Which integrations are available for this server?

Provides a strategy skill for the CoinMarketCap Agent Hub, enabling agents to fetch BTC market regime and risk posture via the MCP server.

How do I use Market Regime Oracle?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Market Regime Oracle what is the current BTC market regime and posture?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Market Regime Oracle

by aggreyeric

Overview Schema Source Related Servers Discussions Admin

Python

Local

📊 Market Regime Oracle

A 5-signal → 5-state BTC market-regime classifier with posture mapping, backtested vs buy-and-hold.

Fuses momentum, sentiment, volatility, funding & flow into one explainable regime — and a documented risk posture per regime.

Python License: MIT Tests Track MCP

What It Does

Ask "What kind of BTC market is this, and how much risk should I take?"

The oracle answers with one of 5 regimes + a documented posture:

Regime	Target Exposure	Posture
🟢 `RISK_ON`	100%	Uptrend — full exposure
🟡 `RANGE_BOUND`	40%	Sideways — light exposure
🔵 `RISK_OFF`	20%	Downtrend — defensive
🔴 `CAPITULATION`	10%	Panic — max defensive
🟣 `EUPHORIA`	30%	Blow-off — take profit

Built as an MCP Strategy Skill for the CoinMarketCap Agent Hub (BNB AI Trading — Track 2). Any MCP-compatible client (Claude Desktop, Cursor, CMC Agent Hub) calls get_market_regime to get a deterministic, no-look-ahead risk posture.

Related MCP server: PreReason-mcp

📈 Headline Result

In a down year for BTC (−37%), the regime strategy did −12.7% — halving drawdown (−24% vs −51%) and halving volatility (19% vs 43%), outperforming buy-and-hold by ~25 points while still going long in uptrends.

Metric	Regime Strategy	Buy & Hold
Total return	−12.7%	−37.4%
Max drawdown	−24.2%	−51.2%
Annualized volatility	19.3%	43.1%
Sharpe (rf 4%)	−0.82	−0.97
Sortino	−1.01	−1.32

Data: CoinGecko BTC daily (2025-06-19 → 2026-06-17, 364 days). Start $10,000. 10 bps/turnover cost.

🖼️ Visual Results

Equity Curve — Regime Strategy vs Buy & Hold

Drawdown — Strategy Stays Shallower

BTC Price with Regime Overlay

Regime Distribution & Target Exposure

🏗️ Architecture

  CoinGecko (BTC OHLCV)         alternative.me (Fear & Greed)
         │                              │
         └──────────────┬───────────────┘
                        ▼
             ┌─────────────────────┐
             │   data/loader.py    │  aligned daily features
             └─────────┬───────────┘
                       ▼
  ┌──────────────────────────────────────────┐
  │     5 independent signal modules         │  each → score in [-1, +1]
  │  momentum(0.30)  fear_greed(0.25)        │
  │  funding(0.15)   flows(0.15)   vol(0.15) │
  └──────────────────────┬───────────────────┘
                         ▼ weighted fusion
                  composite score
                         ▼ priority rules
  ┌──────────────────────────────────────────┐
  │   5-state regime classifier              │  CAPITULATION > EUPHORIA >
  │   (deterministic, no look-ahead)         │  RISK_OFF   > RISK_ON >
  └──────────────────────┬───────────────────┘                          > RANGE_BOUND
                         ▼
                target exposure + action
           ┌───────────────┴───────────────┐
           ▼                               ▼
    vectorized backtest                MCP tool: get_market_regime
    (no look-ahead, w/ costs)          (stdio, Agent Hub skill)

🧩 The 5 Signals

Signal	Source	Weight
RSI / MACD momentum	CoinGecko price	0.30
Fear & Greed Index	alternative.me	0.25
Volatility regime	CoinGecko price	0.15
Funding rate proxy	derived from price	0.15
Exchange flow proxy	derived from volume	0.15

Each signal outputs a normalized bullishness score in [-1, +1]. All 5 are independently unit-tested.

Transparency: Funding rate and exchange flows have no free public feed. We reconstruct them from price/volume data as clearly-labeled proxies. Drop in real feeds anytime — the fusion layer is signal-agnostic.

🚀 Quick Start

git clone https://github.com/aggreyeric/bnb-market-regime-oracle.git
cd bnb-market-regime-oracle
pip install -r requirements.txt

# Run full pipeline: fetch → classify → backtest → charts
python main.py

# Run tests (offline, no network needed)
PYTHONPATH=src python -m pytest tests/

# Run as MCP server
PYTHONPATH=src python -m market_regime_oracle.mcp_server

# Live MCP demo (30 seconds)
./scripts/demo.sh

Docker

docker compose up --build run    # full pipeline
docker compose up --build server # MCP server

📁 Project Layout

market_regime_oracle/
├── src/market_regime_oracle/
│   ├── data/          # CoinGecko + alternative.me loaders
│   ├── signals/       # 5 signal modules (unit-tested)
│   ├── classifier/    # fusion → regime mapping
│   ├── backtest/      # vectorized engine, no look-ahead
│   ├── viz/           # equity/drawdown/regime charts
│   └── mcp_server.py  # MCP stdio server
├── tests/             # 24/24 passing
├── results/           # CSVs, metrics.json, PNG charts
├── scripts/demo.sh    # live MCP round-trip
├── Dockerfile
├── docker-compose.yml
└── README.md

📚 Data Sources

CoinGecko v3 free API — BTC daily close + volume
alternative.me — Fear & Greed Index

Both public, both free. No API keys required.

📜 License

🤖 AI Assistants

→ See CLAUDE.md for AI coding assistant context.

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