argus-mcp/argus_first_idea.md

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.

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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.

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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

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3. Core Signal Domains

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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

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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.

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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.

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Interpretation Model

Because of decay and structure:

• absolute values are unreliable
• relative changes are primary signals

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Operational Interpretation

• VXX rising → increasing stress / volatility expectations
• VXX stable or falling → calm / compression
• UVXY spikes → acute stress events

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Role in System

• VXX → baseline volatility regime
• UVXY → shock / spike detector

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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

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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

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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

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4. Signal Interactions

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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

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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

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4.3 Range-Bound / Compression Regime

Characteristics:

• VXX low and stable
• equities sideways
• DXY neutral

Interpretation:

• low volatility environment
• high suitability for grid strategies

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4.4 Speculative Expansion Phase

Characteristics:

• QQQ rising strongly
• VXX low
• ETH outperforming BTC

Interpretation:

• late-stage risk-on
• increased volatility and instability

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5. Design Philosophy

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5.1 Minimalism

A small number of symbols captures a large portion of global state.

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5.2 Orthogonality

Each signal represents a distinct dimension:

• equities → risk
• volatility proxies → stress dynamics
• dollar → liquidity constraint
• credit → funding conditions
• crypto → internal structure

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5.3 Relative Over Absolute

Particularly for volatility proxies:

Changes and momentum matter more than levels.

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5.4 Regime Awareness

The system classifies conditions, not predictions.

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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.

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ADDENDUM: Data Sources (Finnhub & Twelve Data)

A. Finnhub

Role

Realtime signal ingestion.

Key Usage

• QQQ
• SPY
• BTCUSD
• ETHUSD
• VXX / UVXY

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WebSocket Endpoint

wss://ws.finnhub.io?token=YOUR_API_KEY

Subscribe:

{ "type": "subscribe", "symbol": "QQQ" }

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REST Endpoint

https://finnhub.io/api/v1/quote?symbol=QQQ&token=KEY

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Free Tier Limits

• ~60 requests/min
• limited WebSocket subscriptions (~50 symbols practical)

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Notes

• best source for realtime signals
• ETFs used for volatility and macro proxies

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B. Twelve Data

Role

Context and indicator enrichment.

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Key Usage

• DXY (or proxy)
• BTC/USD, ETH/USD indicators
• optional commodities

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Time Series Endpoint

https://api.twelvedata.com/time_series?symbol=DXY&interval=1min&apikey=KEY

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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

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Free Tier Limits

• ~800 requests/day
• ~8 requests/min

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Notes

• broad asset coverage
• built-in indicators reduce computation overhead
• REST-based (no streaming)

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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.