feat: strategy builder

backend/routers/strategy_builder.py

@@ -171,18 +171,40 @@ def _resolve_terminal_shocks(scenario: "ScenarioIn"):
     )
 
 
-def _build_surfaces(scenario: ScenarioIn):
+def _chain_window_for_legs(scenario: ScenarioIn, legs: Optional[List[Dict[str, Any]]]):
+    """get_chain_slice picks the `n_expiries` expiries CLOSEST to horizon_days, even inside
+    a dte_min/dte_max window — so a calendar/diagonal whose far leg sits well past
+    horizon_days can silently lose that leg's real quote to the trim (confirmed via the
+    strategy_price_debug trace: the far leg's exec_price/mid matched a Black-Scholes+5%-
+    spread FALLBACK price, not its actual bid/ask, because find_quote came up empty against
+    the narrower chain /price had fetched). When we know the exact legs being priced, widen
+    the window to guarantee every one of their expiries survives — no reason to rely on a
+    horizon-proximity heuristic when the expiries are already explicit."""
+    dte_min, dte_max, n_expiries = scenario.dte_min, scenario.dte_max, scenario.n_expiries
+    leg_days = [l["days_to_expiry"] for l in (legs or []) if l.get("option_type") != "stock"]
+    if leg_days:
+        lo, hi = min(leg_days), max(leg_days)
+        dte_min = min(dte_min, lo) if dte_min is not None else lo
+        dte_max = max(dte_max, hi) if dte_max is not None else hi
+        # Enough slots that narrowing-to-window doesn't get re-trimmed by horizon-proximity
+        # sort — same n_expiries=20 the /presets endpoint already uses for this reason.
+        n_expiries = max(n_expiries, 20)
+    return dte_min, dte_max, n_expiries
+
+
+def _build_surfaces(scenario: ScenarioIn, legs: Optional[List[Dict[str, Any]]] = None):
+    dte_min, dte_max, n_expiries = _chain_window_for_legs(scenario, legs)
     chain_slice = get_chain_slice(
-        scenario.symbol, scenario.horizon_days, scenario.n_expiries,
+        scenario.symbol, scenario.horizon_days, n_expiries,
-        dte_min=scenario.dte_min, dte_max=scenario.dte_max, as_of=scenario.as_of,
+        dte_min=dte_min, dte_max=dte_max, as_of=scenario.as_of,
     )
     surface_now = build_surface(chain_slice)
     if scenario.checkpoint_as_of:
         # Real smile-of-the-day, not a hypothesis — same fitting code as surface_now
         # (build_surface), just fed the chain as it stood at the scrubbed-to date.
         checkpoint_chain = get_chain_slice(
-            scenario.symbol, scenario.horizon_days, scenario.n_expiries,
+            scenario.symbol, scenario.horizon_days, n_expiries,
-            dte_min=scenario.dte_min, dte_max=scenario.dte_max, as_of=scenario.checkpoint_as_of,
+            dte_min=dte_min, dte_max=dte_max, as_of=scenario.checkpoint_as_of,
         )
         surface_scenario = build_surface(checkpoint_chain)
     else:
@@ -255,12 +277,12 @@ def price(req: PriceRequest):
     if len(req.legs) > 4:
         raise HTTPException(status_code=400, detail="4 jambes maximum")
 
+    legs = [leg.model_dump() for leg in req.legs]
     try:
-        chain_slice, surface_now, surface_scenario = _build_surfaces(req.scenario)
+        chain_slice, surface_now, surface_scenario = _build_surfaces(req.scenario, legs=legs)
     except ValueError as e:
         raise HTTPException(status_code=404, detail=str(e))
 
-    legs = [leg.model_dump() for leg in req.legs]
     # Paths only drive the day-by-day payoff table, and only make sense for the synthetic
     # parametric scenario — "Analyse période historique" (checkpoint_as_of) prices against
     # a real remembered chain instead, which has no notion of a hypothesized path.