Perfect—that’s the right move. I’ll integrate the volatility proxy logic cleanly into the main paper and attach the API details as a separate addendum. --- # **ARGUS-MCP: A Market Context Sensor System for Crypto Trading (Revised)** ## 1. Purpose **Argus-MCP** is a market context engine designed to observe global financial signals and derive **actionable regime information** for crypto trading systems. It does not predict prices directly. Instead, it determines: > **“What kind of market environment is currently active?”** This enables downstream systems to adapt behavior based on **regime**, not signals alone. --- ## 2. Conceptual Model Crypto markets are modeled as **downstream of global liquidity and risk sentiment**. Argus-MCP constructs a **compressed representation of global state** using a minimal set of cross-market signals. The system acts as a **sensor fusion layer**, combining: * macro risk signals * volatility structure * liquidity constraints * internal crypto dynamics --- ## 3. Core Signal Domains --- ### 3.1 Equity Risk Appetite Primary symbols: * SPY * QQQ Rationale: Equities represent **risk-taking behavior and liquidity availability**. QQQ is emphasized due to: * high sensitivity to liquidity * strong correlation with crypto * concentration of speculative capital Interpretation: * QQQ outperforming SPY → speculative expansion * both rising → broad risk-on * divergence → instability --- ### 3.2 Volatility and Market Stress (Revised) Primary symbols: * VXX * optional: UVXY Rationale: Direct access to the CBOE Volatility Index is often restricted. Instead, Argus-MCP uses **tradable volatility proxies** based on VIX futures. These instruments reflect **market demand for volatility exposure**, which is sufficient—and often advantageous—for detecting stress. --- ### Structural Difference (Critical) Unlike the VIX: * VXX / UVXY are based on **futures**, not options * they exhibit **contango decay** * they are influenced by tradable flows Therefore: > They measure **market stress dynamics**, not absolute implied volatility levels. --- ### Interpretation Model Because of decay and structure: * **absolute values are unreliable** * **relative changes are primary signals** --- ### Operational Interpretation * VXX rising → increasing stress / volatility expectations * VXX stable or falling → calm / compression * UVXY spikes → acute stress events --- ### Role in System * VXX → baseline volatility regime * UVXY → shock / spike detector --- ### 3.3 Currency Pressure (Global Liquidity Constraint) Primary symbol: * DXY (or proxy such as UUP) Rationale: The US dollar acts as a **global liquidity sink**. Crypto is highly sensitive to: * USD strength * global liquidity contraction Interpretation: * DXY rising → tightening conditions → bearish pressure * DXY falling → easing conditions → supportive --- ### 3.4 Credit and Liquidity Stress Primary symbol: * HYG Rationale: High-yield bonds reflect **real credit risk**, often preceding equity stress. Interpretation: * HYG rising → liquidity available * HYG falling → stress building --- ### 3.5 Internal Crypto Structure Primary symbols: * BTCUSD * ETHUSD Rationale: Internal crypto dynamics reveal **capital distribution within the ecosystem**. Interpretation: * ETH outperforming BTC → speculative expansion * BTC dominance → defensive positioning * divergence → internal regime shift --- ## 4. Signal Interactions --- ### 4.1 Liquidity Expansion Regime Characteristics: * QQQ rising * DXY falling * HYG stable or rising * VXX stable or declining Interpretation: * broad liquidity expansion * strong support for crypto --- ### 4.2 Liquidity Contraction / Stress Regime Characteristics: * VXX rising sharply * UVXY spike (optional confirmation) * DXY rising * HYG falling Interpretation: * tightening financial conditions * elevated systemic stress --- ### 4.3 Range-Bound / Compression Regime Characteristics: * VXX low and stable * equities sideways * DXY neutral Interpretation: * low volatility environment * high suitability for grid strategies --- ### 4.4 Speculative Expansion Phase Characteristics: * QQQ rising strongly * VXX low * ETH outperforming BTC Interpretation: * late-stage risk-on * increased volatility and instability --- ## 5. Design Philosophy --- ### 5.1 Minimalism A small number of symbols captures a large portion of global state. --- ### 5.2 Orthogonality Each signal represents a distinct dimension: * equities → risk * volatility proxies → stress dynamics * dollar → liquidity constraint * credit → funding conditions * crypto → internal structure --- ### 5.3 Relative Over Absolute Particularly for volatility proxies: > **Changes and momentum matter more than levels.** --- ### 5.4 Regime Awareness The system classifies **conditions**, not predictions. --- ## 6. Conclusion Argus-MCP models markets as **regime-driven systems shaped by liquidity and stress dynamics**. By using volatility proxies such as VXX and UVXY, it maintains functional awareness of market stress even under data access constraints. Its value lies in: > **accurately interpreting the present environment to guide adaptive behavior.** --- # **ADDENDUM: Data Sources (Finnhub & Twelve Data)** ## A. Finnhub ### Role Realtime signal ingestion. ### Key Usage * QQQ * SPY * BTCUSD * ETHUSD * VXX / UVXY --- ### WebSocket Endpoint ``` wss://ws.finnhub.io?token=YOUR_API_KEY ``` Subscribe: ```json { "type": "subscribe", "symbol": "QQQ" } ``` --- ### REST Endpoint ``` https://finnhub.io/api/v1/quote?symbol=QQQ&token=KEY ``` --- ### Free Tier Limits * ~60 requests/min * limited WebSocket subscriptions (~50 symbols practical) --- ### Notes * best source for realtime signals * ETFs used for volatility and macro proxies --- ## B. Twelve Data ### Role Context and indicator enrichment. --- ### Key Usage * DXY (or proxy) * BTC/USD, ETH/USD indicators * optional commodities --- ### Time Series Endpoint ``` https://api.twelvedata.com/time_series?symbol=DXY&interval=1min&apikey=KEY ``` --- ### Indicator Endpoints RSI: ``` https://api.twelvedata.com/rsi?symbol=BTC/USD&interval=5min&time_period=14&apikey=KEY ``` ATR: ``` https://api.twelvedata.com/atr?symbol=BTC/USD&interval=5min&time_period=14&apikey=KEY ``` --- ### Free Tier Limits * ~800 requests/day * ~8 requests/min --- ### Notes * broad asset coverage * built-in indicators reduce computation overhead * REST-based (no streaming) --- ## Final Integration Summary * Finnhub → **fast, event-driven awareness** * Twelve Data → **slow, contextual understanding** Together they form a **balanced sensing architecture** aligned with Argus-MCP’s design philosophy: > **Fast signals for awareness, slower signals for meaning.**