# QuantSandbox System Architecture & Quantitative Roadmap This document serves as the permanent, centralized system architecture design and master context for all future quantitative feature deployments. --- ## 1. Repository Status & Milestone Log ### Completed Phases & Integrated Silos * **Phase 1.0: Portfolio Sandbox** * *Features*: Real-time volatility estimators, portfolio optimization mechanics, and Swamy-Arora random effects panel regression solvers. * *Status*: **Fully Operational (Production Lock)**. * **Phase 2.0: Live GJR-GARCH Scanners** * *Features*: Real-time rolling volatility forecasting engine that detects asymmetric leverage effects in equity volatility. * *Status*: **Fully Operational (Production Lock)**. * **Phase 3.0: Real FRED Macro Ingestion** * *Features*: Real-time server-side API integration with Federal Reserve Economic Data (FRED). Ingests Personal Savings Rates, Credit Card Delinquencies, Housing Starts, and Case-Shiller indices. * *Status*: **Fully Operational (Production Lock)**. * **Phase 4.7: AI & Tech Hyper-Leverage Silo** * *Features*: Track the AI CapEx-Overinvestment Cycle for NVDA, MSFT, GOOGL, META, and AMD. Calculates ROI-to-CapEx (Monetization Gap), Nvidia Supply-Chain Velocity Index, and Tech Infrastructure Leverage with a 60-minute caching layer. * *Status*: **Fully Operational (Production Lock)**. --- ## 2. Master Backlog Architecture: The 6-Level Cockpit Matrix The system tracks and synthesizes ~50 quantitative metrics divided into 6 distinct analytical levels to form a unified Market Regime Classifier. ### Level 1: Macro & Credit Layer (21 Metrics) * **Inflation Vectors**: CPI YoY, Core CPI, PPI. * **Sovereign Yields & Term Structure**: US 10Y Yield, US 2Y Yield, 2S10S Yield Spread, High-Yield Credit Spreads. * **Central Bank Liquidity**: Fed Balance Sheet Assets, ECB Refinancing Rate, Fed Funds Rate, M2 Money Supply, Reverse Repo (RRP) Volumes, Treasury General Account (TGA) levels. * **Macro Capacity**: S&P 500-to-GDP Ratio (Buffett Indicator Proxy). * **Labor Market Dynamics**: Non-Farm Payrolls (NFP), Unemployment Rate, Initial Jobless Claims. * **Housing & Credit Velocity**: Housing Starts, Mortgage Applications Index Proxy, S&P Case-Shiller Home Price Index. * **Consumer Stress Indexes**: Credit Card Delinquency Rates, Personal Savings Rate. ### Level 2: Market Breadth Layer (8 Metrics) * **Moving Average Spreads**: Percentage of S&P 500 constituents trading above their 50-day and 200-day Simple Moving Averages. * **Volume Accumulation**: Cumulative Advance-Decline Line (A/D Line) scaled by volume. * **McClellan Oscillator**: Index tracking short-term momentum shifts in net advances. * **High-Low Index**: Ratio of stocks making new 52-week highs to total new highs/lows. * **Sector Rotational Momentum**: Relative strength vectors of Defensive (XLU, XLP, XLV) vs. Cyclical/Growth (XLK, XLY, XLI) sectors. * **Beta Distribution spreads**: Dispersion of individual constituent betas relative to index beta. ### Level 3: Sentiment & Positioning Flow Layer (7 Metrics) * **Implied Volatility Structures**: VIX, VIX/VVIX term structure spreads. * **Option Flows**: CBOE Equity Put/Call Volume Ratio (10-day moving average). * **Retail Positioning**: AAII Bulls-Bears Spread, margin debt levels in retail brokerage accounts. * **Institutional Positioning**: NAAIM Exposure Index, CFTC Commitments of Traders (COT) net non-commercial positioning in S&P 500 futures. ### Level 4: Corporate Fundamental & Accruals Layer (6 Metrics) * **Accrual Integrity**: Sloan Ratio tracking earnings quality. * **Bankruptcy Probability**: Altman Z-Score for manufacturing and non-manufacturing firms. * **Earnings Manipulation**: Beneish M-Score tracking probability of financial statement manipulation. * **Financial Strength**: Piotroski F-Score (9-point fundamental health checklist). * **Margin Compression Dynamics**: Operating Margin YoY changes, Gross Margin trends. ### Level 5: Technical Momentum & Volatility Layer (5 Metrics) * **Vol Forecasts**: Rolling GJR-GARCH downside volatility forecast vectors. * **Relative Strength**: 14-day Relative Strength Index (RSI). * **Trend Vectors**: MACD Signal Line Spreads. * **Range Expansion**: Average True Range (ATR) normalized by price. * **Beta Expansion Multipliers**: Realized beta shifts in high-beta tech components. ### Level 6: Alternative Data Layer (3 Metrics) * **Supply Chain Disruption**: Supply-Chain Velocity Index (Aggregate buyer purchase obligations vs. hardware supplier inventories). * **Employment Demand**: Tech sector job postings scraped from aggregators. * **Credit Card Transactions**: Real-time consumer retail spending proxies. --- ## 3. Whale Reconnaissance Layer Designed to track the equity holdings of institutional boutique Value and Small-Cap asset managers via SEC Form 13F filings. ```mermaid graph TD A[SEC 13F Filings Ingestion] --> B{Filter Boutique Managers} B -- AUM < $5B & High Active Share --> C[Extract High-Conviction Long Positions] B -- Large Index Funds --> D[Discard] C --> E[Compute Quarterly Position Shifts] E --> F[Generate Whale Satellite-Screener Score] ``` ### Screener Specifications * **Target Universe**: Boutique managers with Asset Under Management (AUM) between $100M and $5B, exhibiting an Active Share $> 80\%$. * **Quant Filters**: 1. **Concentration Index**: Top 10 holdings must exceed $50\%$ of the total reported portfolio value. 2. **Position Size Changes**: Track quarterly additions ($\Delta W_{i} > 2\%$) where the manager is actively building a stake. 3. **Co-ownership Clusters**: Identify stocks bought by 3 or more selected boutique managers simultaneously. --- ## 4. Deep-Dive Corporate Terminal Specifications When evaluating an individual equity ticker, the terminal computes three quantitative risk markers: ### I. The Sloan Ratio (Earnings Quality Indicator) Measures the proportion of earnings backed by non-cash accruals. A high ratio indicates that earnings are driven by accounting accruals rather than real operating cash flows. #### Mathematical Formulation: $$\text{Sloan Ratio} = \frac{\text{Net Income} - \text{Operating Cash Flow} - \text{Investing Cash Flow}}{\text{Total Assets}}$$ #### Alert Thresholds: * **Stable Accruals**: $\le 5\%$ (Green) * **Elevated Accruals**: $5\% < \text{Sloan Ratio} \le 10\%$ (Amber) * **Toxic Accruals (Manipulative Risk)**: $> 10\%$ (Flashing Neon Rose-Red) --- ### II. Analyst Revision Impulse (ARI) Tracks the momentum of consensus earnings estimates over a rolling 14-day window to identify positive or negative structural inflections before earnings reports. #### Mathematical Formulation: $$\text{ARI}_{t} = \sum_{h=1}^{H} \frac{E_{t}(\text{EPS}_{h}) - E_{t-14}(\text{EPS}_{h})}{E_{t-14}(\text{EPS}_{h})}$$ Where: * $E_{t}(\text{EPS}_{h})$ is the consensus EPS estimate at day $t$ for fiscal period $h$. * $H$ represents the number of forward fiscal quarters modeled (standard $H=4$). --- ### III. GJR-GARCH Downside Buffer Calculates the conditional Value-at-Risk (VaR) and Expected Shortfall (ES) at a $99\%$ confidence level over a 10-day forward horizon using volatility projections from Module 1. #### Mathematical Formulation: $$\sigma_{t}^2 = \omega + \left(\alpha + \gamma I_{t-1}\right) \epsilon_{t-1}^2 + \beta \sigma_{t-1}^2$$ $$\text{VaR}_{99\%, 10D} = P_{t} \times \left(1 - e^{z_{0.01} \times \sqrt{10} \times \sigma_{t}}\right)$$ Where: * $I_{t-1} = 1$ if $\epsilon_{t-1} < 0$, and $0$ otherwise (asymmetric shock multiplier). * $z_{0.01}$ is the $1\%$ quantile of the standardized residual distribution (Student-t or Normal). --- ## 5. Multi-Regime Transition Classifier The core cognitive brain of the sandbox dynamically adjusts allocation weights across our portfolio modules based on estimated macroeconomic and market states. ```mermaid graph LR A[Level 1-6 Inputs] --> B[Dynamic Z-Score Solver] B --> C[Markov-Switching Model] C --> D{Regime Output} D -->|Regime 0: Risk-On| E[Overweight Equities/Growth] D -->|Regime 1: Transition| F[Neutral / Hedge overlay] D -->|Regime 2: Risk-Off| G[Overweight Bonds/Cash/Short Vol] ``` ### Model Specifications 1. **Regime Estimation**: A 3-state Markov-Switching Vector Autoregressive (MS-VAR) model classifying the market into: * **Regime 0 (Expansion/Risk-On)**: Low volatility, positive macro surprise, expanding supply-chain velocity. * **Regime 1 (Late-Cycle/Transition)**: Softening breadth, rising credit spreads, negative monetization gaps. * **Regime 2 (Contraction/Risk-Off)**: High realized volatility, yield curve uninversion, consumer savings depletion. 2. **Dynamic Weight Allocation**: $$\mathbf{W}_{t} = s_t \mathbf{W}_{\text{Risk-On}} + (1 - s_t) \mathbf{W}_{\text{Risk-Off}}$$ Where $s_t \in [0, 1]$ represents the filtered probability of being in the expansionary regime at time $t$.