Macropulse

MacroPulse is a deterministic macro regime engine that classifies markets into Risk-On, Risk-Off, Inflation Shock, or Dollar Stress and turns cross-asset signals into explainable allocation decisions.

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Inspiration

Markets don’t crash or rally randomly — they move through regimes.
Most investors react emotionally and too late. We wanted to build a system that classifies macro environments systematically using cross-asset signals, not headlines.

MacroPulse was inspired by institutional macro desks that combine credit spreads, momentum, inflation expectations, and dollar strength to understand regime shifts — but we made it transparent and accessible.

What it does

MacroPulse is a deterministic macro regime decision engine.

It classifies markets into four regimes:

  • 🟢 Risk-On
  • 🔴 Risk-Off
  • 🟠 Inflation Shock
  • 🔵 Dollar Stress

For each regime, it provides:

  • Allocation recommendations
  • Confidence scoring
  • Explainable trigger conditions
  • Regime shift alerts
  • Backtested performance vs SPY
  • Scenario simulation (Fed hikes, oil shocks, credit stress)

It answers one core question: What regime are we in — and what should we do about it?

How we built it

MacroPulse is built with:

  • React (Vite)
  • Tailwind CSS (dark Bloomberg-style theme)
  • Recharts for interactive financial visualization
  • A fully rule-based JavaScript engine

We separated logic into three deterministic engines:

  • signalEngine.js – Computes normalized macro signals
  • regimeEngine.js – Classifies regimes based on thresholds
  • allocationEngine.js – Generates portfolio weights

No machine learning. No black boxes. Every decision is traceable to a macro signal.

Challenges we ran into

  • Designing regime rules that weren’t overly simplistic or overfit
  • Normalizing cross-asset signals for consistent comparison
  • Building a backtest engine that stayed deterministic
  • Making the UI feel institutional instead of hackathon-grade
  • Handling animated regime alerts cleanly without breaking rendering

The biggest challenge was balancing clarity with realism.

Accomplishments that we're proud of

  • Built a fully explainable macro regime engine
  • Added backtesting with Sharpe ratio and max drawdown
  • Created a regime transition alert system
  • Implemented scenario-based shock simulation
  • Designed an institutional-grade dark fintech UI
  • Structured clean, modular architecture

MacroPulse feels like a quant tool — not just a dashboard.

What we learned

  • Cross-asset relationships tell a clearer story than single indicators
  • Deterministic rule systems can be powerful without ML
  • Explainability builds trust
  • Regime shifts are often visible before narratives catch up
  • Clean architecture matters even in a hackathon

We also learned that presentation and clarity are just as important as technical depth.

What's next for MacroPulse

  • Live data API integration
  • Real-time regime monitoring
  • Volatility regime modeling
  • Risk-parity allocation
  • Regime probability forecasting
  • Institutional API layer
  • Mobile dashboard

Long-term vision:
MacroPulse becomes a systematic macro intelligence layer for investors — retail and institutional alike.

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