify each day’s market conditions into Choppy, Moderate, or Strong regimes by clustering the pair:

( R_vol(t), ADX(14, t) ),
where R_vol(t) = ATR₂₅(t) / ATR₂₀₀(t).

Then assign break-even (BE) option as follows:

1. Choppy → Option C: Zone-Specific BE.
2. Moderate → Option B: GASBET BE = 0.6375 × DAATS.
3. Strong → Option A: BE = f(t) × DeathStop (from Law 5).

Formulas & Definitions

1. Volatility Ratio
   R_vol(t) = ATR₂₅(t) / ATR₂₀₀(t).
   – If R_vol > 1.2 → Choppy.
   – If 0.7 ≤ R_vol ≤ 1.2 → Moderate.
   – If R_vol < 0.7 → Trending/Strong.
2. ADX Bands
   Choppy: ADX < 18
   Moderate: 18 ≤ ADX < 28
   Strong: ADX ≥ 28
3. Clustering
   Apply k-means (k = 3) to historical pairs:
   ( R_vol(k), ADX(k) ) for k = t – 59 … t (60 bars).
   Label clusters “Choppy,” “Moderate,” “Strong.” Then assign today’s (R_vol(t), ADX(t)) to the closest cluster.
4. Assigned BE Option
   
   • Choppy → Option C: Zone-Specific BE = X × ATR_P or f(t) × X × ATR_P (per Law 5).
   • Moderate → Option B: GASBET BE = 0.6375 × DAATS.
   • Strong → Option A: BE = f(t) × DeathStop (per Law 5).

Explanation & Rationale

• In a Choppy regime (R_vol > 1.2, ADX < 18), ATR₂₅ is large relative to ATR₂₀₀. A BE at 20 % of DeathStop (based on ATR₂₀₀) would be too wide. Option C uses a tighter, zone-specific BE to capture micro-trends.
• In a Moderate regime (0.7 ≤ R_vol ≤ 1.2, 18 ≤ ADX < 28), a BE at 63.75 % of DAATS balances between too tight and too loose.
• In a Strong regime (R_vol < 0.7, ADX ≥ 28), Option A’s “Exit Only on Death” (Law 5) gives maximum breathing room for extended trends.

Enhancements & Additional Adjustments

• GMM Clustering with Skew
  Add 5-day return skewness as a third feature, forming:
  ( R_vol(t), ADX(t), Skew_5(t) )
  and cluster using a Gaussian Mixture Model. Detect a “Transitional” cluster (high skew, moderate ADX). If in “Transitional,” default to Option B but set BE = 0.6875 × DAATS (63.75 % + 5 %).
• Adaptive R Thresholds by Dispersion
  Instead of fixed 0.7/1.2, let:
  R_low(t) = 0.7 – 0.05 × [ σ_D(t) – σ̄_D ] / σ̄_D,
  R_high(t) = 1.2 + 0.05 × [ σ_D(t) – σ̄_D ] / σ̄_D,
  where σ_D(t) is current cross-pair DAATS dispersion and σ̄_D its 30-day mean. In extreme dispersion, thresholds widen accordingly.
• Shock Override
  If Law 3’s “super-shock” criterion is met (ΔDAATS_i > 2 × median), force “Strong” regime for the next three M 60 bars, regardless of (R_vol, ADX).

Law 7 – Portfolio-Level Break-Even & Noise-Budget Law (PLBND)

Statement

Treat the 28-pair universe as a single portfolio. Compute:

1. Raw DAATS Sum
   DAATS_sum(t) = Σ_i=1…28 DAATS_i(t).
   (On May 13, 2025, DAATS_sum = 85 127.)
2. Dispersion-Adjusted Sum
   DAATS_adj(t) = Σ_i=1…28 max [ DAATS_i(t) – κ × σ_D(t), 0 ],
   where
   κ = 0.15,
   σ_D(t) = 30-day σ of DAATS across all pairs.
   Subtracting 0.15 × 1184.21 ≈ 177.63 from each DAATS (floored at 0) yields DAATS_adj ≈ 80 153.
3. BE Band
   [ 0.18 × DAATS_adj(t), 0.22 × DAATS_adj(t) ].
   Numeric:
   • Lower = 0.18 × 80 153 ≈ 14 427
   • Upper = 0.22 × 80 153 ≈ 17 633
4. Death Stop (Portfolio)
   DeathStop_port = DAATS_adj(t).
5. Fractional BE & Trail
   BE/Trail_port = 0.20 × DAATS_adj(t) ≈ 16 031.

Exit Conditions

• Fractional Trail Hit: Once combined profit ≥ 17 633 (Upper BE), shift portfolio stop to breakeven. Thereafter, trail by 16 031. If combined losses from its high-water mark ≥ 16 031, exit all positions.
• Death Stop Hit: If combined losses from entry ≥ 80 153, exit all positions.
• No Pair-Level EMA Exits: Only portfolio-level stops (DeathStop or fractional trail) can close positions.

Explanation & Rationale

Subtracting κ × σ_D from each DAATS prevents extreme outliers (e.g. GBPNZD = 5414) from dominating the noise budget. A BE Band [18 %, 22 %] lets you tighten stops gradually as portfolio profit moves from 14 427 → 17 633. Once 17 633 is reached, shift the entire portfolio to break-even.

Enhancements & Additional Adjustments

• Dynamic κ Based on Skew
  Instead of fixed 0.15, let:
  κ(t) = 0.1 + 0.2 × | Skew_D(t) |,
  where Skew_D(t) = cross-pair DAATS skewness.
  Higher skew → larger κ.
• VaR-Adjusted Upper BE
  Weekly, simulate a 5-day 95 % VaR using historical pair returns & DAATS correlations. Then set:
  Upper_BE = max [ 0.22 × DAATS_adj(t), VaR₉₅ ].
• Sectoral Caps
  Group pairs into sub-universes (USD-majors, JPY-crosses, commodity-pairs, etc.). Ensure no sub-universe’s sum of [ DAATS_i – κ × σ_D(t) ] exceeds 40 % of DAATS_adj. If it does, scale down that group’s position sizes proportionally.

Law 8 – Transaction-Cost & Slippage Optimization Law (TCSOL)

Statement

Before any entry or exit, estimate transaction costs (commissions, fees) and expected slippage based on order-book depth and historical slippage. Define each instrument’s slippage coefficient:

α_i = median( | ExecutedPrice – MidQuote | over last 60 trades ) / ATR̄_P,i,

and compute:

Slippage_i(t) = α_i × ATR_P,i(t).

Then adjust:

DeathStop_net,i = DeathStop_i + Slippage_i(t),
BE_net,i = BE_i – Slippage_i(t).

Formulas & Definitions

1. Slippage Coefficient α_i
   α_i = median | ExecutedPrice – MidQuote | over last 60 trades / ATR̄_P,i.
2. Estimated Slippage
   Slippage_i(t) = α_i × ATR_P,i(t).
3. Net Stop Adjustments
   
   • Net Death Stop
     DeathStop_net,i = ( X_i × ATR_P,i ) + Slippage_i(t).
   • Net BE
     BE_net,i = ( Raw BE from Law 5 or Law 6 ) – Slippage_i(t).

Explanation & Rationale

Even the best theoretical stop fails if, by the time an order executes, slippage pushes price beyond it. By adding Slippage_i(t) to DeathStop and subtracting it from BE, we ensure actual executions align with intended risk parameters.

Example (EUR USD, P = 8)

• Suppose:
  ATR̄_8,EURUSD = 0.0016 (16 pips)
  Median slippage = 0.00015 (1.5 pips)
  Then:
  α_EURUSD = 0.00015 / 0.0016 ≈ 0.0938.
  Slippage_EURUSD(t) = 0.0938 × 0.0016 = 0.00015 (1.5 pips).
• Raw DeathStop = 3 × 0.0016 = 0.0048 (48 pips).
  • DeathStop_net = 0.0048 + 0.00015 = 0.00495 (≈ 49.5 pips).
  • If raw BE = 0.0048 (48 pips), then BE_net = 0.0048 – 0.00015 = 0.00465 (≈ 46.5 pips).

Enhancements & Additional Adjustments

• Dynamic EWMA Slippage
  Instead of a static α_i, update it intraday via an EWMA on realized slippage at each execution—captures rapid liquidity changes.
• Transaction-Cost Buffer
  If commissions + fees per roundtrip = γ_i pips, then:
  DeathStop_net = DeathStop_i + Slippage_i(t) + γ_i,
  BE_net = BE_i – Slippage_i(t) – γ_i.
• Liquidity Watchlist Overrides
  Maintain a list of instruments or times with historically poor liquidity (e.g. CHFJPY near Swiss holidays). Multiply α_i by 1.20 in those cases to avoid stops triggered by thin-market spikes.

Law 9 – Continuous Model Validation & “Law Rebirth” Law (CMV)

Statement

Every trade cycle (or weekly), compute out-of-sample performance metrics for each law. If any law’s key metric deviates by > 10 % from its target (e.g. Law 1’s false‐positive rate, Law 2’s forecast RMSE, Law 3’s shock‐day prediction error, Law 4’s premature DeathStop hits, Law 5’s BE‐hit frequency, Law 6’s regime misclassifications, Law 7’s portfolio drawdown exceedances, Law 8’s slippage errors), then retire that law’s parameters and rebirth them—re-estimate using the latest 90 days of data. Any law not revalidated within 30 days is flagged as stale.

Procedures & Metrics

1. Law 1 (CRTL)
   • Metric: False-positive rate (stress flagged without subsequent drawdown) and false-negative rate (drawdown without stress alert) over the past 30 days.
   • Rebirth Trigger: If FP > 10 % or FN > 10 %, re-estimate δ and β using the last 60 days of ( Z_‖V‖, Δλ₁ ) vs. subsequent drawdowns.
2. Law 2 (WDHDI)
   • Metric: One-step-ahead RMSE of DAATS_eff forecasts over the past 90 days.
   • Rebirth Trigger: If RMSE > 1.1 × baseline RMSE, sweep H ∈ [5, 20] days to find new H₀,_i minimizing RMSE.
3. Law 3 (MSPL)
   • Metric: Median absolute error of ΔDAATS_i predictions on shock days over the last 60.
   • Rebirth Trigger: If error > 10 % of average DAATS_i, re-regress φ_i, γ_i on the most recent 90 days of shock data.
4. Law 4 (E & DS)
   • Metric: % of strong-regime trades where price continued > 2 × ATR_P beyond DeathStop (i.e. DeathStop was too tight).
   • Rebirth Trigger: If > 5 % of those trades hit DeathStop prematurely, reduce that zone’s base exposure (X → X – 1).
5. Law 5 (EOD)
   • Metric: BE-hit frequency vs. target (≈ 30 % of winners).
   • Rebirth Trigger: If BE-hit % < 20 % or > 40 % of the last 50 trades, adjust c (Law 5’s dispersion coefficient) to nudge BE-hit % toward 30 %.
6. Law 6 (ADBED)
   • Metric: Regime classification accuracy vs. actual trade outcomes (e.g. % of “Choppy” trades requiring Option C).
   • Rebirth Trigger: If misclassification > 15 % over 30 days, re-train clustering (add skew/kurtosis features if needed).
7. Law 7 (PLBND)
   • Metric: % of portfolio drawdowns exceeding Upper BE (22 % of DAATS_adj).
   • Rebirth Trigger: If > 10 % of 5-day drawdowns exceed the BE band, adjust κ or narrow the BE band (e.g. [16 %, 20 %] instead of [18 %, 22 %]).
8. Law 8 (TCSOL)
   • Metric: Mean absolute slippage error over the last 60 trades: | Slippage_pred – Slippage_actual |.
   • Rebirth Trigger: If error > 5 pips or > 5 % of ATR_P, recalibrate α_i via EWMA on the last 30 trades.
9. Meta-Law (CMV)
   • If a law’s parameters remain unchanged for > 60 days, automatically retrain at half the usual data weight to prevent staleness.

Explanation & Rationale

Markets evolve. A law calibrated in January may be suboptimal by July. By measuring each law’s real-time performance and forcibly “rebirthing” any whose metrics drift, we ensure perpetual alignment with current market dynamics—a crucial competitive advantage.

Implementation

1. Every Monday pre-market:
   1. Compute all nine metrics over their respective rolling windows.
   2. Compare to thresholds.
   3. Retrain any law exceeding its threshold using the last 90 days of data.
   4. Publish a Law Update Log detailing old vs. new parameters.

Enhancements & Additional Adjustments

• Cross-Frequency Validation
  For Laws 4 & 5 (stop-based), validate performance on M 60, M 240, and D 1 to ensure multi-timeframe robustness.
• Ensemble Parameter Pools
  Maintain three “best-fit” parameter sets for each law, weighted by recent out-of-sample performance—guards against overfitting to a single period.
• Automated Model Decay
  If a law’s parameters remain unchanged for > 60 days, automatically retrain at half the usual data weight.

About the Author

Dr. Glen Brown stands at the forefront of the financial and accounting sectors, distinguished by a career spanning over a quarter-century marked by visionary leadership and groundbreaking achievements. As the esteemed President & CEO of both Global Accountancy Institute, Inc., and Global Financial Engineering, Inc., he steers these institutions with a steadfast commitment to integrating the realms of accountancy, finance, investments, trading, and technology. This integrative approach has solidified their status as pioneering entities in global multi-asset class professional proprietary trading and education.

Holding a Doctor of Philosophy (Ph.D.) in Investments and Finance, Dr. Brown possesses profound expertise covering an impressive spectrum of financial disciplines. His knowledge extends from financial accounting and management accounting to intricate areas of finance, investments, strategic management, and risk management. His role transcends traditional leadership; as the Chief Financial Engineer, Head of Trading & Investments, Chief Data Scientist, and Senior Lecturer, Dr. Brown embodies the spirit of hands-on innovation and scholarly excellence.

Dr. Brown’s guiding philosophy, “We must consume ourselves in order to transform ourselves for our rebirth,” encapsulates his holistic approach to professional and personal development. It underscores a belief in the transformative power of self-reflection, creative imagination, and the relentless pursuit of spiritual and intellectual growth. This ethos is the bedrock of his dedication to not just navigating but shaping the future of finance and investments with innovative solutions.

Beyond his executive and academic roles, Dr. Brown is a fervent advocate for continuous learning and innovation. His leadership has catalyzed a culture of forward-thinking at Global Accountancy Institute, Inc., and Global Financial Engineering, Inc., propelling them into the vanguard of financial education and proprietary trading. Under his guidance, these institutions not only adapt to the evolving financial landscape but actively contribute to its development, offering state-of-the-art solutions to the industry’s most complex challenges.

Dr. Brown’s commitment to excellence is matched by his dedication to nurturing the next generation of financial professionals. Through his visionary outlook and unique philosophical approach, he inspires a cadre of students and professionals alike to transcend conventional boundaries, fostering an environment where innovation, leadership, and success flourish.

Closed Business Module

Global Accountancy Institute, Inc. and Global Financial Engineering, Inc. operate a closed, research‐and‐trade model. All intellectual property—GATS algorithms, volatility rules, and automation scripts—is proprietary. Profits derive exclusively from trading, ensuring total alignment between research and results.

Risk Disclaimer

Trading leveraged derivatives and foreign exchange instruments involves significant risk. The strategies and methods presented here are educational and informational only and do not constitute investment advice. Past performance is not indicative of future results. All models (the Nine-Law Framework, DAATS, GNASD, etc.) rely on historical data, which may not predict future volatility regimes or correlation structures. Signals generated by these models can fail, resulting in partial or total loss of capital.

1. No Promise of Profit: There is no guarantee that any trading strategy will achieve profits or that losses will be limited to the specified “Death Stop.”
2. Slippage & Execution Risk: Actual trade executions may incur slippage greater than estimated, leading to larger losses.
3. Liquidity Risk: In low-liquidity conditions, orders may be partially filled or filled at worse prices.
4. Systemic & Counterparty Risk: Market freezes, exchange halts, or counterparty defaults can cause sudden, extreme price movements.
5. Consult Your Advisor: Users should consult their own financial advisor, tax advisor, and legal counsel before implementing any trades.
6. Full Disclosure: All information is provided “as is” without warranties of any kind. Global Accountancy Institute, Inc. and Global Financial Engineering, Inc. disclaim all liability for any losses arising from reliance on this content.