feat: risk

backend/services/portfolio_scenarios.py

@@ -1,7 +1,7 @@
 """
 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?" Deliberately reuses the SAME 8 scenarios as the global macro regime
+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
@@ -14,15 +14,37 @@ Distinct from two other pre-existing, coarser tools:
     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, so a bullish and a bearish position on the same
-    ticker land in the same bucket. Kept as a separate, complementary lens (thematic
-    capital exposure) rather than merged with this one (directional scenario alignment).
+    is long or short its underlying. Kept as a separate, complementary lens.
 
-The evaluation itself is deliberately NOT an LLM call: it reprices each position's REAL legs
-(same Saxo-first pricing as services.portfolio_pricing, used by mark-to-market and the
-payoff chart) under each scenario's spot/vol shock via Black-Scholes. Same positions in,
-same percentages out, every time — auditable and tied to real strikes/greeks, matching how
-Curve Regime and the payoff diagram already work elsewhere in this app.
+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
 
@@ -63,6 +85,10 @@ _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
@@ -92,21 +118,15 @@ def _fx_dollar_sign(ticker: str) -> int:
     return 0
 
 
-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, mirroring
-    services.portfolio_pricing.compute_payoff's methodology but at ONE target spot instead
-    of a curve (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."""
+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, price_leg
-    from services.options_pricer import black_scholes
+    from services.portfolio_pricing import resolve_saxo_chain
     from services.data_fetcher import get_quote, compute_historical_iv
 
     underlying = pos["underlying"]
-    legs = pos.get("legs", [])
-    if not legs:
-        return None
-
     expiry_date = pos.get("expiry_date") or ""
     if expiry_date:
         try:
@@ -118,7 +138,6 @@ def _reprice_position(pos: Dict[str, Any], spot_shock_pct: float, vol_shock_abs:
         entry = datetime.strptime(pos["entry_date"][:10], "%Y-%m-%d").date()
         days_remaining = max(0, pos.get("expiry_days", 90) - (date.today() - entry).days)
 
-    r = 0.05
     chain, surface = resolve_saxo_chain(underlying, target_days=max(days_remaining, 1))
     fallback_spot = pos.get("entry_underlying_price") or 100.0
     fallback_sigma = 0.20
@@ -127,6 +146,21 @@ def _reprice_position(pos: Dict[str, Any], spot_shock_pct: float, vol_shock_abs:
         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
 
@@ -149,23 +183,98 @@ def _reprice_position(pos: Dict[str, Any], spot_shock_pct: float, vol_shock_abs:
     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
-    aggregates two views:
-      - `scenarios`: per-scenario portfolio-wide estimated P&L (the "sensitivity matrix"),
-        sorted by |impact| so the scenarios that matter most float to the top.
-      - `concentration`: for each position, the scenario that would benefit it MOST, then
-        the capital-weighted % of the portfolio sharing that same dominant scenario — the
-        "our book is really one bet, repeated" ranking, using the exact same scenario
-        names/colors as the global macro regime badge.
+    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, "scenarios": [], "concentration": [],
-                "dominant_scenario": None, "unpriced": [], "warning": None}
+        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:
@@ -184,19 +293,26 @@ def compute_scenario_exposure() -> Dict[str, Any]:
             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,
-            "capital_invested": max(pos.get("capital_invested") or 0, 0),
+            "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, "scenarios": [], "concentration": [],
-                "dominant_scenario": None, "unpriced": unpriced, "warning": None}
+        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["capital_invested"] for p in priced) or 1.0
+    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"]
@@ -209,41 +325,86 @@ def compute_scenario_exposure() -> Dict[str, Any]:
                 {
                     "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["capital_invested"], 1) * 100, 1),
+                    "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"]))
 
-    # Concentration: which scenario is each position's single most favorable outcome?
+    # ── 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:
-        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")))
-        weight_by_scenario[best_key] = weight_by_scenario.get(best_key, 0.0) + p["capital_invested"]
+        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": key, **{k: v for k, v in next(s for s in SCENARIOS if s["key"] == key).items() if k != "key"},
-         "pct_of_portfolio": round(w / total_capital * 100, 1)}
-        for key, w in weight_by_scenario.items() if w > 0
+        {"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"] >= 60 and len(priced) >= 3:
+    if dominant_scenario and dominant_scenario["pct_of_portfolio"] >= 40 and n >= 3:
         warning = (
-            f"{dominant_scenario['pct_of_portfolio']:.0f}% du portefeuille gagne surtout dans le même "
-            f"scénario ({dominant_scenario['label']}) — vos {len(priced)} positions ne sont pas aussi "
-            f"diversifiées qu'il n'y paraît, c'est en grande partie un seul pari macro répété."
+            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": len(priced),
+        "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,
     }

frontend/src/pages/RiskDashboard.tsx

@@ -141,6 +141,8 @@ function ScenarioExposureCard() {
 
   const concentration: any[] = exp.concentration ?? []
   const scenarios: any[] = exp.scenarios ?? []
+  const positionDetails: any[] = exp.position_details ?? []
+  const redundant = exp.effective_n_positions != null && exp.effective_n_positions < exp.positions * 0.75
 
   return (
     <div className="card">
@@ -148,9 +150,29 @@ function ScenarioExposureCard() {
         <Layers className="w-4 h-4 text-purple-400" /> Alignement du portefeuille sur le Régime Macro
       </div>
       <div className="text-[10px] text-slate-500 mb-3">
-        Repricing Black-Scholes réel (pricing Saxo-first) de chaque position sous les 8 scénarios du
-        régime macro — révèle quand plusieurs positions différentes sont en réalité le même pari répété.
-        Vue distincte des Risk Factors ci-dessous (thème/classe d'actif touché, pas le sens du pari).
+        Une position ne compte pour un scénario que si elle y gagne réellement (repricing Black-Scholes
+        réel, Saxo-first), répartie entre tous les scénarios où elle est gagnante — pas un vote unique
+        au scénario "le moins pire". Vue distincte des Risk Factors ci-dessous (thème touché, pas le sens du pari).
+      </div>
+
+      {/* Headline stats */}
+      <div className="grid grid-cols-3 gap-2 mb-3">
+        <div className="bg-dark-700/40 rounded px-2 py-1.5 text-center">
+          <div className="text-sm font-bold font-mono text-slate-200">{exp.positions}</div>
+          <div className="text-[9px] text-slate-500">positions</div>
+        </div>
+        <div className="bg-dark-700/40 rounded px-2 py-1.5 text-center">
+          <div className={clsx('text-sm font-bold font-mono', redundant ? 'text-amber-400' : 'text-slate-200')}>
+            {exp.effective_n_positions ?? '—'}
+          </div>
+          <div className="text-[9px] text-slate-500">paris indépendants (N eff.)</div>
+        </div>
+        <div className="bg-dark-700/40 rounded px-2 py-1.5 text-center">
+          <div className={clsx('text-sm font-bold font-mono', exp.pct_unaligned >= 25 ? 'text-amber-400' : 'text-slate-200')}>
+            {exp.pct_unaligned}%
+          </div>
+          <div className="text-[9px] text-slate-500">non couvert par les 8 scénarios</div>
+        </div>
       </div>
 
       {exp.warning && (
@@ -164,25 +186,29 @@ function ScenarioExposureCard() {
         {/* Concentration ranking */}
         <div>
           <div className="text-xs font-semibold text-slate-400 mb-2">
-            % du book dont c'est le scénario le plus favorable
+            % du capital à risque aligné avec chaque scénario
           </div>
-          <div className="space-y-2.5">
-            {concentration.map((c: any) => (
-              <div key={c.key}>
-                <div className="flex justify-between text-xs mb-1">
-                  <span className="text-slate-300 flex items-center gap-1.5">
-                    <span>{c.emoji}</span>{c.label}
-                  </span>
-                  <span className="font-mono font-bold" style={{ color: c.color }}>
-                    {c.pct_of_portfolio}%
-                  </span>
+          {concentration.length === 0 ? (
+            <div className="text-xs text-slate-600">Aucune position ne gagne dans un des 8 scénarios.</div>
+          ) : (
+            <div className="space-y-2.5">
+              {concentration.map((c: any) => (
+                <div key={c.key}>
+                  <div className="flex justify-between text-xs mb-1">
+                    <span className="text-slate-300 flex items-center gap-1.5">
+                      <span>{c.emoji}</span>{c.label}
+                    </span>
+                    <span className="font-mono font-bold" style={{ color: c.color }}>
+                      {c.pct_of_portfolio}%
+                    </span>
+                  </div>
+                  <div className="h-2 bg-dark-700 rounded-full overflow-hidden">
+                    <div className="h-full rounded-full" style={{ width: `${Math.min(c.pct_of_portfolio, 100)}%`, background: c.color }} />
+                  </div>
                 </div>
-                <div className="h-2 bg-dark-700 rounded-full overflow-hidden">
-                  <div className="h-full rounded-full" style={{ width: `${Math.min(c.pct_of_portfolio, 100)}%`, background: c.color }} />
-                </div>
-              </div>
-            ))}
-          </div>
+              ))}
+            </div>
+          )}
         </div>
 
         {/* Sensitivity matrix */}
@@ -206,6 +232,39 @@ function ScenarioExposureCard() {
         </div>
       </div>
 
+      {/* Per-position transparency layer */}
+      {positionDetails.length > 0 && (
+        <div className="mt-4 pt-3 border-t border-slate-700/30">
+          <div className="text-xs font-semibold text-slate-400 mb-2">Détail par position</div>
+          <div className="space-y-1.5">
+            {positionDetails.map((p: any) => (
+              <div key={p.id} className="flex items-center gap-2 text-[11px] bg-dark-700/30 rounded px-2 py-1.5">
+                <span className="text-slate-200 font-medium w-40 truncate shrink-0">{p.title}</span>
+                <span className="text-slate-500 w-32 truncate shrink-0">{p.nature}</span>
+                <span className="text-slate-600 font-mono text-[10px] w-28 shrink-0">
+                  Δ {p.delta_dollars != null ? p.delta_dollars.toFixed(0) : '—'} · v {p.vega_dollars != null ? p.vega_dollars.toFixed(0) : '—'}
+                </span>
+                {p.curve_regime && (
+                  <span className="text-[9px] text-cyan-400/80 bg-cyan-900/20 rounded px-1.5 py-0.5 shrink-0">{p.curve_regime}</span>
+                )}
+                <span className="flex-1 flex items-center gap-1 justify-end flex-wrap">
+                  {p.is_unaligned ? (
+                    <span className="text-[9px] text-slate-600 italic">non aligné (perd dans les 8 scénarios)</span>
+                  ) : (
+                    p.aligned_scenarios.map((a: any) => (
+                      <span key={a.key} className="text-[9px] rounded px-1.5 py-0.5"
+                        style={{ background: `${a.color}22`, color: a.color }}>
+                        {a.emoji} {a.label} {a.share_of_position}%
+                      </span>
+                    ))
+                  )}
+                </span>
+              </div>
+            ))}
+          </div>
+        </div>
+      )}
+
       {exp.unpriced?.length > 0 && (
         <div className="text-[10px] text-slate-600 mt-3 pt-2 border-t border-slate-700/30">
           {exp.unpriced.length} position(s) non pricée(s) (pas de legs/données) : {exp.unpriced.map((u: any) => u.title).join(', ')}