143 lines
8.5 KiB
TypeScript
143 lines
8.5 KiB
TypeScript
import React from 'react';
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import { BookOpen } 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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interface EconometricsMathModalProps {
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isOpen: boolean;
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onClose: () => void;
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}
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export default function EconometricsMathModal({ isOpen, onClose }: EconometricsMathModalProps) {
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React.useEffect(() => {
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const handleKeyDown = (e: KeyboardEvent) => {
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if (e.key === 'Escape') {
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onClose();
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}
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};
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if (isOpen) {
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window.addEventListener('keydown', handleKeyDown);
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}
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return () => {
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window.removeEventListener('keydown', handleKeyDown);
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};
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}, [isOpen, onClose]);
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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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{/* 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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<div>
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<h2 className="text-base font-bold bg-gradient-to-r from-rose-400 to-indigo-400 bg-clip-text text-transparent flex items-center gap-2">
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<BookOpen className="w-5 h-5 text-rose-400" /> Econometrics Workspace - Math & Logic Specification
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</h2>
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<p className="text-[10px] text-slate-500 font-mono">Institutional Specification Manual</p>
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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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>
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Schließen (ESC)
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</button>
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</div>
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{/* Modal Body */}
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<div className="flex-1 overflow-y-auto p-6 sm:p-8 space-y-6 text-slate-300 scrollbar-thin">
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<div className="space-y-6">
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<div className="border-b border-slate-800/80 pb-3">
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<h3 className="text-base font-bold text-slate-200">1. Econometrics Workspace Engine</h3>
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<p className="text-xs text-slate-400 mt-1">Estimates asset reactions to macroeconomic shocks using panel regression, predictions accuracy, and survival durability.</p>
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</div>
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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">A. Ingestion & Storage Pipeline</h4>
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<p className="text-xs leading-relaxed text-slate-400">
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A background manager checks event parameters against the simulated current workstation local time (<code className="bg-slate-950 px-1 py-0.5 rounded text-[10px] text-purple-400">2026-06-11</code>).
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If an active event's date is in the past:
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</p>
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<ul className="list-disc pl-5 text-xs text-slate-400 space-y-1">
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<li>FMP API fetches relative prices $P_t$ for $t \in [T-30, T+30]$ (60-day historical window).</li>
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<li>Asset curves and the user's manual score are frozen under <code className="bg-slate-950 px-1 py-0.5 rounded text-[10px] text-slate-300">archivedEvents</code> in <code className="bg-slate-950 px-1 py-0.5 text-slate-300 rounded text-[10px]">econometrics_storage.json</code>.</li>
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<li>Future edits to the active matrix will <strong>never</strong> modify archived price vectors.</li>
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</ul>
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</div>
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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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</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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</div>
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</div>
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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">C. Linear Mixed Model (LMM) Panel Regression</h4>
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<p className="text-xs leading-relaxed text-slate-400">
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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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<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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</p>
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</div>
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</div>
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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">D. ROC Classifier & Youden Threshold</h4>
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<p className="text-xs leading-relaxed text-slate-400">
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Evaluates prediction accuracy on binary outcomes (rebound return > 0). The Youden index maximizes classifier sensitivity and specificity:
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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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<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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</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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</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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</div>
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</div>
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</div>
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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">E. Kaplan-Meier Survival Curve</h4>
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<p className="text-xs leading-relaxed text-slate-400">
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Measures trend durability. Survival rates represent the probability of an asset holding its predicted direction before reversing:
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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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</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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</div>
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</div>
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</div>
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</div>
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</div>
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</div>
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</div>
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);
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}
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