1098 lines
49 KiB
TypeScript
1098 lines
49 KiB
TypeScript
'use client';
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import React, { useState, useMemo, useEffect } from 'react';
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import { useSandboxStore } from '@/lib/store';
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import { predictCryptoTrend, calculateBetaPosterior } from '@/lib/math/statistics';
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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 CryptoMathModal from './CryptoMathModal';
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import CryptoBlueprintModal from './CryptoBlueprintModal';
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import {
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Cpu, Search, RefreshCw, BarChart2, TrendingUp, AlertCircle, Info,
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ChevronDown, ChevronUp, ArrowUpRight, ArrowDownRight, Compass, ShieldAlert, Sparkles,
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BookOpen, Check
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} from 'lucide-react';
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interface TrackerState {
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alpha: number;
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beta: number;
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}
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type TrackersMap = Record<string, TrackerState>;
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const ESTIMATORS = [
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{ id: 'rf', name: 'Random Forest' },
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{ id: 'gb', name: 'XGBoost / GB' },
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{ id: 'lr', name: 'Logistic Regression' },
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{ id: 'svm', name: 'Support Vector Machine' },
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{ id: 'mlp', name: 'Multi-Layer Perceptron' }
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] as const;
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const HORIZONS = [
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{ id: 'T1', name: 'T+1 Day', days: 1 },
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{ id: 'T5', name: 'T+5 Days', days: 5 },
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{ id: 'T10', name: 'T+10 Days', days: 10 }
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] as const;
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interface CoinData {
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ticker: string;
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name: string;
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price: string;
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change24h: number;
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fundingRate: number; // in %
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openInterestChange: number; // in %
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longShortRatio: number;
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whaleInflow: number; // net flows
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exchangeReserves: number; // in %
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liqLong: string;
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liqShort: string;
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}
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const defaultCoins: Record<string, CoinData> = {
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'BTC': {
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ticker: 'BTC',
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name: 'Bitcoin',
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price: '$69,450',
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change24h: 2.4,
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fundingRate: -0.015,
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openInterestChange: 8.2,
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longShortRatio: 0.92,
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whaleInflow: 480,
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exchangeReserves: -1.4,
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liqLong: '$68,200',
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liqShort: '$70,500'
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},
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'ETH': {
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ticker: 'ETH',
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name: 'Ethereum',
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price: '$3,820',
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change24h: -1.2,
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fundingRate: 0.045,
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openInterestChange: -3.5,
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longShortRatio: 1.34,
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whaleInflow: -120,
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exchangeReserves: 0.8,
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liqLong: '$3,710',
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liqShort: '$3,920'
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},
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'SOL': {
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ticker: 'SOL',
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name: 'Solana',
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price: '$184.20',
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change24h: 5.8,
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fundingRate: 0.082,
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openInterestChange: 14.5,
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longShortRatio: 1.62,
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whaleInflow: 1250,
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exchangeReserves: -2.8,
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liqLong: '$176.00',
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liqShort: '$192.50'
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}
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};
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interface Forecast {
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id: string;
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ticker: string;
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entryPrice: number;
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resolved: boolean;
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timestamp: number;
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predictions: Record<string, Record<string, number>>;
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targetTimes: Record<string, number>;
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results?: Record<string, 'SUCCESS' | 'FAILURE'>;
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}
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export default function CryptoDemo() {
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const { addModelTrial } = useSandboxStore();
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// Local state for alphaSuccess and betaFailure to satisfy SSR hydration safeguarding
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const [alphaSuccess, setAlphaSuccess] = useState<number>(394);
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const [betaFailure, setBetaFailure] = useState<number>(118);
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const [forecasts, setForecasts] = useState<Forecast[]>([]);
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const [learningLoopLog, setLearningLoopLog] = useState<string>('');
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const [activeTicker, setActiveTicker] = useState<string>('BTC');
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const [searchQuery, setSearchQuery] = useState('');
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const [customCoins, setCustomCoins] = useState<Record<string, CoinData>>({});
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const [searchError, setSearchError] = useState(false);
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const [showMathAccordion, setShowMathAccordion] = useState(false);
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const [isMathModalOpen, setIsMathModalOpen] = useState(false);
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const [isBlueprintModalOpen, setIsBlueprintModalOpen] = useState(false);
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const [simulatedTrialLogged, setSimulatedTrialLogged] = useState(false);
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const [lastTrialSuccess, setLastTrialSuccess] = useState(false);
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// 15 independent Beta-Posterior trackers
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const [trackers, setTrackers] = useState<TrackersMap>({});
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const [ensemblePredictions, setEnsemblePredictions] = useState<any>(null);
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const [loadingEnsemble, setLoadingEnsemble] = useState(false);
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const [isShieldActive, setIsShieldActive] = useState(true);
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const [coins, setCoins] = useState<Record<string, CoinData>>(defaultCoins);
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// Safely load counters and forecasts from localStorage on client mount
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useEffect(() => {
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const savedAlpha = localStorage.getItem('crypto_bayes_alpha');
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const savedBeta = localStorage.getItem('crypto_bayes_beta');
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const savedForecasts = localStorage.getItem('crypto_bayes_forecasts');
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let loadedAlpha = 394;
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let loadedBeta = 118;
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if (savedAlpha !== null) {
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loadedAlpha = parseInt(savedAlpha, 10);
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setAlphaSuccess(loadedAlpha);
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} else {
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localStorage.setItem('crypto_bayes_alpha', '394');
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}
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if (savedBeta !== null) {
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loadedBeta = parseInt(savedBeta, 10);
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setBetaFailure(loadedBeta);
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} else {
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localStorage.setItem('crypto_bayes_beta', '118');
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}
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// Load trackers
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const defaultPriors: Record<string, { alpha: number; beta: number }> = {
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'rf_T1': { alpha: 38, beta: 12 }, 'rf_T5': { alpha: 35, beta: 15 }, 'rf_T10': { alpha: 32, beta: 18 },
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'gb_T1': { alpha: 40, beta: 10 }, 'gb_T5': { alpha: 36, beta: 14 }, 'gb_T10': { alpha: 30, beta: 20 },
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'lr_T1': { alpha: 35, beta: 15 }, 'lr_T5': { alpha: 33, beta: 17 }, 'lr_T10': { alpha: 31, beta: 19 },
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'svm_T1': { alpha: 36, beta: 14 }, 'svm_T5': { alpha: 34, beta: 16 }, 'svm_T10': { alpha: 32, beta: 18 },
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'mlp_T1': { alpha: 39, beta: 11 }, 'mlp_T5': { alpha: 35, beta: 15 }, 'mlp_T10': { alpha: 31, beta: 19 }
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};
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const map: TrackersMap = {};
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Object.keys(defaultPriors).forEach((key) => {
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const a = localStorage.getItem(`crypto_bayes_tracker_${key}_alpha`);
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const b = localStorage.getItem(`crypto_bayes_tracker_${key}_beta`);
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const alphaVal = a !== null ? parseInt(a, 10) : defaultPriors[key].alpha;
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const betaVal = b !== null ? parseInt(b, 10) : defaultPriors[key].beta;
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map[key] = { alpha: alphaVal, beta: betaVal };
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if (a === null) {
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localStorage.setItem(`crypto_bayes_tracker_${key}_alpha`, String(alphaVal));
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localStorage.setItem(`crypto_bayes_tracker_${key}_beta`, String(betaVal));
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}
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});
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setTrackers(map);
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// Fetch ensemble predictions
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const fetchEnsemble = async () => {
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setLoadingEnsemble(true);
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try {
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const res = await fetch('/api/crypto/ensemble');
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if (res.ok) {
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const data = await res.json();
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setEnsemblePredictions(data.predictions || null);
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setIsShieldActive(data.isShieldActive !== undefined ? data.isShieldActive : true);
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}
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} catch (err) {
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console.error("Failed to load ensemble predictions:", err);
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} finally {
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setLoadingEnsemble(false);
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}
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};
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fetchEnsemble();
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if (savedForecasts !== null) {
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try {
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const parsed = JSON.parse(savedForecasts);
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// Clean legacy formats if necessary
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if (parsed.length > 0 && parsed[0].predictions === undefined) {
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throw new Error("Legacy forecast format");
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}
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setForecasts(parsed);
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} catch (err) {
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console.log("Resetting legacy forecasts to multi-model format...");
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const now = Date.now();
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const mockForecasts: Forecast[] = [
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{
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id: 'mock-1',
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ticker: 'BTC',
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entryPrice: 65000,
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resolved: true,
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timestamp: now - 86400 * 1000 * 3,
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predictions: {
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rf: { T1: 0.62, T5: 0.58, T10: 0.54 },
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gb: { T1: 0.65, T5: 0.61, T10: 0.51 },
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lr: { T1: 0.58, T5: 0.57, T10: 0.55 },
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svm: { T1: 0.60, T5: 0.59, T10: 0.56 },
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mlp: { T1: 0.64, T5: 0.60, T10: 0.53 }
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},
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targetTimes: {
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T1: now - 86400 * 1000 * 2,
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T5: now - 86400 * 1000 * 2,
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T10: now - 86400 * 1000 * 2
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},
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results: {
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rf_T1: 'SUCCESS', rf_T5: 'SUCCESS', rf_T10: 'SUCCESS',
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gb_T1: 'SUCCESS', gb_T5: 'SUCCESS', gb_T10: 'SUCCESS',
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lr_T1: 'SUCCESS', lr_T5: 'SUCCESS', lr_T10: 'SUCCESS',
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svm_T1: 'SUCCESS', svm_T5: 'SUCCESS', svm_T10: 'SUCCESS',
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mlp_T1: 'SUCCESS', mlp_T5: 'SUCCESS', mlp_T10: 'SUCCESS'
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}
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}
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];
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setForecasts(mockForecasts);
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localStorage.setItem('crypto_bayes_forecasts', JSON.stringify(mockForecasts));
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}
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} else {
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const now = Date.now();
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const mockForecasts: Forecast[] = [
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{
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id: 'mock-1',
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ticker: 'BTC',
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entryPrice: 65000,
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resolved: true,
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timestamp: now - 86400 * 1000 * 3,
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predictions: {
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rf: { T1: 0.62, T5: 0.58, T10: 0.54 },
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gb: { T1: 0.65, T5: 0.61, T10: 0.51 },
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lr: { T1: 0.58, T5: 0.57, T10: 0.55 },
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svm: { T1: 0.60, T5: 0.59, T10: 0.56 },
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mlp: { T1: 0.64, T5: 0.60, T10: 0.53 }
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},
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targetTimes: {
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T1: now - 86400 * 1000 * 2,
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T5: now - 86400 * 1000 * 2,
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T10: now - 86400 * 1000 * 2
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},
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results: {
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rf_T1: 'SUCCESS', rf_T5: 'SUCCESS', rf_T10: 'SUCCESS',
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gb_T1: 'SUCCESS', gb_T5: 'SUCCESS', gb_T10: 'SUCCESS',
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lr_T1: 'SUCCESS', lr_T5: 'SUCCESS', lr_T10: 'SUCCESS',
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svm_T1: 'SUCCESS', svm_T5: 'SUCCESS', svm_T10: 'SUCCESS',
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mlp_T1: 'SUCCESS', mlp_T5: 'SUCCESS', mlp_T10: 'SUCCESS'
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}
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}
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];
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setForecasts(mockForecasts);
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localStorage.setItem('crypto_bayes_forecasts', JSON.stringify(mockForecasts));
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}
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}, []);
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// Poll live price, 24h change, and indicators from the backend API
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useEffect(() => {
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const fetchCryptoPrices = async () => {
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try {
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const res = await fetch('/api/finance?region=crypto');
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if (!res.ok) return;
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const data = await res.json();
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const results = data.results || [];
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setCoins(prevCoins => {
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const updatedCoins = { ...prevCoins };
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results.forEach((r: any) => {
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const cleanTicker = r.ticker.replace('-USD', '');
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if (cleanTicker === 'BTC' || cleanTicker === 'ETH' || cleanTicker === 'SOL') {
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const currentPrice = r.currentPrice;
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const dayChangePercent = r.dayChange * 100;
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// Bind API indicators directly
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const fundingRate = r.fundingRate !== undefined ? r.fundingRate : (cleanTicker === 'BTC' ? -0.015 : cleanTicker === 'ETH' ? 0.045 : 0.082);
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const openInterestChange = r.openInterestChange !== undefined ? r.openInterestChange : (cleanTicker === 'BTC' ? 8.2 : cleanTicker === 'ETH' ? -3.5 : 14.5);
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const longShortRatio = r.longShortRatio !== undefined ? r.longShortRatio : (cleanTicker === 'BTC' ? 0.92 : cleanTicker === 'ETH' ? 1.34 : 1.62);
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const whaleInflow = r.whaleInflow !== undefined ? r.whaleInflow : (cleanTicker === 'BTC' ? 480 : cleanTicker === 'ETH' ? -120 : 1250);
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const exchangeReserves = r.exchangeReserves !== undefined ? r.exchangeReserves : (cleanTicker === 'BTC' ? -1.4 : cleanTicker === 'ETH' ? 0.8 : -2.8);
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// Scale liquidations dynamically relative to the current real price
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const liqLongVal = currentPrice * (cleanTicker === 'BTC' ? 0.982 : cleanTicker === 'ETH' ? 0.971 : 0.955);
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const liqShortVal = currentPrice * (cleanTicker === 'BTC' ? 1.015 : cleanTicker === 'ETH' ? 1.026 : 1.045);
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updatedCoins[cleanTicker] = {
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ticker: cleanTicker,
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name: cleanTicker === 'BTC' ? 'Bitcoin' : cleanTicker === 'ETH' ? 'Ethereum' : 'Solana',
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price: `$${currentPrice.toLocaleString(undefined, { minimumFractionDigits: 2, maximumFractionDigits: 2 })}`,
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change24h: parseFloat(dayChangePercent.toFixed(2)),
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fundingRate,
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openInterestChange,
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longShortRatio,
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whaleInflow,
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exchangeReserves,
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liqLong: `$${liqLongVal.toLocaleString(undefined, { minimumFractionDigits: 2, maximumFractionDigits: 2 })}`,
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liqShort: `$${liqShortVal.toLocaleString(undefined, { minimumFractionDigits: 2, maximumFractionDigits: 2 })}`
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};
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}
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});
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return updatedCoins;
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});
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} catch (err) {
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console.error("Failed to fetch crypto prices:", err);
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}
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};
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fetchCryptoPrices();
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const interval = setInterval(fetchCryptoPrices, 15000); // Poll every 15s
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return () => clearInterval(interval);
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}, []);
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// Client-side background learning loop evaluating forecasts against actual live returns
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useEffect(() => {
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const runLearningLoop = async () => {
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if (Object.keys(trackers).length === 0) return;
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try {
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const res = await fetch('/api/finance?region=crypto');
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if (!res.ok) return;
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const data = await res.json();
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const results = data.results || [];
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const pricesMap: Record<string, number> = {};
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results.forEach((r: any) => {
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pricesMap[r.ticker] = r.currentPrice;
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const cleanTicker = r.ticker.replace('-USD', '');
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pricesMap[cleanTicker] = r.currentPrice;
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});
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let updatedAny = false;
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const nextTrackers = { ...trackers };
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const updatedForecasts = forecasts.map((f) => {
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if (f.resolved) return f;
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const currentPrice = pricesMap[f.ticker] || pricesMap[`${f.ticker}-USD`];
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if (!currentPrice) return f;
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const now = Date.now();
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const resultsMap = { ...(f.results || {}) };
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let modified = false;
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HORIZONS.forEach((h) => {
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const hKey = h.id;
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const targetTime = f.targetTimes[hKey];
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ESTIMATORS.forEach((est) => {
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const trackerKey = `${est.id}_${hKey}`;
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if (now >= targetTime && !resultsMap[trackerKey]) {
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const priceWentUp = currentPrice > f.entryPrice;
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const predProb = f.predictions[est.id]?.[hKey] ?? 0.5;
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const predDir = predProb > 0.5 ? 'UP' : 'DOWN';
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const success = (predDir === 'UP' && priceWentUp) || (predDir === 'DOWN' && !priceWentUp);
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resultsMap[trackerKey] = success ? 'SUCCESS' : 'FAILURE';
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if (success) {
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nextTrackers[trackerKey].alpha += 1;
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localStorage.setItem(`crypto_bayes_tracker_${trackerKey}_alpha`, String(nextTrackers[trackerKey].alpha));
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} else {
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nextTrackers[trackerKey].beta += 1;
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localStorage.setItem(`crypto_bayes_tracker_${trackerKey}_beta`, String(nextTrackers[trackerKey].beta));
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}
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addModelTrial(success);
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updatedAny = true;
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modified = true;
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}
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});
|
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});
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if (modified) {
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const allResolved = ESTIMATORS.every(est =>
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HORIZONS.every(h => resultsMap[`${est.id}_${h.id}`] !== undefined)
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);
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return {
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...f,
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results: resultsMap,
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resolved: allResolved
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};
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}
|
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return f;
|
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});
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if (updatedAny) {
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setTrackers(nextTrackers);
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setForecasts(updatedForecasts);
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localStorage.setItem('crypto_bayes_forecasts', JSON.stringify(updatedForecasts));
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setLearningLoopLog(`Processed active ensemble forecasts. Trackers calibration updated.`);
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setTimeout(() => setLearningLoopLog(''), 6000);
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}
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} catch (err) {
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console.error("Learning loop evaluation error:", err);
|
||
}
|
||
};
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if (forecasts.length > 0) {
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runLearningLoop();
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}
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const interval = setInterval(runLearningLoop, 30000);
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return () => clearInterval(interval);
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}, [forecasts, trackers, addModelTrial]);
|
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// Active Coin data retrieval
|
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const activeCoin = useMemo(() => {
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return customCoins[activeTicker] || coins[activeTicker] || coins['BTC'];
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}, [activeTicker, customCoins, coins]);
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||
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// Helper to fetch/load prediction probabilities
|
||
const getPredictionProb = (estimator: string, horizon: string): number => {
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if (ensemblePredictions && ensemblePredictions[activeTicker] && ensemblePredictions[activeTicker][estimator]) {
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return ensemblePredictions[activeTicker][estimator][horizon] ?? 0.5;
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}
|
||
// Fallback static predictions
|
||
const defaultMapping: Record<string, Record<string, Record<string, number>>> = {
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||
BTC: {
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||
rf: { T1: 0.62, T5: 0.58, T10: 0.54 },
|
||
gb: { T1: 0.65, T5: 0.61, T10: 0.51 },
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lr: { T1: 0.58, T5: 0.57, T10: 0.55 },
|
||
svm: { T1: 0.60, T5: 0.59, T10: 0.56 },
|
||
mlp: { T1: 0.64, T5: 0.60, T10: 0.53 }
|
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},
|
||
ETH: {
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||
rf: { T1: 0.60, T5: 0.59, T10: 0.54 },
|
||
gb: { T1: 0.66, T5: 0.61, T10: 0.48 },
|
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lr: { T1: 0.58, T5: 0.55, T10: 0.56 },
|
||
svm: { T1: 0.59, T5: 0.59, T10: 0.56 },
|
||
mlp: { T1: 0.64, T5: 0.59, T10: 0.55 }
|
||
},
|
||
SOL: {
|
||
rf: { T1: 0.65, T5: 0.58, T10: 0.52 },
|
||
gb: { T1: 0.63, T5: 0.63, T10: 0.54 },
|
||
lr: { T1: 0.59, T5: 0.58, T10: 0.54 },
|
||
svm: { T1: 0.60, T5: 0.62, T10: 0.56 },
|
||
mlp: { T1: 0.66, T5: 0.60, T10: 0.51 }
|
||
}
|
||
};
|
||
const assetKey = defaultMapping[activeTicker] ? activeTicker : 'BTC';
|
||
return defaultMapping[assetKey][estimator]?.[horizon] ?? 0.5;
|
||
};
|
||
|
||
// Compute live Random Forest baseline predictions (for legacy/visual compatibility)
|
||
const mlPredictions = useMemo(() => {
|
||
const inputs = {
|
||
fundingRate: activeCoin.fundingRate,
|
||
openInterestChange: activeCoin.openInterestChange,
|
||
longShortRatio: activeCoin.longShortRatio,
|
||
whaleInflow: activeCoin.whaleInflow
|
||
};
|
||
return predictCryptoTrend(inputs);
|
||
}, [activeCoin]);
|
||
|
||
// Apply Bayesian online learning error-correction posterior update (legacy/visual)
|
||
const correctedPredictions = useMemo(() => {
|
||
const shortTermCorrected = calculateBetaPosterior(
|
||
alphaSuccess,
|
||
betaFailure,
|
||
mlPredictions.shortTermProb,
|
||
12 // confidence scale
|
||
);
|
||
const mediumTermCorrected = calculateBetaPosterior(
|
||
alphaSuccess,
|
||
betaFailure,
|
||
mlPredictions.mediumTermProb,
|
||
12
|
||
);
|
||
|
||
return {
|
||
shortTerm: shortTermCorrected,
|
||
mediumTerm: mediumTermCorrected
|
||
};
|
||
}, [mlPredictions, alphaSuccess, betaFailure]);
|
||
|
||
// Search/add new altcoin
|
||
const handleAltcoinSearch = (e: React.FormEvent) => {
|
||
e.preventDefault();
|
||
setSearchError(false);
|
||
const query = searchQuery.trim().toUpperCase();
|
||
if (!query) return;
|
||
|
||
if (coins[query]) {
|
||
setActiveTicker(query);
|
||
setSearchQuery('');
|
||
return;
|
||
}
|
||
|
||
if (customCoins[query]) {
|
||
setActiveTicker(query);
|
||
setSearchQuery('');
|
||
return;
|
||
}
|
||
|
||
const isBull = Math.random() > 0.45;
|
||
const simulatedChange = isBull ? 3 + Math.random() * 8 : -2 - Math.random() * 6;
|
||
const simulatedPrice = isBull ? 2 + Math.random() * 10 : 0.2 + Math.random() * 3;
|
||
|
||
const newCoin: CoinData = {
|
||
ticker: query,
|
||
name: `${query} Token`,
|
||
price: `$${simulatedPrice.toFixed(4)}`,
|
||
change24h: parseFloat(simulatedChange.toFixed(2)),
|
||
fundingRate: parseFloat((Math.random() * 0.12 - 0.04).toFixed(3)),
|
||
openInterestChange: parseFloat((Math.random() * 30 - 10).toFixed(1)),
|
||
longShortRatio: parseFloat((0.8 + Math.random() * 1.1).toFixed(2)),
|
||
whaleInflow: Math.floor(Math.random() * 1500 - 400),
|
||
exchangeReserves: parseFloat((Math.random() * 4 - 2).toFixed(1)),
|
||
liqLong: `$${(simulatedPrice * 0.9).toFixed(4)}`,
|
||
liqShort: `$${(simulatedPrice * 1.1).toFixed(4)}`
|
||
};
|
||
|
||
setCustomCoins(prev => ({ ...prev, [query]: newCoin }));
|
||
setActiveTicker(query);
|
||
setSearchQuery('');
|
||
};
|
||
|
||
// Manual logging of active forecast for all 15 models & horizons
|
||
const handleLogManualForecast = () => {
|
||
const entryPrice = parseFloat(activeCoin.price.replace(/[^0-9.]/g, ''));
|
||
|
||
// Save snapshot of all predictions
|
||
const predictionsMap: Record<string, Record<string, number>> = {};
|
||
ESTIMATORS.forEach((est) => {
|
||
predictionsMap[est.id] = {
|
||
T1: getPredictionProb(est.id, 'T1'),
|
||
T5: getPredictionProb(est.id, 'T5'),
|
||
T10: getPredictionProb(est.id, 'T10')
|
||
};
|
||
});
|
||
|
||
const now = Date.now();
|
||
const newForecast: Forecast = {
|
||
id: 'fc-' + now,
|
||
ticker: activeCoin.ticker,
|
||
entryPrice,
|
||
resolved: false,
|
||
timestamp: now,
|
||
predictions: predictionsMap,
|
||
targetTimes: {
|
||
T1: now + 60 * 1000, // resolves in 60s for direct visual validation
|
||
T5: now + 300 * 1000, // resolves in 300s
|
||
T10: now + 600 * 1000 // resolves in 600s
|
||
}
|
||
};
|
||
|
||
const nextForecasts = [newForecast, ...forecasts];
|
||
setForecasts(nextForecasts);
|
||
localStorage.setItem('crypto_bayes_forecasts', JSON.stringify(nextForecasts));
|
||
|
||
setLearningLoopLog(`Registered active multi-model forecast for ${activeCoin.ticker} at $${entryPrice}. Evaluating T+1 (60s), T+5 (5m), and T+10 (10m).`);
|
||
setTimeout(() => setLearningLoopLog(''), 8000);
|
||
};
|
||
|
||
// Simulator for ensemble calibration (simulates trials across all 15 trackers)
|
||
const handleSimulateEnsembleTrial = (success: boolean) => {
|
||
if (Object.keys(trackers).length === 0) return;
|
||
const nextTrackers = { ...trackers };
|
||
ESTIMATORS.forEach((est) => {
|
||
HORIZONS.forEach((h) => {
|
||
const trackerKey = `${est.id}_${h.id}`;
|
||
if (!nextTrackers[trackerKey]) {
|
||
nextTrackers[trackerKey] = { alpha: 1, beta: 1 };
|
||
}
|
||
if (success) {
|
||
nextTrackers[trackerKey].alpha += 1;
|
||
localStorage.setItem(`crypto_bayes_tracker_${trackerKey}_alpha`, String(nextTrackers[trackerKey].alpha));
|
||
} else {
|
||
nextTrackers[trackerKey].beta += 1;
|
||
localStorage.setItem(`crypto_bayes_tracker_${trackerKey}_beta`, String(nextTrackers[trackerKey].beta));
|
||
}
|
||
});
|
||
});
|
||
setTrackers(nextTrackers);
|
||
setLastTrialSuccess(success);
|
||
setSimulatedTrialLogged(true);
|
||
setTimeout(() => setSimulatedTrialLogged(false), 2000);
|
||
};
|
||
|
||
const totalTrials = alphaSuccess + betaFailure;
|
||
const priorAccuracy = (alphaSuccess / (totalTrials || 1)) * 100;
|
||
|
||
return (
|
||
<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">
|
||
<div className="absolute top-0 right-0 w-32 h-32 bg-cyan-500/10 rounded-full blur-3xl -z-10" />
|
||
|
||
{/* Header */}
|
||
<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">
|
||
<div>
|
||
<span className="text-cyan-400 text-xs font-semibold uppercase tracking-wider">Level 4</span>
|
||
<h2 className="text-2xl font-bold bg-gradient-to-r from-cyan-400 to-sky-200 bg-clip-text text-transparent">
|
||
Predictive Crypto Models & Bayes Self-Correction
|
||
</h2>
|
||
</div>
|
||
<div className="flex flex-wrap items-center gap-3">
|
||
{isShieldActive ? (
|
||
<span className="inline-flex items-center gap-1.5 px-3 py-2.5 rounded-xl text-xs font-bold bg-amber-500/10 text-amber-400 border border-amber-500/20 shadow-[0_0_15px_rgba(245,158,11,0.15)] h-11 animate-pulse">
|
||
<ShieldAlert className="w-4 h-4 text-amber-400" />
|
||
<span>SYSTEM-AUTARK (OFFLINE-CORE)</span>
|
||
</span>
|
||
) : (
|
||
<span className="inline-flex items-center gap-1.5 px-3 py-2.5 rounded-xl text-xs font-bold bg-emerald-500/10 text-emerald-400 border border-emerald-500/20 shadow-[0_0_15px_rgba(16,185,129,0.15)] h-11">
|
||
<span className="w-2 h-2 rounded-full bg-emerald-500 animate-ping" />
|
||
<span>LIVE-API ENDPUNKT</span>
|
||
</span>
|
||
)}
|
||
|
||
<button
|
||
onClick={() => setIsMathModalOpen(true)}
|
||
className="flex items-center gap-1.5 px-4 py-2 rounded-xl bg-slate-950/80 hover:bg-slate-900 border border-slate-800 hover:border-slate-700 transition-all font-semibold text-xs tracking-wider text-cyan-400 justify-center h-11"
|
||
>
|
||
<BookOpen className="w-3.5 h-3.5" />
|
||
<span>📖 Quantitative Handbook</span>
|
||
</button>
|
||
|
||
<button
|
||
onClick={() => setIsBlueprintModalOpen(true)}
|
||
className="flex items-center gap-1.5 px-4 py-2 rounded-xl bg-slate-950/80 hover:bg-slate-900 border border-slate-800 hover:border-slate-700 transition-all font-semibold text-xs tracking-wider text-cyan-400 justify-center h-11"
|
||
>
|
||
<Cpu className="w-3.5 h-3.5" />
|
||
<span>⚙️ Operational Blueprint</span>
|
||
</button>
|
||
|
||
<div className="bg-slate-900 border border-slate-800 rounded-xl px-4 py-2 flex items-center gap-3 h-11">
|
||
<Cpu className="text-cyan-400 w-5 h-5 animate-spin-slow" />
|
||
<div>
|
||
<p className="text-slate-400 text-xs">Prior Accuracy</p>
|
||
<p className="font-mono text-sm font-bold text-cyan-400">
|
||
{priorAccuracy.toFixed(1)}% (n={totalTrials})
|
||
</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
{/* SECTION 1: Top 3 Cards & Search Mask */}
|
||
<div className="grid grid-cols-1 lg:grid-cols-4 gap-4 mb-6">
|
||
|
||
{/* Status Cards BTC, ETH, SOL */}
|
||
{['BTC', 'ETH', 'SOL'].map((tick) => {
|
||
const coin = coins[tick] || defaultCoins[tick];
|
||
const isActive = activeTicker === tick;
|
||
const isUp = coin.change24h >= 0;
|
||
return (
|
||
<div
|
||
key={tick}
|
||
onClick={() => setActiveTicker(tick)}
|
||
className={`p-4 rounded-xl border cursor-pointer transition-all hover:bg-slate-850 flex items-center justify-between relative overflow-hidden ${isActive ? 'border-cyan-500/40 bg-cyan-500/5 shadow-md shadow-cyan-500/5' : 'border-slate-850 bg-slate-950/20'}`}
|
||
>
|
||
<div>
|
||
<div className="text-xs text-slate-400 font-semibold">{coin.name}</div>
|
||
<div className="text-xl font-extrabold font-mono mt-1 text-slate-100">{coin.price}</div>
|
||
<div className={`text-[10px] font-bold font-mono mt-0.5 flex items-center gap-0.5 ${isUp ? 'text-emerald-400' : 'text-rose-400'}`}>
|
||
{isUp ? <ArrowUpRight className="w-3.5 h-3.5" /> : <ArrowDownRight className="w-3.5 h-3.5" />}
|
||
<span>{isUp ? '+' : ''}{coin.change24h}%</span>
|
||
</div>
|
||
</div>
|
||
<div className="text-right">
|
||
<span className="text-2xl font-bold font-mono text-slate-800">{tick}</span>
|
||
</div>
|
||
</div>
|
||
);
|
||
})}
|
||
|
||
{/* Custom Search bar */}
|
||
<div className="p-4 rounded-xl border border-slate-850 bg-slate-950/20 flex flex-col justify-center gap-2">
|
||
<form onSubmit={handleAltcoinSearch} className="flex gap-2">
|
||
<input
|
||
type="text"
|
||
required
|
||
placeholder="Altcoin Ticker (e.g. LINK)"
|
||
className="bg-slate-900 border border-slate-800 rounded-lg p-2 flex-1 text-slate-100 font-mono text-xs uppercase focus:outline-none focus:border-cyan-500"
|
||
value={searchQuery}
|
||
onChange={(e) => setSearchQuery(e.target.value)}
|
||
/>
|
||
<button
|
||
type="submit"
|
||
className="bg-slate-800 hover:bg-slate-700 text-cyan-400 hover:text-cyan-350 font-bold px-3 py-2 border border-slate-700 rounded-lg transition-colors text-xs"
|
||
>
|
||
<Search className="w-4 h-4" />
|
||
</button>
|
||
</form>
|
||
{Object.keys(customCoins).length > 0 && (
|
||
<div className="flex flex-wrap gap-1.5 mt-1 overflow-x-auto max-h-[32px]">
|
||
{Object.keys(customCoins).map(tick => (
|
||
<button
|
||
key={tick}
|
||
onClick={() => setActiveTicker(tick)}
|
||
className={`px-2 py-0.5 rounded font-mono text-[9px] border ${activeTicker === tick ? 'bg-cyan-500/10 text-cyan-400 border-cyan-500/30' : 'bg-slate-900 text-slate-500 border-slate-800'}`}
|
||
>
|
||
{tick}
|
||
</button>
|
||
))}
|
||
</div>
|
||
)}
|
||
</div>
|
||
</div>
|
||
|
||
{/* SECTION 2: Derivatives & On-Chain Metrics Ledger */}
|
||
<div className="grid grid-cols-1 xl:grid-cols-3 gap-8">
|
||
|
||
{/* Left Column: Metrics Widgets */}
|
||
<div className="xl:col-span-2 space-y-6">
|
||
<h3 className="text-base font-bold text-white flex items-center gap-2 border-b border-slate-800 pb-3">
|
||
<BarChart2 className="text-cyan-400 w-5 h-5" /> On-Chain & Derivative Indicators ({activeCoin.ticker})
|
||
</h3>
|
||
|
||
<div className="grid grid-cols-1 sm:grid-cols-2 gap-4">
|
||
|
||
{/* Funding & Open Interest Widget */}
|
||
<div className="p-4 rounded-xl border border-slate-850 bg-slate-950/40 space-y-3">
|
||
<h4 className="text-xs font-semibold text-slate-400 uppercase tracking-wider">Funding Rates & Open Interest</h4>
|
||
<div className="space-y-2">
|
||
<div className="flex justify-between items-center text-sm font-mono">
|
||
<span className="text-slate-400 text-xs">Daily Funding Rate:</span>
|
||
<span className={`font-bold ${activeCoin.fundingRate < 0 ? 'text-emerald-400' : 'text-rose-400'}`}>
|
||
{activeCoin.fundingRate > 0 ? '+' : ''}{activeCoin.fundingRate.toFixed(3)}%
|
||
</span>
|
||
</div>
|
||
<div className="flex justify-between items-center text-sm font-mono">
|
||
<span className="text-slate-400 text-xs">Open Interest (24h Δ):</span>
|
||
<span className={`font-bold ${activeCoin.openInterestChange >= 0 ? 'text-emerald-400' : 'text-rose-400'}`}>
|
||
{activeCoin.openInterestChange > 0 ? '+' : ''}{activeCoin.openInterestChange.toFixed(1)}%
|
||
</span>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
{/* Long/Short & Liquidation Widget */}
|
||
<div className="p-4 rounded-xl border border-slate-850 bg-slate-950/40 space-y-3">
|
||
<h4 className="text-xs font-semibold text-slate-400 uppercase tracking-wider">Positioning & Liquidations</h4>
|
||
<div className="space-y-2">
|
||
<div className="flex justify-between items-center text-sm font-mono">
|
||
<span className="text-slate-400 text-xs">Long / Short Ratio:</span>
|
||
<span className="text-slate-200 font-bold">{activeCoin.longShortRatio}</span>
|
||
</div>
|
||
<div className="flex justify-between items-center text-xs font-mono">
|
||
<span className="text-slate-400">Liq Cluster:</span>
|
||
<span className="text-rose-400">Long: {activeCoin.liqLong} | Short: {activeCoin.liqShort}</span>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
{/* Whale Flows Widget */}
|
||
<div className="p-4 rounded-xl border border-slate-850 bg-slate-950/40 space-y-3">
|
||
<h4 className="text-xs font-semibold text-slate-400 uppercase tracking-wider">Whale Flows (Net Inflow)</h4>
|
||
<div className="space-y-2">
|
||
<div className="flex justify-between items-center text-sm font-mono">
|
||
<span className="text-slate-400 text-xs">Net Inflow (Wallets):</span>
|
||
<span className={`font-bold ${activeCoin.whaleInflow >= 0 ? 'text-emerald-400' : 'text-rose-400'}`}>
|
||
{activeCoin.whaleInflow > 0 ? '+' : ''}{activeCoin.whaleInflow} {activeCoin.ticker}
|
||
</span>
|
||
</div>
|
||
<div className="text-[10px] text-slate-500 leading-relaxed font-sans">
|
||
Positive values signal that large investors are withdrawing assets from exchanges to private wallets (accumulation).
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
{/* Exchange Reserves Widget */}
|
||
<div className="p-4 rounded-xl border border-slate-850 bg-slate-950/40 space-y-3">
|
||
<h4 className="text-xs font-semibold text-slate-400 uppercase tracking-wider">Exchange Reserves (Spot)</h4>
|
||
<div className="space-y-2">
|
||
<div className="flex justify-between items-center text-sm font-mono">
|
||
<span className="text-slate-400 text-xs">Reserve Change (7d):</span>
|
||
<span className={`font-bold ${activeCoin.exchangeReserves <= 0 ? 'text-emerald-400' : 'text-rose-400'}`}>
|
||
{activeCoin.exchangeReserves > 0 ? '+' : ''}{activeCoin.exchangeReserves}%
|
||
</span>
|
||
</div>
|
||
<div className="text-[10px] text-slate-500 leading-relaxed font-sans">
|
||
Falling reserves at spot exchanges reduce available selling pressure and favor squeezes.
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
{/* Right Column: Multi-Model Ensemble & Walk-Forward Radar Table */}
|
||
<div className="bg-slate-900/60 backdrop-blur-md border border-slate-800 rounded-2xl p-6 text-slate-100 shadow-xl space-y-6">
|
||
<div className="flex justify-between items-center border-b border-slate-800 pb-3">
|
||
<h3 className="text-base font-bold text-white flex items-center gap-2">
|
||
<Compass className="text-cyan-400 w-5 h-5" /> Walk-Forward Ensemble Radar
|
||
</h3>
|
||
{loadingEnsemble && (
|
||
<RefreshCw className="w-4 h-4 text-cyan-400 animate-spin" />
|
||
)}
|
||
</div>
|
||
|
||
<div className="text-xs text-slate-400 leading-relaxed">
|
||
Displays predictions and live calibration metrics (<InlineMath math="E[\theta] = \alpha / (\alpha + \beta)" />) across 15 independent trackers.
|
||
</div>
|
||
|
||
<div className="overflow-x-auto rounded-xl border border-slate-850 bg-slate-950/40">
|
||
<table className="w-full border-collapse text-left text-[11px] font-mono">
|
||
<thead>
|
||
<tr className="border-b border-slate-800 text-slate-400 font-semibold bg-slate-900/40">
|
||
<th className="p-2">Estimator</th>
|
||
<th className="p-2 text-center">T+1</th>
|
||
<th className="p-2 text-center">T+5</th>
|
||
<th className="p-2 text-center">T+10</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
{ESTIMATORS.map((est) => (
|
||
<tr key={est.id} className="border-b border-slate-900 hover:bg-slate-850/10">
|
||
<td className="p-2 font-semibold text-slate-300">{est.name}</td>
|
||
{HORIZONS.map((h) => {
|
||
const trackerKey = `${est.id}_${h.id}`;
|
||
const tracker = trackers[trackerKey] || { alpha: 1, beta: 1 };
|
||
const prob = getPredictionProb(est.id, h.id);
|
||
const direction = prob > 0.5 ? 'UP' : 'DOWN';
|
||
const expValue = tracker.alpha / (tracker.alpha + tracker.beta);
|
||
|
||
return (
|
||
<td key={h.id} className="p-2 text-center border-l border-slate-900">
|
||
<div className="flex flex-col items-center">
|
||
<span className={`font-bold ${direction === 'UP' ? 'text-emerald-400' : 'text-rose-400'}`}>
|
||
{direction === 'UP' ? '▲' : '▼'} {(prob * 100).toFixed(0)}%
|
||
</span>
|
||
<span className="text-[9px] text-slate-500 mt-0.5">
|
||
{tracker.alpha}/{tracker.beta}
|
||
</span>
|
||
<span className="text-[9px] text-cyan-400 font-semibold mt-0.5">
|
||
E: {(expValue * 100).toFixed(1)}%
|
||
</span>
|
||
</div>
|
||
</td>
|
||
);
|
||
})}
|
||
</tr>
|
||
))}
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
|
||
{/* Model Calibration Log & Simulation */}
|
||
<div className="p-4 rounded-xl border border-slate-850 bg-slate-950/40 space-y-3">
|
||
<div className="flex items-center justify-between">
|
||
<h4 className="text-xs font-bold text-slate-300 uppercase">Beta-Posterior Calibration</h4>
|
||
<span className="text-[10px] text-slate-500 font-mono">Simulate Walk-Forward</span>
|
||
</div>
|
||
|
||
<div className="space-y-2">
|
||
<p className="text-[10px] text-slate-400">
|
||
Simulate model drift across all 15 independent trackers to calibrate Beta posterior expectations.
|
||
</p>
|
||
<div className="flex gap-2">
|
||
<button
|
||
onClick={() => handleSimulateEnsembleTrial(true)}
|
||
className="flex-1 bg-emerald-500/10 hover:bg-emerald-500/20 text-emerald-400 border border-emerald-500/20 py-1.5 rounded-lg text-xs font-semibold font-mono"
|
||
>
|
||
+1 Success (All)
|
||
</button>
|
||
<button
|
||
onClick={() => handleSimulateEnsembleTrial(false)}
|
||
className="flex-1 bg-rose-500/10 hover:bg-rose-500/20 text-rose-400 border border-rose-500/20 py-1.5 rounded-lg text-xs font-semibold font-mono"
|
||
>
|
||
+1 False Alarm (All)
|
||
</button>
|
||
</div>
|
||
{simulatedTrialLogged && (
|
||
<div className="text-[10px] text-cyan-400 font-mono text-center animate-pulse">
|
||
Logged trial outcomes across all 15 estimators & horizons!
|
||
</div>
|
||
)}
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
{/* SECTION 3: Active Forecasts (Feedback Loop) */}
|
||
<div className="grid grid-cols-1 xl:grid-cols-3 gap-8 mt-6">
|
||
<div className="xl:col-span-2 bg-slate-950/30 rounded-xl p-4 border border-slate-850 space-y-4">
|
||
<div className="flex justify-between items-center border-b border-slate-800 pb-3">
|
||
<h3 className="text-sm font-bold text-white flex items-center gap-2">
|
||
<TrendingUp className="text-cyan-400 w-4 h-4" /> Active Learning Feedback Loop
|
||
</h3>
|
||
<button
|
||
onClick={handleLogManualForecast}
|
||
className="bg-cyan-500 hover:bg-cyan-600 text-slate-950 font-bold py-1.5 px-3 rounded-lg text-xs transition-all active:scale-[0.96]"
|
||
>
|
||
Log Forecast to Feedback Loop
|
||
</button>
|
||
</div>
|
||
|
||
{learningLoopLog && (
|
||
<div className="p-3 bg-cyan-950/20 border border-cyan-900/40 rounded-lg text-xs text-cyan-300 flex items-center gap-2">
|
||
<Info className="w-4 h-4 shrink-0" />
|
||
<span>{learningLoopLog}</span>
|
||
</div>
|
||
)}
|
||
|
||
<div className="overflow-x-auto max-h-56 scrollbar-thin">
|
||
<table className="w-full border-collapse text-left text-xs font-mono">
|
||
<thead>
|
||
<tr className="border-b border-slate-800 text-slate-400 font-semibold bg-slate-900/40">
|
||
<th className="p-2">Ticker</th>
|
||
<th className="p-2">Entry Price</th>
|
||
<th className="p-2 text-center">T+1 Forecast & Res</th>
|
||
<th className="p-2 text-center">T+5 Forecast & Res</th>
|
||
<th className="p-2 text-center">T+10 Forecast & Res</th>
|
||
<th className="p-2">Status</th>
|
||
<th className="p-2 text-right">Accuracy</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
{forecasts.length === 0 ? (
|
||
<tr>
|
||
<td colSpan={7} className="p-4 text-center text-slate-500 italic">No forecasts registered yet.</td>
|
||
</tr>
|
||
) : (
|
||
forecasts.map((fc) => {
|
||
const now = Date.now();
|
||
|
||
const getHorizonStatus = (hKey: 'T1' | 'T5' | 'T10') => {
|
||
const targetTime = fc.targetTimes[hKey];
|
||
const isPast = now >= targetTime;
|
||
|
||
let successes = 0;
|
||
let total = 0;
|
||
ESTIMATORS.forEach((est) => {
|
||
const rKey = `${est.id}_${hKey}`;
|
||
if (fc.results && fc.results[rKey]) {
|
||
total++;
|
||
if (fc.results[rKey] === 'SUCCESS') successes++;
|
||
}
|
||
});
|
||
|
||
if (total === 5) {
|
||
return (
|
||
<span className="text-emerald-400 font-bold">
|
||
{successes}/5 OK
|
||
</span>
|
||
);
|
||
}
|
||
if (isPast) {
|
||
return <span className="text-cyan-400 animate-pulse">Resolving...</span>;
|
||
}
|
||
const secondsLeft = Math.max(0, Math.ceil((targetTime - now) / 1000));
|
||
return <span className="text-slate-500 font-normal">{secondsLeft}s</span>;
|
||
};
|
||
|
||
const renderHorizonCell = (hKey: 'T1' | 'T5' | 'T10') => {
|
||
let avgProb = 0.5;
|
||
const modelProbs: string[] = [];
|
||
if (fc.predictions) {
|
||
let sum = 0;
|
||
let count = 0;
|
||
ESTIMATORS.forEach((est) => {
|
||
const prob = fc.predictions[est.id]?.[hKey];
|
||
if (prob !== undefined) {
|
||
sum += prob;
|
||
count++;
|
||
modelProbs.push(`${est.id.toUpperCase()}:${(prob * 100).toFixed(0)}%`);
|
||
}
|
||
});
|
||
if (count > 0) avgProb = sum / count;
|
||
}
|
||
const avgDir = avgProb > 0.5 ? 'UP' : 'DOWN';
|
||
|
||
return (
|
||
<td className="p-2 text-center border-r border-slate-900/40 last:border-r-0">
|
||
<div className="flex flex-col items-center gap-1">
|
||
<span className={`px-1.5 py-0.5 rounded text-[10px] font-bold ${avgDir === 'UP' ? 'bg-emerald-500/10 text-emerald-400' : 'bg-rose-500/10 text-rose-400'}`}>
|
||
{avgDir} {(avgProb * 100).toFixed(0)}%
|
||
</span>
|
||
<div className="text-[10px] font-semibold">
|
||
{getHorizonStatus(hKey)}
|
||
</div>
|
||
<div className="text-[8px] text-slate-500 flex flex-wrap justify-center gap-1 max-w-[130px] leading-tight font-sans">
|
||
{modelProbs.join(' | ')}
|
||
</div>
|
||
</div>
|
||
</td>
|
||
);
|
||
};
|
||
|
||
let resolvedCount = 0;
|
||
let successCount = 0;
|
||
if (fc.results) {
|
||
Object.values(fc.results).forEach((r) => {
|
||
resolvedCount++;
|
||
if (r === 'SUCCESS') successCount++;
|
||
});
|
||
}
|
||
|
||
let statusText = 'PENDING';
|
||
if (resolvedCount === 15) {
|
||
statusText = 'RESOLVED';
|
||
} else if (resolvedCount > 0) {
|
||
statusText = `PARTIAL (${resolvedCount}/15)`;
|
||
}
|
||
|
||
return (
|
||
<tr key={fc.id} className="border-b border-slate-900 hover:bg-slate-850/10">
|
||
<td className="p-2 text-slate-200 font-bold">{fc.ticker}</td>
|
||
<td className="p-2 text-slate-350">${fc.entryPrice.toLocaleString()}</td>
|
||
{renderHorizonCell('T1')}
|
||
{renderHorizonCell('T5')}
|
||
{renderHorizonCell('T10')}
|
||
<td className="p-2 text-slate-400 text-[10px]">{statusText}</td>
|
||
<td className="p-2 text-right font-bold text-slate-300">
|
||
{resolvedCount > 0 ? (
|
||
<span className={successCount / resolvedCount >= 0.5 ? 'text-emerald-400' : 'text-rose-400'}>
|
||
{((successCount / resolvedCount) * 100).toFixed(0)}% ({successCount}/{resolvedCount})
|
||
</span>
|
||
) : (
|
||
<span className="text-slate-500">-</span>
|
||
)}
|
||
</td>
|
||
</tr>
|
||
);
|
||
})
|
||
)}
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
</div>
|
||
|
||
{/* Informational overlay */}
|
||
<div className="bg-slate-950/40 rounded-xl p-4 border border-slate-850 text-xs text-slate-400 space-y-2">
|
||
<h4 className="font-bold text-slate-300 uppercase">Econometric Feedback Loop Spec</h4>
|
||
<p className="leading-relaxed">
|
||
The learning loop automatically evaluates active forecast parameters in the background against actual price histories returned by <code className="text-cyan-400 font-mono">/api/finance?region=crypto</code>.
|
||
</p>
|
||
<p className="leading-relaxed">
|
||
When a logged forecast passes its evaluation target timestamp, it resolves against live market data, updating the Bayesian online calibration metrics <InlineMath math="\alpha" /> and <InlineMath math="\beta" />.
|
||
</p>
|
||
</div>
|
||
</div>
|
||
|
||
{/* SECTION 4: 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-cyan-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">Mathematical Formulation (Beta Update & Random Forest)</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-cyan-400 mb-1">1. Bayesian Beta-Conjugate Error Correction</h4>
|
||
<p className="mb-2">
|
||
We model the error rate confidence interval of the model using a Beta distribution. The prior error state is represented by the parameters <InlineMath math="\alpha" /> (Successes) and <InlineMath math="\beta" /> (False Alarms):
|
||
</p>
|
||
<div className="py-2 overflow-x-auto text-slate-200">
|
||
<BlockMath math="P \\sim \\text{Beta}(\\alpha, \\beta) \\quad \\text{with expected value } \\mathbb{E}[P] = \\frac{\\alpha}{\\alpha + \\beta}" />
|
||
</div>
|
||
<p className="mb-2">
|
||
With a new ML signal <InlineMath math="P_{\\text{ML}}" />, we perform a conjugate Bayes update with a confidence weight <InlineMath math="w" />:
|
||
</p>
|
||
<div className="py-2 overflow-x-auto text-slate-200">
|
||
<BlockMath math="\\alpha_{\\text{post}} = \\alpha + w \\cdot P_{\\text{ML}}, \\quad \\beta_{\\text{post}} = \\beta + w \\cdot (1 - P_{\\text{ML}})" />
|
||
</div>
|
||
<div className="py-2 overflow-x-auto text-slate-200">
|
||
<BlockMath math="P_{\\text{Posterior}} = \\frac{\\alpha_{\\text{post}}}{\\alpha_{\\text{post}} + \\beta_{\\text{post}}}" />
|
||
</div>
|
||
<p className="text-slate-400">
|
||
If the model is historically highly unstable (high <InlineMath math="\\beta" />), the Bayesian term corrects an overconfident ML signal downwards, safeguarding the robustness of the system.
|
||
</p>
|
||
</div>
|
||
|
||
<div className="border-t border-slate-900 pt-3">
|
||
<h4 className="font-bold text-cyan-400 mb-1">2. Random Forest Non-Linear Signal Mapping</h4>
|
||
<p className="mb-2">
|
||
The Random Forest simulates an ensemble of 10 weak decision trees. Each tree splits the data based on threshold criteria (e.g., 'Funding Rate < -0.04%' and 'Open Interest > 10%'):
|
||
</p>
|
||
<div className="py-2 overflow-x-auto text-slate-200">
|
||
<BlockMath math="\\text{ML}_{\\text{prob}} = \\frac{1}{M} \\sum_{m=1}^{M} T_m(\\mathbf{x})" />
|
||
</div>
|
||
<p className="text-slate-400">
|
||
where <InlineMath math="T_m(\\mathbf{x})" /> is the predicted output value of the <InlineMath math="m" />-th decision tree for the feature vector <InlineMath math="\\mathbf{x}" />.
|
||
</p>
|
||
</div>
|
||
</div>
|
||
)}
|
||
</div>
|
||
|
||
<CryptoMathModal isOpen={isMathModalOpen} onClose={() => setIsMathModalOpen(false)} />
|
||
<CryptoBlueprintModal isOpen={isBlueprintModalOpen} onClose={() => setIsBlueprintModalOpen(false)} />
|
||
</div>
|
||
);
|
||
}
|