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>

services/common/tests/test_stats.py

@@ -0,0 +1,52 @@
+from __future__ import annotations
+
+import math
+import random
+
+from mcp_common.stats import cointegration_test
+
+
+def test_cointegrated_synthetic_pair():
+    """Costruisco coppia cointegrata: B random walk, A = 2*B + noise stazionario."""
+    r = random.Random(1)
+    b = [100.0]
+    for _ in range(300):
+        b.append(b[-1] + r.gauss(0, 1))
+    a = [2 * b[i] + r.gauss(0, 0.5) for i in range(len(b))]
+    out = cointegration_test(a, b)
+    assert out["cointegrated"] is True
+    assert out["beta"] == pytest_approx(2.0, rel=0.05)
+    assert out["adf_t_stat"] is not None
+    assert out["adf_t_stat"] < -2.86
+
+
+def test_not_cointegrated_independent_walks():
+    """Due random walk indipendenti → spread non stazionario → no cointegration."""
+    r = random.Random(2)
+    a = [100.0]
+    b = [100.0]
+    for _ in range(300):
+        a.append(a[-1] + r.gauss(0, 1))
+        b.append(b[-1] + r.gauss(0, 1))
+    out = cointegration_test(a, b)
+    # Per due RW indipendenti, t-stat ADF è solitamente > -2.86 → non cointegrate
+    assert out["cointegrated"] is False or out["adf_t_stat"] > -3.0
+
+
+def test_cointegration_short_series():
+    out = cointegration_test([1.0, 2.0], [3.0, 4.0])
+    assert out["cointegrated"] is None
+    assert out["beta"] is None
+
+
+def test_cointegration_mismatched_length():
+    out = cointegration_test([1.0, 2.0, 3.0], [1.0, 2.0])
+    assert out["cointegrated"] is None
+
+
+def pytest_approx(value, rel):
+    """Tiny helper to avoid importing pytest just for approx."""
+    class _Approx:
+        def __eq__(self, other):
+            return abs(other - value) <= abs(value) * rel
+    return _Approx()