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