Show semi risk-based open size from ask and unit ratio.

Semi sizing uses market ask with semi units; Plan panel previews option/perp qty under the form.

Co-authored-by: Cursor <cursoragent@cursor.com>

backend/app/strategy/risk_sizing.py

@@ -430,6 +430,8 @@ def compute_risk_sizing(
     db: Database | None = None,
     perp_unit: float | None = None,
     option_unit: float | None = None,
+    exit_unit: float | None = None,
+    leverage_basis: str | None = None,
 ) -> RiskSizingResult:
     database = db or get_db()
     ledger = Ledger(database)
@@ -440,9 +442,16 @@ def compute_risk_sizing(
         perp_u = float(perp_unit)
     if option_unit is not None and float(option_unit) > 0:
         opt_u = float(option_unit)
-    basis_raw = ledger.get_setting_str(
-        "risk_leverage_basis", s.risk_leverage_basis
-    ) or s.risk_leverage_basis
+    if exit_unit is not None and float(exit_unit) > 0:
+        exit_u = float(exit_unit)
+    basis_raw = (
+        leverage_basis
+        if leverage_basis is not None
+        else (
+            ledger.get_setting_str("risk_leverage_basis", s.risk_leverage_basis)
+            or s.risk_leverage_basis
+        )
+    )
     min_opt_lev = ledger.get_setting_float(
         "min_option_leverage", s.min_option_leverage
     )
@@ -690,6 +699,8 @@ def apply_risk_sizing_to_ledger(
     db: Database | None = None,
     perp_unit: float | None = None,
     option_unit: float | None = None,
+    exit_unit: float | None = None,
+    leverage_basis: str | None = None,
 ) -> RiskSizingResult:
     """计算并写入 perp/option/exit；非以损定仓模式直接 ok 跳过。持仓中拒绝改写。"""
     database = db or get_db()
@@ -713,6 +724,8 @@ def apply_risk_sizing_to_ledger(
         db=database,
         perp_unit=perp_unit,
         option_unit=option_unit,
+        exit_unit=exit_unit,
+        leverage_basis=leverage_basis,
     )
     if not r.ok:
         return r
@@ -793,8 +806,38 @@ def preview_risk_sizing(db: Database | None = None) -> dict[str, Any]:
         out["ok"] = False
         out["detail"] = "暂无指数或期权卖一，无法预览"
         return out
-    r = compute_risk_sizing(index_px=float(idx), option_ask=float(ask), db=database)
+
+    # 半自动：用本单配比/净利基数，并以盘口卖一定仓（与开仓一致）
+    semi_perp = semi_opt = semi_exit = None
+    semi_on = False
+    try:
+        from .semi_auto import is_semi_auto, read_semi_params
+
+        if is_semi_auto(ledger):
+            sp = read_semi_params(ledger)
+            semi_on = True
+            semi_perp = float(sp["perp_unit"])
+            semi_opt = float(sp["option_unit"])
+            semi_exit = float(sp["perp_exit_unit"])
+    except Exception:
+        logger.debug("preview semi units skipped", exc_info=True)
+
+    r = compute_risk_sizing(
+        index_px=float(idx),
+        option_ask=float(ask),
+        db=database,
+        perp_unit=semi_perp,
+        option_unit=semi_opt,
+        exit_unit=semi_exit,
+        leverage_basis="actual" if semi_on else None,
+    )
     perp_u, opt_u, exit_u = read_risk_units(ledger)
+    if semi_perp is not None:
+        perp_u = float(semi_perp)
+    if semi_opt is not None:
+        opt_u = float(semi_opt)
+    if semi_exit is not None:
+        exit_u = float(semi_exit)
     mg = resolve_martingale(database, ledger=ledger)
     s = get_settings()
     exit_mode = str(
@@ -806,7 +849,10 @@ def preview_risk_sizing(db: Database | None = None) -> dict[str, Any]:
     )
     exit_target = r.net_profit_target
     exit_label = "基数×k"
-    if exit_mode == "premium_multiple":
+    if semi_on:
+        exit_label = "半自动净利基数×k"
+        exit_mode = "fixed_usdt"
+    elif exit_mode == "premium_multiple":
         exit_label = f"权利金×{prem_mult:g}"
         if r.ok and r.premium_est is not None:
             exit_target = round(float(r.premium_est) * max(0.0, prem_mult), 2)
@@ -833,6 +879,7 @@ def preview_risk_sizing(db: Database | None = None) -> dict[str, Any]:
             "perp_unit": perp_u,
             "option_unit": opt_u,
             "exit_unit": exit_u,
+            "semi_units": semi_on,
             "martingale": mg,
             "risk_effective_loss_pct": mg.get("effective_pct"),
         }