QUANTUM BRO — Multi-Factor Strategy Skill for Crypto AI Agents

🏆 Developed for the CoinMarketCap BNB HACK 2026 🎯 Track Focus: Track 2 — Strategy Skills 🤖 Live Telemetry Channel: t.me/quantumbrolive

QUANTUM BRO is a modular, continuous-loop AI Strategy Skill engineered specifically for Web3 ecosystems. Operating autonomously on a cloud VPS architecture, the system isolates live telemetry broadcasts from standardized programmatic pipelines, serving as a composable data-intelligence layer that exposes ready-to-consume decision vectors for downstream automated trading agents.

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📡 Live Output — Running Since June 12, 2026

The bot has been broadcasting autonomously since June 12, 2026 — 1 signal per hour per token across BTC, ETH, BNB, SOL, HYPE.
Live channel: t.me/quantumbrolive

Each signal includes price, regime, sentiment, RSI, composite score, and a 2-sentence AI tactical note generated by DeepSeek:

Sample output (BNB, June 19 2026): 🤖 QuantumBro AI Strategy Agent | $BNB

⏱ Update

📊 Signal: ⬜ HOLD / WAIT ⬜ ⚙️ METRICS

Score: +0.0/100

Price: $577.41

Regime: SIDEWAYS (60%)

Sentiment: NEUTRAL 😐 (+0)

RSI: 44.9 🧠 AI STRATEGY

Hold BNB as the sideways regime and neutral sentiment suggest no

clear directional edge. Wait for a breakout above $600 or a dip

toward support before adding to your position.

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♾️ Ecosystem Composability & BNB Agent SDK

QUANTUM BRO architecture is strictly tailored to meet Track 2 (Strategy Skills) specifications, focusing on interoperability and standard data distribution rather than direct wallet execution.

Architectural Flow:

1. Telemetry Pipeline (main.py): Runs an autonomous hourly loop querying the CoinMarketCap API, evaluating market indicators (Multi-Frame RSI, Sentiment, Regimes), generating LLM tactical notes, and logging real-time telemetry straight to the public Telegram channel.
2. Programmatic Skill Layer (track2_exporter.py): Acts as a clean, decoupled endpoint designed to interface with the CMC Skills Marketplace. It serializes internal analytical scores into standard machine-readable formats.
3. Downstream Execution: The exposed JSON payload is optimized for native ingestion by cross-chain trading systems or automated bots running the BNB AI Agent SDK or Trust Wallet Agent Kit (TWAK).

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🗂️ Project Architecture

quantum_bro/
│
├── .env                    # Secure environment variables (API keys and tokens)
├── main.py                 # Live telemetry loop (Hourly rotation across 5 core assets)
├── track2_exporter.py      # Track 2 JSON standard data vector generator
├── requirements.txt        # Core software dependencies
│
├── data/
│   └── cmc_fetcher.py      # CoinMarketCap API data integration module
│
├── signals/
│   ├── sentiment.py        # Sentiment analysis matrix processor
│   ├── regime.py           # Trend and technical regime detection engine
│   └── rsi_multiframe.py   # Multi-timeframe RSI analytics engine
│
└── strategy/
    └── core.py             # Multi-factor weighting and logic aggregator

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📈 Core Scoring Logic & Multi-Factor Weights

The engine strips human emotion from market environments by computing multi-layered vectors fed from real-time CoinMarketCap statistics:

Intelligence Layer	Ingestion Source	Role in Matrix Strategy
Market Velocity	CoinMarketCap API	Tracks sudden 1h volume anomalies, price action, and caps
Technical Regime	Multi-Frame RSI Engine	Evaluates overbought/oversold boundaries and structural trends
Ecosystem Sentiment	Sentiment Matrix	Acts as a fundamental macro weight for whale behavior

🤖 Standardized Programmatic Skill Output (JSON)

The decoupled track2_exporter.py translates current technical evaluation matrices into an open-standard payload vector, guaranteeing absolute compatibility with external Web3 bots:

{
  "skill": "quantum_bro_multi_factor",
  "timestamp": 1781845200,
  "token_target": "BNB",
  "market_bias": "LONG",
  "confidence_score": 0.85,
  "metrics_snapshot": {
    "execution_mode": "autonomous_telemetry",
    "scoring_matrix": "multi_factor_rsi_regime_sentiment"
  },
  "composability_interface": "BNB AI Agent SDK Compliant"
}

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🎯 CMC Agent Hub Marketplace Manifest

To integrate QUANTUM BRO into the CoinMarketCap Agent Hub platform ecosystem, execution environments can programmatically call our registered endpoints utilizing the following standard configuration layout:

1. Input Schema Parameters (find_skill)

{
  "unique_name": "quantum_bro_multi_factor",
  "description": "Computes high-speed multi-factor structural bias (RSI, regime, and sentiment scoring matrix) over major assets.",
  "input_schema": {
    "type": "object",
    "properties": {
      "token_target": {
        "type": "string",
        "description": "Target cryptocurrency ticker symbol (e.g., BNB, BTC, ETH)",
        "default": "BNB"
      },
      "preview": {
        "type": "boolean",
        "description": "Return rapid structural summary array telemetry data",
        "default": true
      }
    },
    "required": ["token_target"]
  }
}

2. Programmatic Invocation Command (execute_skill)

Downstream execution runtimes can invoke our computational engine pipeline by passing parameters as a structured, unencoded JSON object block:

python track2_exporter.py '{"token_target": "BNB", "preview": true}'

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🧠 LLM-Agnostic Semantic Validation Layer (DeepSeek, Claude, GPT Ready)

QUANTUM BRO does not lock developers into a single AI ecosystem. While our native cloud deployment utilizes DeepSeek-Chat for cost-efficient, high-speed semantic risk validation, the agent_bridge.py module is built entirely on open-standard OpenAI API specifications.

Third-party developers consuming the QUANTUM BRO Skill can easily hot-swap the underlying LLM via environment variables. Whether your downstream architecture requires Anthropic Claude 3.5 Sonnet, OpenAI GPT-4o, or localized open-source models running via Ollama, QUANTUM BRO adapts instantly without changing a single line of core codebase logic.

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🚀 Deployment & Skill Validation

1. Environmental Setup

Populate your .env structure inside the project directory:

CMC_API_KEY="your_coinmarketcap_api_key"
DEEPSEEK_API_KEY="your_deepseek_api_key"
TELEGRAM_BOT_TOKEN="your_telegram_bot_token"
TELEGRAM_CHAT_ID="your_channel_or_chat_id"

2. Launch the Programmatic Skill Validation (Track 2 Evaluation)

To run and test the standardized interface format without disrupting the live instance, execute:

python track2_exporter.py BNB

3. Start the Live Channel Telemetry Daemon

To initiate or log the continuous multi-token background execution stream:

python main.py