Is This Expansion or Slowdown? The Agent Classifies Macro Regimes in Real-Time

# Macro Regime Detector — Daily
name: macro-regime-detector
schedule: "0 14 * * 1-5"  # 2pm UTC (after US data releases)
channel: telegram

task: |
  Classify current macro regime from indicator data.

  INDICATORS:
  1. Yield Curve: 10Y-2Y Treasury spread
  2. PMI: ISM Manufacturing + Services
  3. Unemployment Claims: weekly initial + continuing
  4. Credit Spreads: Investment Grade + High Yield OAS
  5. ISM New Orders vs Inventories ratio

  REGIME DEFINITIONS:
  - EXPANSION: Curve normal, PMI >50 rising, claims falling, spreads tight
  - SLOWDOWN: Curve flattening, PMI >50 but falling, claims flat, spreads widening
  - CONTRACTION: Curve flat/inverted, PMI <50, claims rising, spreads wide
  - RECOVERY: Curve steepening, PMI <50 but rising, claims peaking, spreads tightening

  OUTPUT:
  - Current regime with confidence %
  - Transition probability (if near boundary)
  - Historical asset class performance in this regime
  - Alert on regime change
  - Recommendation for Hari on allocation bias

class MacroRegimeClassifier:
    def __init__(self):
        self.indicators = {}
        self.history = load_regime_history()

    async def update(self):
        self.indicators = {
            "yield_curve": await fred.get("T10Y2Y"),
            "pmi_mfg": await fred.get("ISM_PMI"),
            "pmi_svc": await fred.get("ISM_SVC_PMI"),
            "claims_initial": await fred.get("ICSA"),
            "claims_continuing": await fred.get("CCSA"),
            "ig_spread": await fred.get("BAMLC0A0CM"),
            "hy_spread": await fred.get("BAMLH0A0HYM2"),
            "new_orders": await fred.get("ISM_NEWORD"),
            "inventories": await fred.get("ISM_INVENT"),
        }

    def classify(self):
        scores = {"expansion": 0, "slowdown": 0,
                  "contraction": 0, "recovery": 0}

        # Yield curve signal
        curve = self.indicators["yield_curve"]
        curve_trend = self.trend(curve, 60)  # 60-day trend
        if curve["value"] > 0.5 and curve_trend > 0:
            scores["expansion"] += 2
        elif curve["value"] > 0 and curve_trend < 0:
            scores["slowdown"] += 2
        elif curve["value"] < 0:
            scores["contraction"] += 2
        elif curve["value"] < 0.5 and curve_trend > 0:
            scores["recovery"] += 2

        # Normalize to percentages
        total = sum(scores.values())
        regime_probs = {k: v/total*100 for k, v in scores.items()}

        primary = max(regime_probs, key=regime_probs.get)
        confidence = regime_probs[primary]

        return primary, confidence, regime_probs

    def get_historical_returns(self, regime):
        """What happened to each asset class in this regime historically."""
        return self.history.query(f"regime == '{regime}'").groupby("asset").agg({
            "return_6m": "mean",
            "return_12m": "mean",
            "max_drawdown": "mean"
        })

⚠️ MACRO REGIME TRANSITION

Previous: EXPANSION (held for 14 months)
Current:  SLOWDOWN — Confidence: 72%

INDICATOR BREAKDOWN:
  Yield Curve:   0.18% (was 0.85%) — flattening rapidly ⚠️
  PMI Mfg:       51.2 (was 54.8) — still >50 but falling ⚠️
  PMI Services:  52.1 (was 55.4) — declining ⚠️
  Initial Claims: 242K (was 218K) — trending up ⚠️
  Credit Spreads: IG +142bps (was +98bps) — widening ⚠️
  New Orders/Inventory: 0.98 (was 1.12) — below 1.0 ⚠️

HISTORICAL CONTEXT — Previous Slowdowns:
  Asset          Avg 6mo Return   Max Drawdown
  S&P 500        -4.2%            -12.8%
  Nasdaq         -8.1%            -18.4%
  BTC            -15.3%           -32.1%
  Treasury 10Y   +5.2%            -1.8%
  Gold           +8.4%            -3.2%
  USD Index      +2.1%            -1.4%

RECOMMENDATION TO HARI:
  Reduce equity overweight → neutral
  Reduce crypto allocation by 30%
  Increase cash/treasury allocation
  Consider gold exposure

  Estimated drawdown avoided if slowdown confirmed: 8-12%