Closes #014 - System-wide handbook sweep, English consolidation & Crypto state persistence
This commit is contained in:
Antigravity Agent
@@ -1,5 +1,5 @@
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import React from 'react';
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import { BookOpen } from 'lucide-react';
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import { BookOpen, X } from 'lucide-react';
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import 'katex/dist/katex.min.css';
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import { BlockMath, InlineMath } from 'react-katex';
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@@ -26,8 +26,8 @@ export default function EconometricsMathModal({ isOpen, onClose }: EconometricsM
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if (!isOpen) return null;
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return (
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<div className="fixed inset-0 z-50 flex items-center justify-center bg-slate-950/85 backdrop-blur-md p-4 sm:p-6 md:p-8">
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<div className="bg-slate-900 border border-slate-800/80 rounded-3xl w-full max-w-4xl h-[80vh] flex flex-col overflow-hidden shadow-2xl relative text-slate-300">
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<div className="fixed inset-0 z-50 flex items-center justify-center bg-slate-955/90 backdrop-blur-md p-4 sm:p-6 md:p-8">
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<div className="bg-slate-900 border border-slate-800/80 rounded-3xl w-full max-w-4xl h-[80vh] flex flex-col overflow-hidden shadow-2xl relative text-slate-350">
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{/* Modal Header */}
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<div className="flex justify-between items-center px-6 py-4 bg-slate-950/40 border-b border-slate-800/60">
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@@ -39,9 +39,10 @@ export default function EconometricsMathModal({ isOpen, onClose }: EconometricsM
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</div>
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<button
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onClick={onClose}
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className="text-slate-400 hover:text-slate-200 bg-slate-950/50 border border-slate-800 hover:border-slate-700 px-3 py-1.5 rounded-lg text-xs font-semibold font-mono transition-all cursor-pointer"
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className="text-slate-400 hover:text-slate-200 bg-slate-950/50 border border-slate-800 hover:border-slate-700 p-2 rounded-xl transition-all cursor-pointer flex items-center justify-center"
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aria-label="Close modal"
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>
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Schließen (ESC)
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<X className="w-4 h-4" />
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</button>
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</div>
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@@ -69,10 +70,10 @@ export default function EconometricsMathModal({ isOpen, onClose }: EconometricsM
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<div className="space-y-3">
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<h4 className="text-xs font-bold text-rose-400 uppercase tracking-wider font-mono">B. Endogenous Calibration</h4>
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<p className="text-xs leading-relaxed text-slate-400">
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Active future matrix cells pre-fill suggested scores by looking up the corresponding historical LMM coefficient <InlineMath math="\beta_{asset\_event\_post}" /> and scaling it to our native score scale:
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Active future matrix cells pre-fill suggested scores by looking up the corresponding historical LMM coefficient <InlineMath math="\\beta_{\\text{asset}\\_\\text{event}\\_\\text{post}}" /> and scaling it to our native score scale:
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</p>
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<div className="bg-slate-950/40 p-4 rounded-xl border border-slate-800/60 my-2">
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<BlockMath math="\text{Score}_{\text{suggested}} = \max\left(-3, \min\left(3, \text{Round}(\beta_{\text{estimate}} \times 100)\right)\right)" />
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<BlockMath math="\\text{Score}_{\\text{suggested}} = \\max\\left(-3, \\min\\left(3, \\text{Round}(\\beta_{\\text{estimate}} \\times 100)\\right)\\right)" />
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</div>
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</div>
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@@ -82,13 +83,17 @@ export default function EconometricsMathModal({ isOpen, onClose }: EconometricsM
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The engine estimates direct event drift and impact returns, isolating asset-level intercepts as random deviances and purging macro volatility using VIX indices:
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</p>
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<div className="bg-slate-950/40 p-4 rounded-xl border border-slate-800/60 my-2">
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<BlockMath math="Y_{it} = X_{it}\beta + Z_{it}b_i + \varepsilon_{it}" />
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<BlockMath math="Y_{it} = X_{it}\\beta + Z_{it}b_i + \\varepsilon_{it}" />
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<p className="text-[11px] text-slate-400 mt-2 font-mono leading-relaxed">
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Where:<br/>
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- <InlineMath math="Y_{it}" /> is the log-return <InlineMath math="\ln(P_t/P_0)" /> of asset <InlineMath math="i" /> at relative index <InlineMath math="t \in [-30, 30]" />.<br/>
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- <InlineMath math="X_{it}" /> design matrix elements isolate Pre-Event Drift (<InlineMath math="t < 0" />) and Post-Event Impact (<InlineMath math="t \ge 0" />) while controlling for systemic covariates (VIX).<br/>
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- <InlineMath math="b_i \sim N(0, \sigma_b^2)" /> random intercept captures unique baseline asset variance.<br/>
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- <InlineMath math="\varepsilon_{it} \sim N(0, \sigma^2)" /> residuals noise.
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{"Where:"}
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<br />
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{"- "}<InlineMath math="Y_{it}" />{" is the log-return "}<InlineMath math="\\ln(P_t/P_0)" />{" of asset "}<InlineMath math="i" />{" at relative index "}<InlineMath math="t \\in [-30, 30]" />{"."}
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<br />
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{"- "}<InlineMath math="X_{it}" />{" design matrix elements isolate Pre-Event Drift ("}<InlineMath math="t < 0" />{") and Post-Event Impact ("}<InlineMath math="t \\ge 0" />{") while controlling for systemic covariates (VIX)."}
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<br />
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{"- "}<InlineMath math="b_i \\sim N(0, \\sigma_b^2)" />{" random intercept captures unique baseline asset variance."}
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<br />
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{"- "}<InlineMath math="\\varepsilon_{it} \\sim N(0, \\sigma^2)" />{" residuals noise."}
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</p>
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</div>
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</div>
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@@ -101,15 +106,15 @@ export default function EconometricsMathModal({ isOpen, onClose }: EconometricsM
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<div className="bg-slate-950/40 p-4 rounded-xl border border-slate-800/60 my-2 space-y-4">
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<div>
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<p className="text-xs text-slate-400 mb-1">Logistic Probability Projection:</p>
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<BlockMath math="P(\text{Bullish}) = \frac{1}{1 + e^{-\text{Score}}}" />
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<BlockMath math="P(\\text{Bullish}) = \\frac{1}{1 + e^{-\\text{Score}}}" />
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</div>
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<div>
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<p className="text-xs text-slate-400 mb-1">Optimal Youden Index (J):</p>
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<BlockMath math="J = \text{Sensitivity} + \text{Specificity} - 1 = \text{TPR} + (1 - \text{FPR}) - 1" />
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<BlockMath math="J = \\text{Sensitivity} + \\text{Specificity} - 1 = \\text{TPR} + (1 - \\text{FPR}) - 1" />
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</div>
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<div>
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<p className="text-xs text-slate-400 mb-1">Inverting probability optimal threshold <InlineMath math="P^*" /> back to native score <InlineMath math="S^*" /> via Logit:</p>
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<BlockMath math="S^* = \ln\left(\frac{P^*}{1 - P^*}\right)" />
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<p className="text-xs text-slate-400 mb-1">{"Inverting probability optimal threshold "}<InlineMath math="P^*" />{" back to native score "}<InlineMath math="S^*" />{" via Logit:"}</p>
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<BlockMath math="S^* = \\ln\\left(\\frac{P^*}{1 - P^*}\\right)" />
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</div>
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</div>
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</div>
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@@ -121,16 +126,18 @@ export default function EconometricsMathModal({ isOpen, onClose }: EconometricsM
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</p>
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<div className="bg-slate-950/40 p-4 rounded-xl border border-slate-800/60 my-2 space-y-4">
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<div>
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<BlockMath math="\hat{S}(t) = \prod_{t_i \le t} \left(1 - \frac{d_i}{n_i}\right)" />
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<p className="text-[11px] text-slate-400 mt-2 font-mono">
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Where:<br/>
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- <InlineMath math="n_i" /> is the number of active asset-run observations at risk at day <InlineMath math="t" />.<br/>
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- <InlineMath math="d_i" /> is the number of trend-reversal events recorded on day <InlineMath math="t" />.
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<BlockMath math="\\hat{S}(t) = \\prod_{t_i \\le t} \\left(1 - \\frac{d_i}{n_i}\\right)" />
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<p className="text-[11px] text-slate-400 mt-2 font-mono leading-relaxed">
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{"Where:"}
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<br />
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{"- "}<InlineMath math="n_i" />{" is the number of active asset-run observations at risk at day "}<InlineMath math="t" />{"."}
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<br />
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{"- "}<InlineMath math="d_i" />{" is the number of trend-reversal events recorded on day "}<InlineMath math="t" />{"."}
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</p>
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</div>
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<div>
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<p className="text-xs text-slate-400 mb-1">Reversal trigger with 1% Volatility Buffer:</p>
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<BlockMath math="\text{Sign}(\text{Score}) \times \text{Return} \le -0.01" />
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<BlockMath math="\\text{Sign}(\\text{Score}) \\times \\text{Return} \\le -0.01" />
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</div>
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</div>
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</div>
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