Files
Cerbero-mcp/services/common/tests/test_options.py
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AdrianoDev a13e3fe045 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

147 lines
4.9 KiB
Python

"""Test puri per mcp_common.options (logiche option-flow indipendenti
dall'exchange).
"""
from __future__ import annotations
import pytest
from mcp_common.options import (
atm_vs_wings_vol,
dealer_gamma_profile,
oi_weighted_skew,
smile_asymmetry,
vanna_charm_aggregate,
)
# ---------- oi_weighted_skew ----------
def test_oi_weighted_skew_balanced():
"""OI distribuito 50/50 calls/puts → skew vicino a 0."""
legs = [
{"iv": 0.5, "delta": 0.5, "oi": 100, "option_type": "call"},
{"iv": 0.5, "delta": -0.5, "oi": 100, "option_type": "put"},
]
out = oi_weighted_skew(legs)
assert abs(out["skew"]) < 0.01
def test_oi_weighted_skew_put_heavy():
"""Put heavy → IV media puts > IV media calls → skew positivo (put > call)."""
legs = [
{"iv": 0.4, "delta": 0.5, "oi": 50, "option_type": "call"},
{"iv": 0.7, "delta": -0.5, "oi": 500, "option_type": "put"},
]
out = oi_weighted_skew(legs)
assert out["skew"] > 0
assert out["call_iv_weighted"] > 0
assert out["put_iv_weighted"] > out["call_iv_weighted"]
def test_oi_weighted_skew_empty():
out = oi_weighted_skew([])
assert out == {"skew": None, "call_iv_weighted": None, "put_iv_weighted": None, "total_oi": 0}
# ---------- smile_asymmetry ----------
def test_smile_asymmetry_symmetric():
"""Smile simmetrico ATM → asymmetry ≈ 0."""
legs = [
{"strike": 80, "iv": 0.55, "option_type": "put"},
{"strike": 90, "iv": 0.50, "option_type": "put"},
{"strike": 100, "iv": 0.45, "option_type": "call"},
{"strike": 110, "iv": 0.50, "option_type": "call"},
{"strike": 120, "iv": 0.55, "option_type": "call"},
]
out = smile_asymmetry(legs, spot=100.0)
assert out["atm_iv"] is not None
assert abs(out["asymmetry"]) < 0.05
def test_smile_asymmetry_put_skew():
"""OTM puts (low strike) IV >> OTM calls (high strike) IV → asymmetry > 0."""
legs = [
{"strike": 80, "iv": 0.80, "option_type": "put"},
{"strike": 100, "iv": 0.50, "option_type": "call"},
{"strike": 120, "iv": 0.45, "option_type": "call"},
]
out = smile_asymmetry(legs, spot=100.0)
assert out["asymmetry"] > 0.1
def test_smile_asymmetry_no_atm():
legs = [{"strike": 200, "iv": 0.5, "option_type": "call"}]
out = smile_asymmetry(legs, spot=100.0)
assert out["atm_iv"] is None
# ---------- atm_vs_wings_vol ----------
def test_atm_vs_wings_vol_basic():
legs = [
{"strike": 90, "iv": 0.55, "delta": -0.25, "option_type": "put"},
{"strike": 100, "iv": 0.45, "delta": 0.5, "option_type": "call"},
{"strike": 110, "iv": 0.50, "delta": 0.25, "option_type": "call"},
]
out = atm_vs_wings_vol(legs, spot=100.0)
assert out["atm_iv"] == pytest.approx(0.45, rel=1e-3)
assert out["wing_25d_call_iv"] == pytest.approx(0.50, rel=1e-3)
assert out["wing_25d_put_iv"] == pytest.approx(0.55, rel=1e-3)
# ATM<wings → richness positiva
assert out["wing_richness"] > 0
def test_atm_vs_wings_vol_no_data():
out = atm_vs_wings_vol([], spot=100.0)
assert out["atm_iv"] is None
# ---------- dealer_gamma_profile ----------
def test_dealer_gamma_profile_assumes_dealer_short_calls():
"""Convention: dealer SHORT calls (sells calls to retail), LONG puts.
Calls oi → negative dealer gamma, puts oi → positive dealer gamma.
"""
legs = [
{"strike": 100, "gamma": 0.01, "oi": 1000, "option_type": "call"},
{"strike": 100, "gamma": 0.01, "oi": 500, "option_type": "put"},
]
out = dealer_gamma_profile(legs, spot=100.0)
# call gamma greater than put gamma at same strike → net dealer short gamma
assert len(out["by_strike"]) == 1
row = out["by_strike"][0]
assert row["call_dealer_gamma"] < 0
assert row["put_dealer_gamma"] > 0
assert row["net_dealer_gamma"] < 0 # calls dominate
assert out["total_net_dealer_gamma"] < 0
def test_dealer_gamma_profile_empty():
out = dealer_gamma_profile([], spot=100.0)
assert out["by_strike"] == []
assert out["total_net_dealer_gamma"] == 0.0
# ---------- vanna_charm_aggregate ----------
def test_vanna_charm_aggregate_basic():
legs = [
{"strike": 100, "vanna": 0.05, "charm": -0.001, "oi": 1000, "option_type": "call"},
{"strike": 100, "vanna": -0.05, "charm": 0.001, "oi": 500, "option_type": "put"},
]
out = vanna_charm_aggregate(legs, spot=100.0)
assert out["total_vanna"] != 0 # some net exposure
assert "total_charm" in out
assert out["legs_analyzed"] == 2
def test_vanna_charm_aggregate_skip_missing_greeks():
legs = [
{"strike": 100, "vanna": None, "charm": -0.001, "oi": 1000, "option_type": "call"},
{"strike": 100, "vanna": 0.05, "charm": None, "oi": 500, "option_type": "put"},
]
out = vanna_charm_aggregate(legs, spot=100.0)
# entrambe le legs hanno almeno una greca None → skippate
assert out["legs_analyzed"] == 0