178 lines
8.0 KiB
Markdown
178 lines
8.0 KiB
Markdown
# Architect Handover Document (`ARCHITECT_HANDOVER.md`)
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This document outlines the functional state of the Quant Terminal, the mathematical thresholds used by the HSL status triggers, and the structures of active data proxies.
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---
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## 1. Functional State of the Station
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The Quant Terminal operates with 7 primary functional modules mounted in [app/page.tsx](file:///c:/Users/jannr/.gemini/antigravity/scratch/investment-sandbox/app/page.tsx):
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1. **Sandbox** (`activeTab === 'sandbox'`): Solves Swamy-Arora random effects regressions.
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2. **Scanner** (`activeTab === 'scanner'`): Visualizes live asset scanners.
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3. **Insider** (`activeTab === 'insider'`): Tracks corporate insider trades.
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4. **Krypto Bayes** (`activeTab === 'crypto'`): Implements Bayesian on-chain learning models, featuring a Walk-Forward Multi-Model Ensemble Radar (RF, XGB/GB, LR, SVM, MLP) and 15 independent Beta-Posterior trackers.
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5. **Ökonometrie** (`activeTab === 'events'`): Conducts econometric event studies.
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6. **Eco Indicators** (`activeTab === 'macro'`): Monitors 21 FRED macroeconomic and credit indicators.
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7. **AI Special Silo** (`activeTab === 'tech'`): Tracks the tech CapEx overinvestment cycle.
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All endpoints support a 60-minute in-memory caching TTL.
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---
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## 2. Core Mathematical Thresholds & Status Triggers
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Indicators in the cockpit cards use HSL-tailored status lights (Emerald-Green, Amber, Flashing Neon Rose-Red).
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### Macroeconomics Silo (Module 6)
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* **Card 1 (Inflation & Consumer)**:
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* `RED`: CPI Inflation YoY $\ge 3.0\%$, Credit Card Delinquencies $> 4.5\%$, or Personal Savings Rate $< 3.0\%$.
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* `AMBER`: CPI Inflation $\ge 2.5\%$, Credit Card Delinquencies $> 3.5\%$, or Personal Savings Rate $< 4.0\%$.
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* `GREEN`: Normal operations.
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* **Card 2 (Valuation & Liquidity)**:
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* `RED`: Buffett Indicator Ratio $> 150\%$, or M2 money supply trend is `DOWN`.
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* `AMBER`: Buffett Indicator Ratio $> 130\%$, or Reverse Repo (RRP) volume $< 400$B$.
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* `GREEN`: Normal valuation limits.
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* **Card 3 (Yield Curve & Credit)**:
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* `RED`: 2S10S yield spread is inverted ($< 0.0\%$), or High-Yield Credit Spread $> 5.0\%$.
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* `AMBER`: 2S10S spread $< 0.1\%$, or High-Yield Credit Spread $> 4.0\%$.
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* `GREEN`: Positive term structure.
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### AI & Tech Silo (Module 7)
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* **Card 1 (Monetization Gap)**:
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* `RED`: Minimum Monetization Gap among constituents $< -15\%$ (diminishing segment returns).
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* `AMBER`: Minimum Monetization Gap $< 0\%$ (CapEx growth outstripping segment revenue growth).
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* `GREEN`: Positive monetization gap (revenue growing faster than capital expenditures).
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* **Card 2 (Supply-Chain Velocity)**:
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* `RED`: Velocity Index $< 1.8x$, or velocity trend is `DOWN` (buyers reducing forward commitments).
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* `AMBER`: Velocity Index $< 3.0x$ (buyer commitments softening relative to supplier inventory).
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* `GREEN`: Normal supply speeds ($\ge 3.0x$).
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* **Card 3 (Infrastructure Leverage)**:
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* `RED`: Maximum CapEx-to-Depreciation Ratio $> 4.0x$, or maximum Debt-to-Equity Ratio $> 1.2$ (highly leveraged node expansion).
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* `AMBER`: Maximum CapEx-to-Depreciation $> 2.5x$, or maximum Debt-to-Equity $> 0.8$.
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* `GREEN`: Asset expansion supported by low-leverage ratios.
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---
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## 3. Active Data Proxy Models
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### AI Special Silo API Response Schema (`/api/tech/ai`)
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```json
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{
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"dates": ["Q3-24", "Q4-24", "Q1-25", "Q2-25", "Q3-25", "Q4-25", "Q1-26", "Q2-26"],
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"liveDataAvailable": false,
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"timestamp": 1781274890000,
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"metrics": {
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"monetizationGap": {
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"name": "ROI-to-CapEx & Monetization Gap",
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"tickers": {
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"NVDA": {
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"current": -5.3,
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"previous": -4.2,
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"trend": "DOWN",
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"segmentRevenueGrowth": 3.1,
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"capexGrowth": 8.4,
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"roiToCapex": 2.6,
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"data": [
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{ "quarter": "Q3-24", "monetizationGap": 0.0, "roiToCapex": 0.0, "segmentRevenueGrowth": 0.0, "capexGrowth": 0.0 }
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]
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}
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}
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},
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"supplyChain": {
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"name": "Nvidia Supply-Chain Velocity Index",
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"unit": "x",
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"currentVelocity": 2.2,
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"previousVelocity": 2.5,
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"currentTurnover": 5.7,
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"currentObligations": 18700,
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"trend": "DOWN",
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"data": [
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{ "quarter": "Q3-24", "nvdaInvTurnover": 5.2, "aggregateObligations": 14500, "velocityIndex": 3.2 }
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]
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},
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"infrastructure": {
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"name": "Tech Infrastructure Leverage & Cluster Expansion",
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"tickers": {
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"MSFT": {
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"currentDE": 0.3,
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"currentCapExDep": 3.9,
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"trendDE": "UP",
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"data": [
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{ "quarter": "Q3-24", "de": 0.34, "capexDep": 3.2, "debt": 77800, "equity": 228900 }
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]
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}
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}
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}
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}
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}
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```
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### Crypto Multi-Model Ensemble API Schema (`/api/crypto/ensemble`)
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```json
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{
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"isShieldActive": true,
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"predictions": {
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"BTC": {
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"rf": { "T1": 0.62, "T5": 0.58, "T10": 0.54 },
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"gb": { "T1": 0.65, "T5": 0.61, "T10": 0.51 },
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"lr": { "T1": 0.58, "T5": 0.57, "T10": 0.55 },
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"svm": { "T1": 0.60, "T5": 0.59, "T10": 0.56 },
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"mlp": { "T1": 0.64, "T5": 0.60, "T10": 0.53 }
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},
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"ETH": {
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"rf": { "T1": 0.60, "T5": 0.59, "T10": 0.54 },
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"gb": { "T1": 0.66, "T5": 0.61, "T10": 0.48 },
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"lr": { "T1": 0.58, "T5": 0.55, "T10": 0.56 },
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"svm": { "T1": 0.59, "T5": 0.59, "T10": 0.56 },
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"mlp": { "T1": 0.64, "T5": 0.59, "T10": 0.55 }
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},
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"SOL": {
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"rf": { "T1": 0.65, "T5": 0.58, "T10": 0.52 },
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"gb": { "T1": 0.63, "T5": 0.63, "T10": 0.54 },
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"lr": { "T1": 0.59, "T5": 0.58, "T10": 0.54 },
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"svm": { "T1": 0.60, "T5": 0.62, "T10": 0.56 },
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"mlp": { "T1": 0.66, "T5": 0.60, "T10": 0.51 }
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}
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}
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}
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```
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### PEAD Drift Radar API Response Schema (`/api/scanner`)
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```json
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{
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"results": [
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{
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"ticker": "NVDA",
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"name": "NVIDIA Corporation",
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"peadSector": "Technology",
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"announcementDate": "2026-05-20",
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"daysElapsed": 25,
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"epsActual": 6.12,
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"epsConsensus": 5.58,
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"surprisePercent": 9.68,
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"driftStatus": "Active Drift",
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"isLiveApi": true
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}
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],
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"isShieldActive": false
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}
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```
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---
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## 4. Fallback Protection & Operational Transparency Levels
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The workstation enforces zero silent caching or historical data ingestion:
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* **PEAD Drift Radar**: Uses a dynamic state flag `isLivePeadApi` to toggle status rendering.
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* *Green Badge (`🟢 LIVE EPS FEED`)*: FMP API endpoint responded with real-world quarterly reports (`isLiveApi: true`).
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* *Amber Badge (`⚠️ ARCHIV-DATEN (API OFFLINE)`)*: Live API timeout, failure, or developer shield fallback active (`isLiveApi: false`).
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* **Crypto Bayes Module**:
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* *Full-Width Scannability*: Layout structured into 100%-width, centered grids containing the Walk-Forward Ensemble Radar and Active Learning Feedback Loop.
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* *Countdown Formatter*: Remaining seconds under pending targets are formatted to human-readable durations (e.g. `Verbleibend: 1 Tag, 19 Std`) using `formatRemainingTime`.
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* *Accordion Matrix*: Each log row is expandable via Chevron toggle, displaying the individual model prediction direction and success/failure correctness status checkmarks upon resolution.
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* *Multi-Accuracy Tracking*: Shows distinct columns for `T+1 Acc`, `T+5 Acc`, and `T+10 Acc` rather than a single aggregated metric.
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* *Global Performance Metrics Panel*: Mounted below the feedback loop, presenting Horizon Efficiency (Section A) and Estimator Hit Distribution (Section B) dynamically evaluated from `localStorage` logs.
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* **Quant Python Pipeline (`pipeline.py`)**:
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* *Intermarket Sentiment Ingestion*: Fetches daily close values for Nasdaq Composite (`^IXIC`), Gold Spot (`GC=F`), VIX (`^VIX`), and Crypto Fear & Greed (Alternative.me API). Incorporates automatic forward-fill (`ffill()`) and backward-fill (`bfill()`) to process data gaps.
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* *Feature selection gateway*: Restricts features passed to the SVM and MLP estimators to those selected by a Random Forest feature importance selector (`SelectFromModel` with threshold `"mean"`), protecting non-linear algorithms from overfitting.
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