feat: 15 nuovi indicatori quant (common + deribit + bybit + macro + sentiment)

Common (mcp_common): - indicators.py: vol_cone, hurst_exponent, half_life_mean_reversion, garch11_forecast, autocorrelation, rolling_sharpe, var_cvar - options.py (nuovo): oi_weighted_skew, smile_asymmetry, atm_vs_wings_vol, dealer_gamma_profile, vanna_charm_aggregate - microstructure.py (nuovo): orderbook_imbalance (ratio + microprice + slope) - stats.py (nuovo): cointegration_test Engle-Granger + ADF helper Deribit (+6 tool MCP): - get_dealer_gamma_profile (net dealer gamma + flip level) - get_vanna_charm (vanna/charm aggregati pesati OI) - get_oi_weighted_skew, get_smile_asymmetry, get_atm_vs_wings_vol - get_orderbook_imbalance Bybit (+2 tool MCP): - get_orderbook_imbalance, get_basis_term_structure (futures dated curve) Macro (+2 tool MCP): - get_yield_curve_slope (2y10y/5y30y + butterfly + regime) - get_breakeven_inflation (FRED T5YIE/T10YIE/T5YIFR) Sentiment (+3 tool MCP): - get_funding_arb_spread (opportunità arb compatte annualizzate) - get_liquidation_heatmap (heuristic da OI delta + funding extreme, no feed paid Coinglass) - get_cointegration_pairs (Engle-Granger su coppie crypto Binance hourly) Tutto in TDD pure-Python (no numpy/scipy in mcp_common). README aggiornato con elenco completo. 442 test totali verdi. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-27 23:58:07 +02:00
parent 867180f4bf
commit a13e3fe045
21 changed files with 1922 additions and 1 deletions
@@ -159,6 +159,100 @@ async def fetch_treasury_yields() -> dict[str, Any]:
    return out


+def yield_curve_metrics(yields: dict[str, float | None]) -> dict[str, Any]:
+    """Slope + convexity da curva yields (us2y, us5y, us10y, us30y).
+    Convexity (butterfly): 2*us10y - us2y - us30y. >0 = curva concava.
+    """
+    y2 = yields.get("us2y")
+    y5 = yields.get("us5y")
+    y10 = yields.get("us10y")
+    y30 = yields.get("us30y")
+
+    slope_2y10y = (y10 - y2) if (y2 is not None and y10 is not None) else None
+    slope_5y30y = (y30 - y5) if (y5 is not None and y30 is not None) else None
+    butterfly_2_10_30 = (2 * y10 - y2 - y30) if (y2 is not None and y10 is not None and y30 is not None) else None
+
+    regime = "unknown"
+    if slope_2y10y is not None:
+        if slope_2y10y >= 0.5:
+            regime = "steep"
+        elif slope_2y10y > 0.1:
+            regime = "normal"
+        elif slope_2y10y > -0.1:
+            regime = "flat"
+        else:
+            regime = "inverted"
+
+    return {
+        "slope_2y10y": round(slope_2y10y, 3) if slope_2y10y is not None else None,
+        "slope_5y30y": round(slope_5y30y, 3) if slope_5y30y is not None else None,
+        "butterfly_2_10_30": round(butterfly_2_10_30, 3) if butterfly_2_10_30 is not None else None,
+        "regime": regime,
+    }
+
+
+async def fetch_yield_curve_slope() -> dict[str, Any]:
+    """Curve slope/convexity metrics su treasury yields correnti."""
+    base = await fetch_treasury_yields()
+    metrics = yield_curve_metrics(base.get("yields") or {})
+    return {
+        "yields": base.get("yields"),
+        **metrics,
+        "data_timestamp": datetime.now(UTC).isoformat(),
+    }
+
+
+async def fetch_breakeven_inflation(fred_api_key: str = "") -> dict[str, Any]:
+    """Breakeven inflation rate via FRED:
+    - T10YIE (10Y breakeven, market expectation 10Y inflation)
+    - T5YIE (5Y breakeven)
+    - T5YIFR (5Y forward 5Y forward inflation expectation)
+    """
+    if not fred_api_key:
+        return {"error": "No FRED API key configured", "breakevens": {}}
+
+    series_map = {
+        "be_5y": "T5YIE",
+        "be_10y": "T10YIE",
+        "be_5y5y_forward": "T5YIFR",
+    }
+    out: dict[str, float | None] = {}
+    async with httpx.AsyncClient(timeout=10) as client:
+        for name, series_id in series_map.items():
+            resp = await client.get(
+                FRED_BASE,
+                params={
+                    "series_id": series_id,
+                    "api_key": fred_api_key,
+                    "file_type": "json",
+                    "sort_order": "desc",
+                    "limit": 1,
+                },
+            )
+            data = resp.json()
+            obs = data.get("observations", [])
+            try:
+                out[name] = float(obs[0]["value"]) if obs and obs[0]["value"] != "." else None
+            except (ValueError, IndexError, KeyError):
+                out[name] = None
+
+    interpretation = "unknown"
+    be10 = out.get("be_10y")
+    if be10 is not None:
+        if be10 > 3.0:
+            interpretation = "high_inflation_expected"
+        elif be10 < 1.5:
+            interpretation = "low_inflation_expected"
+        else:
+            interpretation = "anchored"
+
+    return {
+        "breakevens": out,
+        "interpretation": interpretation,
+        "data_timestamp": datetime.now(UTC).isoformat(),
+    }
+
+
 async def fetch_equity_futures() -> dict[str, Any]:
    """Fetch ES/NQ/YM/RTY futures con session detection."""
    tickers = [("es", "ES=F"), ("nq", "NQ=F"), ("ym", "YM=F"), ("rty", "RTY=F")]