HMM Regime Detection API for Broad Market ETFs
Quant HQ provides a REST API for inferring latent market regimes across broad-market ETFs using an ensemble of Hidden Markov Models. Easily access statistically filtered long/short signals, regime probabilities, and inferred structural shifts across SPY, QQQ, IWM, and more.
In the chart below:
- The solid blue line represents the performance of a simple risk-on / risk-off strategy applied to the
SPYETF using Quant HQ’s Hidden Markov Model (HMM) Regime Detection API. The strategy increases exposure during favorable regimes and reduces risk during unfavorable ones. - The dotted gray line shows the passive performance of
SPYwithout any regime-based filtering — serving as a benchmark for comparison.
This visual comparison highlights how probabilistic regime modeling can improve return consistency while reducing drawdowns in broad-market ETF strategies.
Backtest Metrics
Disclaimer: The data shown represents hypothetical returns generated from backtesting and is for informational purposes only. Quant HQ does not guarantee any future returns or investment performance. Past performance is not indicative of future results. Please consult a financial advisor before making investment decisions.
Key Features
- Access daily regime probabilities and HMM-inferred trend states
- Supports major ETFs like SPY, QQQ, DIA, IWM, VTI
- Backtested performance signals included in API payloads
- Low-latency JSON responses for seamless integration
- Secure, scalable infrastructure with 99.99% uptime SLA
Integrate in Seconds
Skip the boilerplate — here’s exactly how to make your first API request to the HMM Regime Detection endpoint. Whether you're a quant, algo trader, or building a portfolio product, you can be live in minutes with just a few lines of Python.
|Tip: You’ll receive a clean JSON payload with regime states — ready for backtests, dashboards, or automated execution.