nautilus-spark 0.5.1

India Macro-Momentum Regime Dashboard

🧭 Nautilus

Best free open-source India macro-momentum regime dashboard — powered by yfinance, real RBI data, and a 5-state Gaussian HMM.

100% local · zero cloud cost · zero paid APIs · zero synthetic data.

What it does

• Fetches Nifty 50 prices (^NSEI) and the India 10Y G-Sec yield (NIFTYGS10YR.NS) via yfinance.
• Loads RBI repo rate from a bundled historical CSV (public domain).
• Fits a 5-state Gaussian HMM on price volatility + macro features.
• Applies a risk-gate overlay (not aggressive Kelly scaling) that preserves India's upward drift in bull markets while going cash during confirmed Panic regimes.
• Renders an interactive Streamlit dashboard with regime shading, bond yield panel, Williams VixFix, equity curve backtest, and CSV exports.

Quickstart

git clone https://github.com/chrislernunes/Nautilus
cd Nautilus

python -m venv .venv
# Linux/macOS:
source .venv/bin/activate
# Windows MSYS2 UCRT64:
source .venv/Scripts/activate

pip install -e ".[dev]"
pip install hmmlearn   # enables 5-state HMM

streamlit run src/nautilus/dashboard/regime_dashboard.py

v5 Regime Multipliers

Regime	Multiplier	Rationale
Bull Quiet	1.00×	Full exposure — ride India's drift
Bull Volatile	1.00×	Still bullish directionally
Neutral	0.75×	Mild reduction
Stress	0.35×	Meaningful cut
Panic	0.00×	Cash

Previous v4 used 0.40× for Neutral and 0.15× for Stress — this averaged ~0.55× across bull markets, destroying ~45 pp of return with no compensating benefit. BigBeluga is fully removed.

Project structure

nautilus/
├── data/
│   └── rbi_repo_rate.csv       # Bundled RBI repo rate history (public domain)
├── src/nautilus/
│   ├── config.py               # Paths, tickers, defaults
│   ├── etl/
│   │   ├── loader.py           # yfinance + Parquet cache
│   │   └── macro.py            # Real RBI repo + NIFTYGS10YR.NS bond yield
│   ├── strategies/
│   │   ├── momentum.py         # MA signal, WVF, cross-sectional momentum
│   │   └── regime.py           # HMM fitting + regime containers
│   ├── backtests/
│   │   └── engine.py           # Vectorized backtest (no double-shift)
│   └── dashboard/
│       └── regime_dashboard.py # Streamlit app
└── tests/
    └── test_engine.py          # Unit tests

Data sources

Data	Source	Cost
Nifty 50 prices	^NSEI via yfinance	Free
Stock universe	.NS tickers via yfinance	Free
10Y G-Sec yield	NIFTYGS10YR.NS via yfinance	Free
RBI repo rate	Bundled CSV (RBI press releases, public domain)	Free

No Quandl. No FRED. No Bloomberg. No API keys.

Updating RBI repo rate

After each MPC meeting, add a new row to data/rbi_repo_rate.csv:

2025-06-06,5.75

Then click 🔄 Refresh Data in the sidebar.