From 96f7643f8aef2762b5c2c87199762125273fd4fb Mon Sep 17 00:00:00 2001 From: Antigravity Agent Date: Sat, 6 Jun 2026 21:11:16 +0200 Subject: [PATCH] feat: complete core 5 elements and risk layer architecture --- .github/workflows/agent-issue-resolver.yml | 28 + .gitignore | 53 + AGENTS.md | 5 + CLAUDE.md | 1 + GITHUB_WORKFLOW.md | 85 + README.md | 36 + app/favicon.ico | Bin 0 -> 25931 bytes app/globals.css | 26 + app/layout.tsx | 33 + app/page.tsx | 125 + components/modules/crypto/CryptoDemo.tsx | 487 ++ components/modules/events/EventsDemo.tsx | 980 +++ components/modules/insider/InsiderDemo.tsx | 448 ++ components/modules/sandbox/SandboxDemo.tsx | 815 +++ components/modules/scanner/ScannerDemo.tsx | 538 ++ eslint.config.mjs | 18 + github-agent-config.json | 36 + lib/math/statistics.ts | 717 ++ lib/store.ts | 666 ++ next.config.ts | 7 + package-lock.json | 7154 ++++++++++++++++++++ package.json | 32 + postcss.config.mjs | 7 + public/file.svg | 1 + public/globe.svg | 1 + public/next.svg | 1 + public/vercel.svg | 1 + public/window.svg | 1 + tsconfig.json | 34 + 29 files changed, 12336 insertions(+) create mode 100644 .github/workflows/agent-issue-resolver.yml create mode 100644 .gitignore create mode 100644 AGENTS.md create mode 100644 CLAUDE.md create mode 100644 GITHUB_WORKFLOW.md create mode 100644 README.md create mode 100644 app/favicon.ico create mode 100644 app/globals.css create mode 100644 app/layout.tsx create mode 100644 app/page.tsx create mode 100644 components/modules/crypto/CryptoDemo.tsx create mode 100644 components/modules/events/EventsDemo.tsx create mode 100644 components/modules/insider/InsiderDemo.tsx create mode 100644 components/modules/sandbox/SandboxDemo.tsx create mode 100644 components/modules/scanner/ScannerDemo.tsx create mode 100644 eslint.config.mjs create mode 100644 github-agent-config.json create mode 100644 lib/math/statistics.ts create mode 100644 lib/store.ts create mode 100644 next.config.ts create mode 100644 package-lock.json create mode 100644 package.json create mode 100644 postcss.config.mjs create mode 100644 public/file.svg create mode 100644 public/globe.svg create mode 100644 public/next.svg create mode 100644 public/vercel.svg create mode 100644 public/window.svg create mode 100644 tsconfig.json diff --git a/.github/workflows/agent-issue-resolver.yml b/.github/workflows/agent-issue-resolver.yml new file mode 100644 index 0000000..2858d14 --- /dev/null +++ b/.github/workflows/agent-issue-resolver.yml @@ -0,0 +1,28 @@ +name: Antigravity Agent Issue Resolver + +on: + issues: + types: [labeled] + +jobs: + resolve_issue: + if: github.event.label.name == 'enhancement' || github.event.label.name == 'bug' || github.event.label.name == 'agent-resolve' + runs-on: ubuntu-latest + steps: + - name: Checkout Repository + uses: actions/checkout@v4 + + - name: Trigger Agentic Workflow + run: | + echo "Triggering Antigravity agentic workflow for Issue #${{ github.event.issue.number }}" + curl -X POST \ + -H "Authorization: Bearer ${{ secrets.ANTIGRAVITY_AGENT_TOKEN }}" \ + -H "Content-Type: application/json" \ + -d '{ + "issue_number": ${{ github.event.issue.number }}, + "title": "${{ github.event.issue.title }}", + "body": "${{ github.event.issue.body }}", + "label": "${{ github.event.label.name }}", + "repository": "${{ github.repository }}" + }' \ + https://api.antigravity.ai/v1/workflows/trigger diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..b479b45 --- /dev/null +++ b/.gitignore @@ -0,0 +1,53 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.* +.yarn/* +!.yarn/patches +!.yarn/plugins +!.yarn/releases +!.yarn/versions + +# testing +/coverage + +# next.js +/.next/ +/out/ + +# production +/build + +# misc +.DS_Store +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* +.pnpm-debug.log* + +# env files (can opt-in for committing if needed) +.env* + +# vercel +.vercel + +# typescript +*.tsbuildinfo +next-env.d.ts + +# conda envs, databases, and logs +.conda/ +conda-meta/ +envs/ +*.log +*.sqlite +.env.local +.env.development.local +.env.test.local +.env.production.local + diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..8bd0e39 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,5 @@ + +# This is NOT the Next.js you know + +This version has breaking changes — APIs, conventions, and file structure may all differ from your training data. Read the relevant guide in `node_modules/next/dist/docs/` before writing any code. Heed deprecation notices. + diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..43c994c --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1 @@ +@AGENTS.md diff --git a/GITHUB_WORKFLOW.md b/GITHUB_WORKFLOW.md new file mode 100644 index 0000000..052fc22 --- /dev/null +++ b/GITHUB_WORKFLOW.md @@ -0,0 +1,85 @@ +# Antigravity Automated Issue-Resolution Workflow + +This guide details the automated pipeline for listening to, processing, resolving, and verifying GitHub issues using the Antigravity agentic system. + +--- + +## 1. Workflow Architecture + +```mermaid +sequenceDiagram + participant GH as GitHub Webhook / Action + participant OR as Orchestrator Agent + participant IP as Implementation Planner Agent + participant DEV as Developer Agent + participant CI as CI Build & Browser Verifier + + GH->>OR: Webhook: Issue Labeled ('agent-resolve') + Note over OR: Parses issue details &
checks config + OR->>OR: Git Branch Checkout (feature/issue-N) + OR->>IP: Spawns Planner Agent with prompt + IP->>IP: Researches codebase & generates plan + IP-->>OR: Returns plan + OR->>DEV: Invokes Developer Agent to write code + DEV->>DEV: Modifies files & implements feature + DEV-->>OR: Code complete + OR->>CI: Runs verification (npm run build & test) + CI-->>OR: Compilation / Test Success + OR->>GH: Git Commit & Push. Auto-creates Pull Request + Note over GH: GitHub Action checks pass.
Human reviews & merges PR. +``` + +--- + +## 2. Webhook Payload Specifications +When an issue is labeled (e.g. `agent-resolve`, `enhancement`, `bug`), GitHub triggers a webhook with the following JSON structure sent to the Orchestrator endpoint: + +```json +{ + "action": "labeled", + "issue": { + "number": 12, + "title": "IMPLEMENT RISK MANAGEMENT UPGRADE: #ISSUE-005 - Kelly Allocation", + "body": "Detailed objective description...", + "state": "open", + "labels": [ + { + "name": "agent-resolve" + } + ] + }, + "repository": { + "full_name": "jannr/investment-sandbox", + "html_url": "https://github.com/jannr/investment-sandbox" + }, + "sender": { + "login": "jannr" + } +} +``` + +--- + +## 3. Step-by-Step Execution Lifecycle + +### Step 3.1: Parsing and Routing +The Orchestrator Agent parses the webhook payload, maps labels using `github-agent-config.json`, and triggers the workflow. + +### Step 3.2: Branching Strategy +The system automatically creates a branch: +- For enhancements: `feature/issue-{issue_number}` +- For bugs: `bugfix/issue-{issue_number}` + +### Step 3.3: Planning & Coding +1. The Orchestrator launches an **Implementation Planner Agent** to search the codebase and write an `implementation_plan.md` draft. +2. Once the plan is approved (autonomously or by a maintainer), the **Developer Agent** modifies the target code files. + +### Step 3.4: Automated Verification +Before pushing changes, the Orchestrator runs: +- `npm run build` to verify Next.js/TypeScript compilations. +- Virtual browser tests (Playwright/Puppeteer stubs) to simulate UI button interactions, state modifications, and graph renderings. + +### Step 3.5: PR Creation and Merge +1. The agent pushes the branch to remote and creates a PR using the GitHub API: + `POST /repos/{owner}/{repo}/pulls` +2. Once merged by a human, the branch is deleted and the issue is closed. diff --git a/README.md b/README.md new file mode 100644 index 0000000..e215bc4 --- /dev/null +++ b/README.md @@ -0,0 +1,36 @@ +This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app). + +## Getting Started + +First, run the development server: + +```bash +npm run dev +# or +yarn dev +# or +pnpm dev +# or +bun dev +``` + +Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. + +You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file. + +This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel. + +## Learn More + +To learn more about Next.js, take a look at the following resources: + +- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API. +- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial. + +You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome! + +## Deploy on Vercel + +The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js. + +Check out our [Next.js deployment 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zj3dyiwW4Ki2QG{@i>yuZXQizw_OkZI^-3otXC{!(lUpJF33gI60ak;Uqitp74|B6I zgg{b=Iz}WkhCGj1M=hu4#Aw173YxIVbISaoc z-nLZC*6Tgivd5V`K%GxhBsp@SUU60-rfc$=wb>zdJzXS&-5(NRRodFk;Kxk!S(O(a0e7oY=E( zAyS;Ow?6Q&XA+cnkCb{28_1N8H#?J!*$MmIwLq^*T_9-z^&UE@A(z9oGYtFy6EZef LrJugUA?W`A8`#=m literal 0 HcmV?d00001 diff --git a/app/globals.css b/app/globals.css new file mode 100644 index 0000000..a2dc41e --- /dev/null +++ b/app/globals.css @@ -0,0 +1,26 @@ +@import "tailwindcss"; + +:root { + --background: #ffffff; + --foreground: #171717; +} + +@theme inline { + --color-background: var(--background); + --color-foreground: var(--foreground); + --font-sans: var(--font-geist-sans); + --font-mono: var(--font-geist-mono); +} + +@media (prefers-color-scheme: dark) { + :root { + --background: #0a0a0a; + --foreground: #ededed; + } +} + +body { + background: var(--background); + color: var(--foreground); + font-family: Arial, Helvetica, sans-serif; +} diff --git a/app/layout.tsx b/app/layout.tsx new file mode 100644 index 0000000..c62093c --- /dev/null +++ b/app/layout.tsx @@ -0,0 +1,33 @@ +import type { Metadata } from "next"; +import { Geist, Geist_Mono } from "next/font/google"; +import "./globals.css"; + +const geistSans = Geist({ + variable: "--font-geist-sans", + subsets: ["latin"], +}); + +const geistMono = Geist_Mono({ + variable: "--font-geist-mono", + subsets: ["latin"], +}); + +export const metadata: Metadata = { + title: "QuantSandbox Ökonometrie - KI-gestützte Investment-Sandbox", + description: "Sophisticated econometric and statistical investment analysis dashboard, including GJR-GARCH, EWMA volatility modeling, insider whale trackers, and Bayesian online learning.", +}; + +export default function RootLayout({ + children, +}: Readonly<{ + children: React.ReactNode; +}>) { + return ( + + {children} + + ); +} diff --git a/app/page.tsx b/app/page.tsx new file mode 100644 index 0000000..ef5cbdb --- /dev/null +++ b/app/page.tsx @@ -0,0 +1,125 @@ +'use client'; + +import React, { useState } from 'react'; +import SandboxDemo from '@/components/modules/sandbox/SandboxDemo'; +import ScannerDemo from '@/components/modules/scanner/ScannerDemo'; +import InsiderDemo from '@/components/modules/insider/InsiderDemo'; +import CryptoDemo from '@/components/modules/crypto/CryptoDemo'; +import EventsDemo from '@/components/modules/events/EventsDemo'; +import { BarChart3, TrendingUp, ShieldAlert, Radio, Landmark, RefreshCw } from 'lucide-react'; + +export default function Home() { + const [activeTab, setActiveTab] = useState<'sandbox' | 'scanner' | 'insider' | 'crypto' | 'events'>('sandbox'); + + return ( +
+ {/* Background Decorative Blur Gradients */} +
+
+ + {/* Main Header / Navigation */} +
+
+
+
+ +
+
+

+ QuantSandbox Ökonometrie +

+

KI-gestützte Investment-Sandbox

+
+
+ +
+
+ + Verbindung aktiv +
+
+ Workspace: + investment-sandbox +
+
+
+
+ + {/* Hero Banner Section */} +
+
+
+
+ + Scaffolding Version 1.0.0 + +

+ Statistische Investment-Analyse & Sandbox +

+

+ Diese modulare Architektur bündelt fortgeschrittene ökonometrische Modelle – von EWMA und GJR-GARCH Volatilitätsprognosen über Bayesianisches On-Chain-Lernen bis hin zu Überlebens- und LMM-Regressionsmodellen. +

+
+ +
+ + + + + +
+
+
+
+ + {/* Main Content Area */} +
+ {/* Active Module Rendering */} +
+ {activeTab === 'sandbox' && } + {activeTab === 'scanner' && } + {activeTab === 'insider' && } + {activeTab === 'crypto' && } + {activeTab === 'events' && } +
+
+ + {/* Footer */} +
+
+

© 2026 QuantSandbox Inc. Alle Rechte vorbehalten.

+
+ Zustand State Engine + | + Next.js App Router (TypeScript + Tailwind v4) +
+
+
+
+ ); +} diff --git a/components/modules/crypto/CryptoDemo.tsx b/components/modules/crypto/CryptoDemo.tsx new file mode 100644 index 0000000..e33a603 --- /dev/null +++ b/components/modules/crypto/CryptoDemo.tsx @@ -0,0 +1,487 @@ +'use client'; + +import React, { useState, useMemo } from 'react'; +import { useSandboxStore } from '@/lib/store'; +import { predictCryptoTrend, calculateBetaPosterior } from '@/lib/math/statistics'; +import 'katex/dist/katex.min.css'; +import { BlockMath, InlineMath } from 'react-katex'; +import { + Cpu, Search, RefreshCw, BarChart2, TrendingUp, AlertCircle, Info, + ChevronDown, ChevronUp, ArrowUpRight, ArrowDownRight, Compass, ShieldAlert, Sparkles +} from 'lucide-react'; + +interface CoinData { + ticker: string; + name: string; + price: string; + change24h: number; + fundingRate: number; // in % + openInterestChange: number; // in % + longShortRatio: number; + whaleInflow: number; // net flows + exchangeReserves: number; // in % + liqLong: string; + liqShort: string; +} + +const defaultCoins: Record = { + 'BTC': { + ticker: 'BTC', + name: 'Bitcoin', + price: '$69,450', + change24h: 2.4, + fundingRate: -0.015, + openInterestChange: 8.2, + longShortRatio: 0.92, + whaleInflow: 480, + exchangeReserves: -1.4, + liqLong: '$68,200', + liqShort: '$70,500' + }, + 'ETH': { + ticker: 'ETH', + name: 'Ethereum', + price: '$3,820', + change24h: -1.2, + fundingRate: 0.045, + openInterestChange: -3.5, + longShortRatio: 1.34, + whaleInflow: -120, + exchangeReserves: 0.8, + liqLong: '$3,710', + liqShort: '$3,920' + }, + 'SOL': { + ticker: 'SOL', + name: 'Solana', + price: '$184.20', + change24h: 5.8, + fundingRate: 0.082, + openInterestChange: 14.5, + longShortRatio: 1.62, + whaleInflow: 1250, + exchangeReserves: -2.8, + liqLong: '$176.00', + liqShort: '$192.50' + } +}; + +export default function CryptoDemo() { + const { alphaSuccess, betaFailure, addModelTrial } = useSandboxStore(); + + const [activeTicker, setActiveTicker] = useState('BTC'); + const [searchQuery, setSearchQuery] = useState(''); + const [customCoins, setCustomCoins] = useState>({}); + const [searchError, setSearchError] = useState(false); + const [showMathAccordion, setShowMathAccordion] = useState(false); + const [simulatedTrialLogged, setSimulatedTrialLogged] = useState(false); + const [lastTrialSuccess, setLastTrialSuccess] = useState(false); + + // Active Coin data retrieval + const activeCoin = useMemo(() => { + return customCoins[activeTicker] || defaultCoins[activeTicker] || defaultCoins['BTC']; + }, [activeTicker, customCoins]); + + // Compute live Random Forest baseline predictions + 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 + const correctedPredictions = useMemo(() => { + // Correct short term probability + const shortTermCorrected = calculateBetaPosterior( + alphaSuccess, + betaFailure, + mlPredictions.shortTermProb, + 12 // pseudo weight/confidence scale + ); + // Correct medium term probability + const mediumTermCorrected = calculateBetaPosterior( + alphaSuccess, + betaFailure, + mlPredictions.mediumTermProb, + 12 + ); + + return { + shortTerm: shortTermCorrected, + mediumTerm: mediumTermCorrected + }; + }, [mlPredictions, alphaSuccess, betaFailure]); + + // Perform search check for Altcoins + const handleAltcoinSearch = (e: React.FormEvent) => { + e.preventDefault(); + setSearchError(false); + const query = searchQuery.trim().toUpperCase(); + if (!query) return; + + if (defaultCoins[query]) { + setActiveTicker(query); + setSearchQuery(''); + return; + } + + if (customCoins[query]) { + setActiveTicker(query); + setSearchQuery(''); + return; + } + + // Generate simulated data for Altcoin + 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(''); + }; + + const handleSimulateTrial = (success: boolean) => { + addModelTrial(success); + setLastTrialSuccess(success); + setSimulatedTrialLogged(true); + setTimeout(() => setSimulatedTrialLogged(false), 2500); + }; + + const totalTrials = alphaSuccess + betaFailure; + const priorAccuracy = (alphaSuccess / (totalTrials || 1)) * 100; + + return ( +
+
+ + {/* Header */} +
+
+ Element 4 +

+ Predictive Krypto-Modelle & Bayes Self-Correction +

+
+
+ +
+

A-Priori Genauigkeit

+

+ {priorAccuracy.toFixed(1)}% (n={totalTrials}) +

+
+
+
+ + {/* SECTION 1: Top 3 Cards & Search Mask */} +
+ + {/* Status Cards BTC, ETH, SOL */} + {['BTC', 'ETH', 'SOL'].map((tick) => { + const coin = defaultCoins[tick]; + const isActive = activeTicker === tick; + const isUp = coin.change24h >= 0; + return ( +
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'}`} + > +
+
{coin.name}
+
{coin.price}
+
+ {isUp ? : } + {isUp ? '+' : ''}{coin.change24h}% +
+
+
+ {tick} +
+
+ ); + })} + + {/* Custom Search bar */} +
+
+ setSearchQuery(e.target.value)} + /> + +
+ {Object.keys(customCoins).length > 0 && ( +
+ {Object.keys(customCoins).map(tick => ( + + ))} +
+ )} +
+
+ + {/* SECTION 2: Derivatives & On-Chain Metrics Ledger */} +
+ + {/* Left Column: Metrics Widgets */} +
+

+ On-Chain & Derivate-Indikatoren ({activeCoin.ticker}) +

+ +
+ + {/* Funding & Open Interest Widget */} +
+

Ref-Zinssatz & Kontrakte (OI)

+
+
+ Daily Funding Rate: + + {activeCoin.fundingRate > 0 ? '+' : ''}{activeCoin.fundingRate.toFixed(3)}% + +
+
+ Open Interest (24h Δ): + = 0 ? 'text-emerald-400' : 'text-rose-400'}`}> + {activeCoin.openInterestChange > 0 ? '+' : ''}{activeCoin.openInterestChange.toFixed(1)}% + +
+
+
+ + {/* Long/Short & Liquidation Widget */} +
+

Positionierung & Liquidationen

+
+
+ Long / Short Ratio: + {activeCoin.longShortRatio} +
+
+ Liq-Cluster: + Long: {activeCoin.liqLong} | Short: {activeCoin.liqShort} +
+
+
+ + {/* Whale Flows Widget */} +
+

Whale-Ströme (Nettozufluss)

+
+
+ Netto Inflow (Wallets): + = 0 ? 'text-emerald-400' : 'text-rose-400'}`}> + {activeCoin.whaleInflow > 0 ? '+' : ''}{activeCoin.whaleInflow} {activeCoin.ticker} + +
+
+ Positive Werte signalisieren, dass Großinvestoren Bestände von Börsen auf private Wallets (Akkumulation) abziehen. +
+
+
+ + {/* Exchange Reserves Widget */} +
+

Börsenreserven (Spot)

+
+
+ Reservenänderung (7d): + + {activeCoin.exchangeReserves > 0 ? '+' : ''}{activeCoin.exchangeReserves}% + +
+
+ Sinkende Reserven an den Spot-Börsen reduzieren den verfügbaren Verkaufsdruck und begünstigen Squeezes. +
+
+
+ +
+
+ + {/* Right Column: Predictive Gauges & Correction Calibration */} +
+

+ Vorhersage-Wahrscheinlichkeiten +

+ + {/* Gauges / Progress Bars */} +
+ + {/* 24h Gauge */} +
+
+ 24h Volatility Squeeze (Short-Term) + {(correctedPredictions.shortTerm * 100).toFixed(0)}% +
+
+ {/* ML Baseline Overlay */} +
+ {/* Corrected Posterior Marker */} +
+
+
+ ML-Signal: {(mlPredictions.shortTermProb * 100).toFixed(0)}% + Bayes-Korrigiert: {(correctedPredictions.shortTerm * 100).toFixed(0)}% +
+
+ + {/* 14d Gauge */} +
+
+ 14d Struktureller Bullish-Trend (Medium-Term) + {(correctedPredictions.mediumTerm * 100).toFixed(0)}% +
+
+
+
+
+
+ ML-Signal: {(mlPredictions.mediumTermProb * 100).toFixed(0)}% + Bayes-Korrigiert: {(correctedPredictions.mediumTerm * 100).toFixed(0)}% +
+
+ +
+ + {/* Model Calibration Log & Simulation */} +
+
+

Bayes Modell-Kalibrierung

+ n = {totalTrials} Trials +
+
+
Erfolge (α):
+
{alphaSuccess}
+
Fehlalarme (β):
+
{betaFailure}
+
+ + {/* Trial simulator */} +
+

Modell-Drift simulieren: Fügen Sie richtige/falsche Outcomes hinzu, um die Beta-Verteilung anzupassen.

+
+ + +
+ {simulatedTrialLogged && ( +
+ Trial geloggt! Bayes-Prior wurde auf {lastTrialSuccess ? 'Erfolg' : 'Fehlalarm'} aktualisiert. +
+ )} +
+
+
+ +
+ + {/* SECTION 3: Mathematical LaTeX Accordion */} +
+ + + {showMathAccordion && ( +
+
+

1. Bayesianische Beta-Konjugierte Fehlerkorrektur

+

+ Wir modellieren das Vertrauensintervall über die Fehlerraten des Modells mittels einer Beta-Verteilung. Der A-Priori Fehlerzustand wird durch die Parameter (Erfolge) und (Fehlalarme) dargestellt: +

+
+ +
+

+ Bei einem neuen ML-Signal führen wir ein konjugiertes Bayes-Update mit einem Vertrauensgewicht aus: +

+
+ +
+
+ +
+

+ Ist das Modell historisch sehr instabil (hohes ), korrigiert der Bayesianische Term ein übermütiges ML-Signal nach unten, was die Robustheit des Gesamtsystems schützt. +

+
+ +
+

2. Random Forest Nicht-Lineares Signalmapping

+

+ Der Random Forest simuliert ein Ensemble von 10 schwachen Entscheidungsbäumen. Jeder Baum spaltet die Daten nach Schwellenwerten (z.B. „Funding-Rate < -0.04%“ und „Open Interest > 10%“) auf: +

+
+ +
+

+ wobei der prognostizierte Ausgabewert des -ten Entscheidungsbaumes für den Featurevektor ist. +

+
+
+ )} +
+
+ ); +} diff --git a/components/modules/events/EventsDemo.tsx b/components/modules/events/EventsDemo.tsx new file mode 100644 index 0000000..b2ad537 --- /dev/null +++ b/components/modules/events/EventsDemo.tsx @@ -0,0 +1,980 @@ +'use client'; + +import React, { useState, useMemo } from 'react'; +import { useSandboxStore } from '@/lib/store'; +import { calculateEventROC, calculateEventSurvival, runEventLMM } from '@/lib/math/statistics'; +import { + ResponsiveContainer, + AreaChart, + Area, + XAxis, + YAxis, + CartesianGrid, + Tooltip, + LineChart, + Line, + ReferenceLine, + Legend +} from 'recharts'; +import 'katex/dist/katex.min.css'; +import { BlockMath, InlineMath } from 'react-katex'; +import { + Activity, + BarChart4, + Compass, + GitMerge, + Plus, + Trash2, + Calendar, + Sparkles, + AlertCircle, + ChevronDown, + ChevronUp, + BookOpen, + RefreshCw, + Info, + Check, + TrendingUp, + TrendingDown, + Sliders, + Database +} from 'lucide-react'; + +// Predefined archetypes for Event Creation +const ARCHETYPES: Record }> = { + 'FED Zinsentscheid': { + name: 'FED Zinsentscheid', + defaultScores: { Apple: 1, NASDAQ: 2, Gold: -1, Bitcoin: 2 } + }, + 'US Wahlen (Präsidentschaft)': { + name: 'US Wahlen', + defaultScores: { Apple: 2, NASDAQ: 1, Gold: 3, Bitcoin: 2 } + }, + 'SpaceX IPO (Gerüchte)': { + name: 'SpaceX IPO', + defaultScores: { Apple: 0, NASDAQ: 2, Gold: -1, Bitcoin: 1 } + }, + 'CPI Inflationsdaten': { + name: 'CPI Inflationsdaten', + defaultScores: { Apple: 1, NASDAQ: 2, Gold: -2, Bitcoin: 1 } + }, + 'US Non-Farm Payrolls': { + name: 'US Non-Farm Payrolls', + defaultScores: { Apple: 0, NASDAQ: 1, Gold: -1, Bitcoin: 0 } + }, + 'EZB Pressekonferenz': { + name: 'EZB Pressekonferenz', + defaultScores: { Apple: -1, NASDAQ: -1, Gold: 2, Bitcoin: 1 } + } +}; + +const ASSETS = ['Apple', 'NASDAQ', 'Gold', 'Bitcoin']; + +export default function EventsDemo() { + const { + selectedModel, + setSelectedModel, + eventsMatrix, + calendarProposals, + lmmObservations, + addEventToMatrix, + updateMatrixCell, + runEndogenousLMMCalibration + } = useSandboxStore(); + + // Local State + const [tauPre, setTauPre] = useState(7); + const [tauPost, setTauPost] = useState(3); + const [showMath, setShowMath] = useState(false); + const [selectedSurvivalAsset, setSelectedSurvivalAsset] = useState('Apple'); + + // Custom Event Form State + const [customName, setCustomName] = useState(''); + const [customDate, setCustomDate] = useState('2026-06-15'); + const [selectedArchetype, setSelectedArchetype] = useState('Custom'); + + // Calibration feedback states + const [isCalibrating, setIsCalibrating] = useState(false); + const [calibrationSuccess, setCalibrationSuccess] = useState(false); + const [lastCalibrationTime, setLastCalibrationTime] = useState(null); + + // Current baseline date for relative time calculations + const CURRENT_DATE_STR = '2026-06-06'; + + // Helper to calculate time kernel weight + const getWeightAndDays = (eventDateStr: string) => { + const eventDate = new Date(eventDateStr); + const currentDate = new Date(CURRENT_DATE_STR); + const diffTime = eventDate.getTime() - currentDate.getTime(); + const d = diffTime / (1000 * 60 * 60 * 24); // days relative to today + + let weight = 0; + if (d >= 0) { + weight = Math.exp(-d / tauPre); + } else { + const daysSince = -d; + weight = 1 / (1 + Math.log(1 + daysSince / tauPost)); + } + return { + d: Math.round(d), + weight: Math.round(weight * 100) / 100 + }; + }; + + // 1. Time Weighted Net Impact Scores & Final Action Signals + const actionSignals = useMemo(() => { + const totals: Record = { Apple: 0, NASDAQ: 0, Gold: 0, Bitcoin: 0 }; + eventsMatrix.forEach((ev) => { + const { weight } = getWeightAndDays(ev.date); + ASSETS.forEach((asset) => { + const score = ev.scores[asset] || 0; + totals[asset] += score * weight; + }); + }); + + const signals: Record = {}; + ASSETS.forEach((asset) => { + const netScore = Math.round(totals[asset] * 100) / 100; + let signal = 'NEUTRAL / HOLD'; + let colorClass = 'bg-slate-800/40 border-slate-700/60 text-slate-400'; + let textClass = 'text-slate-400'; + let glowClass = 'shadow-slate-500/5'; + + if (netScore > 1.5) { + signal = 'STRONG BUY'; + colorClass = 'bg-emerald-950/40 border-emerald-800/80 text-emerald-400'; + textClass = 'text-emerald-400 font-bold'; + glowClass = 'shadow-emerald-500/10 shadow-[0_0_15px_rgba(16,185,129,0.15)]'; + } else if (netScore > 0.4) { + signal = 'ACCUMULATE'; + colorClass = 'bg-teal-950/30 border-teal-800/50 text-teal-400'; + textClass = 'text-teal-300 font-semibold'; + glowClass = 'shadow-teal-500/5 shadow-[0_0_10px_rgba(20,184,166,0.1)]'; + } else if (netScore < -1.5) { + signal = 'STRONG SELL / RISK OFF'; + colorClass = 'bg-rose-950/40 border-rose-800/80 text-rose-400'; + textClass = 'text-rose-400 font-bold'; + glowClass = 'shadow-rose-500/10 shadow-[0_0_15px_rgba(244,63,94,0.15)]'; + } else if (netScore < -0.4) { + signal = 'REDUCE / HEDGE'; + colorClass = 'bg-amber-950/30 border-amber-800/50 text-amber-400'; + textClass = 'text-amber-300 font-semibold'; + glowClass = 'shadow-amber-500/5 shadow-[0_0_10px_rgba(245,158,11,0.1)]'; + } + + signals[asset] = { netScore, signal, colorClass, textClass, glowClass }; + }); + + return signals; + }, [eventsMatrix, tauPre, tauPost]); + + // 2. Dynamic Decay Curve Chart Data + const decayCurveData = useMemo(() => { + const pts = []; + // Generate weight for each day relative to event (d = E - T) + for (let d = -30; d <= 30; d++) { + let weight = 0; + if (d >= 0) { + weight = Math.exp(-d / tauPre); + } else { + const daysSince = -d; + weight = 1 / (1 + Math.log(1 + daysSince / tauPost)); + } + pts.push({ + days: d, + weight: Math.round(weight * 1000) / 1000 + }); + } + return pts; + }, [tauPre, tauPost]); + + // 3. Dynamic ROC Data + const { rocData, optimalThreshold, maxYouden, auc } = useMemo(() => { + const predictions: number[] = []; + const labels: number[] = []; + + lmmObservations.forEach((obs) => { + // Find average event score of this asset in matrix to use as indicator bias + const assetScores = eventsMatrix.map(ev => ev.scores[obs.asset] || 0); + const avgScore = assetScores.reduce((sum, s) => sum + s, 0) / (assetScores.length || 1); + + // Construct a predictor between 0 and 1 + let basePred = obs.eventType === 'BULLISH' ? 0.65 : 0.35; + basePred += avgScore * 0.04 + obs.trend * 0.5 - obs.vix * 0.002; + const finalPred = Math.min(0.99, Math.max(0.01, basePred)); + + predictions.push(finalPred); + labels.push(obs.returnVal > 0.012 ? 1 : 0); // label 1 if return > 1.2% + }); + + const res = calculateEventROC(predictions, labels); + + // Trapezoidal Area Under Curve (AUC) + let computedAuc = 0; + const sorted = [...res.points].sort((a, b) => a.fpr - b.fpr); + for (let i = 1; i < sorted.length; i++) { + const w = sorted[i].fpr - sorted[i - 1].fpr; + const h = (sorted[i].tpr + sorted[i - 1].tpr) / 2; + computedAuc += w * h; + } + + return { + rocData: res.points, + optimalThreshold: res.optimalThreshold, + maxYouden: res.maxYouden, + auc: Math.round(Math.max(0.5, Math.min(0.99, computedAuc)) * 1000) / 1000 + }; + }, [eventsMatrix, lmmObservations]); + + // 4. Dynamic Survival Curve Data for selected asset + const survivalData = useMemo(() => { + const assetScores = eventsMatrix.map(ev => ev.scores[selectedSurvivalAsset] || 0); + const sumScore = assetScores.reduce((sum, s) => sum + s, 0); + + const timesLong: number[] = []; + const eventsLong: number[] = []; + const timesShort: number[] = []; + const eventsShort: number[] = []; + + // Simulate 15 historical events outcomes per direction + for (let i = 0; i < 15; i++) { + // LONG: Positive scores reduce time-to-event (gain target hit faster) + let tLong = 35 - sumScore * 3.5 + (Math.sin(i * 1.5) * 12); + let evLong = 1; + if (tLong > 60 || sumScore < -1) { + tLong = 60; + evLong = 0; // right censored + } + timesLong.push(Math.round(Math.max(3, tLong))); + eventsLong.push(evLong); + + // SHORT: Negative scores reduce time-to-event (loss target hit faster) + let tShort = 35 + sumScore * 3.5 + (Math.cos(i * 1.9) * 12); + let evShort = 1; + if (tShort > 60 || sumScore > 1) { + tShort = 60; + evShort = 0; // right censored + } + timesShort.push(Math.round(Math.max(3, tShort))); + eventsShort.push(evShort); + } + + const curveLong = calculateEventSurvival(timesLong, eventsLong, 'LONG'); + const curveShort = calculateEventSurvival(timesShort, eventsShort, 'SHORT'); + + // Merge for chart mapping + const timeMap: Record = {}; + for (let t = 0; t <= 60; t += 2) { + timeMap[t] = { time: t }; + } + + curveLong.forEach(pt => { + const roundedT = Math.round(pt.time / 2) * 2; + if (timeMap[roundedT]) timeMap[roundedT].longRate = pt.survivalRate; + }); + + curveShort.forEach(pt => { + const roundedT = Math.round(pt.time / 2) * 2; + if (timeMap[roundedT]) timeMap[roundedT].shortRate = pt.survivalRate; + }); + + const sortedMerged = Object.values(timeMap).sort((a, b) => a.time - b.time); + + let lastLong = 1.0; + let lastShort = 1.0; + + return sortedMerged.map(pt => { + if (pt.longRate !== undefined) lastLong = pt.longRate; + else pt.longRate = lastLong; + + if (pt.shortRate !== undefined) lastShort = pt.shortRate; + else pt.shortRate = lastShort; + + return pt; + }); + }, [eventsMatrix, selectedSurvivalAsset]); + + // 5. Dynamic LMM regression fitting + const lmmResults = useMemo(() => { + return runEventLMM(lmmObservations); + }, [lmmObservations]); + + // Custom Event Handler + const handleAddCustomEvent = (e: React.FormEvent) => { + e.preventDefault(); + let name = customName.trim(); + let scores: Record = { Apple: 0, NASDAQ: 0, Gold: 0, Bitcoin: 0 }; + + if (selectedArchetype !== 'Custom') { + const arch = ARCHETYPES[selectedArchetype]; + name = name || arch.name; + scores = { ...arch.defaultScores }; + } else { + name = name || 'Benutzerdefiniertes Ereignis'; + } + + addEventToMatrix(name, customDate, scores); + setCustomName(''); + setSelectedArchetype('Custom'); + }; + + // Calibration Action Trigger + const handleTriggerCalibration = () => { + setIsCalibrating(true); + // Simulate complex econometric iterative calibration + setTimeout(() => { + runEndogenousLMMCalibration(); + setIsCalibrating(false); + setCalibrationSuccess(true); + setLastCalibrationTime(new Date().toLocaleTimeString()); + setTimeout(() => { + setCalibrationSuccess(false); + }, 4000); + }, 1200); + }; + + return ( +
+ + {/* 1. Header with Model Selector */} +
+
+
+ +
+
+
+ + Element 5 + + Status: Calibrated & Active +
+

+ Advanced Econometric Event-Analysis Matrix +

+

+ Analyzes multi-asset cross-impact networks under logarithmic decay timelines. Evaluates predictive efficiency via ROC, models target boundaries with directional survival, and performs endogenous regressions via LMM feedback. +

+
+ +
+ + + +
+
+
+ + {/* 2. Main Dashboard: Clean View Matrix & Action Signals */} +
+ + {/* Left/Middle Matrix & Settings (2/3 width) */} +
+ + {/* A. Event-Asset Matrix Table */} +
+
+

+ Event Impact Matrix +

+
+ - + Bearish (-3) + + + Bullish (+3) +
+
+ +
+ + + + + + {ASSETS.map(asset => ( + + ))} + + + + + {eventsMatrix.map((ev) => { + const { d, weight } = getWeightAndDays(ev.date); + + return ( + + + + + {ASSETS.map((asset) => { + const score = ev.scores[asset] || 0; + + // Determine color style based on score value + let badgeStyle = 'text-slate-400 bg-slate-950 border-slate-800/60'; + if (score > 1.5) badgeStyle = 'text-emerald-400 bg-emerald-950/30 border-emerald-800/50'; + else if (score > 0) badgeStyle = 'text-teal-400 bg-teal-950/20 border-teal-900/30'; + else if (score < -1.5) badgeStyle = 'text-rose-400 bg-rose-950/30 border-rose-800/50'; + else if (score < 0) badgeStyle = 'text-orange-400 bg-orange-950/20 border-orange-900/30'; + + return ( + + ); + })} + + + + ); + })} + +
Makro-EreignisDatum{asset}Kernel-Gewicht
+ {ev.name} + + {ev.date} + + {d === 0 ? 'Heute' : d > 0 ? `In ${d} Tagen` : `Vor ${-d} Tagen`} + + +
+ + + {score > 0 ? `+${score}` : score} + + +
+ 		+
+ {Math.round(weight * 100)}% + ({weight.toFixed(2)}) +
+
+
+
+ + {/* B. Add Event Form & Time Kernel Weights config (split) */} +
+ + {/* Form to Add Event */} +
+

+ Event hinzufügen +

+
+
+ + +
+ +
+ + setCustomName(e.target.value)} + placeholder="z.B. OPEC Treffen" + className="w-full bg-slate-950 border border-slate-800 rounded-lg p-2 text-slate-200 focus:outline-none focus:border-rose-500/50 font-sans" + /> +
+ +
+ + setCustomDate(e.target.value)} + className="w-full bg-slate-950 border border-slate-800 rounded-lg p-2 text-slate-200 focus:outline-none focus:border-rose-500/50 font-mono" + /> +
+ + +
+
+ + {/* Time Decay Kernel Sliders & Live Decay Curve Chart */} +
+
+

+ Time-Kernel Decay Parameters +

+ +
+
+
+ Pre-Event Slope () + {tauPre} Tage +
+ setTauPre(Number(e.target.value))} + className="w-full accent-purple-500 bg-slate-950 h-1 rounded-lg appearance-none cursor-pointer" + /> + Schnellerer Anstieg (kleinerer Wert) nähert sich dem Ereignis. +
+ +
+
+ Post-Event Half-Life () + {tauPost} Tage +
+ setTauPost(Number(e.target.value))} + className="w-full accent-purple-500 bg-slate-950 h-1 rounded-lg appearance-none cursor-pointer" + /> + Langsameres Abklingen logarithmisch nach dem Stichtag. +
+
+
+ + {/* Mini Kernel Chart showing bell curve of relevance */} +
+ + + `Relative Tage: ${label}`} + /> + + + + +
+
+ +
+ +
+ + {/* Right Sidebar: Suggestions & Final Action Signals (1/3 width) */} +
+ + {/* Calendar Inbox Panel */} +
+
+ +

Wirtschaftskalender Vorschläge

+
+ + {calendarProposals.length === 0 ? ( +
+ Keine ausstehenden Vorschläge im Posteingang. +
+ ) : ( +
+ {calendarProposals.map((cp) => ( +
+
+
{cp.name}
+
+ {cp.date} + • + {cp.archetype} +
+
+ +
+ ))} +
+ )} +
+ + {/* Action Signals Dashboard */} +
+

+ Aggregated Trade Signals +

+ +
+ {ASSETS.map((asset) => { + const { netScore, signal, colorClass, textClass, glowClass } = actionSignals[asset]; + + return ( +
+
+ {asset} + + Weighted Score: {netScore > 0 ? `+${netScore}` : netScore} + +
+ +
+
+ {signal} +
+
+ {netScore > 0.4 ? ( + + ) : netScore < -0.4 ? ( + + ) : ( + + )} + {netScore > 0 ? 'Bullish Drift' : netScore < 0 ? 'Bearish Risk' : 'Stationary'} +
+
+
+ ); + })} +
+
+ + {/* LMM Feedback Trigger */} +
+

Endogenous Calibration

+

+ Updates manual matrix scores with optimal significant coefficients estimated from historical Linear Mixed Models, calibrating feedback parameters dynamically. +

+ + {calibrationSuccess && ( +
+ Kalibrierung erfolgreich abgeschlossen! +
+ )} + + + + {lastCalibrationTime && ( +
+ Letzter Durchlauf: {lastCalibrationTime} ({lmmObservations.length} Obs.) +
+ )} +
+ +
+ +
+ + {/* 3. Bottom: Econometric Charts & Show Math Panel */} +
+ + {/* Model Tabs Header */} +
+
+ +

+ {selectedModel === 'ROC' && 'ROC Model Diagnostics'} + {selectedModel === 'SURVIVAL' && 'Survival Analysis (Time-to-Event)'} + {selectedModel === 'LMM' && 'LMM Panel Regression Summary'} +

+
+ + +
+ + {/* Collapsible LaTeX equations */} + {showMath && ( +
+ +
+

ROC Model Diagnostics

+

+ Sensitivity (TPR) maps positive asset breakouts, while Specificity (1-FPR) maps false alerts. +

+
+ + + +
+
+ +
+

Kaplan-Meier Survival

+

+ Calculates probability of NOT hitting target thresholds over 60 days. Events beyond 60 days are mathematically censored. +

+
+ + +
+
+ +
+

Linear Mixed Model (LMM)

+

+ Estimates pure event returns controlling for systemic covariates. Assets are modeled as random effect intercept adjustments. +

+
+ + +
+
+ +
+ )} + + {/* Tab Content */} +
+ + {/* Left Panel: Description & Stats Cards (1/3 width) */} +
+ {selectedModel === 'ROC' && ( +
+

+ The Receiver Operating Characteristic (ROC) curve evaluates classifier strength on binary events (e.g. Return > +3.0% within 14 days). An AUC of 0.5 denotes a random baseline, while 1.0 represents a perfect oracle. +

+
+
+ Area Under Curve (AUC) + {auc} +
+
+ Max Youden Index (J) + {maxYouden} +
+
+ Optimal Youden Threshold + + Score ≥ {optimalThreshold} + +
+
+
+ )} + + {selectedModel === 'SURVIVAL' && ( +
+

+ Kaplan-Meier survival curves map time-to-rebound (Long target: +5%) and time-to-drawdown (Short target: -5%). Separation of long and short tracks prevents arithmetic zero-sum cancellation. +

+ +
+ + +
+ +
+
+ Right Censoring + 60 Tage +
+
+ Observation Count + 30 Event Runs +
+
+
+ )} + + {selectedModel === 'LMM' && ( +
+

+ Linear Mixed Model estimates the true impact of events on returns, isolating asset-level intercepts as random deviations. Standard Errors, t-stats, and p-values determine significance. +

+ +
+
+ AIC + {lmmResults.aic} +
+
+ BIC + {lmmResults.bic} +
+
+ Adj. R² + {(lmmResults.rSquared * 100).toFixed(1)}% +
+
+
+ )} +
+ + {/* Right Panel: Charts / Regression Table (2/3 width) */} +
+ + {selectedModel === 'ROC' && ( +
+
+ Modell-Klassifikationstrennung (FPR vs TPR) +
+ + + + v.toFixed(1)} /> + + [parseFloat(value).toFixed(2), 'True Positive Rate']} + /> + + {/* Diagonal baseline */} + + + +
+ )} + + {selectedModel === 'SURVIVAL' && ( +
+
+ LONG Rebound (+5%) + SHORT Drawdown (-5%) +
+
+ + + + + `${(v * 100).toFixed(0)}%`} /> + `${(parseFloat(v) * 100).toFixed(1)}%`} + /> + + + + +
+
+ )} + + {selectedModel === 'LMM' && ( +
+
+ Fixed Effects Coefficients + Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 +
+ + + + + + + + + + + + + {lmmResults.fixedEffects.map((coeff) => ( + + + + + + + + + ))} + +
ParameterEstimateStd. Errorp-valueSig.95% Conf. Interval
{coeff.name}{coeff.estimate.toFixed(4)}{coeff.se.toFixed(4)}{coeff.pVal.toFixed(5)}{coeff.sig} + [{coeff.ciLower.toFixed(4)}, {coeff.ciUpper.toFixed(4)}] +
+ +
+ Random Effects Asset (intercepts): + {lmmResults.randomEffects.map((re) => ( + + {re.asset}: {re.intercept > 0 ? `+${re.intercept.toFixed(4)}` : re.intercept.toFixed(4)} + + ))} +
+
+ )} + +
+ +
+ +
+ +
+ ); +} diff --git a/components/modules/insider/InsiderDemo.tsx b/components/modules/insider/InsiderDemo.tsx new file mode 100644 index 0000000..5b32c0f --- /dev/null +++ b/components/modules/insider/InsiderDemo.tsx @@ -0,0 +1,448 @@ +'use client'; + +import React, { useState, useMemo } from 'react'; +import { useSandboxStore, InsiderTrade, CongressTrade, WhaleTrade } from '@/lib/store'; +import { calculateRollingZScore, detectInsiderClusters, coupleBayesianRebound } from '@/lib/math/statistics'; +import { BarChart, Bar, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer, ReferenceLine } from 'recharts'; +import 'katex/dist/katex.min.css'; +import { BlockMath, InlineMath } from 'react-katex'; +import { + Shield, User, ArrowDownRight, ArrowUpRight, DollarSign, Calendar, Landmark, + ChevronDown, ChevronUp, Search, Radio, Building2, AlertTriangle, Layers, Percent +} from 'lucide-react'; + +export default function InsiderDemo() { + const { + insiderTrades, + congressTrades, + whaleTrades, + insiderVolumes, + priorProbability + } = useSandboxStore(); + + // Component local UI states + const [activeSegment, setActiveSegment] = useState<'executives' | 'congress' | 'whales'>('executives'); + const [searchQuery, setSearchQuery] = useState(''); + const [selectedTicker, setSelectedTicker] = useState(null); + + const [scanning, setScanning] = useState(false); + const [scanResults, setScanResults] = useState<{ + ticker: string; + zScore: number; + clusterCount: number; + multiplier: number; + isAnomaly: boolean; + coupledRebound: number; + }[] | null>(null); + + const [showMathAccordion, setShowMathAccordion] = useState(false); + + // Run Global Flow Scan + const handleGlobalFlowScan = () => { + setScanning(true); + setTimeout(() => { + const results = Object.keys(insiderVolumes).map((ticker) => { + const volumes = insiderVolumes[ticker]; + const zResult = calculateRollingZScore(volumes); + + // Filter trades for this ticker to detect clusters + const tickerTrades = insiderTrades.filter(t => t.ticker === ticker); + const clusterResult = detectInsiderClusters(tickerTrades); + + // Bayesian coupling + const coupled = coupleBayesianRebound(priorProbability, zResult.latest); + + return { + ticker, + zScore: parseFloat(zResult.latest.toFixed(2)), + clusterCount: clusterResult.count, + multiplier: parseFloat(clusterResult.multiplier.toFixed(2)), + isAnomaly: zResult.isAnomaly || clusterResult.isCluster, + coupledRebound: coupled, + }; + }); + + // Sort anomalies to the top + results.sort((a, b) => (b.isAnomaly ? 1 : 0) - (a.isAnomaly ? 1 : 0) || b.zScore - a.zScore); + + setScanResults(results); + setScanning(false); + }, 1000); + }; + + // Perform Ticker Lookup + const handleTickerLookup = (e: React.FormEvent) => { + e.preventDefault(); + const query = searchQuery.trim().toUpperCase(); + if (query) { + setSelectedTicker(query); + } else { + setSelectedTicker(null); + } + }; + + // Compute stats for selected ticker + const tickerStats = useMemo(() => { + if (!selectedTicker) return null; + + const volumes = insiderVolumes[selectedTicker] || [5000, 4800, 5200, 6000, 4500, 5000]; // fallback + const zResult = calculateRollingZScore(volumes); + + const tickerTrades = insiderTrades.filter(t => t.ticker === selectedTicker); + const clusterResult = detectInsiderClusters(tickerTrades); + const coupled = coupleBayesianRebound(priorProbability, zResult.latest); + + return { + zScore: zResult.latest, + isAnomaly: zResult.isAnomaly, + clusterCount: clusterResult.count, + isCluster: clusterResult.isCluster, + multiplier: clusterResult.multiplier, + coupledRebound: coupled, + volumes, + }; + }, [selectedTicker, insiderVolumes, insiderTrades, priorProbability]); + + // Volumes chart data for selected ticker + const volumeChartData = useMemo(() => { + if (!tickerStats || !selectedTicker) return []; + return tickerStats.volumes.map((vol, idx) => ({ + month: `M-${tickerStats.volumes.length - idx - 1}`, + 'Volumen (Shares)': vol, + })); + }, [tickerStats, selectedTicker]); + + return ( +
+
+ + {/* Header */} +
+
+ Element 3 +

+ Institutional & Insider Flow Tracker +

+
+
+ +
+

Bayesianische Kopplung

+

+ Prior Rebound: {(priorProbability * 100).toFixed(0)}% +

+
+
+
+ + {/* SECTION 1: Dual-Query Actions */} +
+
+
+

Global Flow Outlier Scan

+

Filtert den Markt nach statistisch signifikanten Kaufvolumina (Z-Score > 2.0) und konzertierten Käufen (Insider-Cluster).

+
+ +
+ +
+
+

Ticker Lookup (Einzelauswertung)

+

Gezielte Abfrage von Insider-Z-Scores und Bayesianischen Kopplungs-Updates (z.B. PLTR, RACE, AMZN, AAPL).

+
+
+ setSearchQuery(e.target.value)} + /> + +
+
+
+ + {/* Ticker Stats / Lookup Section */} + {selectedTicker && tickerStats && ( +
+
+
+

{selectedTicker} Einzelauswertung

+ {tickerStats.isAnomaly && FLOW OUTLIER} +
+ +
+
+ Volumetrischer Z-Score: + + Z = {tickerStats.zScore.toFixed(2)} + +
+
+ Konzertiertes Cluster (14 Tage): + + {tickerStats.isCluster ? `JA (${tickerStats.clusterCount} Insiders)` : `NEIN (${tickerStats.clusterCount})`} + +
+ {tickerStats.isCluster && ( +
+ Cluster Exponent-Multiplikator: + x{tickerStats.multiplier.toFixed(2)} +
+ )} +
+ Gekoppelte Rebound-Wahrsch.: + + + {(tickerStats.coupledRebound * 100).toFixed(0)}% + +
+
+
+ + {/* Volume chart */} +
+
Insider Handelsvolumen (24 Monate Baseline)
+ + + + + + + + + +
+
+ )} + + {/* Global Scan Outlier List */} + {scanResults && ( +
+

Ergebnisse des Global Flow Scans

+
+ {scanResults.map((res) => ( +
setSelectedTicker(res.ticker)} + className={`p-3 rounded-xl border cursor-pointer hover:bg-slate-900/60 transition-colors ${res.isAnomaly ? 'border-purple-500/40 bg-purple-500/5' : 'border-slate-850 bg-slate-900/40'}`} + > +
+ {res.ticker} + {res.isAnomaly && } +
+
+
Z-Score: {res.zScore}
+
Cluster: {res.clusterCount} Traders
+
Rebound: {Math.round(res.coupledRebound * 100)}%
+
+
+ ))} +
+
+ )} + + {/* SECTION 2: Smart Money Segment Tabs */} +
+
+ + + +
+ + {/* Ledger displays */} +
+ {activeSegment === 'executives' && ( + + + + + + + + + + + + + {insiderTrades.map((t) => { + const isBuy = t.type === 'BUY'; + return ( + + + + + + + + + ); + })} + +
TickerInsider NamePositionTransaktionStückeWert ($)
{t.ticker}{t.insiderName}{t.relation} + + {isBuy ? 'KAUF' : 'VERKAUF'} + + {t.shares.toLocaleString()} + ${t.value.toLocaleString()} +
+ )} + + {activeSegment === 'congress' && ( +
+
+ +
+ U.S. Congress Stock Act Offenlegungslags: Abgeordnete müssen Käufe innerhalb von 30 bis 45 Tagen offenlegen. Der Alpha-Lag in der Tabelle visualisiert diese Meldeverzögerung. Käufe können verzögert eingepreist sein. +
+
+ + + + + + + + + + + + + + + {congressTrades.map((c) => { + const isBuy = c.type === 'BUY'; + return ( + + + + + + + + + + + ); + })} + +
TickerAbgeordneterKammerTransaktionVolumen-SpanneHandelsdatumMeldedatumAlpha-Lag (Tage)
{c.ticker}{c.representative}{c.chamber} + + {isBuy ? 'KAUF' : 'VERKAUF'} + + {c.valueRange}{c.transactionDate}{c.filingDate}{c.lagDays} Tage
+
+ )} + + {activeSegment === 'whales' && ( + + + + + + + + + + + + + + {whaleTrades.map((w) => { + const isBuy = w.type === 'BUY' || w.type === 'NEW'; + return ( + + + + + + + + + + ); + })} + +
TickerInstitution (13F Filers)ArtGehandelte AnteileAktueller BestandMeldedatumGeschätzter Wert ($)
{w.ticker}{w.institution} + + {w.type} + + {w.sharesTraded.toLocaleString()}{w.sharesHeld.toLocaleString()}{w.filingDate} + ${w.estimatedValue.toLocaleString()} +
+ )} +
+
+ + {/* SECTION 3: Mathematical LaTeX Accordion */} +
+ + + {showMathAccordion && ( +
+
+

1. Volumetrischer Z-Score (Statistische Signifikanz)

+

+ Der Z-Score gibt an, um wie viele Standardabweichungen das aktuelle Transaktionsvolumen vom historischen Durchschnitt abweicht: +

+
+ +
+

+ Ein Z-Score > 2.0 wird als Ausreißer (Anomaly Trigger) eingestuft, was einer Wahrscheinlichkeit von unter 2.27% für einen zufälligen Anstieg entspricht (einseitiger Test bei Normalverteilung). +

+
+ +
+

2. Bayesianische Kopplung (Rebound-Wahrscheinlichkeit)

+

+ Wir verknüpfen den Preisdrop-Sentiment (Element 2) mit den Insider-Z-Scores (Element 3), um die A-Posteriori-Wahrscheinlichkeit eines echten Rebounds zu ermitteln: +

+
+ +
+

+ wobei die Prior-Wahrscheinlichkeit ist. Ist der Z-Score hoch (Käufe), steigt die Likelihood stark an und maximiert den Rebound-Erwartungswert. +

+
+
+ )} +
+
+ ); +} diff --git a/components/modules/sandbox/SandboxDemo.tsx b/components/modules/sandbox/SandboxDemo.tsx new file mode 100644 index 0000000..45e592c --- /dev/null +++ b/components/modules/sandbox/SandboxDemo.tsx @@ -0,0 +1,815 @@ +'use client'; + +import React, { useState, useMemo } from 'react'; +import { useSandboxStore, PortfolioHolding, Transaction } from '@/lib/store'; +import { calculateEWMA, calculateKellyFraction, calculateAssetCovariance } from '@/lib/math/statistics'; +import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer, Legend } from 'recharts'; +import 'katex/dist/katex.min.css'; +import { BlockMath, InlineMath } from 'react-katex'; +import { + TrendingUp, Wallet, ArrowDownRight, ArrowUpRight, Percent, Plus, FolderSync, + HelpCircle, Settings, Calendar, DollarSign, Tag, Check, AlertCircle, ChevronDown, ChevronUp, Sparkles +} from 'lucide-react'; + +export default function SandboxDemo() { + const { + portfolios, + activePortfolioId, + ewmaLambda, + createPortfolio, + setActivePortfolio, + executeTransaction, + setEwmaLambda, + scannerAlerts, + posteriorProbability + } = useSandboxStore(); + + // Selected portfolio + const activePortfolio = useMemo(() => { + return portfolios.find(p => p.id === activePortfolioId) || portfolios[0]; + }, [portfolios, activePortfolioId]); + + const [mounted, setMounted] = useState(false); + React.useEffect(() => { + setMounted(true); + }, []); + + if (!mounted) { + return ( +
+
Lade Sandbox-Modul...
+
+ ); + } + + // UI state + const [showNewPortfolioModal, setShowNewPortfolioModal] = useState(false); + const [newPortfolioName, setNewPortfolioName] = useState(''); + const [newStartingBalance, setNewStartingBalance] = useState(50000); + + const [tradeSymbol, setTradeSymbol] = useState('AAPL'); + const [tradeWknOrIsin, setTradeWknOrIsin] = useState('865985'); + const [tradeShares, setTradeShares] = useState(10); + const [tradePrice, setTradePrice] = useState(182); + const [tradeType, setTradeType] = useState<'BUY' | 'SELL'>('BUY'); + const [simulateFees, setSimulateFees] = useState(true); + const [isBackfill, setIsBackfill] = useState(false); + const [backfillDate, setBackfillDate] = useState('2026-05-20'); + const [hypothesisTag, setHypothesisTag] = useState('Fokus auf KI-Infrastruktur'); + const [orderError, setOrderError] = useState(null); + const [orderSuccess, setOrderSuccess] = useState(false); + + const [showMathAccordion, setShowMathAccordion] = useState(false); + const [showMsciBenchmark, setShowMsciBenchmark] = useState(true); + + // Kelly Position Sizing states + const [kellySource, setKellySource] = useState<'scanner' | 'crypto' | 'econometric' | 'custom'>('custom'); + const [customProb, setCustomProb] = useState(0.60); + const [oddsRatio, setOddsRatio] = useState(1.5); + + // Compute Net Worth + const netWorth = useMemo(() => { + const assetsVal = activePortfolio.holdings.reduce((sum, h) => sum + h.shares * h.currentPrice, 0); + return Math.round((activePortfolio.cash + assetsVal) * 100) / 100; + }, [activePortfolio]); + + // Dynamic winning probability (p) based on selected source + const kellyProbability = useMemo(() => { + if (kellySource === 'scanner') { + const alert = scannerAlerts.find(a => a.ticker.toUpperCase() === tradeSymbol.toUpperCase()); + return alert ? alert.overreactionScore / 100 : 0.52; + } + if (kellySource === 'crypto') { + return posteriorProbability; // e.g. 0.72 + } + if (kellySource === 'econometric') { + return 0.65; // ROC target probability + } + return customProb; + }, [kellySource, customProb, tradeSymbol, scannerAlerts, posteriorProbability]); + + // Check potential cluster risk for the input symbol + const potentialClusterRisk = useMemo(() => { + if (!tradeSymbol) return false; + const holdingsWithWeights = activePortfolio.holdings.map(h => ({ + symbol: h.symbol, + weight: (h.shares * h.currentPrice) / (netWorth || 1.0) + })); + const covResult = calculateAssetCovariance(holdingsWithWeights, tradeSymbol); + return covResult.clusterRisk; + }, [activePortfolio.holdings, tradeSymbol, netWorth]); + + // Compute Kelly fraction and recommended cash amount + const kellyFraction = useMemo(() => { + const rawKelly = calculateKellyFraction(kellyProbability, oddsRatio); + // Cap at Half-Kelly already done in calculateKellyFraction, but we can scale by 50% if there is cluster risk + return potentialClusterRisk ? rawKelly * 0.5 : rawKelly; + }, [kellyProbability, oddsRatio, potentialClusterRisk]); + + const recommendedKellyCash = useMemo(() => { + return activePortfolio.cash * kellyFraction; + }, [activePortfolio.cash, kellyFraction]); + + // Compute returns based on active portfolio's historical value series + const portfolioReturns = useMemo(() => { + const vals = activePortfolio.historicalValues; + if (vals.length < 2) return []; + const r: number[] = []; + for (let i = 1; i < vals.length; i++) { + r.push((vals[i].value - vals[i - 1].value) / vals[i - 1].value); + } + return r; + }, [activePortfolio.historicalValues]); + + // Calculate EWMA Volatility live + const ewmaResult = useMemo(() => { + return calculateEWMA(portfolioReturns, ewmaLambda); + }, [portfolioReturns, ewmaLambda]); + + // Combine data for charting + const chartData = useMemo(() => { + const vals = activePortfolio.historicalValues; + if (vals.length === 0) return []; + + // Normalize MSCI World index from the same starting value of the portfolio + const baseValue = vals[0].value; + let msciVal = baseValue; + + return vals.map((hv, idx) => { + // Deterministic pseudo-random walk for MSCI World + if (idx > 0) { + const rand = Math.sin(idx * 57.8) * 0.45 + 0.05; // range: -0.4% to +0.5% return + msciVal = msciVal * (1 + rand * 0.015); + } + + const vol = ewmaResult.series[idx - 1] || 0; + + return { + date: hv.date, + Portfolio: hv.value, + 'MSCI World (Benchmark)': Math.round(msciVal), + 'EWMA Vol (%)': parseFloat(vol.toFixed(2)), + }; + }); + }, [activePortfolio.historicalValues, ewmaResult]); + + // Total gain/loss + const totalGainLoss = netWorth - activePortfolio.startingBalance; + const totalGainLossPct = (totalGainLoss / activePortfolio.startingBalance) * 100; + const isPositiveOverall = totalGainLoss >= 0; + + const handleCreatePortfolio = (e: React.FormEvent) => { + e.preventDefault(); + if (!newPortfolioName.trim()) return; + createPortfolio(newPortfolioName, newStartingBalance); + setNewPortfolioName(''); + setShowNewPortfolioModal(false); + }; + + const handleTransactionSubmit = (e: React.FormEvent) => { + e.preventDefault(); + setOrderError(null); + setOrderSuccess(false); + + if (tradeShares <= 0 || tradePrice <= 0) { + setOrderError('Bitte geben Sie eine gültige Stückzahl und einen Kurs an.'); + return; + } + + const ok = executeTransaction( + activePortfolio.id, + tradeSymbol, + tradeWknOrIsin, + tradeType, + tradeShares, + tradePrice, + simulateFees, + isBackfill, + backfillDate, + hypothesisTag + ); + + if (ok) { + setOrderSuccess(true); + setTimeout(() => setOrderSuccess(false), 3000); + } else { + setOrderError( + tradeType === 'BUY' + ? 'Unzureichendes Barguthaben (inklusive allfälliger Transaktionsgebühren).' + : 'Unzureichende Anteile im Depot für den Verkauf.' + ); + } + }; + + return ( +
+ + {activePortfolio.riskProfile?.status === 'RED' && ( +
+ +
+ Kritische Klumpenrisiken (Kovarianz RED): {activePortfolio.riskProfile.message} +
+
+ )} + + {/* SECTION 1: Portfolio Selector & Stats Bar */} +
+
+ +
+
+ Strategic Sandbox +
+ + + +
+
+ +
+ {/* Net Worth Card */} +
+
Gesamtwert
+
+ ${netWorth.toLocaleString(undefined, { minimumFractionDigits: 2, maximumFractionDigits: 2 })} +
+
+ + {/* Performance Card */} +
+
GuV (Gesamt)
+
+ {isPositiveOverall ? : } + {isPositiveOverall ? '+' : ''}{totalGainLossPct.toFixed(2)}% +
+
+ + {/* Live EWMA Vol Card */} +
+
+ EWMA Volatilität + + + +
+
+ {ewmaResult.latest.toFixed(2)}% +
+
+ + {/* Covariance Risk Traffic Light Card */} +
+
+ Kovarianz-Ampel + + + +
+
+ + + {activePortfolio.riskProfile?.status || 'GREEN'} RISK + +
+
+
+
+ + {/* Modal for creating a new Portfolio */} + {showNewPortfolioModal && ( +
+
+

Neues Sandbox-Portfolio

+
+
+ + setNewPortfolioName(e.target.value)} + /> +
+
+ + setNewStartingBalance(Number(e.target.value))} + /> +
+
+ + +
+
+
+
+ )} + + {/* Accordion / Math Button */} +
+ + + {showMathAccordion && ( +
+
+

1. EWMA Volatilitätsmodell

+

+ Die Volatilität wird mittels des Exponentially Weighted Moving Average (EWMA)-Modells ermittelt. Jüngere Renditen erhalten hierbei ein höheres Gewicht als weiter in der Vergangenheit liegende Renditen, gesteuert durch den Zerfallsparameter (Lambda). +

+
+ +
+

+ Die tägliche Volatilität wird auf ein ganzes Jahr hochgerechnet (Annualisierung) unter der Annahme von 252 Handelstagen: +

+
+ +
+

+ RiskMetrics empfiehlt für tägliche Finanzdaten einen Lambda-Wert von . +

+
+ +
+

2. Kelly-Kriterium zur Positionsgrößenbestimmung

+

+ Die Kelly-Formel bestimmt den optimalen Anteil des Kapitals (), der in ein Geschäft investiert werden soll, um das exponentielle Wachstum des Kapitals zu maximieren: +

+
+ +
+

+ Um Risiken durch ungenaue Schätzungen zu verringern, wenden wir das konservative Half-Kelly-Sizing an und begrenzen das Ergebnis auf (zusätzlich begrenzt auf ). +

+
+ +
+

3. Covariance & Cluster Risk (Kovarianz-Ampel)

+

+ Die Kovarianz zwischen Assets wird durch Multiplikation ihrer paarweisen Korrelation mit ihren jeweiligen Standardabweichungen (Volatilitäten) bestimmt: +

+
+ +
+

+ Ein Klumpenrisiko (Risk RED) wird ausgelöst, wenn ein Asset eine Korrelation zu bestehenden Positionen aufweist und diese Positionen jeweils mehr als 15% des Portfolios ausmachen. +

+
+
+ )} +
+
+ + {/* SECTION 2: Chart / Analytics & Order Form */} +
+ + {/* Left 2 Columns: Analytics Performance Plot */} +
+
+

+ Portfolio Wertentwicklung & Benchmark +

+
+ +
+
+ +
+ + + + + `$${v.toLocaleString()}`} /> + + + + {showMsciBenchmark && ( + + )} + + +
+ + {/* EWMA parameter tuner slider */} +
+
+

Parameteranpassung EWMA Lambda (λ)

+

Zerfallsfaktor steuert die Schock-Sensitivität des Volatilitätsmodells.

+
+
+ setEwmaLambda(parseFloat(e.target.value))} + className="w-40 accent-emerald-400 cursor-pointer" + /> + λ = {ewmaLambda.toFixed(2)} +
+
+
+ + {/* Right 1 Column: Advanced Order Mask */} +
+
+
+

+ Order-Maske (Simuliert) +

+
+ +
+
+
+ + setTradeSymbol(e.target.value.toUpperCase())} + /> +
+
+ + setTradeWknOrIsin(e.target.value)} + /> +
+
+ + setTradeShares(Number(e.target.value))} + /> +
+
+ + setTradePrice(Number(e.target.value))} + /> +
+
+ + {/* Order direction buttons */} +
+ + +
+ + {/* Kelly Sizing Risk Recommendation Widget */} + {tradeType === 'BUY' && ( +
+
+ + Risk-Engine Sizing + + Kelly recommendation +
+ +
+
+ + +
+ +
+ + setOddsRatio(Number(e.target.value))} + className="w-full bg-slate-900 border border-slate-800 rounded px-1.5 py-1 text-[10px] text-slate-200 font-mono focus:outline-none" + /> +
+
+ + {kellySource === 'custom' && ( +
+
+ Erfolgswahrscheinlichkeit: + {(kellyProbability * 100).toFixed(0)}% +
+ setCustomProb(Number(e.target.value))} + className="w-full accent-emerald-500 h-1 bg-slate-900 rounded" + /> +
+ )} + + {kellySource !== 'custom' && ( +
+ System-Wahrscheinlichkeit: + {(kellyProbability * 100).toFixed(1)}% +
+ )} + + {potentialClusterRisk && ( +
+ +
+ Klumpenrisiko! Korrelation > 0.70 zu bestehenden Positionen. Kelly-Empfehlung wurde um 50% halbiert. +
+
+ )} + +
+
+ Kelly-Anteil: + {(kellyFraction * 100).toFixed(1)}% des Cashs +
+
+ Kaufvolumen: + ${Math.round(recommendedKellyCash).toLocaleString()} +
+
+
+ )} + + {/* Hypothesis input */} +
+ + setHypothesisTag(e.target.value)} + /> +
+ + {/* Fees Toggle */} +
+ + Ordergebühren simulieren + + setSimulateFees(e.target.checked)} + className="rounded border-slate-800 text-emerald-500 focus:ring-0 accent-emerald-500 w-4 h-4 cursor-pointer" + /> +
+ + {/* Backfill Date Picker Toggle */} +
+
+ + Historischer Backfill + + setIsBackfill(e.target.checked)} + className="rounded border-slate-800 text-emerald-500 focus:ring-0 accent-emerald-500 w-4 h-4 cursor-pointer" + /> +
+ + {isBackfill && ( + setBackfillDate(e.target.value)} + /> + )} +
+ + {orderError && ( +
+ + {orderError} +
+ )} + + {orderSuccess && ( +
+ + Transaktion erfolgreich gebucht! +
+ )} + + +
+
+
+ +
+ + {/* SECTION 3: Holdings Table & Transactions Log */} +
+ + {/* Left 2 Columns: Holdings List */} +
+
+

+ Depotbestände ({activePortfolio.holdings.length}) +

+
+ Barguthaben: ${activePortfolio.cash.toLocaleString()} +
+
+ +
+ + + + + + + + + + + + + {activePortfolio.holdings.length === 0 ? ( + + + + ) : ( + activePortfolio.holdings.map((hold) => { + const profitLoss = (hold.currentPrice - hold.avgPrice) * hold.shares; + const isPositive = profitLoss >= 0; + return ( + + + + + + + + + ); + }) + )} + +
AssetStückeEinstandKursHypotheseGuV
+ Keine Bestände in diesem Sandbox-Portfolio. Nutzen Sie die Order-Maske, um Werte hinzuzufügen. +
+
{hold.symbol}
+ {hold.wknOrIsin &&
WKN: {hold.wknOrIsin}
} + 		{hold.shares}${hold.avgPrice.toFixed(2)}${hold.currentPrice.toFixed(2)} + {hold.hypothesisTag || '-'} + + {isPositive ? : } + ${Math.abs(profitLoss).toLocaleString(undefined, { minimumFractionDigits: 2, maximumFractionDigits: 2 })} +
+
+
+ + {/* Right 1 Column: Transactions History */} +
+
+

+ Letzte Orderbuch-Einträge +

+
+ +
+ {activePortfolio.transactions.length === 0 ? ( +

Bislang keine Transaktionen in diesem Portfolio.

+ ) : ( + activePortfolio.transactions.map((tx) => { + const isBuy = tx.type === 'BUY'; + return ( +
+
+
+ + {isBuy ? 'KAUF' : 'VERKAUF'} + + {tx.symbol} +
+ {tx.timestamp} +
+ +
+ {tx.shares} Stk @ ${tx.price.toFixed(2)} + Gebühr: ${tx.feeApplied.toFixed(2)} +
+ + {tx.hypothesisTag && ( +
+ + {tx.hypothesisTag} +
+ )} +
+ ); + }) + )} +
+
+ +
+ +
+ ); +} diff --git a/components/modules/scanner/ScannerDemo.tsx b/components/modules/scanner/ScannerDemo.tsx new file mode 100644 index 0000000..144d585 --- /dev/null +++ b/components/modules/scanner/ScannerDemo.tsx @@ -0,0 +1,538 @@ +'use client'; + +import React, { useState, useMemo } from 'react'; +import { useSandboxStore, ScannerAlert, WatchlistItem } from '@/lib/store'; +import { calculateGJRGARCH } from '@/lib/math/statistics'; +import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer, Legend } from 'recharts'; +import 'katex/dist/katex.min.css'; +import { BlockMath, InlineMath } from 'react-katex'; +import { + ShieldAlert, Sparkles, RefreshCw, Flame, Search, Plus, Trash2, + ChevronDown, ChevronUp, AlertTriangle, CheckCircle2, XCircle, Info, Clock, Play +} from 'lucide-react'; + +// Predefined mock database for deep-check searches +interface SearchResult { + ticker: string; + name: string; + priceChange: number; + sentiment: 'GREEN' | 'YELLOW' | 'RED'; + whyDropped: string; + gjrGarchVol: number; + reboundScore: number; + returns: number[]; +} + +const mockSearchDatabase: Record = { + 'RACE': { + ticker: 'RACE', + name: 'Ferrari N.V.', + priceChange: -0.065, + sentiment: 'GREEN', + whyDropped: 'Emotionaler Abverkauf nach viralem Video von Cristiano Ronaldo, der sich über Autoprobleme beschwert. Keine fundamentalen Schäden.', + gjrGarchVol: 0.048, + reboundScore: 88, + returns: [0.01, -0.005, 0.012, -0.008, 0.003, -0.065] + }, + 'KO': { + ticker: 'KO', + name: 'The Coca-Cola Co.', + priceChange: -0.052, + sentiment: 'GREEN', + whyDropped: 'Berühmter Influencer entfernt Coca-Cola Flasche während Pressekonferenz. Reine Social-Media-Hype Reaktion.', + gjrGarchVol: 0.021, + reboundScore: 82, + returns: [0.002, 0.005, -0.003, 0.001, -0.002, -0.052] + }, + 'TSLA': { + ticker: 'TSLA', + name: 'Tesla Inc.', + priceChange: -0.084, + sentiment: 'YELLOW', + whyDropped: 'Auslieferungszahlen leicht unter Analystenschätzungen. Margenentwicklung bleibt jedoch stabil.', + gjrGarchVol: 0.062, + reboundScore: 65, + returns: [-0.012, 0.008, -0.025, 0.015, -0.005, -0.084] + }, + 'SMCI': { + ticker: 'SMCI', + name: 'Super Micro Computer', + priceChange: -0.124, + sentiment: 'RED', + whyDropped: 'Hindenburg Research Short-Seller-Report bezüglich mutmaßlicher Bilanzmanipulationen veröffentlicht.', + gjrGarchVol: 0.085, + reboundScore: 24, + returns: [0.035, -0.018, 0.042, -0.051, 0.012, -0.124] + }, + 'BA': { + ticker: 'BA', + name: 'Boeing Co.', + priceChange: -0.071, + sentiment: 'RED', + whyDropped: 'FAA verhängt vorübergehendes Flugverbot nach erneutem technischen Zwischenfall mit Rumpftür.', + gjrGarchVol: 0.058, + reboundScore: 18, + returns: [-0.005, -0.012, 0.005, -0.021, -0.008, -0.071] + }, + 'NFLX': { + ticker: 'NFLX', + name: 'Netflix Inc.', + priceChange: -0.058, + sentiment: 'GREEN', + whyDropped: 'Gerüchte über angebliche Preissenkungen in Schwellenländern belasten Kurs kurzfristig.', + gjrGarchVol: 0.038, + reboundScore: 78, + returns: [0.015, -0.002, 0.008, 0.005, -0.011, -0.058] + } +}; + +export default function ScannerDemo() { + const { watchlist, addToWatchlist, removeFromWatchlist, simulateWatchlistTick } = useSandboxStore(); + + // Component local states + const [scanning, setScanning] = useState(false); + const [scanProgress, setScanProgress] = useState(''); + const [activeAlerts, setActiveAlerts] = useState([ + { id: 'sa1', ticker: 'TSLA', priceChange: -0.084, gjrGarchVol: 0.062, overreactionScore: 65, status: 'UNDEREVALUATED' }, + { id: 'sa2', ticker: 'SMCI', priceChange: -0.124, gjrGarchVol: 0.085, overreactionScore: 24, status: 'FAIR' }, + ]); + + const [searchQuery, setSearchQuery] = useState(''); + const [searchResult, setSearchResult] = useState(null); + const [searchError, setSearchError] = useState(false); + + const [showMathAccordion, setShowMathAccordion] = useState(false); + + // Run market scan simulator + const handleMarketScan = () => { + setScanning(true); + setScanProgress('Verbinde mit Börsenfeeds...'); + + setTimeout(() => { + setScanProgress('Berechne historische Volatilitätsmatrizen...'); + setTimeout(() => { + setScanProgress('Filtere abnormale Abweichungen (Asset > -5%, Index stabil)...'); + setTimeout(() => { + // Scan isolated anomalies + setActiveAlerts([ + { id: 'sa3', ticker: 'RACE', priceChange: -0.065, gjrGarchVol: 0.048, overreactionScore: 88, status: 'UNDEREVALUATED' }, + { id: 'sa4', ticker: 'KO', priceChange: -0.052, gjrGarchVol: 0.021, overreactionScore: 82, status: 'UNDEREVALUATED' }, + { id: 'sa5', ticker: 'TSLA', priceChange: -0.084, gjrGarchVol: 0.062, overreactionScore: 65, status: 'UNDEREVALUATED' }, + { id: 'sa6', ticker: 'SMCI', priceChange: -0.124, gjrGarchVol: 0.085, overreactionScore: 24, status: 'FAIR' }, + { id: 'sa7', ticker: 'BA', priceChange: -0.071, gjrGarchVol: 0.058, overreactionScore: 18, status: 'OVERVALUATED' }, + ]); + setScanning(false); + setScanProgress(''); + }, 600); + }, 500); + }, 400); + }; + + // Perform a manual deep check + const handleDeepCheck = (e: React.FormEvent) => { + e.preventDefault(); + setSearchError(false); + setSearchResult(null); + const query = searchQuery.trim().toUpperCase(); + if (!query) return; + + if (mockSearchDatabase[query]) { + setSearchResult(mockSearchDatabase[query]); + } else { + // Simulate dynamic result for unknown assets + const simulatedVol = 0.03 + Math.random() * 0.04; + const simulatedScore = Math.floor(40 + Math.random() * 50); + const isNegative = Math.random() > 0.4; + const simulatedChange = -0.05 - Math.random() * 0.06; + + const res: SearchResult = { + ticker: query, + name: `${query} Corp.`, + priceChange: simulatedChange, + sentiment: isNegative ? (simulatedScore > 75 ? 'GREEN' : 'YELLOW') : 'RED', + whyDropped: 'Simulierte Marktabweichung basierend auf automatischem Sentiment-Scanning der Finanzberichte.', + gjrGarchVol: simulatedVol, + reboundScore: simulatedScore, + returns: [0.005, -0.008, 0.012, -0.015, 0.004, simulatedChange] + }; + setSearchResult(res); + } + }; + + const handleAddToWatchlist = (ticker: string, priceChange: number, sentiment: 'GREEN' | 'YELLOW' | 'RED', whyDropped: string) => { + // Determine a mock initial price based on ticker + let initialPrice = 150; + if (ticker === 'RACE') initialPrice = 380; + if (ticker === 'KO') initialPrice = 60; + if (ticker === 'TSLA') initialPrice = 175; + if (ticker === 'NFLX') initialPrice = 610; + + addToWatchlist({ + ticker, + priceChange, + sentiment, + whyDropped, + initialPrice, + currentPrice: initialPrice * (1 + priceChange), // current price after drop + }); + }; + + // Compute GJR-GARCH forecasting series for the math accordion/visual validation + const gjrGarchMathResult = useMemo(() => { + const mockReturns = [0.015, -0.022, 0.008, -0.031, 0.014, -0.055, 0.012, -0.008, 0.024, -0.065]; + return calculateGJRGARCH(mockReturns); + }, []); + + const mathChartData = useMemo(() => { + return gjrGarchMathResult.series.map((vol, idx) => ({ + day: `T-${gjrGarchMathResult.series.length - idx - 1}`, + 'GJR-GARCH Vol (%)': parseFloat(vol.toFixed(2)), + })); + }, [gjrGarchMathResult]); + + return ( +
+ + {/* SECTION 1: Overreaction Scan Action */} +
+
+ +
+
+ Market Scanner Engine +

+ Anomalien-Scanner & Marktverzerrungen +

+

+ Isoliert Kursstürze > 5% bei relativem Gesamtmarkt-Stopp (S&P 500 driftet seitwärts oder steigt). Misst die Asymmetrie mittels GJR-GARCH, um Panik von strukturellen Risiken zu separieren. +

+
+ +
+ + {scanning && ( + {scanProgress} + )} +
+
+ + {/* Collapsible Math Accordion */} +
+ + + {showMathAccordion && ( +
+
+

+ Das GJR-GARCH(1,1)-Modell erfasst die Asymmetrie im Volatilitätsprozess von Renditen. Es besitzt einen zusätzlichen Term (), der aktiviert wird, wenn der gestrige Schock negativ war. +

+
+ +
+

+ Die Indikatorvariable nimmt den Wert 1 bei einem Kurssturz an: +

+
+ +
+

+ Sind die Volatilitätsschocks asymmetrisch (), führt ein Kurssturz (Bad News) zu einer signifikant höheren Volatilität als ein gleich großer Kursgewinn (Good News). +

+
+ +
+
Simulierter GJR-GARCH Volatilitätsprozess nach Schocks
+
+ + + + + + + + + +
+
+ Spike am Tag T-4 repräsentiert die asymmetrische Reaktion auf einen Schock von -6.5%. +
+
+
+ )} +
+
+ + {/* SECTION 2: Scanned Anomalies & Sentiment Traffic Light */} +
+ + {/* Left 2 Columns: Scanned anomalies details */} +
+
+

+ Gefundene Anomalien (Sentiment-Ampel) +

+ +
+ {activeAlerts.map((alert) => { + // Fetch associated info from mockDB if available, else generic mock + const dbInfo = mockSearchDatabase[alert.ticker] || { + name: `${alert.ticker} Corp.`, + sentiment: alert.overreactionScore > 75 ? 'GREEN' : (alert.overreactionScore > 40 ? 'YELLOW' : 'RED'), + whyDropped: 'Kurzfristige Eindeckungen und Gewinnmitnahmen an den Terminmärkten belasten das Sentiment.' + }; + + const isGreen = dbInfo.sentiment === 'GREEN'; + const isYellow = dbInfo.sentiment === 'YELLOW'; + const isRed = dbInfo.sentiment === 'RED'; + + return ( +
+
+
+
+ {alert.ticker} + ({dbInfo.name}) +
+
+ Kurssturz: {(alert.priceChange * 100).toFixed(1)}% + | + GJR-GARCH Vol: {(alert.gjrGarchVol * 100).toFixed(1)}% +
+
+ + {/* Traffic Light Sentiment Badge */} +
+ {isGreen && ( + + EMOTIONALER OVERREACTION (KAUF) + + )} + {isYellow && ( + + UNSICHERHEIT (HALTEN) + + )} + {isRed && ( + + FUNDAMENTALER SCHADEN (MEIDEN) + + )} +
+
+ +
+ {/* Analysis Block */} +
+
+ KI-Ursachenanalyse: +
+

+ „{dbInfo.whyDropped}“ +

+
+ + {/* Actions & Score */} +
+
+ Rebound Score + + {alert.overreactionScore}% + +
+ + {(isGreen || isYellow) && ( + + )} +
+
+
+ ); + })} +
+
+
+ + {/* Right Column: Deep-Check & Search Mask */} +
+
+

+ Deep-Check Suchmaske +

+

+ Suchen Sie gezielt nach Werten, um Anomalien-Analysen abzurufen. (z.B. RACE, KO, TSLA, SMCI, NFLX) +

+
+ +
+ setSearchQuery(e.target.value)} + /> + +
+ + {searchResult && ( +
+
+
+

{searchResult.ticker}

+ {searchResult.name} +
+
+ {searchResult.sentiment === 'GREEN' && GREEN} + {searchResult.sentiment === 'YELLOW' && YELLOW} + {searchResult.sentiment === 'RED' && RED} +
+
+ +
+
+ Aktueller Sturz: + {(searchResult.priceChange * 100).toFixed(1)}% +
+
+ GJR-GARCH Volatilität: + {(searchResult.gjrGarchVol * 100).toFixed(1)}% +
+
+ Rebound Score: + + {searchResult.reboundScore}/100 + +
+ +
+ KI-Kommentar: +

{searchResult.whyDropped}

+
+ + {(searchResult.sentiment === 'GREEN' || searchResult.sentiment === 'YELLOW') && ( + + )} +
+
+ )} +
+
+ + {/* SECTION 3: Hot Watchlist & Rebound-Tracker (48 Hours) */} +
+
+
+ +

Hot Rebound Watchlist & Tracker (48h)

+
+ + {watchlist.length > 0 && ( + + )} +
+ + {watchlist.length === 0 ? ( +
+ Bislang keine Assets auf der Rebound-Watchlist. Nutzen Sie die Markt-Scanergebnisse oben, um Werte hinzuzufügen. +
+ ) : ( +
+ {watchlist.map((item) => { + const perf = item.reboundPerformance; + const isPositive = perf >= 0; + const progressPct = (item.hoursTracked / 48) * 100; + + return ( +
+
+ +
+ +
+
+
+ {item.ticker} + + {item.sentiment} + +
+ Gezogen bei: {(item.priceChange * 100).toFixed(1)}% +
+ +
+
Rebound Performance
+
+ {isPositive ? '+' : ''}{perf.toFixed(2)}% +
+
+
+ + {/* Progress timeline */} +
+
+ Tracking-Dauer: {item.hoursTracked}/48 Std. + {Math.round(progressPct)}% abgeschlossen +
+
+
+
+
+ +

+ Hintergrund: {item.whyDropped} +

+
+ ); + })} +
+ )} +
+ +
+ ); +} diff --git a/eslint.config.mjs b/eslint.config.mjs new file mode 100644 index 0000000..05e726d --- /dev/null +++ b/eslint.config.mjs @@ -0,0 +1,18 @@ +import { defineConfig, globalIgnores } from "eslint/config"; +import nextVitals from "eslint-config-next/core-web-vitals"; +import nextTs from "eslint-config-next/typescript"; + +const eslintConfig = defineConfig([ + ...nextVitals, + ...nextTs, + // Override default ignores of eslint-config-next. + globalIgnores([ + // Default ignores of eslint-config-next: + ".next/**", + "out/**", + "build/**", + "next-env.d.ts", + ]), +]); + +export default eslintConfig; diff --git a/github-agent-config.json b/github-agent-config.json new file mode 100644 index 0000000..3df3ccc --- /dev/null +++ b/github-agent-config.json @@ -0,0 +1,36 @@ +{ + "agentWorkflow": { + "enabled": true, + "version": "1.0.0", + "orchestratorUrl": "https://api.antigravity.ai/v1/workflows/trigger", + "webhookSecretName": "ANTIGRAVITY_AGENT_TOKEN", + "mappings": [ + { + "trigger": "issue_labeled", + "labels": ["enhancement", "feature", "agent-resolve"], + "assignedAgent": "orchestrator", + "mode": "autonomous", + "createBranchPattern": "feature/issue-{issue_number}" + }, + { + "trigger": "issue_labeled", + "labels": ["bug", "hotfix"], + "assignedAgent": "orchestrator", + "mode": "autonomous", + "createBranchPattern": "bugfix/issue-{issue_number}" + } + ], + "verification": { + "requireBuild": true, + "runTests": true, + "buildScript": "npm run build", + "testScript": "npm test" + }, + "pullRequest": { + "autoCreate": true, + "draft": false, + "assignee": "jannr", + "reviewers": [] + } + } +} diff --git a/lib/math/statistics.ts b/lib/math/statistics.ts new file mode 100644 index 0000000..b107b00 --- /dev/null +++ b/lib/math/statistics.ts @@ -0,0 +1,717 @@ +/** + * Statistical and Econometric Utilities for Investment Sandbox + */ + +/** + * Calculates the Exponentially Weighted Moving Average (EWMA) Volatility for asset returns + * Formula: sigma_t^2 = lambda * sigma_{t-1}^2 + (1 - lambda) * r_{t-1}^2 + * Annualized Volatility: sigma_ann = sqrt(sigma_t^2 * 252) + */ +export function calculateEWMA( + returns: number[], + lambda: number = 0.94 +): { series: number[]; latest: number } { + if (returns.length === 0) { + return { series: [], latest: 0 }; + } + + const series: number[] = new Array(returns.length).fill(0); + + // Calculate initial variance as average of squared returns (mean = 0) + let currentVariance = returns.reduce((sum, r) => sum + r * r, 0) / returns.length; + if (currentVariance === 0) { + currentVariance = 0.0004; // Seed variance (2% daily standard deviation squared) + } + + // Initial annualized volatility + series[0] = Math.sqrt(currentVariance * 252); + + for (let t = 1; t < returns.length; t++) { + const rPrev = returns[t - 1]; + currentVariance = lambda * currentVariance + (1 - lambda) * rPrev * rPrev; + series[t] = Math.sqrt(currentVariance * 252); + } + + return { + series, + latest: series[series.length - 1], + }; +} + +/** + * Calculates asymmetric GJR-GARCH(1,1) volatility series and next-day forecast + * Formula: sigma_t^2 = omega + alpha * epsilon_{t-1}^2 + gamma * epsilon_{t-1}^2 * I_{t-1} + beta * sigma_{t-1}^2 + * Where returns are scaled to percentages (e.g. 5.0 instead of 0.05) to align with default parameters. + */ +export function calculateGJRGARCH( + returns: number[], + omega: number = 0.02, + alpha: number = 0.05, + gamma: number = 0.10, + beta: number = 0.80 +): { + series: number[]; + forecast: number; +} { + if (returns.length === 0) { + return { series: [], forecast: 0 }; + } + + // Standardize return inputs to percentages + const isDecimal = returns.some(r => Math.abs(r) > 0 && Math.abs(r) < 0.2); + const scaledReturns = isDecimal ? returns.map(r => r * 100) : returns; + + const series: number[] = new Array(scaledReturns.length).fill(0); + + // Set initial variance to simple variance of returns + let currentVariance = scaledReturns.reduce((sum, r) => sum + r * r, 0) / scaledReturns.length; + if (currentVariance === 0) { + currentVariance = 4.0; // Seed variance (2% daily vol squared) + } + + series[0] = Math.sqrt(currentVariance); + + for (let t = 1; t < scaledReturns.length; t++) { + const epsPrev = scaledReturns[t - 1]; + const indicator = epsPrev < 0 ? 1 : 0; + currentVariance = omega + alpha * epsPrev * epsPrev + gamma * epsPrev * epsPrev * indicator + beta * currentVariance; + series[t] = Math.sqrt(currentVariance); + } + + // Forecast next day's volatility (e.g., after a shock) + const lastEps = scaledReturns[scaledReturns.length - 1] || 0; + const lastIndicator = lastEps < 0 ? 1 : 0; + const forecastVariance = omega + alpha * lastEps * lastEps + gamma * lastEps * lastEps * lastIndicator + beta * currentVariance; + + return { + series, + forecast: Math.sqrt(forecastVariance), + }; +} + +/** + * Performs a Bayesian Online Learning update for expected returns + * Prior: N(priorMean, priorVar) + * Likelihood: N(measurement, measurementVar) + * Posterior: N(postMean, postVar) + */ +export function calculateBayesianUpdate( + priorMean: number, + priorVar: number, + measurement: number, + measurementVar: number +): { mean: number; variance: number } { + // Kalman filter styled 1D update + const gain = priorVar / (priorVar + measurementVar); + const postMean = priorMean + gain * (measurement - priorMean); + const postVar = (1 - gain) * priorVar; + + return { + mean: postMean, + variance: postVar, + }; +} + +/** + * Generates ROC Curve coordinates (FPR, TPR) based on predicted probabilities and binary labels + */ +export function calculateROCCurve( + predictions: number[], + labels: number[] +): { fpr: number; tpr: number; threshold: number }[] { + const data = predictions.map((p, idx) => ({ pred: p, label: labels[idx] })); + // Sort descending by predictions + data.sort((a, b) => b.pred - a.pred); + + const totalPositives = labels.filter(l => l === 1).length; + const totalNegatives = labels.length - totalPositives; + + if (totalPositives === 0 || totalNegatives === 0) { + return [ + { fpr: 0, tpr: 0, threshold: 1 }, + { fpr: 1, tpr: 1, threshold: 0 } + ]; + } + + const roc = [{ fpr: 0, tpr: 0, threshold: 1 }]; + let currentTP = 0; + let currentFP = 0; + + for (let i = 0; i < data.length; i++) { + if (data[i].label === 1) { + currentTP++; + } else { + currentFP++; + } + roc.push({ + fpr: currentFP / totalNegatives, + tpr: currentTP / totalPositives, + threshold: data[i].pred + }); + } + + roc.push({ fpr: 1, tpr: 1, threshold: 0 }); + return roc; +} + +/** + * Generates Kaplan-Meier Survival Curve coordinates + * Used for Event-driven time-to-insolvency or time-to-rebound analyses + */ +export function calculateSurvivalAnalysis( + times: number[], + events: number[] // 1 for event (e.g. default), 0 for censoring +): { time: number; survivalRate: number }[] { + const data = times.map((t, idx) => ({ time: t, event: events[idx] })); + data.sort((a, b) => a.time - b.time); + + const curve: { time: number; survivalRate: number }[] = [{ time: 0, survivalRate: 1 }]; + let n = data.length; // Number of subjects at risk + let currentSurvival = 1; + + for (let i = 0; i < data.length; i++) { + const t = data[i].time; + // Count how many events happened at this time + let d = data[i].event; + let c = data[i].event === 0 ? 1 : 0; + + // Group ties + while (i + 1 < data.length && data[i + 1].time === t) { + i++; + if (data[i].event === 1) d++; + else c++; + } + + if (d > 0) { + currentSurvival = currentSurvival * (1 - d / n); + curve.push({ time: t, survivalRate: currentSurvival }); + } + + n -= (d + c); + } + + return curve; +} + +/** + * Calculates a rolling Z-Score for volumetric data. + * Formula: Z = (X_t - mean) / stdDev + * Returns the latest Z-score and flags if it represents an outlier (Z > 2.0) + */ +export function calculateRollingZScore( + volumes: number[] +): { zScores: number[]; latest: number; isAnomaly: boolean } { + if (volumes.length < 2) { + return { zScores: new Array(volumes.length).fill(0), latest: 0, isAnomaly: false }; + } + + const mean = volumes.reduce((sum, v) => sum + v, 0) / volumes.length; + const variance = volumes.reduce((sum, v) => sum + (v - mean) * (v - mean), 0) / volumes.length; + const stdDev = Math.sqrt(variance) || 1.0; + + const zScores = volumes.map((v) => (v - mean) / stdDev); + const latest = zScores[zScores.length - 1]; + + return { + zScores, + latest, + isAnomaly: latest > 2.0, + }; +} + +/** + * Time-Window Cluster Detection: Aggregates multiple trades. + * If 3 or more distinct insiders of the same corporation trade within a moving 14-day window, + * return a cluster flag and scale the signal exponentially. + */ +export function detectInsiderClusters( + trades: { date: string; insiderName: string }[] +): { isCluster: boolean; count: number; multiplier: number } { + if (trades.length < 3) { + return { isCluster: false, count: trades.length, multiplier: 1.0 }; + } + + // Sort trades by date + const sorted = [...trades].sort((a, b) => new Date(a.date).getTime() - new Date(b.date).getTime()); + const fourteenDays = 14 * 24 * 60 * 60 * 1000; + let maxClusterSize = 0; + + for (let i = 0; i < sorted.length; i++) { + const startWindow = new Date(sorted[i].date).getTime(); + const uniqueInsiders = new Set(); + + for (let j = i; j < sorted.length; j++) { + const tradeTime = new Date(sorted[j].date).getTime(); + if (tradeTime - startWindow <= fourteenDays) { + uniqueInsiders.add(sorted[j].insiderName); + } else { + break; + } + } + + if (uniqueInsiders.size > maxClusterSize) { + maxClusterSize = uniqueInsiders.size; + } + } + + const isCluster = maxClusterSize >= 3; + // Exponential scaling multiplier: e^(N - 3) if N >= 3, else 1.0 + const multiplier = isCluster ? Math.exp(maxClusterSize - 3) : 1.0; + + return { + isCluster, + count: maxClusterSize, + multiplier, + }; +} + +/** + * Bayesian Probability Coupling: updates posterior rebound probability + * by linking price drop overreactions (Element 2) with insider Z-Scores (Element 3). + * Prior: P(R) [rebound probability] + * Likelihood: P(Z | R) vs P(Z | ~R) + */ +export function coupleBayesianRebound( + priorProbability: number, + insiderZScore: number +): number { + let likelihoodPos = 0.5; + let likelihoodNeg = 0.5; + + if (insiderZScore >= 2.0) { + likelihoodPos = 0.88; // 88% chance of high buying Z-score if there's a true rebound + likelihoodNeg = 0.12; // 12% false positive rate + } else if (insiderZScore > 0) { + // Linear scale between 0.5 and 0.88 + likelihoodPos = 0.5 + (insiderZScore / 2.0) * 0.38; + likelihoodNeg = 0.5 - (insiderZScore / 2.0) * 0.38; + } else { + // Negative Z-score (selling) reduces probability + const absZ = Math.min(2.0, Math.abs(insiderZScore)); + likelihoodPos = 0.5 - (absZ / 2.0) * 0.35; // drops to 15% + likelihoodNeg = 0.5 + (absZ / 2.0) * 0.35; // rises to 85% + } + + const marginal = likelihoodPos * priorProbability + likelihoodNeg * (1 - priorProbability); + const posterior = (likelihoodPos * priorProbability) / (marginal || 1.0); + + return Math.round(posterior * 100) / 100; +} + +/** + * Simulates a non-linear Random Forest decision baseline for crypto. + * Ensemble of 10 decision trees mapping Funding, Open Interest, Long/Short ratio, and Whale flows. + */ +export function predictCryptoTrend(inputs: { + fundingRate: number; // e.g. 0.05 for 0.05% + openInterestChange: number; // e.g. 10.0 for 10% + longShortRatio: number; // e.g. 1.2 + whaleInflow: number; // net flows +}): { shortTermProb: number; mediumTermProb: number } { + let stVotes = 0; // Short Term (24h Volatility Squeeze) + let mtVotes = 0; // Medium Term (14d Structural Trend) + + const { fundingRate, openInterestChange, longShortRatio, whaleInflow } = inputs; + + // Tree 1: Squeeze Detector + if (fundingRate < -0.02 && openInterestChange > 5) stVotes += 1; + if (whaleInflow > 100) mtVotes += 1; + + // Tree 2: Funding Extreme Counter-trade + if (fundingRate > 0.08) stVotes -= 0.6; + if (longShortRatio > 1.8) mtVotes -= 0.6; + + // Tree 3: Whale Inflow Momentum + if (whaleInflow > 500) { + stVotes += 0.8; + mtVotes += 1; + } + + // Tree 4: Long/Short Extreme Capitulation + if (longShortRatio < 0.8 && fundingRate < -0.05) { + stVotes += 1; + mtVotes += 0.6; + } + + // Tree 5: Open Interest Build-up Trend + if (openInterestChange > 15 && longShortRatio > 1.2) { + stVotes += 0.5; + mtVotes += 0.5; + } + + // Tree 6: High Funding Squeeze + if (fundingRate < -0.04) { + stVotes += 0.8; + } else { + stVotes -= 0.2; + } + + // Tree 7: Whale Inflow + High OI + if (whaleInflow > 200 && openInterestChange > 8) { + mtVotes += 0.8; + } + + // Tree 8: Low OI + Neutral Funding + if (openInterestChange < -10) { + stVotes -= 0.5; + mtVotes -= 0.3; + } + + // Tree 9: Long/Short Ratio divergence + if (longShortRatio > 1.5 && fundingRate > 0.04) { + stVotes -= 0.8; + } + + // Tree 10: General trend + if (whaleInflow > 0 && fundingRate < 0.02) { + mtVotes += 0.6; + } + + // Map votes to probabilities (logistic scale) + const stScore = 0.5 + (stVotes / 10); + const shortTermProb = Math.min(0.95, Math.max(0.05, stScore)); + + const mtScore = 0.5 + (mtVotes / 10); + const mediumTermProb = Math.min(0.95, Math.max(0.05, mtScore)); + + return { + shortTermProb, + mediumTermProb, + }; +} + +/** + * Beta-conjugate Bayesian self-correcting update. + * Treats history (alphaPrior successes, betaPrior failures) as the prior distribution, + * and the current ML prediction as pseudo-observations of trials. + * Returns the posterior mean probability. + */ +export function calculateBetaPosterior( + alphaPrior: number, + betaPrior: number, + mlProbability: number, + pseudoWeight: number = 10 +): number { + const successes = mlProbability * pseudoWeight; + const failures = (1 - mlProbability) * pseudoWeight; + + const alphaPost = alphaPrior + successes; + const betaPost = betaPrior + failures; + + const posteriorMean = alphaPost / (alphaPost + betaPost); + return Math.round(posteriorMean * 100) / 100; +} + +/** + * ROC Analysis: Evaluates predictive performance of scores over binary outcomes. + * Returns coordinates (FPR, TPR) and optimal threshold based on the Youden Index (J = Sensitivity + Specificity - 1). + */ +export interface ROCPoint { + fpr: number; + tpr: number; + threshold: number; + youdenIndex: number; +} + +export function calculateEventROC( + predictions: number[], + labels: number[] +): { points: ROCPoint[]; optimalThreshold: number; maxYouden: number } { + if (predictions.length === 0 || labels.length === 0) { + return { + points: [ + { fpr: 0, tpr: 0, threshold: 1, youdenIndex: 0 }, + { fpr: 1, tpr: 1, threshold: 0, youdenIndex: 0 } + ], + optimalThreshold: 0.5, + maxYouden: 0 + }; + } + + const data = predictions.map((p, idx) => ({ pred: p, label: labels[idx] })); + data.sort((a, b) => b.pred - a.pred); + + const totalPos = labels.filter(l => l === 1).length; + const totalNeg = labels.length - totalPos; + + if (totalPos === 0 || totalNeg === 0) { + return { + points: [ + { fpr: 0, tpr: 0, threshold: 1, youdenIndex: 0 }, + { fpr: 1, tpr: 1, threshold: 0, youdenIndex: 0 } + ], + optimalThreshold: 0.5, + maxYouden: 0 + }; + } + + const points: ROCPoint[] = [{ fpr: 0, tpr: 0, threshold: 1, youdenIndex: 0 }]; + let tp = 0; + let fp = 0; + let maxYouden = -1; + let optimalThreshold = 0.5; + + for (let i = 0; i < data.length; i++) { + if (data[i].label === 1) { + tp++; + } else { + fp++; + } + const tpr = tp / totalPos; + const fpr = fp / totalNeg; + const youdenIndex = tpr - fpr; + + if (youdenIndex > maxYouden) { + maxYouden = youdenIndex; + optimalThreshold = data[i].pred; + } + + points.push({ fpr, tpr, threshold: data[i].pred, youdenIndex }); + } + + points.push({ fpr: 1, tpr: 1, threshold: 0, youdenIndex: 0 }); + + return { + points, + optimalThreshold: Math.round(optimalThreshold * 100) / 100, + maxYouden: Math.round(maxYouden * 100) / 100 + }; +} + +/** + * Survival Analysis: Models Time-to-Event until asset hits ±5% target. + * Assets not hitting the target within 60 days are right-censored (event = 0). + */ +export function calculateEventSurvival( + times: number[], + events: number[], // 1 if event occurred (target hit), 0 if censored + direction: 'LONG' | 'SHORT' +): { time: number; survivalRate: number }[] { + // Model Kaplan-Meier Survival Rate + const data = times.map((t, idx) => ({ time: Math.min(60, t), event: t > 60 ? 0 : events[idx] })); + data.sort((a, b) => a.time - b.time); + + const curve: { time: number; survivalRate: number }[] = [{ time: 0, survivalRate: 1.0 }]; + let n = data.length; + let currentSurvival = 1.0; + + for (let i = 0; i < data.length; i++) { + const t = data[i].time; + let d = data[i].event; + let c = data[i].event === 0 ? 1 : 0; + + // Handle ties + while (i + 1 < data.length && data[i + 1].time === t) { + i++; + if (data[i].event === 1) d++; + else c++; + } + + if (d > 0) { + currentSurvival = currentSurvival * (1 - d / n); + curve.push({ time: t, survivalRate: Math.round(currentSurvival * 1000) / 1000 }); + } else { + // Just record censoring point at same survival + curve.push({ time: t, survivalRate: Math.round(currentSurvival * 1000) / 1000 }); + } + + n -= (d + c); + } + + return curve; +} + +/** + * Linear Mixed Model (LMM) Estimator: Estimates the pure event impact on asset returns, + * controlling for covariates: VIX, Sector Trend, and Asset Beta. + * Model: Return = beta_0 + beta_1(Event) + beta_2(VIX) + beta_3(Trend) + b_i(Asset) + e + */ +export interface LMMCoefficient { + name: string; + estimate: number; + se: number; + pVal: number; + sig: string; + ciLower: number; + ciUpper: number; +} + +export interface LMMResult { + fixedEffects: LMMCoefficient[]; + randomEffects: { asset: string; intercept: number }[]; + aic: number; + bic: number; + rSquared: number; +} + +export function runEventLMM( + data: { asset: string; eventType: string; vix: number; trend: number; returnVal: number }[] +): LMMResult { + if (data.length < 5) { + // Default baseline values + return { + fixedEffects: [ + { name: '(Intercept)', estimate: 0.005, se: 0.002, pVal: 0.012, sig: '*', ciLower: 0.001, ciUpper: 0.009 }, + { name: 'EventTypeBullish', estimate: 0.024, se: 0.004, pVal: 0.0001, sig: '***', ciLower: 0.016, ciUpper: 0.032 }, + { name: 'VIX', estimate: -0.0015, se: 0.0005, pVal: 0.003, sig: '**', ciLower: -0.0025, ciUpper: -0.0005 }, + { name: 'SectorTrend', estimate: 0.450, se: 0.080, pVal: 0.00001, sig: '***', ciLower: 0.290, ciUpper: 0.610 } + ], + randomEffects: [ + { asset: 'Apple', intercept: 0.003 }, + { asset: 'NASDAQ', intercept: 0.001 }, + { asset: 'Gold', intercept: -0.002 }, + { asset: 'Bitcoin', intercept: 0.008 } + ], + aic: -1420.5, + bic: -1395.2, + rSquared: 0.642 + }; + } + + const n = data.length; + const meanReturn = data.reduce((sum, d) => sum + d.returnVal, 0) / n; + + // Compute LMM coefficients (simulated fit with randomized small variation to reflect new data points) + const seed = Math.sin(n) * 0.002; + const eventEst = 0.024 + seed; + const vixEst = -0.0015 + seed * 0.1; + const trendEst = 0.45 + seed * 10; + + return { + fixedEffects: [ + { name: '(Intercept)', estimate: Math.round(meanReturn * 10000) / 10000, se: 0.0015, pVal: 0.018, sig: '*', ciLower: Math.round((meanReturn - 0.003) * 10000) / 10000, ciUpper: Math.round((meanReturn + 0.003) * 10000) / 10000 }, + { name: 'EventTypeBullish', estimate: Math.round(eventEst * 1000) / 1000, se: 0.003, pVal: 0.0001, sig: '***', ciLower: Math.round((eventEst - 0.006) * 1000) / 1000, ciUpper: Math.round((eventEst + 0.006) * 1000) / 1000 }, + { name: 'VIX', estimate: Math.round(vixEst * 10000) / 10000, se: 0.0004, pVal: 0.002, sig: '**', ciLower: Math.round((vixEst - 0.0008) * 10000) / 10000, ciUpper: Math.round((vixEst + 0.0008) * 10000) / 10000 }, + { name: 'SectorTrend', estimate: Math.round(trendEst * 1000) / 1000, se: 0.05, pVal: 0.00001, sig: '***', ciLower: Math.round((trendEst - 0.10) * 1000) / 1000, ciUpper: Math.round((trendEst + 0.10) * 1000) / 1000 } + ], + randomEffects: [ + { asset: 'Apple', intercept: 0.0035 }, + { asset: 'NASDAQ', intercept: 0.0012 }, + { asset: 'Gold', intercept: -0.0025 }, + { asset: 'Bitcoin', intercept: 0.0078 } + ], + aic: Math.round((-1420.5 - n * 1.8) * 10) / 10, + bic: Math.round((-1395.2 - n * 1.5) * 10) / 10, + rSquared: Math.min(0.95, Math.round((0.642 + (n - 5) * 0.001) * 1000) / 1000) + }; +} + +/** + * Calculates the optimal position size using the Kelly Criterion. + * Formula: f* = (p * b - q) / b + * Capped at Half-Kelly (0.5 * f*) and clamped at 0. + */ +export function calculateKellyFraction(p: number, b: number = 1.5): number { + if (p <= 0 || b <= 0) return 0; + const q = 1 - p; + const fStar = (p * b - q) / b; + return Math.max(0, 0.5 * fStar); +} + +/** + * Returns a historical correlation lookup for sandbox assets. + */ +export function calculateAssetCorrelation(assetA: string, assetB: string): number { + const a = assetA.toUpperCase().trim(); + const b = assetB.toUpperCase().trim(); + if (a === b) return 1.0; + + const correlationMap: Record> = { + AAPL: { MSFT: 0.75, NVDA: 0.65, KO: 0.15, JNJ: 0.18, BTC: 0.25, ETH: 0.22, SOL: 0.20, GOLD: 0.05, NASDAQ: 0.85 }, + MSFT: { AAPL: 0.75, NVDA: 0.72, KO: 0.12, JNJ: 0.15, BTC: 0.28, ETH: 0.26, SOL: 0.23, GOLD: 0.02, NASDAQ: 0.88 }, + NVDA: { AAPL: 0.65, MSFT: 0.72, KO: 0.08, JNJ: 0.10, BTC: 0.35, ETH: 0.32, SOL: 0.38, GOLD: -0.05, NASDAQ: 0.80 }, + KO: { AAPL: 0.15, MSFT: 0.12, NVDA: 0.08, JNJ: 0.55, BTC: -0.05, ETH: -0.08, SOL: -0.10, GOLD: 0.20, NASDAQ: 0.10 }, + JNJ: { AAPL: 0.18, MSFT: 0.15, NVDA: 0.10, KO: 0.55, BTC: -0.02, ETH: -0.05, SOL: -0.07, GOLD: 0.22, NASDAQ: 0.12 }, + BTC: { AAPL: 0.25, MSFT: 0.28, NVDA: 0.35, KO: -0.05, JNJ: -0.02, ETH: 0.82, SOL: 0.78, GOLD: -0.10, NASDAQ: 0.30 }, + ETH: { AAPL: 0.22, MSFT: 0.26, NVDA: 0.32, KO: -0.08, JNJ: -0.05, BTC: 0.82, SOL: 0.80, GOLD: -0.08, NASDAQ: 0.28 }, + SOL: { AAPL: 0.20, MSFT: 0.23, NVDA: 0.38, KO: -0.10, JNJ: -0.07, BTC: 0.78, ETH: 0.80, GOLD: -0.12, NASDAQ: 0.25 }, + GOLD: { AAPL: 0.05, MSFT: 0.02, NVDA: -0.05, KO: 0.20, JNJ: 0.22, BTC: -0.10, ETH: -0.08, SOL: -0.12, NASDAQ: -0.15 }, + NASDAQ: { AAPL: 0.85, MSFT: 0.88, NVDA: 0.80, KO: 0.10, JNJ: 0.12, BTC: 0.30, ETH: 0.28, SOL: 0.25, GOLD: -0.15 } + }; + + // Check lookup + if (correlationMap[a] && correlationMap[a][b] !== undefined) { + return correlationMap[a][b]; + } + if (correlationMap[b] && correlationMap[b][a] !== undefined) { + return correlationMap[b][a]; + } + + // Fallbacks + const techOrCrypto = ['AAPL', 'MSFT', 'NVDA', 'BTC', 'ETH', 'SOL', 'NASDAQ']; + if (techOrCrypto.includes(a) && techOrCrypto.includes(b)) return 0.50; + return 0.20; +} + +/** + * Calculates asset covariance matrix and checks for portfolio-level cluster risk. + */ +export function calculateAssetCovariance( + holdings: { symbol: string; weight: number }[], + newAsset?: string +): { + covarianceMatrix: Record>; + clusterRisk: boolean; + highCorrHoldings: string[]; +} { + const getVol = (sym: string) => { + const s = sym.toUpperCase().trim(); + if (['BTC', 'ETH', 'SOL'].includes(s)) return 0.50; // crypto vol + if (s === 'GOLD') return 0.10; // low gold vol + return 0.20; // default stock vol + }; + + const covarianceMatrix: Record> = {}; + const symbols = holdings.map(h => h.symbol.toUpperCase().trim()); + + if (newAsset) { + const na = newAsset.toUpperCase().trim(); + if (!symbols.includes(na)) { + symbols.push(na); + } + } + + symbols.forEach(s1 => { + covarianceMatrix[s1] = {}; + symbols.forEach(s2 => { + const corr = calculateAssetCorrelation(s1, s2); + const vol1 = getVol(s1); + const vol2 = getVol(s2); + covarianceMatrix[s1][s2] = Math.round(corr * vol1 * vol2 * 10000) / 10000; + }); + }); + + // Cluster Risk check + let clusterRisk = false; + const highCorrHoldings: string[] = []; + + if (newAsset) { + const na = newAsset.toUpperCase().trim(); + holdings.forEach(hold => { + const holdSym = hold.symbol.toUpperCase().trim(); + if (holdSym === na) return; + const corr = calculateAssetCorrelation(na, holdSym); + if (hold.weight > 0.15 && corr > 0.70) { + clusterRisk = true; + highCorrHoldings.push(hold.symbol); + } + }); + } else { + // Portfolio level risk check + for (let i = 0; i < holdings.length; i++) { + for (let j = i + 1; j < holdings.length; j++) { + const corr = calculateAssetCorrelation(holdings[i].symbol, holdings[j].symbol); + if (corr > 0.70 && holdings[i].weight > 0.15 && holdings[j].weight > 0.15) { + clusterRisk = true; + highCorrHoldings.push(`${holdings[i].symbol}-${holdings[j].symbol}`); + } + } + } + } + + return { + covarianceMatrix, + clusterRisk, + highCorrHoldings + }; +} diff --git a/lib/store.ts b/lib/store.ts new file mode 100644 index 0000000..c12ec07 --- /dev/null +++ b/lib/store.ts @@ -0,0 +1,666 @@ +import { create } from 'zustand'; +import { calculateAssetCorrelation, calculateAssetCovariance } from './math/statistics'; + +// --- Interfaces for Sandbox Portfolio --- +export interface PortfolioHolding { + symbol: string; + wknOrIsin?: string; + shares: number; + avgPrice: number; + currentPrice: number; + hypothesisTag?: string; +} + +export interface Transaction { + id: string; + type: 'BUY' | 'SELL'; + symbol: string; + wknOrIsin?: string; + shares: number; + price: number; + timestamp: string; // date/time string + hypothesisTag?: string; + feeApplied: number; +} + +export interface HistoricalValue { + date: string; + value: number; // portfolio value (cash + assets) +} + +export interface RiskProfile { + status: 'GREEN' | 'YELLOW' | 'RED'; + clusterRisk: boolean; + highCorrAssets: string[]; + message: string; +} + +export interface Portfolio { + id: string; + name: string; + startingBalance: number; + cash: number; + holdings: PortfolioHolding[]; + transactions: Transaction[]; + historicalValues: HistoricalValue[]; + riskProfile: RiskProfile; +} + +export function computePortfolioRiskProfile( + cash: number, + holdings: PortfolioHolding[] +): RiskProfile { + if (holdings.length === 0) { + return { + status: 'GREEN', + clusterRisk: false, + highCorrAssets: [], + message: 'Portfolio ist leer. Keine Risiken vorhanden.' + }; + } + + const assetsVal = holdings.reduce((sum, h) => sum + h.shares * h.currentPrice, 0); + const totalVal = cash + assetsVal; + if (totalVal <= 0) { + return { status: 'GREEN', clusterRisk: false, highCorrAssets: [], message: 'Gesamtwert ist Null.' }; + } + + const holdingsWithWeights = holdings.map(h => ({ + symbol: h.symbol, + weight: (h.shares * h.currentPrice) / totalVal + })); + + const covResult = calculateAssetCovariance(holdingsWithWeights); + + let status: 'GREEN' | 'YELLOW' | 'RED' = 'GREEN'; + let message = 'Gut diversifiziert. Geringe Gesamtkovarianz.'; + + if (covResult.clusterRisk) { + status = 'RED'; + message = 'Achtung: Hohe Kovarianz festgestellt. Reduziere Positionsgröße um 50%.'; + } else { + let yellowFlag = false; + const yellowAssets: string[] = []; + for (let i = 0; i < holdingsWithWeights.length; i++) { + for (let j = i + 1; j < holdingsWithWeights.length; j++) { + const h1 = holdingsWithWeights[i]; + const h2 = holdingsWithWeights[j]; + const corr = calculateAssetCorrelation(h1.symbol, h2.symbol); + if (corr > 0.50 && h1.weight > 0.10 && h2.weight > 0.10) { + yellowFlag = true; + yellowAssets.push(`${h1.symbol}-${h2.symbol}`); + } + } + } + if (yellowFlag) { + status = 'YELLOW'; + message = `Moderate Überschneidungen festgestellt zwischen: ${yellowAssets.join(', ')}.`; + } + } + + return { + status, + clusterRisk: covResult.clusterRisk, + highCorrAssets: covResult.highCorrHoldings, + message + }; +} + +// --- Interfaces for Insider & Whale Trades --- +export interface InsiderTrade { + id: string; + ticker: string; + insiderName: string; + relation: string; + type: 'BUY' | 'SELL'; + shares: number; + value: number; + date: string; +} + +export interface CongressTrade { + id: string; + ticker: string; + representative: string; + chamber: 'HOUSE' | 'SENATE'; + type: 'BUY' | 'SELL'; + valueRange: string; + transactionDate: string; + filingDate: string; + lagDays: number; +} + +export interface WhaleTrade { + id: string; + ticker: string; + institution: string; + type: 'BUY' | 'SELL' | 'NEW' | 'EXIT'; + sharesTraded: number; + sharesHeld: number; + filingDate: string; + estimatedValue: number; +} + +// --- Interfaces for Overreaction Scanner --- +export interface ScannerAlert { + id: string; + ticker: string; + priceChange: number; // e.g. -0.12 for -12% + gjrGarchVol: number; + overreactionScore: number; + status: 'UNDEREVALUATED' | 'FAIR' | 'OVERVALUATED'; +} + +export interface WatchlistItem { + id: string; + ticker: string; + priceChange: number; + sentiment: 'GREEN' | 'YELLOW' | 'RED'; + whyDropped: string; + addedAt: string; + hoursTracked: number; + initialPrice: number; + currentPrice: number; + reboundPerformance: number; +} + +// --- Zustand Store Interface --- +interface SandboxState { + // 1. Sandbox Portfolios + portfolios: Portfolio[]; + activePortfolioId: string; + ewmaLambda: number; + + // 2. Overreaction Scanner State + scanThreshold: number; + scannerAlerts: ScannerAlert[]; + watchlist: WatchlistItem[]; + + // 3. Insider / Whale Tracker State + insiderTrades: InsiderTrade[]; + congressTrades: CongressTrade[]; + whaleTrades: WhaleTrade[]; + insiderVolumes: Record; // Ticker -> 24 months volumes + + // 4. Crypto Bayesian State + priorProbability: number; + likelihoodPositive: number; + posteriorProbability: number; + alphaSuccess: number; + betaFailure: number; + + // 5. Econometric Events State + selectedModel: 'ROC' | 'SURVIVAL' | 'LMM'; + eventsMatrix: { + id: string; + name: string; + date: string; + scores: Record; // asset -> score + }[]; + calendarProposals: { + id: string; + name: string; + date: string; + archetype: string; + defaultScores: Record; + }[]; + lmmObservations: { + asset: string; + eventType: string; + vix: number; + trend: number; + returnVal: number; + }[]; + + // Actions + createPortfolio: (name: string, startingBalance: number) => void; + setActivePortfolio: (id: string) => void; + executeTransaction: ( + portfolioId: string, + symbol: string, + wknOrIsin: string, + type: 'BUY' | 'SELL', + shares: number, + price: number, + simulateFees: boolean, + isBackfill: boolean, + backfillDate: string, + hypothesisTag: string + ) => boolean; // returns success + setEwmaLambda: (lambda: number) => void; + updateScannerAlerts: (alerts: ScannerAlert[]) => void; + addToWatchlist: (item: Omit) => void; + removeFromWatchlist: (id: string) => void; + simulateWatchlistTick: () => void; + addInsiderTrade: (trade: Omit) => void; + addCongressTrade: (trade: Omit) => void; + addWhaleTrade: (trade: Omit) => void; + addModelTrial: (isSuccess: boolean) => void; + addEventToMatrix: (name: string, date: string, scores: Record) => void; + updateMatrixCell: (eventId: string, asset: string, score: number) => void; + runEndogenousLMMCalibration: () => void; + updateBayesPrior: (prior: number) => void; + updateBayesLikelihood: (likelihood: number) => void; + setSelectedModel: (model: 'ROC' | 'SURVIVAL' | 'LMM') => void; +} + +// --- Helper: Generate Initial Historical Data --- +const generateHistoricalData = (startVal: number, days: number, growthRate: number): HistoricalValue[] => { + const data: HistoricalValue[] = []; + const date = new Date('2026-05-15'); + let currentVal = startVal; + for (let i = 0; i < days; i++) { + const dStr = date.toISOString().slice(0, 10); + data.push({ date: dStr, value: Math.round(currentVal) }); + date.setDate(date.getDate() + 1); + currentVal = currentVal * (1 + (Math.random() - 0.45) * growthRate); + } + return data; +}; + +// --- Zustand Store Implementation --- +export const useSandboxStore = create((set, get) => ({ + // 1. Portfolio State + portfolios: [ + { + id: 'p1', + name: 'Tech Breakout Sandbox', + startingBalance: 100000, + cash: 21374, + holdings: [ + { symbol: 'AAPL', wknOrIsin: '865985', shares: 150, avgPrice: 172.5, currentPrice: 182.2, hypothesisTag: 'Premium Product Lock-in' }, + { symbol: 'MSFT', wknOrIsin: '870747', shares: 80, avgPrice: 388.0, currentPrice: 415.5, hypothesisTag: 'Enterprise AI Lead' }, + { symbol: 'NVDA', wknOrIsin: '918422', shares: 45, avgPrice: 910.0, currentPrice: 945.0, hypothesisTag: 'GPU Demand Dominance' }, + ], + transactions: [ + { 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 }, + { 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 }, + { 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 }, + ], + historicalValues: generateHistoricalData(100000, 22, 0.018), + riskProfile: { + status: 'RED', + clusterRisk: true, + highCorrAssets: ['AAPL', 'MSFT', 'NVDA'], + message: 'Achtung: Hohe Kovarianz festgestellt. Reduziere Positionsgröße um 50%.' + } + }, + { + id: 'p2', + name: 'Dividenden Defensive Sandbox', + startingBalance: 50000, + cash: 14750, + holdings: [ + { symbol: 'KO', wknOrIsin: '850663', shares: 350, avgPrice: 58.5, currentPrice: 62.4, hypothesisTag: 'Inflation-resistant Consumer Goods' }, + { symbol: 'JNJ', wknOrIsin: '853260', shares: 80, avgPrice: 152.0, currentPrice: 158.3, hypothesisTag: 'Stable Healthcare Cashflows' }, + ], + transactions: [ + { 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 }, + { 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 }, + ], + historicalValues: generateHistoricalData(50000, 22, 0.007), + riskProfile: { + status: 'YELLOW', + clusterRisk: false, + highCorrAssets: [], + message: 'Moderate Überschneidungen festgestellt zwischen: KO-JNJ.' + } + } + ], + activePortfolioId: 'p1', + ewmaLambda: 0.94, + + // 2. Overreaction Scanner Defaults + scanThreshold: -0.05, + scannerAlerts: [ + { id: '1', ticker: 'NVDA', priceChange: -0.082, gjrGarchVol: 0.034, overreactionScore: 82, status: 'UNDEREVALUATED' }, + { id: '2', ticker: 'AMD', priceChange: -0.061, gjrGarchVol: 0.041, overreactionScore: 68, status: 'UNDEREVALUATED' }, + { id: '3', ticker: 'SMCI', priceChange: -0.124, gjrGarchVol: 0.068, overreactionScore: 91, status: 'UNDEREVALUATED' }, + ], + watchlist: [ + { + id: 'w1', + ticker: 'RACE', + priceChange: -0.065, + sentiment: 'GREEN', + whyDropped: 'Emotionaler Abverkauf nach viralem Video von Cristiano Ronaldo, der sich über Autoprobleme beschwert. Keine fundamentalen Schäden.', + addedAt: '2026-06-05 14:00', + hoursTracked: 24, + initialPrice: 380, + currentPrice: 394.5, + reboundPerformance: 3.81 + } + ], + + // 3. Insider / Whale Defaults + insiderTrades: [ + { id: '1', ticker: 'AMZN', insiderName: 'Bezos Jeff', relation: 'Director', type: 'SELL', shares: 50000, value: 9200000, date: '2026-06-05' }, + { id: '2', ticker: 'META', insiderName: 'Zuckerberg Mark', relation: 'CEO', type: 'SELL', shares: 12000, value: 5760000, date: '2026-06-04' }, + { id: '3', ticker: 'PLTR', insiderName: 'Karp Alexander', relation: 'CEO', type: 'BUY', shares: 150000, value: 3300000, date: '2026-06-03' }, + { id: '4', ticker: 'PLTR', insiderName: 'Thiel Peter', relation: 'Director', type: 'BUY', shares: 100000, value: 2200000, date: '2026-06-02' }, + { id: '5', ticker: 'PLTR', insiderName: 'Cohen Stephen', relation: 'President', type: 'BUY', shares: 80000, value: 1760000, date: '2026-06-01' }, + { id: '6', ticker: 'RACE', insiderName: 'Vigna Benedetto', relation: 'CEO', type: 'BUY', shares: 8000, value: 3040000, date: '2026-06-04' }, + { id: '7', ticker: 'RACE', insiderName: 'Elkann John', relation: 'Director', type: 'BUY', shares: 12000, value: 4560000, date: '2026-06-03' }, + { id: '8', ticker: 'RACE', insiderName: 'Ferrari Piero', relation: 'Vice Chairman', type: 'BUY', shares: 10000, value: 3800000, date: '2026-06-02' } + ], + congressTrades: [ + { 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 }, + { 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 }, + { 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 } + ], + whaleTrades: [ + { id: 'w1', ticker: 'AAPL', institution: 'Berkshire Hathaway', type: 'SELL', sharesTraded: 10000000, sharesHeld: 789000000, filingDate: '2026-05-15', estimatedValue: 1820000000 }, + { id: 'w2', ticker: 'PLTR', institution: 'Renaissance Technologies', type: 'BUY', sharesTraded: 5400000, sharesHeld: 12500000, filingDate: '2026-05-15', estimatedValue: 118800000 }, + { id: 'w3', ticker: 'NVDA', institution: 'BlackRock Inc.', type: 'BUY', sharesTraded: 15400000, sharesHeld: 182400000, filingDate: '2026-05-15', estimatedValue: 14553000000 } + ], + insiderVolumes: { + 'PLTR': [30000, 25000, 45000, 18000, 22000, 31000, 27000, 36000, 29000, 40000, 33000, 150000], // 12-month rolling (scaled down representation for monthly) + 'RACE': [8000, 6000, 7500, 9000, 5200, 7100, 6800, 9500, 8100, 10200, 9300, 30000], + 'AMZN': [45000, 52000, 48000, 61000, 49000, 53000, 50000, 55000, 42000, 59000, 48000, 50000], + 'AAPL': [12000, 15000, 11000, 13000, 14000, 16000, 12000, 13000, 15000, 11000, 13000, 14000], + 'MSFT': [10000, 8000, 12000, 9000, 11000, 13000, 10000, 14000, 11000, 10000, 12000, 15000] + }, + + // 4. Crypto Bayes Defaults + priorProbability: 0.45, + likelihoodPositive: 0.72, + posteriorProbability: 0.72, + alphaSuccess: 394, + betaFailure: 118, + + // 5. Econometric Events Defaults + selectedModel: 'ROC', + eventsMatrix: [ + { id: 'ev1', name: 'FED Zinsentscheid', date: '2026-05-14', scores: { Apple: 1, NASDAQ: 2, Gold: -1, Bitcoin: 2 } }, + { id: 'ev2', name: 'US Wahlen (Präsidentschaft)', date: '2026-11-03', scores: { Apple: 2, NASDAQ: 1, Gold: 3, Bitcoin: 2 } }, + { id: 'ev3', name: 'SpaceX IPO (Gerüchte)', date: '2026-06-25', scores: { Apple: 0, NASDAQ: 2, Gold: -1, Bitcoin: 1 } }, + ], + calendarProposals: [ + { id: 'cp1', name: 'CPI Inflationsdaten', date: '2026-06-12', archetype: 'Macro Announcement', defaultScores: { Apple: 1, NASDAQ: 2, Gold: -2, Bitcoin: 1 } }, + { id: 'cp2', name: 'US Non-Farm Payrolls', date: '2026-06-15', archetype: 'Employment Report', defaultScores: { Apple: 0, NASDAQ: 1, Gold: -1, Bitcoin: 0 } }, + { id: 'cp3', name: 'EZB Pressekonferenz', date: '2026-06-18', archetype: 'Central Bank Policy', defaultScores: { Apple: -1, NASDAQ: -1, Gold: 2, Bitcoin: 1 } }, + ], + lmmObservations: [ + { 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] + }; + }), +})); diff 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build", + "start": "next start", + "lint": "eslint" + }, + "dependencies": { + "katex": "^0.17.0", + "lucide-react": "^1.17.0", + "next": "16.2.7", + "react": "19.2.4", + "react-dom": "19.2.4", + "react-katex": "^3.1.0", + "recharts": "^3.8.1", + "zustand": "^5.0.14" + }, + "devDependencies": { + "@tailwindcss/postcss": "^4", + "@types/node": "^20", + "@types/react": "^19", + "@types/react-dom": "^19", + "@types/react-katex": "^3.0.4", + "eslint": "^9", + "eslint-config-next": "16.2.7", + "tailwindcss": "^4", + "typescript": "^5" + } +} diff --git a/postcss.config.mjs b/postcss.config.mjs new file mode 100644 index 0000000..61e3684 --- /dev/null +++ b/postcss.config.mjs @@ -0,0 +1,7 @@ +const config = { + plugins: { + "@tailwindcss/postcss": {}, + }, +}; + +export default config; diff --git a/public/file.svg b/public/file.svg new file mode 100644 index 0000000..004145c --- /dev/null +++ b/public/file.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/public/globe.svg b/public/globe.svg new file mode 100644 index 0000000..567f17b --- /dev/null +++ b/public/globe.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/public/next.svg b/public/next.svg new file mode 100644 index 0000000..5174b28 --- /dev/null +++ b/public/next.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/public/vercel.svg b/public/vercel.svg new file mode 100644 index 0000000..7705396 --- /dev/null +++ b/public/vercel.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/public/window.svg b/public/window.svg new file mode 100644 index 0000000..b2b2a44 --- /dev/null +++ b/public/window.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/tsconfig.json b/tsconfig.json new file mode 100644 index 0000000..3a13f90 --- /dev/null +++ b/tsconfig.json @@ -0,0 +1,34 @@ +{ + "compilerOptions": { + "target": "ES2017", + "lib": ["dom", "dom.iterable", "esnext"], + "allowJs": true, + "skipLibCheck": true, + "strict": true, + "noEmit": true, + "esModuleInterop": 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