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>
This commit is contained in:
AdrianoDev
@@ -6,6 +6,8 @@ from typing import Any
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import httpx
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from mcp_common import indicators as ind
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from mcp_common import microstructure as micro
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from mcp_common import options as opt
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BASE_LIVE = "https://www.deribit.com/api/v2"
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BASE_TESTNET = "https://test.deribit.com/api/v2"
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@@ -262,6 +264,18 @@ class DeribitClient:
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"data_timestamp": _dt.datetime.now(_dt.UTC).isoformat(),
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}
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async def get_orderbook_imbalance(self, instrument_name: str, depth: int = 10) -> dict:
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"""Microstructure: bid/ask imbalance + microprice + slope su top-N livelli."""
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ob = await self.get_orderbook(instrument_name, depth=max(depth, 10))
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result = micro.orderbook_imbalance(ob.get("bids") or [], ob.get("asks") or [], depth=depth)
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return {
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"instrument_name": instrument_name,
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"depth": depth,
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**result,
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"timestamp": ob.get("timestamp"),
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"testnet": self.testnet,
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}
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async def get_positions(self, currency: str = "USDC") -> list:
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raw = await self._request("private/get_positions", {"currency": currency})
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result = raw.get("result") or []
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@@ -525,6 +539,159 @@ class DeribitClient:
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"testnet": self.testnet,
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}
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async def _fetch_chain_legs(
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self,
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currency: str,
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expiry_from: str | None = None,
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expiry_to: str | None = None,
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top_n_strikes: int = 50,
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) -> tuple[float, list[dict[str, Any]]]:
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"""Fetch chain options + ticker per top-N strikes per OI; restituisce
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(spot, legs[]) con campi normalizzati per le funzioni in mcp_common.options.
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"""
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import asyncio
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currency = currency.upper()
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try:
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idx_tk = await self.get_ticker(f"{currency}-PERPETUAL")
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spot = float(idx_tk.get("mark_price") or 0)
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except Exception:
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spot = 0.0
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chain = await self.get_instruments(
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currency=currency,
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kind="option",
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expiry_from=expiry_from,
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expiry_to=expiry_to,
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limit=2000,
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)
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items = chain.get("instruments", [])
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items.sort(key=lambda x: -(x.get("open_interest") or 0))
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top = items[:top_n_strikes]
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async def _ticker(name: str) -> dict:
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try:
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return await self.get_ticker(name)
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except Exception:
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return {}
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tickers = await asyncio.gather(*[_ticker(i["name"]) for i in top])
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legs: list[dict[str, Any]] = []
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for meta, tk in zip(top, tickers, strict=True):
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greeks = tk.get("greeks") or {}
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legs.append({
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"strike": meta.get("strike"),
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"option_type": meta.get("option_type"),
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"oi": meta.get("open_interest") or 0,
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"iv": tk.get("mark_iv"),
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"delta": greeks.get("delta"),
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"gamma": greeks.get("gamma"),
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"vanna": greeks.get("vanna"),
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"charm": greeks.get("charm"),
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"vega": greeks.get("vega"),
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})
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return spot, legs
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async def get_dealer_gamma_profile(
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self,
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currency: str,
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expiry_from: str | None = None,
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expiry_to: str | None = None,
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top_n_strikes: int = 50,
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) -> dict:
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"""Net dealer gamma per strike (assume dealer short calls/long puts).
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Identifica il gamma flip level: sopra → mercato pinning, sotto → squeeze.
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"""
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import datetime as _dt
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spot, legs = await self._fetch_chain_legs(currency, expiry_from, expiry_to, top_n_strikes)
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result = opt.dealer_gamma_profile(legs, spot)
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return {
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"currency": currency.upper(),
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"spot_price": spot,
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**result,
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"strikes_analyzed": len(result["by_strike"]),
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"data_timestamp": _dt.datetime.now(_dt.UTC).isoformat(),
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"testnet": self.testnet,
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}
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async def get_vanna_charm(
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self,
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currency: str,
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expiry_from: str | None = None,
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expiry_to: str | None = None,
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top_n_strikes: int = 50,
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) -> dict:
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"""Vanna (∂delta/∂IV) e Charm (∂delta/∂t) aggregati pesati per OI.
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Vanna positiva: dealer compra spot quando IV sale.
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Charm negativa: time decay erode delta hedging.
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"""
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import datetime as _dt
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spot, legs = await self._fetch_chain_legs(currency, expiry_from, expiry_to, top_n_strikes)
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result = opt.vanna_charm_aggregate(legs, spot)
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return {
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"currency": currency.upper(),
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**result,
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"data_timestamp": _dt.datetime.now(_dt.UTC).isoformat(),
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"testnet": self.testnet,
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}
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async def get_oi_weighted_skew(
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self,
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currency: str,
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expiry_from: str | None = None,
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expiry_to: str | None = None,
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top_n_strikes: int = 100,
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) -> dict:
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"""Skew aggregato pesato OI: IV media puts - calls. Positivo = paura.
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"""
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import datetime as _dt
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_, legs = await self._fetch_chain_legs(currency, expiry_from, expiry_to, top_n_strikes)
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result = opt.oi_weighted_skew(legs)
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return {
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"currency": currency.upper(),
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**result,
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"data_timestamp": _dt.datetime.now(_dt.UTC).isoformat(),
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"testnet": self.testnet,
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}
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async def get_smile_asymmetry(
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self,
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currency: str,
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expiry_from: str | None = None,
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expiry_to: str | None = None,
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top_n_strikes: int = 100,
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) -> dict:
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"""Asymmetry IV otm-puts vs otm-calls. Positivo = put-side richer."""
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import datetime as _dt
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spot, legs = await self._fetch_chain_legs(currency, expiry_from, expiry_to, top_n_strikes)
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result = opt.smile_asymmetry(legs, spot)
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return {
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"currency": currency.upper(),
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"spot_price": spot,
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**result,
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"data_timestamp": _dt.datetime.now(_dt.UTC).isoformat(),
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"testnet": self.testnet,
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}
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async def get_atm_vs_wings_vol(
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self,
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currency: str,
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expiry_from: str | None = None,
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expiry_to: str | None = None,
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top_n_strikes: int = 100,
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) -> dict:
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"""IV ATM vs IV alle ali 25-delta. wing_richness > 0 → smile (kurtosis)."""
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import datetime as _dt
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spot, legs = await self._fetch_chain_legs(currency, expiry_from, expiry_to, top_n_strikes)
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result = opt.atm_vs_wings_vol(legs, spot)
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return {
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"currency": currency.upper(),
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"spot_price": spot,
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**result,
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"data_timestamp": _dt.datetime.now(_dt.UTC).isoformat(),
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"testnet": self.testnet,
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}
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async def get_pc_ratio(self, currency: str) -> dict:
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import datetime as _dt
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