449 lines
23 KiB
TypeScript
449 lines
23 KiB
TypeScript
'use client';
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import React, { useState, useMemo } from 'react';
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import { useSandboxStore, InsiderTrade, CongressTrade, WhaleTrade } from '@/lib/store';
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import { calculateRollingZScore, detectInsiderClusters, coupleBayesianRebound } from '@/lib/math/statistics';
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import { BarChart, Bar, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer, ReferenceLine } from 'recharts';
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import 'katex/dist/katex.min.css';
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import { BlockMath, InlineMath } from 'react-katex';
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import {
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Shield, User, ArrowDownRight, ArrowUpRight, DollarSign, Calendar, Landmark,
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ChevronDown, ChevronUp, Search, Radio, Building2, AlertTriangle, Layers, Percent
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} from 'lucide-react';
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export default function InsiderDemo() {
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const {
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insiderTrades,
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congressTrades,
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whaleTrades,
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insiderVolumes,
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priorProbability
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} = useSandboxStore();
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// Component local UI states
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const [activeSegment, setActiveSegment] = useState<'executives' | 'congress' | 'whales'>('executives');
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const [searchQuery, setSearchQuery] = useState('');
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const [selectedTicker, setSelectedTicker] = useState<string | null>(null);
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const [scanning, setScanning] = useState(false);
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const [scanResults, setScanResults] = useState<{
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ticker: string;
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zScore: number;
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clusterCount: number;
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multiplier: number;
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isAnomaly: boolean;
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coupledRebound: number;
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}[] | null>(null);
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const [showMathAccordion, setShowMathAccordion] = useState(false);
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// Run Global Flow Scan
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const handleGlobalFlowScan = () => {
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setScanning(true);
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setTimeout(() => {
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const results = Object.keys(insiderVolumes).map((ticker) => {
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const volumes = insiderVolumes[ticker];
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const zResult = calculateRollingZScore(volumes);
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// Filter trades for this ticker to detect clusters
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const tickerTrades = insiderTrades.filter(t => t.ticker === ticker);
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const clusterResult = detectInsiderClusters(tickerTrades);
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// Bayesian coupling
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const coupled = coupleBayesianRebound(priorProbability, zResult.latest);
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return {
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ticker,
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zScore: parseFloat(zResult.latest.toFixed(2)),
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clusterCount: clusterResult.count,
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multiplier: parseFloat(clusterResult.multiplier.toFixed(2)),
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isAnomaly: zResult.isAnomaly || clusterResult.isCluster,
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coupledRebound: coupled,
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};
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});
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// Sort anomalies to the top
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results.sort((a, b) => (b.isAnomaly ? 1 : 0) - (a.isAnomaly ? 1 : 0) || b.zScore - a.zScore);
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setScanResults(results);
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setScanning(false);
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}, 1000);
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};
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// Perform Ticker Lookup
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const handleTickerLookup = (e: React.FormEvent) => {
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e.preventDefault();
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const query = searchQuery.trim().toUpperCase();
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if (query) {
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setSelectedTicker(query);
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} else {
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setSelectedTicker(null);
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}
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};
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// Compute stats for selected ticker
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const tickerStats = useMemo(() => {
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if (!selectedTicker) return null;
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const volumes = insiderVolumes[selectedTicker] || [5000, 4800, 5200, 6000, 4500, 5000]; // fallback
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const zResult = calculateRollingZScore(volumes);
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const tickerTrades = insiderTrades.filter(t => t.ticker === selectedTicker);
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const clusterResult = detectInsiderClusters(tickerTrades);
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const coupled = coupleBayesianRebound(priorProbability, zResult.latest);
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return {
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zScore: zResult.latest,
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isAnomaly: zResult.isAnomaly,
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clusterCount: clusterResult.count,
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isCluster: clusterResult.isCluster,
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multiplier: clusterResult.multiplier,
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coupledRebound: coupled,
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volumes,
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};
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}, [selectedTicker, insiderVolumes, insiderTrades, priorProbability]);
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// Volumes chart data for selected ticker
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const volumeChartData = useMemo(() => {
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if (!tickerStats || !selectedTicker) return [];
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return tickerStats.volumes.map((vol, idx) => ({
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month: `M-${tickerStats.volumes.length - idx - 1}`,
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'Volumen (Shares)': vol,
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}));
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}, [tickerStats, selectedTicker]);
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return (
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<div className="bg-slate-900/60 backdrop-blur-md border border-slate-800 rounded-2xl p-6 text-slate-100 shadow-xl relative overflow-hidden">
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<div className="absolute top-0 right-0 w-32 h-32 bg-purple-500/10 rounded-full blur-3xl -z-10" />
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{/* Header */}
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<div className="flex flex-col lg:flex-row justify-between items-start lg:items-center gap-4 border-b border-slate-800 pb-4 mb-6">
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<div>
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<span className="text-purple-400 text-xs font-semibold uppercase tracking-wider">Element 3</span>
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<h2 className="text-2xl font-bold bg-gradient-to-r from-purple-400 to-indigo-200 bg-clip-text text-transparent">
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Institutional & Insider Flow Tracker
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</h2>
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</div>
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<div className="bg-slate-900 border border-slate-800 rounded-xl px-4 py-2 flex items-center gap-3">
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<Shield className="text-purple-400 w-5 h-5" />
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<div>
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<p className="text-slate-400 text-xs">Bayesianische Kopplung</p>
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<p className="font-mono text-sm font-bold text-purple-400">
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Prior Rebound: {(priorProbability * 100).toFixed(0)}%
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</p>
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</div>
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</div>
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</div>
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{/* SECTION 1: Dual-Query Actions */}
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<div className="grid grid-cols-1 lg:grid-cols-2 gap-6 mb-6">
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<div className="p-4 rounded-xl border border-slate-800 bg-slate-950/30 flex flex-col justify-between gap-4">
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<div className="space-y-1">
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<h4 className="text-sm font-semibold text-slate-300">Global Flow Outlier Scan</h4>
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<p className="text-xs text-slate-400">Filtert den Markt nach statistisch signifikanten Kaufvolumina (Z-Score > 2.0) und konzertierten Käufen (Insider-Cluster).</p>
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</div>
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<button
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onClick={handleGlobalFlowScan}
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disabled={scanning}
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className="w-full bg-gradient-to-r from-purple-500 to-indigo-500 hover:from-purple-600 hover:to-indigo-600 disabled:from-purple-900 text-white font-bold py-2.5 px-4 rounded-xl transition-all shadow-lg shadow-purple-500/10 flex items-center justify-center gap-2"
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>
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<Radio className={`w-4 h-4 ${scanning ? 'animate-pulse' : ''}`} />
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<span>{scanning ? 'Scanne Transaktionen...' : 'Global Flow Scan starten'}</span>
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</button>
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</div>
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<div className="p-4 rounded-xl border border-slate-800 bg-slate-950/30 flex flex-col justify-between gap-4">
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<div className="space-y-1">
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<h4 className="text-sm font-semibold text-slate-300">Ticker Lookup (Einzelauswertung)</h4>
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<p className="text-xs text-slate-400">Gezielte Abfrage von Insider-Z-Scores und Bayesianischen Kopplungs-Updates (z.B. PLTR, RACE, AMZN, AAPL).</p>
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</div>
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<form onSubmit={handleTickerLookup} className="flex gap-2">
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<input
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type="text"
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required
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placeholder="z.B. PLTR"
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className="bg-slate-950 border border-slate-800 rounded-lg p-2.5 flex-1 text-slate-100 font-mono text-sm uppercase focus:outline-none focus:border-purple-500"
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value={searchQuery}
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onChange={(e) => setSearchQuery(e.target.value)}
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/>
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<button
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type="submit"
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className="bg-slate-800 hover:bg-slate-700 text-purple-400 hover:text-purple-300 font-bold px-4 py-2 border border-slate-700 rounded-lg transition-colors text-sm"
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>
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Lookup
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</button>
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</form>
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</div>
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</div>
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{/* Ticker Stats / Lookup Section */}
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{selectedTicker && tickerStats && (
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<div className="p-5 rounded-2xl border border-purple-500/30 bg-purple-500/5 grid grid-cols-1 lg:grid-cols-3 gap-6 mb-6 animate-fade-in">
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<div className="space-y-3">
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<div className="flex justify-between items-center border-b border-slate-800 pb-2">
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<h3 className="font-mono font-bold text-lg text-slate-100">{selectedTicker} Einzelauswertung</h3>
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{tickerStats.isAnomaly && <span className="px-2 py-0.5 rounded bg-purple-500/20 text-purple-300 text-[10px] font-bold border border-purple-500/30 animate-pulse">FLOW OUTLIER</span>}
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</div>
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<div className="space-y-2 text-xs">
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<div className="flex justify-between font-mono">
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<span className="text-slate-400">Volumetrischer Z-Score:</span>
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<span className={`font-bold ${tickerStats.isAnomaly ? 'text-purple-400' : 'text-slate-300'}`}>
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Z = {tickerStats.zScore.toFixed(2)}
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</span>
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</div>
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<div className="flex justify-between font-mono">
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<span className="text-slate-400">Konzertiertes Cluster (14 Tage):</span>
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<span className={`font-bold ${tickerStats.isCluster ? 'text-emerald-400' : 'text-slate-400'}`}>
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{tickerStats.isCluster ? `JA (${tickerStats.clusterCount} Insiders)` : `NEIN (${tickerStats.clusterCount})`}
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</span>
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</div>
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{tickerStats.isCluster && (
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<div className="flex justify-between font-mono text-emerald-400">
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<span>Cluster Exponent-Multiplikator:</span>
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<span>x{tickerStats.multiplier.toFixed(2)}</span>
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</div>
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)}
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<div className="flex justify-between font-mono border-t border-slate-800/80 pt-2 text-sm">
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<span className="text-slate-300">Gekoppelte Rebound-Wahrsch.:</span>
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<span className="text-purple-400 font-bold flex items-center gap-1">
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<Percent className="w-3.5 h-3.5" />
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{(tickerStats.coupledRebound * 100).toFixed(0)}%
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</span>
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</div>
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</div>
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</div>
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{/* Volume chart */}
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<div className="lg:col-span-2 h-44 w-full">
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<div className="text-[10px] text-slate-400 mb-1 text-center font-mono font-semibold">Insider Handelsvolumen (24 Monate Baseline)</div>
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<ResponsiveContainer width="100%" height="90%">
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<BarChart data={volumeChartData}>
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<CartesianGrid strokeDasharray="3 3" stroke="#1e293b" />
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<XAxis dataKey="month" stroke="#64748b" fontSize={9} />
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<YAxis stroke="#64748b" fontSize={9} />
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<Tooltip contentStyle={{ backgroundColor: '#0f172a', borderColor: '#334155', borderRadius: '8px' }} />
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<Bar dataKey="Volumen (Shares)" fill="#8b5cf6" radius={[3, 3, 0, 0]} />
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</BarChart>
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</ResponsiveContainer>
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</div>
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</div>
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)}
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{/* Global Scan Outlier List */}
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{scanResults && (
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<div className="p-5 rounded-2xl border border-slate-800 bg-slate-950/20 mb-6 space-y-3 animate-fade-in">
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<h3 className="text-sm font-bold text-slate-200 uppercase tracking-wider">Ergebnisse des Global Flow Scans</h3>
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<div className="grid grid-cols-2 md:grid-cols-4 gap-4">
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{scanResults.map((res) => (
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<div
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key={res.ticker}
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onClick={() => setSelectedTicker(res.ticker)}
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className={`p-3 rounded-xl border cursor-pointer hover:bg-slate-900/60 transition-colors ${res.isAnomaly ? 'border-purple-500/40 bg-purple-500/5' : 'border-slate-850 bg-slate-900/40'}`}
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>
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<div className="flex justify-between items-center">
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<span className="font-mono font-bold text-slate-200">{res.ticker}</span>
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{res.isAnomaly && <span className="w-2.5 h-2.5 rounded-full bg-purple-500 animate-pulse" />}
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</div>
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<div className="text-[10px] text-slate-400 mt-2 space-y-1">
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<div>Z-Score: <span className="font-mono text-slate-300 font-bold">{res.zScore}</span></div>
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<div>Cluster: <span className="font-mono text-slate-300">{res.clusterCount} Traders</span></div>
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<div>Rebound: <span className="font-mono text-purple-400 font-bold">{Math.round(res.coupledRebound * 100)}%</span></div>
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</div>
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</div>
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))}
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</div>
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</div>
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)}
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{/* SECTION 2: Smart Money Segment Tabs */}
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<div className="space-y-4">
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<div className="flex bg-slate-950/80 p-1 rounded-xl border border-slate-800/80 max-w-md">
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<button
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onClick={() => setActiveSegment('executives')}
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className={`flex-1 py-2 rounded-lg text-xs font-semibold font-sans transition-all flex items-center justify-center gap-1.5 ${activeSegment === 'executives' ? 'bg-purple-500 text-white font-bold' : 'text-slate-400 hover:text-slate-200'}`}
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>
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<User className="w-3.5 h-3.5" /> Vorstände (Form 4)
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</button>
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<button
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onClick={() => setActiveSegment('congress')}
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className={`flex-1 py-2 rounded-lg text-xs font-semibold font-sans transition-all flex items-center justify-center gap-1.5 ${activeSegment === 'congress' ? 'bg-purple-500 text-white font-bold' : 'text-slate-400 hover:text-slate-200'}`}
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>
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<Landmark className="w-3.5 h-3.5" /> Kongress (Stock Act)
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</button>
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<button
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onClick={() => setActiveSegment('whales')}
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className={`flex-1 py-2 rounded-lg text-xs font-semibold font-sans transition-all flex items-center justify-center gap-1.5 ${activeSegment === 'whales' ? 'bg-purple-500 text-white font-bold' : 'text-slate-400 hover:text-slate-200'}`}
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>
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<Building2 className="w-3.5 h-3.5" /> Whales (13F Filings)
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</button>
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</div>
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{/* Ledger displays */}
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<div className="border border-slate-800 rounded-xl overflow-hidden bg-slate-950/40">
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{activeSegment === 'executives' && (
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<table className="w-full border-collapse text-left text-xs">
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<thead>
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<tr className="border-b border-slate-800 text-slate-400 font-semibold bg-slate-900/40">
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<th className="p-3">Ticker</th>
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<th className="p-3">Insider Name</th>
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<th className="p-3">Position</th>
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<th className="p-3">Transaktion</th>
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<th className="p-3 font-mono">Stücke</th>
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<th className="p-3 text-right">Wert ($)</th>
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</tr>
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</thead>
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<tbody>
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{insiderTrades.map((t) => {
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const isBuy = t.type === 'BUY';
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return (
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<tr key={t.id} className="border-b border-slate-850/50 hover:bg-slate-850/10 transition-colors">
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<td className="p-3 font-bold font-mono text-purple-400">{t.ticker}</td>
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<td className="p-3 text-slate-200 font-semibold">{t.insiderName}</td>
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<td className="p-3 text-slate-400">{t.relation}</td>
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<td className="p-3">
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<span className={`px-1.5 py-0.5 rounded text-[9px] font-bold ${isBuy ? 'bg-emerald-500/10 text-emerald-400 border border-emerald-500/20' : 'bg-rose-500/10 text-rose-400 border border-rose-500/20'}`}>
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{isBuy ? 'KAUF' : 'VERKAUF'}
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</span>
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</td>
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<td className="p-3 font-mono text-slate-300">{t.shares.toLocaleString()}</td>
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<td className={`p-3 font-mono text-right font-bold ${isBuy ? 'text-emerald-400' : 'text-rose-400'}`}>
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${t.value.toLocaleString()}
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</td>
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</tr>
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);
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})}
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</tbody>
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</table>
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)}
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{activeSegment === 'congress' && (
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<div className="space-y-4">
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<div className="p-3 bg-amber-500/10 border-b border-slate-800 text-xs text-amber-400 flex items-start gap-2.5">
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<AlertTriangle className="w-5 h-5 shrink-0" />
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<div>
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<span className="font-bold">U.S. Congress Stock Act Offenlegungslags:</span> Abgeordnete müssen Käufe innerhalb von 30 bis 45 Tagen offenlegen. Der Alpha-Lag in der Tabelle visualisiert diese Meldeverzögerung. Käufe können verzögert eingepreist sein.
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</div>
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</div>
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<table className="w-full border-collapse text-left text-xs">
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<thead>
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<tr className="border-b border-slate-800 text-slate-400 font-semibold bg-slate-900/40">
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<th className="p-3">Ticker</th>
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<th className="p-3">Abgeordneter</th>
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<th className="p-3">Kammer</th>
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<th className="p-3">Transaktion</th>
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<th className="p-3">Volumen-Spanne</th>
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<th className="p-3 font-mono">Handelsdatum</th>
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<th className="p-3 font-mono">Meldedatum</th>
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<th className="p-3 text-right">Alpha-Lag (Tage)</th>
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</tr>
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</thead>
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<tbody>
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{congressTrades.map((c) => {
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const isBuy = c.type === 'BUY';
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return (
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<tr key={c.id} className="border-b border-slate-850/50 hover:bg-slate-850/10 transition-colors">
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<td className="p-3 font-bold font-mono text-purple-400">{c.ticker}</td>
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<td className="p-3 text-slate-200 font-semibold">{c.representative}</td>
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<td className="p-3 text-slate-400">{c.chamber}</td>
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<td className="p-3">
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<span className={`px-1.5 py-0.5 rounded text-[9px] font-bold ${isBuy ? 'bg-emerald-500/10 text-emerald-400 border border-emerald-500/20' : 'bg-rose-500/10 text-rose-400 border border-rose-500/20'}`}>
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{isBuy ? 'KAUF' : 'VERKAUF'}
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</span>
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</td>
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<td className="p-3 text-slate-300 font-mono">{c.valueRange}</td>
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<td className="p-3 font-mono text-slate-400">{c.transactionDate}</td>
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<td className="p-3 font-mono text-slate-400">{c.filingDate}</td>
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<td className="p-3 font-mono text-right text-amber-400 font-bold">{c.lagDays} Tage</td>
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</tr>
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);
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})}
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</tbody>
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</table>
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</div>
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)}
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{activeSegment === 'whales' && (
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<table className="w-full border-collapse text-left text-xs">
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<thead>
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<tr className="border-b border-slate-800 text-slate-400 font-semibold bg-slate-900/40">
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<th className="p-3">Ticker</th>
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<th className="p-3">Institution (13F Filers)</th>
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<th className="p-3">Art</th>
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<th className="p-3 font-mono">Gehandelte Anteile</th>
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<th className="p-3 font-mono">Aktueller Bestand</th>
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<th className="p-3 font-mono">Meldedatum</th>
|
|
<th className="p-3 text-right">Geschätzter Wert ($)</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
{whaleTrades.map((w) => {
|
|
const isBuy = w.type === 'BUY' || w.type === 'NEW';
|
|
return (
|
|
<tr key={w.id} className="border-b border-slate-850/50 hover:bg-slate-850/10 transition-colors">
|
|
<td className="p-3 font-bold font-mono text-purple-400">{w.ticker}</td>
|
|
<td className="p-3 text-slate-200 font-semibold">{w.institution}</td>
|
|
<td className="p-3">
|
|
<span className={`px-1.5 py-0.5 rounded text-[9px] font-bold ${isBuy ? 'bg-emerald-500/10 text-emerald-400 border border-emerald-500/20' : 'bg-rose-500/10 text-rose-400 border border-rose-500/20'}`}>
|
|
{w.type}
|
|
</span>
|
|
</td>
|
|
<td className="p-3 font-mono text-slate-300">{w.sharesTraded.toLocaleString()}</td>
|
|
<td className="p-3 font-mono text-slate-400">{w.sharesHeld.toLocaleString()}</td>
|
|
<td className="p-3 font-mono text-slate-400">{w.filingDate}</td>
|
|
<td className={`p-3 font-mono text-right font-bold ${isBuy ? 'text-emerald-400' : 'text-rose-400'}`}>
|
|
${w.estimatedValue.toLocaleString()}
|
|
</td>
|
|
</tr>
|
|
);
|
|
})}
|
|
</tbody>
|
|
</table>
|
|
)}
|
|
</div>
|
|
</div>
|
|
|
|
{/* SECTION 3: Mathematical LaTeX Accordion */}
|
|
<div className="border-t border-slate-850 pt-4 mt-6">
|
|
<button
|
|
onClick={() => setShowMathAccordion(!showMathAccordion)}
|
|
className="flex items-center gap-1.5 text-xs text-slate-400 hover:text-purple-400 transition-colors focus:outline-none"
|
|
>
|
|
<span>{showMathAccordion ? <ChevronUp className="w-4 h-4" /> : <ChevronDown className="w-4 h-4" />}</span>
|
|
<span className="font-semibold uppercase tracking-wider">Mathematische Formulierung (Z-Score & Bayesianische Kopplung)</span>
|
|
</button>
|
|
|
|
{showMathAccordion && (
|
|
<div className="mt-4 p-4 rounded-xl border border-slate-850 bg-slate-950/40 text-xs text-slate-300 space-y-4">
|
|
<div>
|
|
<h4 className="font-bold text-purple-400 mb-1">1. Volumetrischer Z-Score (Statistische Signifikanz)</h4>
|
|
<p className="mb-2">
|
|
Der Z-Score gibt an, um wie viele Standardabweichungen das aktuelle Transaktionsvolumen <InlineMath math="X_t" /> vom historischen Durchschnitt <InlineMath math="\mu" /> abweicht:
|
|
</p>
|
|
<div className="py-2 overflow-x-auto text-slate-200">
|
|
<BlockMath math="Z = \frac{X_t - \mu}{\sigma}" />
|
|
</div>
|
|
<p className="text-slate-400">
|
|
Ein Z-Score > 2.0 wird als Ausreißer (Anomaly Trigger) eingestuft, was einer Wahrscheinlichkeit von unter 2.27% für einen zufälligen Anstieg entspricht (einseitiger Test bei Normalverteilung).
|
|
</p>
|
|
</div>
|
|
|
|
<div className="border-t border-slate-900 pt-3">
|
|
<h4 className="font-bold text-purple-400 mb-1">2. Bayesianische Kopplung (Rebound-Wahrscheinlichkeit)</h4>
|
|
<p className="mb-2">
|
|
Wir verknüpfen den Preisdrop-Sentiment (Element 2) mit den Insider-Z-Scores (Element 3), um die A-Posteriori-Wahrscheinlichkeit eines echten Rebounds zu ermitteln:
|
|
</p>
|
|
<div className="py-2 overflow-x-auto text-slate-200">
|
|
<BlockMath math="P(R|Z) = \frac{P(Z|R) \cdot P(R)}{P(Z|R) \cdot P(R) + P(Z|\neg R) \cdot (1 - P(R))}" />
|
|
</div>
|
|
<p className="text-slate-400">
|
|
wobei <InlineMath math="P(R)" /> die Prior-Wahrscheinlichkeit ist. Ist der Z-Score hoch (Käufe), steigt die Likelihood <InlineMath math="P(Z|R)" /> stark an und maximiert den Rebound-Erwartungswert.
|
|
</p>
|
|
</div>
|
|
</div>
|
|
)}
|
|
</div>
|
|
</div>
|
|
);
|
|
}
|