AdrianoDev
a13e3fe045
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>
78 lines
2.4 KiB
Python
78 lines
2.4 KiB
Python
"""Microstructure indicators: orderbook imbalance, slope, microprice.
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Tutte le funzioni accettano bids/asks come list[list[price, qty]] (formato
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standard dei ticker exchange) e ritornano metriche aggregate exchange-agnostic.
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"""
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from __future__ import annotations
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def orderbook_imbalance(
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bids: list[list[float]],
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asks: list[list[float]],
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depth: int = 10,
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) -> dict[str, float | None]:
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"""Imbalance ratio = (bid_vol - ask_vol) / (bid_vol + ask_vol) sui top-`depth`
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livelli. Range [-1, +1]. Positivo = bid pressure, negativo = ask pressure.
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Microprice (Stoll-Bertsimas): mid pesato dalla size opposta
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→ P_micro = (P_bid * Q_ask + P_ask * Q_bid) / (Q_bid + Q_ask). Best level only.
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Slope: variazione cumulata di volume per unità di prezzo (proxy per
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liquidità in profondità).
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"""
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if not bids and not asks:
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return {
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"imbalance_ratio": None,
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"bid_volume": 0.0,
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"ask_volume": 0.0,
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"microprice": None,
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"bid_slope": None,
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"ask_slope": None,
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}
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top_bids = bids[:depth]
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top_asks = asks[:depth]
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bid_vol = sum(q for _, q in top_bids)
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ask_vol = sum(q for _, q in top_asks)
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total = bid_vol + ask_vol
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if total == 0:
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ratio = None
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else:
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ratio = (bid_vol - ask_vol) / total
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# Microprice: best bid, best ask. Weighted by opposite-side size.
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microprice = None
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if top_bids and top_asks:
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bp, bq = top_bids[0]
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ap, aq = top_asks[0]
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denom = bq + aq
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if denom > 0:
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microprice = (bp * aq + ap * bq) / denom
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bid_slope = _depth_slope(top_bids, ascending_price=False)
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ask_slope = _depth_slope(top_asks, ascending_price=True)
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return {
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"imbalance_ratio": ratio,
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"bid_volume": bid_vol,
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"ask_volume": ask_vol,
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"microprice": microprice,
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"bid_slope": bid_slope,
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"ask_slope": ask_slope,
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}
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def _depth_slope(levels: list[list[float]], ascending_price: bool) -> float | None:
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"""Calcola |Δq / Δp| sul primo vs penultimo livello.
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Slope alto = liquidità che crolla rapidamente in profondità (book sottile).
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"""
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if len(levels) < 2:
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return None
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p_first, q_first = levels[0]
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p_last, q_last = levels[-1]
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dp = abs(p_last - p_first)
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if dp == 0:
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return None
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return abs(q_first - q_last) / dp
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