Dynamic Demo Library

The curated source of truth for every interactive build—code excerpts, trusted data feeds, and reusable hooks.

What's inside

Each entry documents the exact snippet that powers a marquee interaction, the vetted data streams behind it, and the open hooks you can import today.

How to use

Drop these hooks into your own projects, clone the data source configs, or fork the demo shell to stitch new financial experiences together faster.

New here?

Prefer an opinionated path? Start with the newcomer journey for a guided tour through flagship posts, demos, and builds.

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quality score engine

Quality Score Engine

Explainable quality scoring across 12 dimensions with Graham-aligned weighting and ESG overlays.

Launch Demo

View Case Study

Read Deep Dive

Weighted Quality Model

Transforms raw fundamentals into weighted quality dimensions and a 0-100 composite score.

export function calculateQualityDimensions(fm: FinancialMetrics): QualityDimension[] {
  return DIMENSIONS.map(cfg => {
    const score = clamp(cfg.scorer(fm), 0, 100)
    return {
      name: cfg.name,
      score,
      weight: cfg.weight,
      description: cfg.description,
      value: cfg.raw(fm),
      benchmark: cfg.benchmark
    }
  })
}

export function calculateOverallScore(dimensions: QualityDimension[]): number {
  const weighted = dimensions.reduce((sum, d) => sum + d.score * (d.weight / 100), 0)
  return clamp(weighted, 0, 100)
}

Data Sources

Polygon Fundamentals
Quarterly fundamentals, balance sheet depth, and sector benchmarks.
View documentation →
Alpha Vantage
Historical ratios and earnings trends powering momentum overlays.
View documentation →
IEX Cloud Snapshot
Live price and volume snapshots for intraday confidence adjustments.
View documentation →

Open Hooks

useRealTimeData
Streams authenticated market snapshots for live scoring overlays.
app/hooks/useRealTimeData.ts
useAnimationPerformance
Dial down heavy visualizations on lower-tier devices and reduced-motion profiles.
app/hooks/useAnimationPerformance.ts

analytics dashboard

Enterprise Analytics Intelligence

Server-driven dashboards with predictive benchmarks, conversion funnels, and anomaly triage.

Launch Demo

View Case Study

Metric Icon Hydrator

Maps API-delivered metrics onto UI affordances so KPIs stay readable in dense layouts.

const METRIC_ICON_MAP: Record<string, React.ComponentType<any>> = {
  'total-users': Users,
  'page-views': Eye,
  'avg-session': Clock,
  'bounce-rate': TrendingDown,
  'conversion-rate': TrendingUp,
  revenue: DollarSign,
}

const attachMetricIcons = (metrics: AnalyticsMetricSnapshot[]): MetricWithIcon[] =>
  metrics.map(metric => ({
    ...metric,
    icon: METRIC_ICON_MAP[metric.id] ?? DEFAULT_METRIC_ICON,
  }))

Data Sources

Vercel Analytics
Edge-collected vitals feed for real-time performance scoring.
View documentation →
Core Web Vitals API
Native browser metrics for CLS, LCP, and INP trendlines.
View documentation →
Mixpanel Export
Cohort and funnel data hydrated into the realtime hub.
View documentation →

Open Hooks

useAnalyticsData
Generates realistic analytics telemetry with auto-refresh and trend lines.
app/hooks/useAnalyticsData.ts
useAnimationPerformance
Toggles motion affordances for dense dashboard canvases.
app/hooks/useAnimationPerformance.ts

margin of safety

Margin of Safety Calculator

Multi-model valuation stack with DCF, Graham, EPV, and Monte Carlo scenario blends.

Launch Demo

View Case Study

Three-Stage DCF Core

Implements staged growth projections plus terminal value for intrinsic pricing.

const stage1Years = 5
const stage2Years = 5
let dcfValue = 0

for (let year = 1; year <= stage1Years; year++) {
  const projected = fcf * Math.pow(1 + fcfGrowth, year)
  dcfValue += projected / Math.pow(1 + discountRate, year)
}

const stage2Base = fcf * Math.pow(1 + fcfGrowth, stage1Years)
for (let year = 1; year <= stage2Years; year++) {
  const decay = stage2InitialGrowth * (1 - (year - 1) / stage2Years)
  const projected = stage2Base * Math.pow(1 + terminalGrowthRate + decay, year)
  dcfValue += projected / Math.pow(1 + discountRate, stage1Years + year)
}

Data Sources

SEC EDGAR
Filings feed for historical financials and share count adjustments.
View documentation →
St. Louis FRED
Macro series for discount rates and inflation assumptions.
View documentation →
Macrotrends
Longitudinal growth curves backing scenario stress tests.
View documentation →

Open Hooks

useRealTimePerformance
Surfaces FPS and memory deltas when Monte Carlo runs on weaker hardware.
app/hooks/useRealTimePerformance.ts
useAnimationPerformance
Keeps heavy SVG charts accessible under prefers-reduced-motion.
app/hooks/useAnimationPerformance.ts

crypto sentiment analyzer

Crypto Sentiment Analyzer

Real-time NLP scoring with social velocity, fear/greed normalization, and alerting.

Launch Demo

View Case Study

Sentiment Labelling Heuristics

Maps the normalized 0-1 sentiment score to badge states for the UI.

const getSentimentColor = (score: number) => {
  if (score >= 0.7) return 'text-emerald-600 bg-emerald-100 border-emerald-300'
  if (score >= 0.4) return 'text-blue-600 bg-blue-100 border-blue-300'
  if (score >= 0) return 'text-yellow-600 bg-yellow-100 border-yellow-300'
  return 'text-red-600 bg-red-100 border-red-300'
}

const getSentimentLabel = (score: number) => {
  if (score >= 0.7) return 'Very Bullish'
  if (score >= 0.4) return 'Bullish'
  if (score >= 0) return 'Neutral'
  return 'Bearish'
}

Data Sources

Twitter API v2
Streams crypto hashtag velocity and engagement metadata.
View documentation →
Reddit Pushshift
Historical subreddit sentiment for r/CryptoCurrency and project channels.
View documentation →
Telegram Bot API
Group message deltas for crowd sentiment confirmation.
View documentation →

Open Hooks

useRealTimeData
Normalizes websocket payloads into ticker-keyed sentiment objects.
app/hooks/useRealTimeData.ts
useScrollReveal
Progressively reveals sentiment tiles as the analysis completes.
app/hooks/useScrollReveal.ts

ai portfolio manager

AI Portfolio Manager

Goal-based optimizer with reinforcement signals, risk bands, and tax-loss harvesting.

Launch Demo

View Case Study

Risk-Aware Weighting

Blends ML allocation suggestions with user-configured constraints.

const blendedWeights = useMemo(() => {
  const optimized = aiAllocation.current
  const constraints = userConstraints.current
  return tickers.reduce<Record<string, number>>((acc, symbol) => {
    const target = optimized[symbol] ?? 0
    const floor = constraints[symbol]?.floor ?? 0
    const ceiling = constraints[symbol]?.ceiling ?? 1
    acc[symbol] = clamp(target, floor, ceiling)
    return acc
  }, {})
}, [aiAllocation, userConstraints, tickers])

Data Sources

Polygon Aggregates
Minute bars for multi-asset covariance matrices.
View documentation →
Quandl Treasury Yields
Risk-free curves for modern portfolio theory inputs.
View documentation →
IRS Tax-Loss Guidance
Wash sale windows & tax bracket tables powering compliance checks.
View documentation →

Open Hooks

useAnalyticsData
Feeds simulated benchmark performance for attribution panels.
app/hooks/useAnalyticsData.ts
useAnimationPerformance
Keeps allocation charts interactive on mid-tier GPUs.
app/hooks/useAnimationPerformance.ts

earnings prediction engine

Earnings Prediction Engine

Inference gradients over 200+ factors with surprise probability curves and trade signals.

Launch Demo

View Case Study

Surprise Classification

Turns model logits into human-readable surprise levels and confidence bands.

const surpriseBand = (score: number) => {
  if (score >= 0.7) return { label: 'Likely Beat', tone: 'success' }
  if (score >= 0.55) return { label: 'Modest Beat', tone: 'info' }
  if (score <= 0.3) return { label: 'Likely Miss', tone: 'danger' }
  return { label: 'In-Line', tone: 'neutral' }
}

export function formatSurprise(prediction: EarningsPrediction) {
  const band = surpriseBand(prediction.beatProbability)
  return {
    ...prediction,
    surpriseLabel: band.label,
    tone: band.tone
  }
}

Data Sources

SEC EDGAR
Quarterly reports filtered through the feature engineering pipeline.
View documentation →
Seeking Alpha Transcripts
Contextual sentiment cues for conference call remarks.
View documentation →
Quandl Earnings Calendar
Ticker-level schedule to align inference runs.
View documentation →

Open Hooks

useRealTimeData
Hydrates earnings calendar widgets with live ticker metadata.
app/hooks/useRealTimeData.ts
useScrollReveal
Stages earning cards into view after inference completes.
app/hooks/useScrollReveal.ts

dividend arbitrage scanner

Dividend Arbitrage Scanner

AI-guided dividend capture scoring with borrow constraints, risk gating, and urgency signals.

Launch Demo

View Case Study

Risk & Urgency Badges

Translates risk tolerance and timing windows into actionable UI states.

const getRiskColor = (risk: string) => {
  switch (risk) {
    case 'low': return 'text-emerald-600 bg-emerald-100 border-emerald-300'
    case 'medium': return 'text-blue-600 bg-blue-100 border-blue-300'
    case 'high': return 'text-red-600 bg-red-100 border-red-300'
    default: return 'text-gray-600 bg-gray-100 border-gray-300'
  }
}

const getUrgencyLabel = (days: number) => {
  if (days <= 3) return 'Critical'
  if (days <= 7) return 'High'
  if (days <= 14) return 'Medium'
  return 'Low'
}

Data Sources

Nasdaq Dividend Calendar
Daily ex-dividend schedules with declared amounts and pay dates.
View documentation →
OPRA Options Feed
Options chain data for pricing capture and hedging overlays.
View documentation →
Interactive Brokers Securities Lending
Borrow availability and rate intel for realistic profit modeling.
View documentation →

Open Hooks

useRealTimeData
Streams option and borrow deltas into the dividend opportunity grid.
app/hooks/useRealTimeData.ts
useScrollDirection
Keeps the timing CTA sticky when scrolling long lists of events.
app/hooks/useScrollDirection.ts

esg impact investing platform

ESG Impact Investing Platform

Carbon-aware impact analytics with configurable focus modes and portfolio scoring.

Launch Demo

View Case Study

Weighted ESG Focus

Rebalances pillar weights when investors prioritise E, S, or G outcomes.

const getFocusWeights = () => {
  switch (focus) {
    case 'environmental': return { environmental: 0.5, social: 0.25, governance: 0.25 }
    case 'social': return { environmental: 0.25, social: 0.5, governance: 0.25 }
    case 'governance': return { environmental: 0.25, social: 0.25, governance: 0.5 }
    default: return { environmental: 0.33, social: 0.33, governance: 0.34 }
  }
}

const weightedScore = useMemo(() => {
  if (!selectedData) return 0
  const weights = getFocusWeights()
  return Math.round(
    selectedData.environmental * weights.environmental +
    selectedData.social * weights.social +
    selectedData.governance * weights.governance
  )
}, [selectedData, focus])

Data Sources

MSCI ESG Ratings
Issuer-level ESG pillar scores for public equities.
View documentation →
Sustainalytics Controversy Monitor
Realtime controversy incidents for governance overlays.
View documentation →
CDP Carbon Disclosures
Scope 1/2 emissions and renewable adoption metrics.
View documentation →

Open Hooks

useAnimationPerformance
Keeps animated meter rings smooth while respecting reduced motion.
app/hooks/useAnimationPerformance.ts
useScrollReveal
Progressively reveals focus cards to avoid overwhelming new users.
app/hooks/useScrollReveal.ts

ai options strategy engine

AI Options Strategy Engine

Strategy generation with win-rate telemetry, Greeks, and confidence-adjusted recommendations.

Launch Demo

View Case Study

Outlook Styling Map

Keeps risk posture explicit by mapping strategy outlooks to semantic badges.

const getStrategyColor = (type: string) => {
  switch (type) {
    case 'bullish': return 'text-emerald-600 bg-emerald-100 border-emerald-300'
    case 'bearish': return 'text-red-600 bg-red-100 border-red-300'
    case 'neutral': return 'text-blue-600 bg-blue-100 border-blue-300'
    default: return 'text-gray-600 bg-gray-100 border-gray-300'
  }
}

Data Sources

Cboe Options Data
Live option chains and Greeks for institutional equities.
View documentation →
Polygon Options Snapshot
Bid/ask and IV surfaces powering payoff diagrams.
View documentation →
CME Volatility Indexes
Forward volatility curves for scenario stress tests.
View documentation →

Open Hooks

useRealTimeData
Pumps option chain updates into the strategy cards.
app/hooks/useRealTimeData.ts
useAnimationPerformance
Suspends heavy payoff animations on low-end hardware.
app/hooks/useAnimationPerformance.ts

deep value screener

Deep Value Screener

Benjamin Graham net-net detection with statistical cheapness scoring and safety overlays.

Launch Demo

View Case Study

Graham Score Heuristics

Encodes defensive investor rules into additive scoring bands.

const getGrahamScore = (stock: Stock): number => {
  let score = 0
  if (stock.peRatio < 10) score += 20
  else if (stock.peRatio < 15) score += 10
  if (stock.pbRatio < 1) score += 20
  else if (stock.pbRatio < 1.5) score += 10
  if (stock.currentRatio > 2) score += 15
  else if (stock.currentRatio > 1.5) score += 8
  if (stock.debtToEquity < 0.3) score += 15
  else if (stock.debtToEquity < 0.5) score += 8
  if (stock.roe > 15) score += 15
  else if (stock.roe > 10) score += 8
  const ncav = stock.workingCapital - stock.totalDebt
  if (ncav > stock.marketCap * 0.66) score += 25
  return score
}

Data Sources

SEC 10-K Filings
Primary source for balance sheet strength and working capital inputs.
View documentation →
QuickFS Fundamentals
Normalized financial ratios for screening across global equities.
View documentation →
Value Line Screening
Historical safety ranks to validate defensive picks.
View documentation →

Open Hooks

useSearchV2
Provides fuzzy filtering for tickers, sectors, and factors.
app/hooks/useSearchV2.ts
useScrollDirection
Enables compact filters bar that pins while browsing long tables.
app/hooks/useScrollDirection.ts

portfolio stress testing

Portfolio Stress Testing

Monte Carlo drawdown modeling blended with historical crisis templates and VaR bands.

Launch Demo

View Case Study

Jump-Diffusion Monte Carlo

Adds fat-tail jumps to better approximate crisis day behaviour.

const runMonteCarloSimulation = useMemo(() => {
  return (scenario: HistoricalScenario, simCount: number) => {
    const results: number[] = []
    const riskMultiplier = riskTolerance === 'conservative' ? 0.8 : riskTolerance === 'aggressive' ? 1.3 : 1.0
    for (let i = 0; i < simCount; i++) {
      let value = portfolioValue
      for (let day = 0; day < scenario.duration * 20; day++) {
        const baseVol = 0.02 * scenario.volatilityMultiplier
        const drift = scenario.marketDrawdown / (100 * scenario.duration * 20)
        const jump = Math.random() < (scenario.severity === 'Extreme' ? 0.05 : 0.02)
          ? -Math.abs(normalRandom() * 0.1)
          : 0
        const dailyReturn = drift + normalRandom() * baseVol + jump
        value *= 1 + dailyReturn * riskMultiplier
      }
      results.push(value)
    }
    return results.sort((a, b) => a - b)
  }
}, [portfolioValue, riskTolerance])

Data Sources

FRED Historical Rates
Recession markers and Treasury data for crisis templates.
View documentation →
Stooq Index Histories
Long-horizon market pricing used to backfill scenarios.
View documentation →
Kensho Events DB
Event sets for defining shock magnitudes and durations.
View documentation →

Open Hooks

useRealTimePerformance
Reports FPS during large simulation batches to throttle complexity.
app/hooks/useRealTimePerformance.ts
useAnimationPerformance
Aborts expensive chart transitions when reduced motion is requested.
app/hooks/useAnimationPerformance.ts

advanced ml screener

Advanced ML Value Screener

Ensemble ML scoring across 1,200+ factors with ESG, sentiment, and regime adjustments.

Launch Demo

View Case Study

Ensemble Voting Engine

Weights model outputs by out-of-sample accuracy for a blended ML score.

const computeEnsembleScore = (scores: Record<string, number>): number => {
  const weightSum = Object.keys(scores).reduce((sum, key) => {
    return sum + (ML_MODELS as any)[key].accuracy
  }, 0)
  return Object.entries(scores).reduce((sum, [model, score]) => {
    return sum + score * (ML_MODELS as any)[model].accuracy
  }, 0) / weightSum
}

Data Sources

Polygon Fundamentals
Income statement, balance sheet, and cash-flow series feeding feature engineering.
View documentation →
Koyfin ESG & Sentiment
Alternative datasets augmenting ESG and news-derived features.
View documentation →
Quandl Alt Data
Macroeconomic and industry datasets for macro regime tagging.
View documentation →

Open Hooks

useAnalyticsData
Generates benchmark curves for ensemble vs single-model comparisons.
app/hooks/useAnalyticsData.ts
useAnimationPerformance
Controls heavy GL visualizations in the model gallery.
app/hooks/useAnimationPerformance.ts

epv visualizations

EPV Visualizations

Bruce Greenwald EPV modelling with cyclical adjustments and risk-weighted valuations.

Launch Demo

View Case Study

Cyclical & Risk Adjustments

Calculates adjusted earnings power accounting for cycle and competitive dynamics.

const epvCalculation = useMemo(() => {
  const cyclicalAnalysis = {
    normalizedEarnings: revenue * ebitMargin * cyclicalAdjustment,
    industryPhase: cyclicalAdjustment > 1.1 ? 'peak' :
      cyclicalAdjustment < 0.9 ? 'trough' : 'normal'
  }
  const competitiveAnalysis = {
    moatStrength: competitivePosition,
    sustainabilityScore: (competitivePosition + managementQuality + businessQuality + marketPosition) / 4
  }
  const riskAssessment = {
    businessRisk: (1 - businessQuality) * 0.8 + (industryRisk * 0.2),
    financialRisk: Math.min(0.9, (debtToEquity * 0.3) + (1 / Math.max(1, interestCoverage)) * 0.7)
  }
  return { cyclicalAnalysis, competitiveAnalysis, riskAssessment }
}, [inputs])

Data Sources

S&P Global Capital IQ
Normalized revenue, EBIT, and capital intensity feeds.
View documentation →
Damodaran Cost of Capital
Market risk premiums and WACC reference tables.
View documentation →
OECD Industry Cycles
Cyclical adjustment factors by sector.
View documentation →

Open Hooks

useAnimationPerformance
Adapts heavy GL charts when users prefer reduced motion.
app/hooks/useAnimationPerformance.ts
useRealTimePerformance
Monitors render cost of Monte Carlo surfaces in the dashboard.
app/hooks/useRealTimePerformance.ts

gpu simulation

GPU Simulation Engine

Particle-based WebGL simulation with presets for galaxy, fireworks, and rain environments.

Launch Demo

View Case Study

Particle Integrator

Updates velocity with gravity, mouse fields, and preset-specific behaviours.

particlesRef.current.forEach(particle => {
  if (config.gravity) particle.vy += 0.1 * config.speed
  if (config.simulationPreset === 'galaxy') {
    const dx = centerX - particle.x
    const dy = centerY - particle.y
    const dist = Math.sqrt(dx * dx + dy * dy)
    particle.vx += (dx / dist) * 0.01
    particle.vy += (dy / dist) * 0.01
  }
  particle.x += particle.vx * config.speed
  particle.y += particle.vy * config.speed
})

Data Sources

WebGL Capability DB
GPU feature detection and max texture sizes across browsers.
View documentation →
MDN Device Memory API
Informs adaptive particle counts by device memory hints.
View documentation →
W3C Pointer Events
Pointer events spec powering attract/repel interactions.
View documentation →

Open Hooks

useAnimationPerformance
Detects device performance tiers and toggles trail effects.
app/hooks/useAnimationPerformance.ts
useMotionPreferences
Syncs simulation intensity with prefers-reduced-motion.
app/hooks/useMotionPreferences.ts

neural network

Neural Network Visualization

Interactive training playground with configurable layers, activations, and live metrics.

Launch Demo

View Case Study

Parameter Counter

Keeps the UI aware of total weights and biases as layers change.

const totalParameters = useMemo(() => {
  let params = 0
  let prevLayer = config.inputNodes
  config.hiddenLayers.forEach(layer => {
    params += prevLayer * layer + layer
    prevLayer = layer
  })
  params += prevLayer * config.outputNodes + config.outputNodes
  return params
}, [config])

Data Sources

TensorFlow.js Datasets
Starter datasets for in-browser experimentation.
View documentation →
Kaggle Sample Data
CSV samples for quick supervised learning experiments.
View documentation →
OpenML Repository
Benchmark classification datasets for experimentation.
View documentation →

Open Hooks

useAnimationPerformance
Limits neuron animations while models are training.
app/hooks/useAnimationPerformance.ts
useMotionPreferences
Syncs training animations with system motion preferences.
app/hooks/useMotionPreferences.ts

trading dashboard

Institutional Trading Dashboard

Level II-ready trading cockpit with mock positions, risk controls, and auto-refresh controls.

Launch Demo

View Case Study

Mock Market Generator

Creates realistic ticker states for performance demos without external feeds.

export const generateMockStock = (symbol: string, basePrice: number) => {
  const change = randomInRange(-5, 5)
  const changePercent = change / basePrice * 100
  return {
    symbol,
    name: \`\${symbol} Corporation\`,
    price: basePrice + change,
    change,
    changePercent,
    volume: Math.floor(randomInRange(1_000_000, 50_000_000)),
    marketCap: basePrice * randomInRange(1e9, 5e11),
    bid: basePrice - 0.01,
    ask: basePrice + 0.01
  }
}

Data Sources

Polygon WebSocket
Real-time top-of-book quotes for U.S. equities.
View documentation →
IEX Cloud TOPS
TOB depth for latency-sensitive dashboards.
View documentation →
Tradier Options API
Supplemental options chains for multi-asset views.
View documentation →

Open Hooks

useRealTimeData
Feeds live quotes into positions, order books, and risk panes.
app/hooks/useRealTimeData.ts
useAnimationPerformance
Throttles grid animations when order flow spikes.
app/hooks/useAnimationPerformance.ts

3d visualization

3D Data Visualization Engine

WebGL-powered point clouds, network graphs, and surfaces with adaptive rendering.

Launch Demo

View Case Study

Procedural Point Cloud

Generates clustered 3D datasets for scatter, network, and surface modes.

const generateData = useCallback((type: string, count: number) => {
  const points: DataPoint3D[] = []
  const colors = ['#ff6b35', '#f7931e', '#ffd23f', '#06ffa5', '#1b8ce5', '#8b5cf6']
  if (type === 'scatter') {
    for (let i = 0; i < count; i++) {
      const cluster = Math.floor(Math.random() * 3)
      const angle = Math.random() * Math.PI * 2
      const radius = (Math.random() * 0.5 + 0.5) * (50 + cluster * 30)
      points.push({
        id: \`scatter-\${i}\`,
        x: Math.cos(angle) * radius + (Math.random() - 0.5) * 20,
        y: (Math.random() - 0.5) * 100 + cluster * 40,
        z: Math.sin(angle) * radius + (Math.random() - 0.5) * 20,
        value: Math.random() * 100,
        category: \`Cluster \${cluster + 1}\`,
        color: colors[cluster % colors.length],
        size: Math.random() * 3 + 1
      })
    }
  }
  return points
}, [])

Data Sources

USGS Earthquake API
Latitude/longitude/depth triplets ideal for scatter visualizations.
View documentation →
OpenFlights Route Network
Graph-based international route data for network mode.
View documentation →
NOAA Bathymetry Grids
Surface meshes for geographic height-field rendering.
View documentation →

Open Hooks

useAnimationPerformance
Adjusts particle counts based on detected GPU capabilities.
app/hooks/useAnimationPerformance.ts
useMotionPreferences
Disables auto-rotation for visitors who prefer reduced motion.
app/hooks/useMotionPreferences.ts

live market data

Live Market Data Terminal

Multi-asset streaming terminal with watchlists, alerts, and global news aggregation.

Launch Demo

View Case Study

Streaming Price Simulation

Keeps demo data fresh by perturbing last prices with asset-class volatility.

const simulateMarketData = useCallback(() => {
  setMarketData(prev => {
    const next = new Map(prev)
    prev.forEach((data, symbol) => {
      const lastPrice = lastPricesRef.current.get(symbol) || data.price
      const volatility = data.assetClass === 'crypto' ? 0.002 : 0.001
      const priceChange = (Math.random() - 0.5) * lastPrice * volatility
      const newPrice = Math.max(0.01, lastPrice + priceChange)
      next.set(symbol, {
        ...data,
        price: newPrice,
        change: newPrice - data.previousClose,
        changePercent: ((newPrice - data.previousClose) / data.previousClose) * 100,
        volume: data.volume + Math.floor(Math.random() * 10000),
        high: Math.max(data.high, newPrice),
        low: Math.min(data.low, newPrice),
        timestamp: new Date()
      })
      lastPricesRef.current.set(symbol, newPrice)
    })
    return next
  })
}, [])

Data Sources

Polygon.io Tick Data
Aggregated price and volume feeds across equities, FX, and crypto.
View documentation →
Refinitiv News Stream
Headline feed for contextual alerts around moves.
View documentation →
twelve Data FX
Forex quotes to populate cross-asset watchlists.
View documentation →

Open Hooks

useRealTimeData
Streams live SSE payloads into the market map and alerts table.
app/hooks/useRealTimeData.ts
useAnimationPerformance
Auto-disables heatmap transitions on devices flagged as low power.
app/hooks/useAnimationPerformance.ts

Initializing quantum processors... (0%)

export function calculateQualityDimensions(fm: FinancialMetrics): QualityDimension[] { return DIMENSIONS.map(cfg => { const score = clamp(cfg.scorer(fm), 0, 100) return { name: cfg.name, score, weight: cfg.weight, description: cfg.description, value: cfg.raw(fm), benchmark: cfg.benchmark } }) } export function calculateOverallScore(dimensions: QualityDimension[]): number { const weighted = dimensions.reduce((sum, d) => sum + d.score * (d.weight / 100), 0) return clamp(weighted, 0, 100) }

const METRIC_ICON_MAP: Record<string, React.ComponentType<any>> = { 'total-users': Users, 'page-views': Eye, 'avg-session': Clock, 'bounce-rate': TrendingDown, 'conversion-rate': TrendingUp, revenue: DollarSign, } const attachMetricIcons = (metrics: AnalyticsMetricSnapshot[]): MetricWithIcon[] => metrics.map(metric => ({ ...metric, icon: METRIC_ICON_MAP[metric.id] ?? DEFAULT_METRIC_ICON, }))

const stage1Years = 5 const stage2Years = 5 let dcfValue = 0 for (let year = 1; year <= stage1Years; year++) { const projected = fcf * Math.pow(1 + fcfGrowth, year) dcfValue += projected / Math.pow(1 + discountRate, year) } const stage2Base = fcf * Math.pow(1 + fcfGrowth, stage1Years) for (let year = 1; year <= stage2Years; year++) { const decay = stage2InitialGrowth * (1 - (year - 1) / stage2Years) const projected = stage2Base * Math.pow(1 + terminalGrowthRate + decay, year) dcfValue += projected / Math.pow(1 + discountRate, stage1Years + year) }

const getSentimentColor = (score: number) => { if (score >= 0.7) return 'text-emerald-600 bg-emerald-100 border-emerald-300' if (score >= 0.4) return 'text-blue-600 bg-blue-100 border-blue-300' if (score >= 0) return 'text-yellow-600 bg-yellow-100 border-yellow-300' return 'text-red-600 bg-red-100 border-red-300' } const getSentimentLabel = (score: number) => { if (score >= 0.7) return 'Very Bullish' if (score >= 0.4) return 'Bullish' if (score >= 0) return 'Neutral' return 'Bearish' }

const blendedWeights = useMemo(() => { const optimized = aiAllocation.current const constraints = userConstraints.current return tickers.reduce<Record<string, number>>((acc, symbol) => { const target = optimized[symbol] ?? 0 const floor = constraints[symbol]?.floor ?? 0 const ceiling = constraints[symbol]?.ceiling ?? 1 acc[symbol] = clamp(target, floor, ceiling) return acc }, {}) }, [aiAllocation, userConstraints, tickers])

const surpriseBand = (score: number) => { if (score >= 0.7) return { label: 'Likely Beat', tone: 'success' } if (score >= 0.55) return { label: 'Modest Beat', tone: 'info' } if (score <= 0.3) return { label: 'Likely Miss', tone: 'danger' } return { label: 'In-Line', tone: 'neutral' } } export function formatSurprise(prediction: EarningsPrediction) { const band = surpriseBand(prediction.beatProbability) return { ...prediction, surpriseLabel: band.label, tone: band.tone } }

const getRiskColor = (risk: string) => { switch (risk) { case 'low': return 'text-emerald-600 bg-emerald-100 border-emerald-300' case 'medium': return 'text-blue-600 bg-blue-100 border-blue-300' case 'high': return 'text-red-600 bg-red-100 border-red-300' default: return 'text-gray-600 bg-gray-100 border-gray-300' } } const getUrgencyLabel = (days: number) => { if (days <= 3) return 'Critical' if (days <= 7) return 'High' if (days <= 14) return 'Medium' return 'Low' }

const getFocusWeights = () => { switch (focus) { case 'environmental': return { environmental: 0.5, social: 0.25, governance: 0.25 } case 'social': return { environmental: 0.25, social: 0.5, governance: 0.25 } case 'governance': return { environmental: 0.25, social: 0.25, governance: 0.5 } default: return { environmental: 0.33, social: 0.33, governance: 0.34 } } } const weightedScore = useMemo(() => { if (!selectedData) return 0 const weights = getFocusWeights() return Math.round( selectedData.environmental * weights.environmental + selectedData.social * weights.social + selectedData.governance * weights.governance ) }, [selectedData, focus])

const getStrategyColor = (type: string) => { switch (type) { case 'bullish': return 'text-emerald-600 bg-emerald-100 border-emerald-300' case 'bearish': return 'text-red-600 bg-red-100 border-red-300' case 'neutral': return 'text-blue-600 bg-blue-100 border-blue-300' default: return 'text-gray-600 bg-gray-100 border-gray-300' } }

const getGrahamScore = (stock: Stock): number => { let score = 0 if (stock.peRatio < 10) score += 20 else if (stock.peRatio < 15) score += 10 if (stock.pbRatio < 1) score += 20 else if (stock.pbRatio < 1.5) score += 10 if (stock.currentRatio > 2) score += 15 else if (stock.currentRatio > 1.5) score += 8 if (stock.debtToEquity < 0.3) score += 15 else if (stock.debtToEquity < 0.5) score += 8 if (stock.roe > 15) score += 15 else if (stock.roe > 10) score += 8 const ncav = stock.workingCapital - stock.totalDebt if (ncav > stock.marketCap * 0.66) score += 25 return score }

const computeEnsembleScore = (scores: Record<string, number>): number => { const weightSum = Object.keys(scores).reduce((sum, key) => { return sum + (ML_MODELS as any)[key].accuracy }, 0) return Object.entries(scores).reduce((sum, [model, score]) => { return sum + score * (ML_MODELS as any)[model].accuracy }, 0) / weightSum }

const epvCalculation = useMemo(() => { const cyclicalAnalysis = { normalizedEarnings: revenue * ebitMargin * cyclicalAdjustment, industryPhase: cyclicalAdjustment > 1.1 ? 'peak' : cyclicalAdjustment < 0.9 ? 'trough' : 'normal' } const competitiveAnalysis = { moatStrength: competitivePosition, sustainabilityScore: (competitivePosition + managementQuality + businessQuality + marketPosition) / 4 } const riskAssessment = { businessRisk: (1 - businessQuality) * 0.8 + (industryRisk * 0.2), financialRisk: Math.min(0.9, (debtToEquity * 0.3) + (1 / Math.max(1, interestCoverage)) * 0.7) } return { cyclicalAnalysis, competitiveAnalysis, riskAssessment } }, [inputs])

particlesRef.current.forEach(particle => { if (config.gravity) particle.vy += 0.1 * config.speed if (config.simulationPreset === 'galaxy') { const dx = centerX - particle.x const dy = centerY - particle.y const dist = Math.sqrt(dx * dx + dy * dy) particle.vx += (dx / dist) * 0.01 particle.vy += (dy / dist) * 0.01 } particle.x += particle.vx * config.speed particle.y += particle.vy * config.speed })

const totalParameters = useMemo(() => { let params = 0 let prevLayer = config.inputNodes config.hiddenLayers.forEach(layer => { params += prevLayer * layer + layer prevLayer = layer }) params += prevLayer * config.outputNodes + config.outputNodes return params }, [config])

export const generateMockStock = (symbol: string, basePrice: number) => { const change = randomInRange(-5, 5) const changePercent = change / basePrice * 100 return { symbol, name: \`\${symbol} Corporation\`, price: basePrice + change, change, changePercent, volume: Math.floor(randomInRange(1_000_000, 50_000_000)), marketCap: basePrice * randomInRange(1e9, 5e11), bid: basePrice - 0.01, ask: basePrice + 0.01 } }

const generateData = useCallback((type: string, count: number) => { const points: DataPoint3D[] = [] const colors = ['#ff6b35', '#f7931e', '#ffd23f', '#06ffa5', '#1b8ce5', '#8b5cf6'] if (type === 'scatter') { for (let i = 0; i < count; i++) { const cluster = Math.floor(Math.random() * 3) const angle = Math.random() * Math.PI * 2 const radius = (Math.random() * 0.5 + 0.5) * (50 + cluster * 30) points.push({ id: \`scatter-\${i}\`, x: Math.cos(angle) * radius + (Math.random() - 0.5) * 20, y: (Math.random() - 0.5) * 100 + cluster * 40, z: Math.sin(angle) * radius + (Math.random() - 0.5) * 20, value: Math.random() * 100, category: \`Cluster \${cluster + 1}\`, color: colors[cluster % colors.length], size: Math.random() * 3 + 1 }) } } return points }, [])

const simulateMarketData = useCallback(() => { setMarketData(prev => { const next = new Map(prev) prev.forEach((data, symbol) => { const lastPrice = lastPricesRef.current.get(symbol) || data.price const volatility = data.assetClass === 'crypto' ? 0.002 : 0.001 const priceChange = (Math.random() - 0.5) * lastPrice * volatility const newPrice = Math.max(0.01, lastPrice + priceChange) next.set(symbol, { ...data, price: newPrice, change: newPrice - data.previousClose, changePercent: ((newPrice - data.previousClose) / data.previousClose) * 100, volume: data.volume + Math.floor(Math.random() * 10000), high: Math.max(data.high, newPrice), low: Math.min(data.low, newPrice), timestamp: new Date() }) lastPricesRef.current.set(symbol, newPrice) }) return next }) }, [])