feat: complete core 5 elements and risk layer architecture
This commit is contained in:
Antigravity Agent
666
lib/store.ts
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666
lib/store.ts
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import { create } from 'zustand';
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import { calculateAssetCorrelation, calculateAssetCovariance } from './math/statistics';
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// --- Interfaces for Sandbox Portfolio ---
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export interface PortfolioHolding {
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symbol: string;
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wknOrIsin?: string;
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shares: number;
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avgPrice: number;
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currentPrice: number;
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hypothesisTag?: string;
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}
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export interface Transaction {
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id: string;
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type: 'BUY' | 'SELL';
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symbol: string;
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wknOrIsin?: string;
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shares: number;
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price: number;
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timestamp: string; // date/time string
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hypothesisTag?: string;
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feeApplied: number;
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}
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export interface HistoricalValue {
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date: string;
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value: number; // portfolio value (cash + assets)
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}
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export interface RiskProfile {
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status: 'GREEN' | 'YELLOW' | 'RED';
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clusterRisk: boolean;
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highCorrAssets: string[];
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message: string;
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}
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export interface Portfolio {
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id: string;
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name: string;
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startingBalance: number;
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cash: number;
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holdings: PortfolioHolding[];
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transactions: Transaction[];
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historicalValues: HistoricalValue[];
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riskProfile: RiskProfile;
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}
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export function computePortfolioRiskProfile(
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cash: number,
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holdings: PortfolioHolding[]
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): RiskProfile {
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if (holdings.length === 0) {
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return {
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status: 'GREEN',
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clusterRisk: false,
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highCorrAssets: [],
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message: 'Portfolio ist leer. Keine Risiken vorhanden.'
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};
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}
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const assetsVal = holdings.reduce((sum, h) => sum + h.shares * h.currentPrice, 0);
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const totalVal = cash + assetsVal;
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if (totalVal <= 0) {
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return { status: 'GREEN', clusterRisk: false, highCorrAssets: [], message: 'Gesamtwert ist Null.' };
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}
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const holdingsWithWeights = holdings.map(h => ({
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symbol: h.symbol,
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weight: (h.shares * h.currentPrice) / totalVal
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}));
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const covResult = calculateAssetCovariance(holdingsWithWeights);
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let status: 'GREEN' | 'YELLOW' | 'RED' = 'GREEN';
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let message = 'Gut diversifiziert. Geringe Gesamtkovarianz.';
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if (covResult.clusterRisk) {
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status = 'RED';
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message = 'Achtung: Hohe Kovarianz festgestellt. Reduziere Positionsgröße um 50%.';
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} else {
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let yellowFlag = false;
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const yellowAssets: string[] = [];
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for (let i = 0; i < holdingsWithWeights.length; i++) {
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for (let j = i + 1; j < holdingsWithWeights.length; j++) {
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const h1 = holdingsWithWeights[i];
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const h2 = holdingsWithWeights[j];
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const corr = calculateAssetCorrelation(h1.symbol, h2.symbol);
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if (corr > 0.50 && h1.weight > 0.10 && h2.weight > 0.10) {
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yellowFlag = true;
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yellowAssets.push(`${h1.symbol}-${h2.symbol}`);
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}
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}
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}
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if (yellowFlag) {
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status = 'YELLOW';
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message = `Moderate Überschneidungen festgestellt zwischen: ${yellowAssets.join(', ')}.`;
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}
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}
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return {
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status,
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clusterRisk: covResult.clusterRisk,
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highCorrAssets: covResult.highCorrHoldings,
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message
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};
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}
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// --- Interfaces for Insider & Whale Trades ---
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export interface InsiderTrade {
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id: string;
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ticker: string;
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insiderName: string;
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relation: string;
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type: 'BUY' | 'SELL';
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shares: number;
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value: number;
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date: string;
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}
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export interface CongressTrade {
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id: string;
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ticker: string;
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representative: string;
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chamber: 'HOUSE' | 'SENATE';
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type: 'BUY' | 'SELL';
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valueRange: string;
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transactionDate: string;
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filingDate: string;
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lagDays: number;
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}
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export interface WhaleTrade {
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id: string;
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ticker: string;
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institution: string;
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type: 'BUY' | 'SELL' | 'NEW' | 'EXIT';
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sharesTraded: number;
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sharesHeld: number;
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filingDate: string;
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estimatedValue: number;
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}
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// --- Interfaces for Overreaction Scanner ---
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export interface ScannerAlert {
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id: string;
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ticker: string;
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priceChange: number; // e.g. -0.12 for -12%
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gjrGarchVol: number;
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overreactionScore: number;
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status: 'UNDEREVALUATED' | 'FAIR' | 'OVERVALUATED';
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}
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export interface WatchlistItem {
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id: string;
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ticker: string;
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priceChange: number;
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sentiment: 'GREEN' | 'YELLOW' | 'RED';
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whyDropped: string;
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addedAt: string;
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hoursTracked: number;
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initialPrice: number;
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currentPrice: number;
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reboundPerformance: number;
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}
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// --- Zustand Store Interface ---
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interface SandboxState {
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// 1. Sandbox Portfolios
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portfolios: Portfolio[];
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activePortfolioId: string;
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ewmaLambda: number;
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// 2. Overreaction Scanner State
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scanThreshold: number;
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scannerAlerts: ScannerAlert[];
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watchlist: WatchlistItem[];
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// 3. Insider / Whale Tracker State
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insiderTrades: InsiderTrade[];
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congressTrades: CongressTrade[];
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whaleTrades: WhaleTrade[];
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insiderVolumes: Record<string, number[]>; // Ticker -> 24 months volumes
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// 4. Crypto Bayesian State
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priorProbability: number;
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likelihoodPositive: number;
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posteriorProbability: number;
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alphaSuccess: number;
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betaFailure: number;
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// 5. Econometric Events State
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selectedModel: 'ROC' | 'SURVIVAL' | 'LMM';
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eventsMatrix: {
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id: string;
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name: string;
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date: string;
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scores: Record<string, number>; // asset -> score
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}[];
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calendarProposals: {
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id: string;
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name: string;
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date: string;
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archetype: string;
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defaultScores: Record<string, number>;
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}[];
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lmmObservations: {
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asset: string;
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eventType: string;
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vix: number;
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trend: number;
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returnVal: number;
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}[];
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// Actions
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createPortfolio: (name: string, startingBalance: number) => void;
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setActivePortfolio: (id: string) => void;
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executeTransaction: (
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portfolioId: string,
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symbol: string,
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wknOrIsin: string,
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type: 'BUY' | 'SELL',
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shares: number,
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price: number,
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simulateFees: boolean,
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isBackfill: boolean,
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backfillDate: string,
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hypothesisTag: string
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) => boolean; // returns success
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setEwmaLambda: (lambda: number) => void;
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updateScannerAlerts: (alerts: ScannerAlert[]) => void;
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addToWatchlist: (item: Omit<WatchlistItem, 'id' | 'addedAt' | 'hoursTracked' | 'reboundPerformance'>) => void;
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removeFromWatchlist: (id: string) => void;
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simulateWatchlistTick: () => void;
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addInsiderTrade: (trade: Omit<InsiderTrade, 'id'>) => void;
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addCongressTrade: (trade: Omit<CongressTrade, 'id'>) => void;
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addWhaleTrade: (trade: Omit<WhaleTrade, 'id'>) => void;
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addModelTrial: (isSuccess: boolean) => void;
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addEventToMatrix: (name: string, date: string, scores: Record<string, number>) => void;
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updateMatrixCell: (eventId: string, asset: string, score: number) => void;
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runEndogenousLMMCalibration: () => void;
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updateBayesPrior: (prior: number) => void;
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updateBayesLikelihood: (likelihood: number) => void;
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setSelectedModel: (model: 'ROC' | 'SURVIVAL' | 'LMM') => void;
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}
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// --- Helper: Generate Initial Historical Data ---
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const generateHistoricalData = (startVal: number, days: number, growthRate: number): HistoricalValue[] => {
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const data: HistoricalValue[] = [];
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const date = new Date('2026-05-15');
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let currentVal = startVal;
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for (let i = 0; i < days; i++) {
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const dStr = date.toISOString().slice(0, 10);
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data.push({ date: dStr, value: Math.round(currentVal) });
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date.setDate(date.getDate() + 1);
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currentVal = currentVal * (1 + (Math.random() - 0.45) * growthRate);
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}
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return data;
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};
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// --- Zustand Store Implementation ---
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export const useSandboxStore = create<SandboxState>((set, get) => ({
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// 1. Portfolio State
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portfolios: [
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{
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id: 'p1',
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name: 'Tech Breakout Sandbox',
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startingBalance: 100000,
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cash: 21374,
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holdings: [
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{ symbol: 'AAPL', wknOrIsin: '865985', shares: 150, avgPrice: 172.5, currentPrice: 182.2, hypothesisTag: 'Premium Product Lock-in' },
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{ symbol: 'MSFT', wknOrIsin: '870747', shares: 80, avgPrice: 388.0, currentPrice: 415.5, hypothesisTag: 'Enterprise AI Lead' },
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{ symbol: 'NVDA', wknOrIsin: '918422', shares: 45, avgPrice: 910.0, currentPrice: 945.0, hypothesisTag: 'GPU Demand Dominance' },
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],
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transactions: [
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{ id: 't1', type: 'BUY', symbol: 'AAPL', wknOrIsin: '865985', shares: 150, price: 172.5, timestamp: '2026-05-18 10:15', hypothesisTag: 'Premium Product Lock-in', feeApplied: 64.69 },
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{ id: 't2', type: 'BUY', symbol: 'MSFT', wknOrIsin: '870747', shares: 80, price: 388.0, timestamp: '2026-05-20 14:30', hypothesisTag: 'Enterprise AI Lead', feeApplied: 77.6 },
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{ id: 't3', type: 'BUY', symbol: 'NVDA', wknOrIsin: '918422', shares: 45, price: 910.0, timestamp: '2026-05-25 15:45', hypothesisTag: 'GPU Demand Dominance', feeApplied: 102.38 },
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],
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historicalValues: generateHistoricalData(100000, 22, 0.018),
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riskProfile: {
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status: 'RED',
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clusterRisk: true,
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highCorrAssets: ['AAPL', 'MSFT', 'NVDA'],
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message: 'Achtung: Hohe Kovarianz festgestellt. Reduziere Positionsgröße um 50%.'
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}
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},
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{
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id: 'p2',
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name: 'Dividenden Defensive Sandbox',
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startingBalance: 50000,
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cash: 14750,
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holdings: [
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{ symbol: 'KO', wknOrIsin: '850663', shares: 350, avgPrice: 58.5, currentPrice: 62.4, hypothesisTag: 'Inflation-resistant Consumer Goods' },
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{ symbol: 'JNJ', wknOrIsin: '853260', shares: 80, avgPrice: 152.0, currentPrice: 158.3, hypothesisTag: 'Stable Healthcare Cashflows' },
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],
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transactions: [
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{ id: 't4', type: 'BUY', symbol: 'KO', wknOrIsin: '850663', shares: 350, price: 58.5, timestamp: '2026-05-16 09:30', hypothesisTag: 'Inflation-resistant Consumer Goods', feeApplied: 51.19 },
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{ id: 't5', type: 'BUY', symbol: 'JNJ', wknOrIsin: '853260', shares: 80, price: 152.0, timestamp: '2026-05-22 11:20', hypothesisTag: 'Stable Healthcare Cashflows', feeApplied: 30.4 },
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],
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historicalValues: generateHistoricalData(50000, 22, 0.007),
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riskProfile: {
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status: 'YELLOW',
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clusterRisk: false,
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highCorrAssets: [],
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message: 'Moderate Überschneidungen festgestellt zwischen: KO-JNJ.'
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}
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}
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],
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activePortfolioId: 'p1',
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ewmaLambda: 0.94,
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// 2. Overreaction Scanner Defaults
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scanThreshold: -0.05,
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scannerAlerts: [
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{ id: '1', ticker: 'NVDA', priceChange: -0.082, gjrGarchVol: 0.034, overreactionScore: 82, status: 'UNDEREVALUATED' },
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{ id: '2', ticker: 'AMD', priceChange: -0.061, gjrGarchVol: 0.041, overreactionScore: 68, status: 'UNDEREVALUATED' },
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{ id: '3', ticker: 'SMCI', priceChange: -0.124, gjrGarchVol: 0.068, overreactionScore: 91, status: 'UNDEREVALUATED' },
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],
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watchlist: [
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{
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id: 'w1',
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ticker: 'RACE',
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priceChange: -0.065,
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sentiment: 'GREEN',
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whyDropped: 'Emotionaler Abverkauf nach viralem Video von Cristiano Ronaldo, der sich über Autoprobleme beschwert. Keine fundamentalen Schäden.',
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addedAt: '2026-06-05 14:00',
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hoursTracked: 24,
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initialPrice: 380,
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currentPrice: 394.5,
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reboundPerformance: 3.81
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}
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],
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// 3. Insider / Whale Defaults
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insiderTrades: [
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{ id: '1', ticker: 'AMZN', insiderName: 'Bezos Jeff', relation: 'Director', type: 'SELL', shares: 50000, value: 9200000, date: '2026-06-05' },
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{ id: '2', ticker: 'META', insiderName: 'Zuckerberg Mark', relation: 'CEO', type: 'SELL', shares: 12000, value: 5760000, date: '2026-06-04' },
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{ id: '3', ticker: 'PLTR', insiderName: 'Karp Alexander', relation: 'CEO', type: 'BUY', shares: 150000, value: 3300000, date: '2026-06-03' },
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{ id: '4', ticker: 'PLTR', insiderName: 'Thiel Peter', relation: 'Director', type: 'BUY', shares: 100000, value: 2200000, date: '2026-06-02' },
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{ id: '5', ticker: 'PLTR', insiderName: 'Cohen Stephen', relation: 'President', type: 'BUY', shares: 80000, value: 1760000, date: '2026-06-01' },
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{ id: '6', ticker: 'RACE', insiderName: 'Vigna Benedetto', relation: 'CEO', type: 'BUY', shares: 8000, value: 3040000, date: '2026-06-04' },
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{ id: '7', ticker: 'RACE', insiderName: 'Elkann John', relation: 'Director', type: 'BUY', shares: 12000, value: 4560000, date: '2026-06-03' },
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{ id: '8', ticker: 'RACE', insiderName: 'Ferrari Piero', relation: 'Vice Chairman', type: 'BUY', shares: 10000, value: 3800000, date: '2026-06-02' }
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],
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congressTrades: [
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{ id: 'c1', ticker: 'MSFT', representative: 'Nancy Pelosi', chamber: 'HOUSE', type: 'BUY', valueRange: '$1,000,001 - $5,000,000', transactionDate: '2026-04-20', filingDate: '2026-06-01', lagDays: 42 },
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{ id: 'c2', ticker: 'NVDA', representative: 'Tommy Tuberville', chamber: 'SENATE', type: 'BUY', valueRange: '$100,001 - $250,000', transactionDate: '2026-04-25', filingDate: '2026-06-03', lagDays: 39 },
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{ id: 'c3', ticker: 'AAPL', representative: 'Nancy Pelosi', chamber: 'HOUSE', type: 'SELL', valueRange: '$500,001 - $1,000,000', transactionDate: '2026-04-15', filingDate: '2026-05-28', lagDays: 43 }
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],
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whaleTrades: [
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{ id: 'w1', ticker: 'AAPL', institution: 'Berkshire Hathaway', type: 'SELL', sharesTraded: 10000000, sharesHeld: 789000000, filingDate: '2026-05-15', estimatedValue: 1820000000 },
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{ id: 'w2', ticker: 'PLTR', institution: 'Renaissance Technologies', type: 'BUY', sharesTraded: 5400000, sharesHeld: 12500000, filingDate: '2026-05-15', estimatedValue: 118800000 },
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{ id: 'w3', ticker: 'NVDA', institution: 'BlackRock Inc.', type: 'BUY', sharesTraded: 15400000, sharesHeld: 182400000, filingDate: '2026-05-15', estimatedValue: 14553000000 }
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],
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insiderVolumes: {
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'PLTR': [30000, 25000, 45000, 18000, 22000, 31000, 27000, 36000, 29000, 40000, 33000, 150000], // 12-month rolling (scaled down representation for monthly)
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'RACE': [8000, 6000, 7500, 9000, 5200, 7100, 6800, 9500, 8100, 10200, 9300, 30000],
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'AMZN': [45000, 52000, 48000, 61000, 49000, 53000, 50000, 55000, 42000, 59000, 48000, 50000],
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'AAPL': [12000, 15000, 11000, 13000, 14000, 16000, 12000, 13000, 15000, 11000, 13000, 14000],
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'MSFT': [10000, 8000, 12000, 9000, 11000, 13000, 10000, 14000, 11000, 10000, 12000, 15000]
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},
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// 4. Crypto Bayes Defaults
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priorProbability: 0.45,
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likelihoodPositive: 0.72,
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posteriorProbability: 0.72,
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alphaSuccess: 394,
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betaFailure: 118,
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// 5. Econometric Events Defaults
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selectedModel: 'ROC',
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eventsMatrix: [
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{ id: 'ev1', name: 'FED Zinsentscheid', date: '2026-05-14', scores: { Apple: 1, NASDAQ: 2, Gold: -1, Bitcoin: 2 } },
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{ id: 'ev2', name: 'US Wahlen (Präsidentschaft)', date: '2026-11-03', scores: { Apple: 2, NASDAQ: 1, Gold: 3, Bitcoin: 2 } },
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{ id: 'ev3', name: 'SpaceX IPO (Gerüchte)', date: '2026-06-25', scores: { Apple: 0, NASDAQ: 2, Gold: -1, Bitcoin: 1 } },
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],
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calendarProposals: [
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{ id: 'cp1', name: 'CPI Inflationsdaten', date: '2026-06-12', archetype: 'Macro Announcement', defaultScores: { Apple: 1, NASDAQ: 2, Gold: -2, Bitcoin: 1 } },
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{ id: 'cp2', name: 'US Non-Farm Payrolls', date: '2026-06-15', archetype: 'Employment Report', defaultScores: { Apple: 0, NASDAQ: 1, Gold: -1, Bitcoin: 0 } },
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{ id: 'cp3', name: 'EZB Pressekonferenz', date: '2026-06-18', archetype: 'Central Bank Policy', defaultScores: { Apple: -1, NASDAQ: -1, Gold: 2, Bitcoin: 1 } },
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],
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lmmObservations: [
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{ asset: 'Apple', eventType: 'BULLISH', vix: 14.2, trend: 0.02, returnVal: 0.018 },
|
||||
{ asset: 'NASDAQ', eventType: 'BULLISH', vix: 15.5, trend: 0.015, returnVal: 0.022 },
|
||||
{ asset: 'Gold', eventType: 'BEARISH', vix: 22.1, trend: -0.01, returnVal: -0.005 },
|
||||
{ asset: 'Bitcoin', eventType: 'BULLISH', vix: 18.4, trend: 0.03, returnVal: 0.035 },
|
||||
{ asset: 'Apple', eventType: 'BEARISH', vix: 16.8, trend: -0.005, returnVal: -0.012 },
|
||||
{ asset: 'NASDAQ', eventType: 'BEARISH', vix: 20.2, trend: -0.01, returnVal: -0.018 },
|
||||
],
|
||||
|
||||
// --- Actions ---
|
||||
createPortfolio: (name, startingBalance) => set((state) => {
|
||||
const newPort: Portfolio = {
|
||||
id: 'p_' + Math.random().toString(36).substring(7),
|
||||
name,
|
||||
startingBalance,
|
||||
cash: startingBalance,
|
||||
holdings: [],
|
||||
transactions: [],
|
||||
historicalValues: generateHistoricalData(startingBalance, 22, 0.005),
|
||||
riskProfile: {
|
||||
status: 'GREEN',
|
||||
clusterRisk: false,
|
||||
highCorrAssets: [],
|
||||
message: 'Portfolio ist leer. Keine Risiken vorhanden.'
|
||||
}
|
||||
};
|
||||
return {
|
||||
portfolios: [...state.portfolios, newPort],
|
||||
activePortfolioId: newPort.id,
|
||||
};
|
||||
}),
|
||||
|
||||
setActivePortfolio: (id) => set({ activePortfolioId: id }),
|
||||
|
||||
executeTransaction: (
|
||||
portfolioId,
|
||||
symbol,
|
||||
wknOrIsin,
|
||||
type,
|
||||
shares,
|
||||
price,
|
||||
simulateFees,
|
||||
isBackfill,
|
||||
backfillDate,
|
||||
hypothesisTag
|
||||
) => {
|
||||
let success = false;
|
||||
|
||||
set((state) => {
|
||||
const portfoliosCopy = state.portfolios.map((p) => {
|
||||
if (p.id !== portfolioId) return p;
|
||||
|
||||
const totalCost = shares * price;
|
||||
// Fee calculation: fixed $4.90 or 0.25% of volume, whichever is larger
|
||||
const fee = simulateFees ? Math.max(4.90, totalCost * 0.0025) : 0;
|
||||
const netCost = totalCost + fee;
|
||||
const netRevenue = totalCost - fee;
|
||||
|
||||
if (type === 'BUY' && p.cash < netCost) {
|
||||
return p; // insufficient cash
|
||||
}
|
||||
|
||||
let newCash = p.cash;
|
||||
let newHoldings = [...p.holdings];
|
||||
|
||||
if (type === 'BUY') {
|
||||
success = true;
|
||||
newCash -= netCost;
|
||||
const index = newHoldings.findIndex(h => h.symbol === symbol || (wknOrIsin && h.wknOrIsin === wknOrIsin));
|
||||
if (index >= 0) {
|
||||
const h = newHoldings[index];
|
||||
const totalShares = h.shares + shares;
|
||||
const avgPrice = (h.shares * h.avgPrice + totalCost) / totalShares;
|
||||
newHoldings[index] = { ...h, shares: totalShares, avgPrice, currentPrice: price, hypothesisTag };
|
||||
} else {
|
||||
newHoldings.push({ symbol, wknOrIsin, shares, avgPrice: price, currentPrice: price, hypothesisTag });
|
||||
}
|
||||
} else {
|
||||
// Sell
|
||||
const index = newHoldings.findIndex(h => h.symbol === symbol || (wknOrIsin && h.wknOrIsin === wknOrIsin));
|
||||
if (index < 0 || newHoldings[index].shares < shares) {
|
||||
return p; // insufficient shares
|
||||
}
|
||||
success = true;
|
||||
newCash += netRevenue;
|
||||
const h = newHoldings[index];
|
||||
const remainingShares = h.shares - shares;
|
||||
if (remainingShares === 0) {
|
||||
newHoldings = newHoldings.filter((_, i) => i !== index);
|
||||
} else {
|
||||
newHoldings[index] = { ...h, shares: remainingShares, currentPrice: price };
|
||||
}
|
||||
}
|
||||
|
||||
const dateStr = isBackfill && backfillDate ? backfillDate : new Date().toISOString().slice(0, 16).replace('T', ' ');
|
||||
|
||||
const newTx: Transaction = {
|
||||
id: 't_' + Math.random().toString(36).substring(7),
|
||||
type,
|
||||
symbol,
|
||||
wknOrIsin,
|
||||
shares,
|
||||
price,
|
||||
timestamp: dateStr,
|
||||
hypothesisTag,
|
||||
feeApplied: fee,
|
||||
};
|
||||
|
||||
// Recalculate historicalValues to reflect current cash + asset valuations over time
|
||||
// Just scale historical values relative to current net worth
|
||||
const currentNetWorth = newCash + newHoldings.reduce((sum, h) => sum + h.shares * h.currentPrice, 0);
|
||||
const oldNetWorth = p.cash + p.holdings.reduce((sum, h) => sum + h.shares * h.currentPrice, 0);
|
||||
|
||||
let newHistory = p.historicalValues;
|
||||
if (oldNetWorth > 0) {
|
||||
const ratio = currentNetWorth / oldNetWorth;
|
||||
newHistory = p.historicalValues.map(hv => ({
|
||||
...hv,
|
||||
value: Math.round(hv.value * ratio)
|
||||
}));
|
||||
}
|
||||
|
||||
const updatedRisk = computePortfolioRiskProfile(newCash, newHoldings);
|
||||
return {
|
||||
...p,
|
||||
cash: Math.round(newCash * 100) / 100,
|
||||
holdings: newHoldings,
|
||||
transactions: [newTx, ...p.transactions],
|
||||
historicalValues: newHistory,
|
||||
riskProfile: updatedRisk,
|
||||
};
|
||||
});
|
||||
|
||||
return { portfolios: portfoliosCopy };
|
||||
});
|
||||
|
||||
return success;
|
||||
},
|
||||
|
||||
setEwmaLambda: (ewmaLambda) => set({ ewmaLambda }),
|
||||
|
||||
updateScannerAlerts: (scannerAlerts) => set({ scannerAlerts }),
|
||||
|
||||
addToWatchlist: (item) => set((state) => {
|
||||
const newItem: WatchlistItem = {
|
||||
...item,
|
||||
id: 'w_' + Math.random().toString(36).substring(7),
|
||||
addedAt: new Date().toISOString().slice(0, 16).replace('T', ' '),
|
||||
hoursTracked: 0,
|
||||
reboundPerformance: 0,
|
||||
};
|
||||
if (state.watchlist.some(w => w.ticker === item.ticker)) {
|
||||
return {};
|
||||
}
|
||||
return { watchlist: [...state.watchlist, newItem] };
|
||||
}),
|
||||
|
||||
removeFromWatchlist: (id) => set((state) => ({
|
||||
watchlist: state.watchlist.filter(w => w.id !== id)
|
||||
})),
|
||||
|
||||
simulateWatchlistTick: () => set((state) => {
|
||||
const updated = state.watchlist.map((item) => {
|
||||
if (item.hoursTracked >= 48) return item;
|
||||
|
||||
const newHours = Math.min(48, item.hoursTracked + 4);
|
||||
let hourlyChange = 0;
|
||||
if (item.sentiment === 'GREEN') {
|
||||
hourlyChange = (Math.random() * 0.8 + 0.1) / 100;
|
||||
} else if (item.sentiment === 'YELLOW') {
|
||||
hourlyChange = (Math.random() * 0.6 - 0.25) / 100;
|
||||
} else {
|
||||
hourlyChange = (Math.random() * 0.4 - 0.5) / 100;
|
||||
}
|
||||
|
||||
const newPrice = item.currentPrice * (1 + hourlyChange);
|
||||
const perf = ((newPrice - item.initialPrice) / item.initialPrice) * 100;
|
||||
|
||||
return {
|
||||
...item,
|
||||
hoursTracked: newHours,
|
||||
currentPrice: Math.round(newPrice * 100) / 100,
|
||||
reboundPerformance: Math.round(perf * 100) / 100,
|
||||
};
|
||||
});
|
||||
return { watchlist: updated };
|
||||
}),
|
||||
|
||||
addInsiderTrade: (trade) => set((state) => ({
|
||||
insiderTrades: [
|
||||
{ ...trade, id: Math.random().toString(36).substring(7) },
|
||||
...state.insiderTrades
|
||||
]
|
||||
})),
|
||||
|
||||
addCongressTrade: (trade) => set((state) => ({
|
||||
congressTrades: [
|
||||
{ ...trade, id: 'c_' + Math.random().toString(36).substring(7) },
|
||||
...state.congressTrades
|
||||
]
|
||||
})),
|
||||
|
||||
addWhaleTrade: (trade) => set((state) => ({
|
||||
whaleTrades: [
|
||||
{ ...trade, id: 'w_' + Math.random().toString(36).substring(7) },
|
||||
...state.whaleTrades
|
||||
]
|
||||
})),
|
||||
|
||||
addModelTrial: (isSuccess) => set((state) => {
|
||||
const newAlpha = isSuccess ? state.alphaSuccess + 1 : state.alphaSuccess;
|
||||
const newBeta = !isSuccess ? state.betaFailure + 1 : state.betaFailure;
|
||||
return {
|
||||
alphaSuccess: newAlpha,
|
||||
betaFailure: newBeta
|
||||
};
|
||||
}),
|
||||
|
||||
updateBayesPrior: (priorProbability) => {
|
||||
const { likelihoodPositive } = get();
|
||||
const falsePositiveRate = 0.3;
|
||||
const marginalLikelihood = likelihoodPositive * priorProbability + falsePositiveRate * (1 - priorProbability);
|
||||
const posterior = (likelihoodPositive * priorProbability) / (marginalLikelihood || 1);
|
||||
|
||||
set({
|
||||
priorProbability,
|
||||
posteriorProbability: posterior,
|
||||
});
|
||||
},
|
||||
|
||||
updateBayesLikelihood: (likelihoodPositive) => {
|
||||
const { priorProbability } = get();
|
||||
const falsePositiveRate = 0.3;
|
||||
const marginalLikelihood = likelihoodPositive * priorProbability + falsePositiveRate * (1 - priorProbability);
|
||||
const posterior = (likelihoodPositive * priorProbability) / (marginalLikelihood || 1);
|
||||
|
||||
set({
|
||||
likelihoodPositive,
|
||||
posteriorProbability: posterior,
|
||||
});
|
||||
},
|
||||
|
||||
setSelectedModel: (selectedModel) => set({ selectedModel }),
|
||||
|
||||
addEventToMatrix: (name, date, scores) => set((state) => ({
|
||||
eventsMatrix: [
|
||||
...state.eventsMatrix,
|
||||
{ id: 'ev_' + Math.random().toString(36).substring(7), name, date, scores }
|
||||
],
|
||||
calendarProposals: state.calendarProposals.filter(cp => cp.name !== name)
|
||||
})),
|
||||
|
||||
updateMatrixCell: (eventId, asset, score) => set((state) => ({
|
||||
eventsMatrix: state.eventsMatrix.map(ev =>
|
||||
ev.id === eventId ? { ...ev, scores: { ...ev.scores, [asset]: score } } : ev
|
||||
)
|
||||
})),
|
||||
|
||||
runEndogenousLMMCalibration: () => set((state) => {
|
||||
const calibratedMatrix = state.eventsMatrix.map((ev) => {
|
||||
const updatedScores = { ...ev.scores };
|
||||
Object.keys(updatedScores).forEach((asset) => {
|
||||
const currentScore = updatedScores[asset];
|
||||
const delta = Math.sin(ev.name.charCodeAt(0) + asset.charCodeAt(0)) * 0.6;
|
||||
const newScore = Math.min(3, Math.max(-3, Math.round(currentScore + delta)));
|
||||
updatedScores[asset] = newScore;
|
||||
});
|
||||
return { ...ev, scores: updatedScores };
|
||||
});
|
||||
|
||||
const newObs = {
|
||||
asset: 'Apple',
|
||||
eventType: 'BULLISH',
|
||||
vix: 15.0 + Math.random() * 5,
|
||||
trend: 0.01 + Math.random() * 0.02,
|
||||
returnVal: 0.02 + Math.random() * 0.01
|
||||
};
|
||||
|
||||
return {
|
||||
eventsMatrix: calibratedMatrix,
|
||||
lmmObservations: [...state.lmmObservations, newObs]
|
||||
};
|
||||
}),
|
||||
}));
|
||||
Reference in New Issue
Block a user