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aggreyeric

Market Regime Oracle

by aggreyeric
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📊 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

MIT © 2026

🤖 AI Assistants

→ See CLAUDE.md for AI coding assistant context.

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