411 lines
21 KiB
Python
411 lines
21 KiB
Python
"""
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Portfolio scenario-exposure — answers "which of our 8 macro scenarios is my ACTUAL book of
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open positions really a bet on, and how many differently-named positions are secretly the
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same bet?" Reuses the SAME 8 scenarios as the global macro regime
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(services.data_fetcher.SCENARIO_META / SCENARIO_ASSET_BIAS — goldilocks, desinflation,
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soft_landing, reflation, stagflation, inflation_shock, recession, crise_liquidite) as its
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single source of truth for labels/colors/emoji and directional bias, so this tool can never
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show a scenario name or direction the rest of the app doesn't already agree with.
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Distinct from two other pre-existing, coarser tools:
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- services.data_fetcher.score_macro_scenarios(): scores which of the 8 scenarios the
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CURRENT macro gauges look like (top-down, market-wide) — this module instead scores
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which of the 8 scenarios OUR OWN POSITIONS are betting on (bottom-up, portfolio-wide).
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Same 8 buckets, opposite direction of inference.
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- services.database.get_risk_dashboard()/get_risk_clusters(): buckets capital by
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asset_class and by a geopolitical-trigger keyword match — blind to whether a position
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is long or short its underlying. Kept as a separate, complementary lens.
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v2 methodology (v1 assigned each position to a single "best" scenario via argmax, which
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collapsed multi-dimensional option payoffs into one arbitrary bucket and let ties resolve
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by scenario list order rather than economics):
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- A position "aligns" with a scenario only if repricing it under that scenario's shock
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actually produces a POSITIVE P&L — not merely "the least bad of 8", so a structurally
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one-sided axis (e.g. metals is bullish/neutral in all 8 scenarios, never bearish) no
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longer forces a bearish gold position into a fake "best" scenario; it shows up as
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genuinely unaligned instead.
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- A position's weight is split PROPORTIONALLY across every scenario it aligns with
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(weighted by how much it gains in each), instead of winner-take-all — so a position
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that profits comparably in two scenarios (e.g. Recession and Crise de liquidité often
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carry the same "bearish+" bias for indices) contributes to both instead of an arbitrary
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single pick.
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- Weight itself is the position's actual capital-at-risk (worst point on its real
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at-expiry payoff curve, from services.portfolio_pricing.compute_payoff, already used by
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the payoff-diagram feature) rather than the user-entered capital_invested field, which
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isn't guaranteed to equal true max loss for spreads.
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- Redundant correlated bets (e.g. Crude & Brent short call spreads) are surfaced via an
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"effective N" independent-bets count, reusing services.portfolio_risk's existing
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pairwise-correlation helper — the same diversification math already used by the risk
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radar's Correlation axis.
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- Each position is annotated with its real dollar delta/vega (from
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services.options_pricer.black_scholes, which already computes them on every call — just
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not extracted before now) and its current services.curve_regime classification, so the
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UI can show the position's actual nature instead of guessing from its strategy name.
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The evaluation itself is still NOT an LLM call: same positions in, same numbers out, every
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time — auditable and tied to real strikes/greeks, matching how Curve Regime and the payoff
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diagram already work elsewhere in this app.
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"""
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from typing import Any, Dict, List, Optional, Tuple
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from services.data_fetcher import SCENARIO_META, SCENARIO_ASSET_BIAS
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SCENARIOS: List[Dict[str, str]] = [
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{"key": key, "label": meta["label"], "color": meta["color"], "emoji": meta["emoji"]}
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for key, meta in SCENARIO_META.items()
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]
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# Qualitative bias label (as used in SCENARIO_ASSET_BIAS) -> (spot_shock_pct, vol_shock_abs
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# added to the leg's resolved sigma). The only numeric calibration this module adds on top
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# of the existing qualitative macro-regime table.
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_BIAS_TO_SHOCK: Dict[str, Tuple[float, float]] = {
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"bullish+": (0.08, -0.03),
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"bullish": (0.04, -0.015),
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"neutral": (0.0, 0.0),
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"bearish": (-0.04, 0.015),
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"bearish+": (-0.08, 0.03),
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}
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# SCENARIO_ASSET_BIAS has no "rates" axis (bond ETFs like IEF/TLT) — extended here with the
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# same 8 scenario keys, standard macro logic: bonds rally when yields fall (recession,
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# desinflation, flight-to-quality in a liquidity crisis), sell off when inflation runs hot
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# (reflation, stagflation, inflation shock).
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_RATES_BIAS: Dict[str, str] = {
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"goldilocks": "neutral", "desinflation": "bullish+", "soft_landing": "bullish",
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"reflation": "bearish", "stagflation": "bearish+", "inflation_shock": "bearish+",
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"recession": "bullish+", "crise_liquidite": "bullish",
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}
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# SCENARIO_ASSET_BIAS's forex axis is only ever "neutral" or "defensive" (a flight-to-USD
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# flag, not a direction) — translated here into a dollar-strength shock. "defensive"
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# scenarios push capital into USD (dollar up); "neutral" scenarios leave it flat. The actual
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# sign applied to a given pair (EURUSD falls / USDJPY rises on dollar strength) is resolved
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# per-position by _fx_dollar_sign().
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_FOREX_DEFENSIVE_SHOCK: Tuple[float, float] = (0.04, 0.02)
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_DIRECT_ASSET_CLASSES = {"energy", "metals", "indices", "equities", "agriculture"}
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# Same futures/ETF proxy mapping as frontend/src/pages/Dashboard.tsx's PROXY_TICKER_ALIASES
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# — BNO (Brent ETF, used as the options proxy) isn't itself a Watchlist ticker, BRENT is.
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_CURVE_REGIME_TICKER_ALIASES = {"BNO": "BRENT"}
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def _dimension_shock(asset_class: str, scenario_key: str) -> Tuple[float, float]:
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"""Spot/vol shock for one asset_class under one of the 8 canonical scenarios, derived
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from SCENARIO_ASSET_BIAS wherever that axis exists, with a documented extension for
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rates (not covered by the shared table) and forex (covered only as a non-directional
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flight-to-USD flag there)."""
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bias_row = SCENARIO_ASSET_BIAS[scenario_key]
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if asset_class in _DIRECT_ASSET_CLASSES:
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return _BIAS_TO_SHOCK.get(bias_row.get(asset_class, "neutral"), (0.0, 0.0))
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if asset_class == "rates":
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return _BIAS_TO_SHOCK.get(_RATES_BIAS.get(scenario_key, "neutral"), (0.0, 0.0))
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if asset_class == "forex":
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return _FOREX_DEFENSIVE_SHOCK if bias_row.get("forex") == "defensive" else (0.0, 0.0)
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# Unknown/uncategorized asset_class — fall back to the indices axis (equity-like default).
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return _BIAS_TO_SHOCK.get(bias_row.get("indices", "neutral"), (0.0, 0.0))
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def _fx_dollar_sign(ticker: str) -> int:
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"""+1 if USD is the base currency (pair rises when USD strengthens, e.g. USDJPY),
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-1 if USD is the quote currency (pair falls when USD strengthens, e.g. EURUSD),
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0 for a non-USD cross where the dollar-strength dimension doesn't clearly apply."""
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t = (ticker or "").upper().replace("=X", "").replace("/", "")
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if t.startswith("USD"):
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return 1
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if t.endswith("USD"):
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return -1
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return 0
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def _resolve_position_market(pos: Dict[str, Any]):
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"""Shared market-data resolution (Saxo chain if linked, else yfinance fallback) used by
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both the scenario reprice and the greeks snapshot, so both read the exact same spot/vol
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the payoff chart and mark-to-market already use."""
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from datetime import date, datetime
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from services.portfolio_pricing import resolve_saxo_chain
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from services.data_fetcher import get_quote, compute_historical_iv
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underlying = pos["underlying"]
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expiry_date = pos.get("expiry_date") or ""
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if expiry_date:
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try:
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exp = datetime.strptime(expiry_date[:10], "%Y-%m-%d").date()
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days_remaining = max(0, (exp - date.today()).days)
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except ValueError:
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days_remaining = 0
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else:
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entry = datetime.strptime(pos["entry_date"][:10], "%Y-%m-%d").date()
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days_remaining = max(0, pos.get("expiry_days", 90) - (date.today() - entry).days)
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chain, surface = resolve_saxo_chain(underlying, target_days=max(days_remaining, 1))
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fallback_spot = pos.get("entry_underlying_price") or 100.0
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fallback_sigma = 0.20
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if chain is None:
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q = get_quote(underlying)
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fallback_spot = (q.get("price") if q else None) or fallback_spot
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fallback_sigma = compute_historical_iv(underlying)
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S = chain["spot"] if chain else fallback_spot
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return chain, surface, S, days_remaining, expiry_date, fallback_spot, fallback_sigma
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def _reprice_position(pos: Dict[str, Any], spot_shock_pct: float, vol_shock_abs: float) -> Optional[float]:
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"""Real Black-Scholes reprice of this position's legs at a shocked spot/vol (no time
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decay applied — a "if this happened right now" snapshot). Returns estimated P&L in
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currency units, or None if the position has no legs to price."""
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from services.portfolio_pricing import price_leg
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from services.options_pricer import black_scholes
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legs = pos.get("legs", [])
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if not legs:
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return None
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chain, surface, S, days_remaining, expiry_date, fallback_spot, fallback_sigma = _resolve_position_market(pos)
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r = 0.05
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S_shocked = S * (1 + spot_shock_pct)
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T_remaining = days_remaining / 365
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pnl = -pos.get("ib_fees_entry", 0)
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for leg in legs:
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K = leg.get("strike") or S
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opt_type = leg.get("option_type", "call")
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qty = leg.get("quantity", 1)
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sign = 1 if leg.get("position", "long") == "long" else -1
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priced_now = price_leg(K, opt_type, days_remaining, r, chain, surface, expiry_date, fallback_spot, fallback_sigma)
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entry_premium = leg.get("premium_paid")
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if entry_premium is None:
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entry_premium = priced_now["price"]
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sigma = max(0.01, priced_now["sigma"] + vol_shock_abs)
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if T_remaining > 0:
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shocked_price = black_scholes(S_shocked, K, T_remaining, r, sigma, opt_type)["price"]
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else:
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shocked_price = max(0.0, S_shocked - K) if opt_type == "call" else max(0.0, K - S_shocked)
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pnl += sign * qty * 100 * (shocked_price - entry_premium)
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return pnl
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def _position_greeks(pos: Dict[str, Any]) -> Dict[str, Optional[float]]:
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"""Real position-level dollar delta/vega at the CURRENT (unshocked) market — from
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services.options_pricer.black_scholes, which computes them on every pricing call
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already. Used purely to annotate each position's actual nature (directional vs.
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volatility play), not fed back into the scenario P&L math above."""
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from services.options_pricer import black_scholes
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legs = pos.get("legs", [])
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if not legs:
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return {"delta_dollars": None, "vega_dollars": None, "nature": None}
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_, _, S, days_remaining, _, _, fallback_sigma = _resolve_position_market(pos)
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r = 0.05
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T_remaining = max(days_remaining, 1) / 365
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delta_shares = 0.0
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vega_dollars = 0.0
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for leg in legs:
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K = leg.get("strike") or S
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opt_type = leg.get("option_type", "call")
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qty = leg.get("quantity", 1)
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sign = 1 if leg.get("position", "long") == "long" else -1
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g = black_scholes(S, K, T_remaining, r, fallback_sigma, opt_type)
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mult = sign * qty * 100
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delta_shares += mult * g["delta"]
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vega_dollars += mult * g["vega"]
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delta_dollars = delta_shares * S / 100 # P&L per 1% move in the underlying
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capital = max(pos.get("capital_invested") or 1000.0, 1.0)
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d_impact = abs(delta_dollars) # P&L for a 1% move
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v_impact = abs(vega_dollars) * 3 # P&L for a typical 3-vol-point move
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if v_impact > d_impact * 1.5:
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nature = "Volatilité longue" if vega_dollars > 0 else "Volatilité courte"
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elif d_impact < 0.01 * capital:
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nature = "Neutre / range"
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else:
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nature = "Directionnelle haussière" if delta_dollars > 0 else "Directionnelle baissière"
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return {"delta_dollars": round(delta_dollars, 2), "vega_dollars": round(vega_dollars, 2), "nature": nature}
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def _capital_at_risk(pos: Dict[str, Any]) -> float:
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"""Worst point on the position's real at-expiry payoff curve (±25% spot range,
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services.portfolio_pricing.compute_payoff — same curve the payoff-diagram chart shows)
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as a proxy for capital genuinely at risk, since the user-entered capital_invested field
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isn't guaranteed to equal true max loss for a spread. Falls back to capital_invested if
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the curve never dips negative within that range (e.g. a well-covered structure) or
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can't be computed."""
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try:
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from services.portfolio_pricing import compute_payoff
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curve = compute_payoff(pos).get("at_expiry") or []
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worst = min(curve) if curve else None
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if worst is not None and worst < 0:
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return abs(worst)
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except Exception:
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pass
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return max(pos.get("capital_invested") or 1000.0, 1.0)
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def _curve_regime_lookup() -> Dict[str, str]:
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from services.database import get_all_curve_regime_cache
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out: Dict[str, str] = {}
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for row in get_all_curve_regime_cache():
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if row.get("regime_label"):
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out[row["ticker"].upper()] = row["regime_label"]
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return out
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def compute_scenario_exposure() -> Dict[str, Any]:
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"""Reprices every open position under each of the 8 canonical macro scenarios, then
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builds three complementary views:
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- `scenarios`: per-scenario portfolio-wide estimated P&L (the sensitivity matrix),
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sorted by |impact|, covering every priced position regardless of alignment.
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- `concentration`: capital-at-risk-weighted % of the book aligned with each scenario
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— a position only counts toward a scenario if it genuinely profits there, and its
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weight is split across every scenario it profits in (proportional to the gain), not
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assigned to a single "winner". Percentages need not sum to 100 — the gap is
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`pct_unaligned`, positions that don't profit in ANY of the 8 scenarios.
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- `position_details`: per-position nature (real dollar delta/vega), current Curve
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Regime, and which scenarios it aligns with — the transparency layer behind the
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ranking, so the numbers are inspectable rather than a black box.
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- `effective_n_positions`: correlation-adjusted count of genuinely independent bets
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(services.portfolio_risk's pairwise-correlation helper), since e.g. two positions on
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Crude and Brent are not two independent risks.
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"""
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from services.database import get_positions
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from services.portfolio_risk import _compute_avg_pairwise_correlation
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positions = get_positions("open")
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if not positions:
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return {"positions": 0, "total_capital": 0, "effective_n_positions": 0,
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"avg_pairwise_correlation": None, "scenarios": [], "concentration": [],
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"position_details": [], "dominant_scenario": None, "pct_unaligned": 0,
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"unpriced": [], "warning": None}
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curve_regimes = _curve_regime_lookup()
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priced: List[Dict[str, Any]] = []
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unpriced: List[Dict[str, Any]] = []
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for pos in positions:
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ac = (pos.get("asset_class") or "indices").lower()
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fx_sign = _fx_dollar_sign(pos["underlying"]) if ac == "forex" else 1
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scenario_pnl: Dict[str, Optional[float]] = {}
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for scen in SCENARIOS:
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key = scen["key"]
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spot_shock, vol_shock = _dimension_shock(ac, key)
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if ac == "forex":
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spot_shock = spot_shock * fx_sign
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scenario_pnl[key] = _reprice_position(pos, spot_shock, vol_shock)
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if all(v is None for v in scenario_pnl.values()):
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unpriced.append({"id": pos["id"], "title": pos.get("title", pos["underlying"])})
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continue
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ticker_key = pos["underlying"].upper()
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ticker_key = _CURVE_REGIME_TICKER_ALIASES.get(ticker_key, ticker_key)
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priced.append({
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"id": pos["id"], "title": pos.get("title", pos["underlying"]),
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"underlying": pos["underlying"], "asset_class": ac,
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"risk_weight": _capital_at_risk(pos),
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"scenario_pnl": scenario_pnl,
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"greeks": _position_greeks(pos),
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"curve_regime": curve_regimes.get(ticker_key),
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})
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if not priced:
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return {"positions": len(positions), "total_capital": 0, "effective_n_positions": 0,
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"avg_pairwise_correlation": None, "scenarios": [], "concentration": [],
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"position_details": [], "dominant_scenario": None, "pct_unaligned": 0,
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"unpriced": unpriced, "warning": None}
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total_capital = sum(p["risk_weight"] for p in priced) or 1.0
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# ── Sensitivity matrix — every priced position, every scenario, no alignment filter ──
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scenario_results = []
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for scen in SCENARIOS:
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key = scen["key"]
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total_pnl = sum(p["scenario_pnl"].get(key) or 0 for p in priced)
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scenario_results.append({
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"key": key, "label": scen["label"], "color": scen["color"], "emoji": scen["emoji"],
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"portfolio_pnl": round(total_pnl, 2),
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"portfolio_pnl_pct": round(total_pnl / total_capital * 100, 2),
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"positions": [
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{
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"id": p["id"], "title": p["title"],
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"pnl": round(p["scenario_pnl"].get(key) or 0, 2),
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"pnl_pct": round((p["scenario_pnl"].get(key) or 0) / max(p["risk_weight"], 1) * 100, 1),
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}
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for p in priced
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],
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})
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scenario_results.sort(key=lambda s: -abs(s["portfolio_pnl_pct"]))
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# ── Alignment — proportional split across every genuinely profitable scenario ──
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weight_by_scenario: Dict[str, float] = {s["key"]: 0.0 for s in SCENARIOS}
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position_details = []
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unaligned_weight = 0.0
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for p in priced:
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positive = {k: v for k, v in p["scenario_pnl"].items() if v is not None and v > 0}
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aligned_scenarios = []
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if positive:
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total_positive = sum(positive.values())
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for k, v in sorted(positive.items(), key=lambda kv: -kv[1]):
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share = v / total_positive
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weight_by_scenario[k] += p["risk_weight"] * share
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meta = next(s for s in SCENARIOS if s["key"] == k)
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aligned_scenarios.append({
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"key": k, "label": meta["label"], "color": meta["color"], "emoji": meta["emoji"],
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"share_of_position": round(share * 100, 1),
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"pnl_pct": round(v / max(p["risk_weight"], 1) * 100, 1),
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})
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else:
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unaligned_weight += p["risk_weight"]
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position_details.append({
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"id": p["id"], "title": p["title"], "underlying": p["underlying"],
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"asset_class": p["asset_class"], "risk_weight": round(p["risk_weight"], 2),
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"nature": p["greeks"]["nature"], "delta_dollars": p["greeks"]["delta_dollars"],
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"vega_dollars": p["greeks"]["vega_dollars"], "curve_regime": p["curve_regime"],
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"aligned_scenarios": aligned_scenarios[:2],
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"is_unaligned": not positive,
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})
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concentration = [
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{"key": s["key"], "label": s["label"], "color": s["color"], "emoji": s["emoji"],
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"pct_of_portfolio": round(weight_by_scenario[s["key"]] / total_capital * 100, 1)}
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for s in SCENARIOS if weight_by_scenario[s["key"]] > 0
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]
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concentration.sort(key=lambda c: -c["pct_of_portfolio"])
|
|
dominant_scenario = concentration[0] if concentration else None
|
|
pct_unaligned = round(unaligned_weight / total_capital * 100, 1)
|
|
|
|
underlyings = sorted({p["underlying"] for p in priced})
|
|
avg_corr = _compute_avg_pairwise_correlation(underlyings) if len(underlyings) >= 2 else None
|
|
n = len(priced)
|
|
if avg_corr is not None:
|
|
effective_n = round(n / (1 + (n - 1) * max(avg_corr, 0.0)), 2)
|
|
else:
|
|
effective_n = n
|
|
|
|
warning = None
|
|
if dominant_scenario and dominant_scenario["pct_of_portfolio"] >= 40 and n >= 3:
|
|
warning = (
|
|
f"{dominant_scenario['pct_of_portfolio']:.0f}% du portefeuille (pondéré par capital à "
|
|
f"risque) gagne dans le même scénario ({dominant_scenario['label']})"
|
|
+ (f", avec seulement {effective_n:.1f} paris réellement indépendants sur {n} positions "
|
|
f"(sous-jacents corrélés)" if effective_n < n * 0.7 else "")
|
|
+ " — vos positions ne sont pas aussi diversifiées qu'il n'y paraît."
|
|
)
|
|
elif pct_unaligned >= 25 and n >= 3:
|
|
warning = (
|
|
f"{pct_unaligned:.0f}% du portefeuille ne profite d'aucun des 8 scénarios macro "
|
|
f"(pari structurel non couvert par cette grille de lecture, ex. positions courtes "
|
|
f"sur un actif refuge)."
|
|
)
|
|
|
|
return {
|
|
"positions": n,
|
|
"total_capital": round(total_capital, 2),
|
|
"effective_n_positions": effective_n,
|
|
"avg_pairwise_correlation": round(avg_corr, 3) if avg_corr is not None else None,
|
|
"scenarios": scenario_results,
|
|
"concentration": concentration,
|
|
"position_details": position_details,
|
|
"dominant_scenario": dominant_scenario,
|
|
"pct_unaligned": pct_unaligned,
|
|
"unpriced": unpriced,
|
|
"warning": warning,
|
|
}
|