230 lines
11 KiB
Python
230 lines
11 KiB
Python
"""
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Portfolio scenario-exposure — answers "if macro scenario X happens, how does my ACTUAL
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book of open positions react, and how many of my positions are really the same bet wearing
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different tickers?" Distinct from two pre-existing, coarser tools:
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- services.data_fetcher.score_macro_scenarios(): the GLOBAL 8-scenario macro regime,
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qualitative asset-class bias only (bullish/bearish/neutral), used for the top-level
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regime badge — not calibrated to numeric spot shocks and has no gold-bearish or
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forex-directional case, so it can't tell two option positions apart.
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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, so a bullish and a bearish position on the same
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ticker land in the same bucket.
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This module instead reprices each position's REAL legs (same Saxo-first pricing as
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services.portfolio_pricing, used by mark-to-market and the payoff chart) under a small set
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of named spot/vol shocks, so positions on different tickers that both profit from the same
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shock get flagged as the SAME risk bet — e.g. a short S&P call spread, a long gold put
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spread and a short crude call spread can all really be "one Risk-Off bet, three times."
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"""
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from typing import Any, Dict, List, Optional, Tuple
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SCENARIOS: List[Dict[str, str]] = [
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{"key": "risk_off", "label": "Risk-Off / Ralentissement"},
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{"key": "risk_on", "label": "Reprise économique / Risk-On"},
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{"key": "inflation_persistante", "label": "Inflation persistante"},
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{"key": "dollar_fort", "label": "Dollar fort"},
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{"key": "commodities_baisse", "label": "Baisse des matières premières"},
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]
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# asset_class -> scenario_key -> (spot_shock_pct, vol_shock_abs added to the leg's resolved sigma)
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_DIMENSION_SHOCKS: Dict[str, Dict[str, Tuple[float, float]]] = {
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"indices": {
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"risk_off": (-0.08, 0.06), "risk_on": (0.07, -0.03), "inflation_persistante": (-0.05, 0.04),
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"dollar_fort": (-0.02, 0.01), "commodities_baisse": (0.01, -0.01),
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},
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"equities": {
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"risk_off": (-0.08, 0.06), "risk_on": (0.07, -0.03), "inflation_persistante": (-0.05, 0.04),
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"dollar_fort": (-0.02, 0.01), "commodities_baisse": (0.01, -0.01),
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},
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"energy": {
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"risk_off": (-0.10, 0.08), "risk_on": (0.08, -0.04), "inflation_persistante": (0.10, 0.05),
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"dollar_fort": (-0.05, 0.02), "commodities_baisse": (-0.12, 0.03),
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},
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"metals": {
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"risk_off": (0.05, 0.03), "risk_on": (-0.04, -0.02), "inflation_persistante": (0.08, 0.04),
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"dollar_fort": (-0.06, 0.02), "commodities_baisse": (-0.08, 0.02),
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},
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"agriculture": {
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"risk_off": (-0.03, 0.03), "risk_on": (0.03, -0.02), "inflation_persistante": (0.09, 0.04),
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"dollar_fort": (-0.04, 0.01), "commodities_baisse": (-0.10, 0.03),
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},
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"forex": {
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# Expressed as "USD strength" moves — sign is flipped per-pair by _fx_dollar_sign()
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# depending on whether USD is the base or quote currency.
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"risk_off": (0.03, 0.03), "risk_on": (-0.03, -0.02), "inflation_persistante": (-0.02, 0.03),
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"dollar_fort": (0.05, 0.01), "commodities_baisse": (0.01, -0.01),
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},
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"rates": {
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"risk_off": (0.04, 0.02), "risk_on": (-0.03, -0.01), "inflation_persistante": (-0.06, 0.03),
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"dollar_fort": (0.01, 0.01), "commodities_baisse": (0.01, -0.01),
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},
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}
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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 _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, mirroring
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services.portfolio_pricing.compute_payoff's methodology but at ONE target spot instead
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of a curve (no time decay applied — a "if this happened right now" snapshot). Returns
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estimated P&L in currency units, or None if the position has no legs to price."""
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from datetime import date, datetime
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from services.portfolio_pricing import resolve_saxo_chain, price_leg
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from services.options_pricer import black_scholes
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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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legs = pos.get("legs", [])
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if not legs:
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return None
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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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r = 0.05
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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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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 compute_scenario_exposure() -> Dict[str, Any]:
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"""Reprices every open position under each named scenario, then aggregates two views:
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- `scenarios`: per-scenario portfolio-wide estimated P&L (the "sensitivity matrix").
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- `concentration`: for each position, the scenario that would benefit it MOST, then
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the capital-weighted % of the portfolio sharing that same dominant scenario (the
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"X% of your book is really one bet" bars) — the whole point being to surface when
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several differently-named positions are actually the same directional wager.
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"""
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from services.database import get_positions
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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, "scenarios": [], "concentration": [],
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"dominant_scenario": None, "unpriced": [], "warning": None}
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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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dims = _DIMENSION_SHOCKS.get(ac, _DIMENSION_SHOCKS["indices"])
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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 = dims.get(key, (0.0, 0.0))
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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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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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"capital_invested": max(pos.get("capital_invested") or 0, 0),
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"scenario_pnl": scenario_pnl,
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})
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if not priced:
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return {"positions": len(positions), "total_capital": 0, "scenarios": [], "concentration": [],
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"dominant_scenario": None, "unpriced": unpriced, "warning": None}
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total_capital = sum(p["capital_invested"] for p in priced) or 1.0
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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"],
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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["capital_invested"], 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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# Concentration: which scenario is each position's single most favorable outcome?
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weight_by_scenario: Dict[str, float] = {s["key"]: 0.0 for s in SCENARIOS}
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for p in priced:
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best_key = max(p["scenario_pnl"], key=lambda k: (p["scenario_pnl"].get(k) if p["scenario_pnl"].get(k) is not None else float("-inf")))
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weight_by_scenario[best_key] = weight_by_scenario.get(best_key, 0.0) + p["capital_invested"]
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concentration = [
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{"key": key, "label": next(s["label"] for s in SCENARIOS if s["key"] == key),
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"pct_of_portfolio": round(w / total_capital * 100, 1)}
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for key, w in weight_by_scenario.items() if w > 0
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]
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concentration.sort(key=lambda c: -c["pct_of_portfolio"])
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dominant_scenario = concentration[0] if concentration else None
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warning = None
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if dominant_scenario and dominant_scenario["pct_of_portfolio"] >= 60 and len(priced) >= 3:
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warning = (
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f"{dominant_scenario['pct_of_portfolio']:.0f}% du portefeuille gagne surtout dans le même "
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f"scénario ({dominant_scenario['label']}) — vos {len(priced)} positions ne sont pas aussi "
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f"diversifiées qu'il n'y paraît, c'est en grande partie un seul pari macro répété."
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)
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return {
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"positions": len(priced),
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"total_capital": round(total_capital, 2),
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"scenarios": scenario_results,
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"concentration": concentration,
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"dominant_scenario": dominant_scenario,
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"unpriced": unpriced,
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"warning": warning,
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}
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