fix: hk_backtest用真实净值曲线(8槽资金管理)算窗口收益+加exit_date,修正复利失真

deploy/profile-scripts/hk_backtest.py

@@ -96,8 +96,11 @@ def gen_trades(panel, strat):
                 break
         if exit_p is None:
             exit_p, reason, hold = fut.iloc[-1]["close"], "time", maxh
+        # 退出日期（用该股票日历往后推 hold 个交易日）
+        exit_date = fut.iloc[min(hold - 1, len(fut) - 1)]["date"] if hold > 0 else s["date"]
         trades.append({
             "code": s["code"], "name": s["code"], "entry_date": s["date"],
+            "exit_date": exit_date,
             "entry_price": round(ep, 2), "exit_price": round(exit_p, 2),
             "profit_pct": round((exit_p - ep) / ep * 100, 2),
             "exit_reason": reason, "hold_days": hold,
@@ -108,28 +111,94 @@ def gen_trades(panel, strat):
     return trades
 
 
+def portfolio_nav(trades, capital=1000000, slots=8):
+    """8槽资金管理净值曲线：每日结算到期→入场(仓位满跳过)→持仓按成本估值。
+    返回 (nav_series: dict date->nav, stats)"""
+    if not trades:
+        return {}, {}
+    dates = sorted({t["entry_date"] for t in trades} | {t.get("exit_date", t["entry_date"]) for t in trades})
+    if not dates:
+        return {}, {}
+    # 用真实日历（stock_daily 港股日K日期）
+    conn = sqlite3.connect(DB)
+    cal = [r[0] for r in conn.execute(
+        "SELECT DISTINCT date FROM stock_daily WHERE date>=? AND date<=? AND length(code)=5 ORDER BY date",
+        (dates[0], dates[-1])).fetchall()]
+    conn.close()
+    if not cal:
+        cal = dates
+    cal_idx = {d: i for i, d in enumerate(cal)}
+    alloc = capital / slots
+    open_pos = []   # {exit_date, alloc, pnl}
+    cash = capital
+    nav_series = {}
+    skipped = 0
+    by_entry = collections.defaultdict(list)
+    for t in trades:
+        by_entry[t["entry_date"]].append(t)
+    for day in cal:
+        # 结算到期
+        still = []
+        for p in open_pos:
+            if p["exit_date"] <= day:
+                cash += p["alloc"] * (1 + p["pnl"] / 100)
+            else:
+                still.append(p)
+        open_pos = still
+        # 入场
+        for t in by_entry.get(day, []):
+            if len(open_pos) >= slots or cash < alloc:
+                skipped += 1
+                continue
+            open_pos.append({"exit_date": t.get("exit_date", day), "alloc": alloc,
+                             "pnl": t["profit_pct"]})
+            cash -= alloc
+        # 净值
+        held_val = sum(p["alloc"] * (1 + p["pnl"] / 100) for p in open_pos)
+        nav_series[day] = cash + held_val
+    nav_series = {d: v for d, v in sorted(nav_series.items())}
+    return nav_series, {"skipped": skipped}
+
+
+def window_returns(nav_series):
+    """从净值曲线算窗口收益（近1年/6月/3月，对照最后日期）"""
+    if not nav_series:
+        return {"year1": None, "month6": None, "month3": None}
+    items = sorted(nav_series.items())
+    last_d, last_v = items[-1]
+    last_dt = datetime.strptime(last_d, "%Y-%m-%d")
+    out = {}
+    for label, days in [("year1", 365), ("month6", 182), ("month3", 91)]:
+        cutoff = (last_dt - timedelta(days=days)).strftime("%Y-%m-%d")
+        # 取 cutoff 后最近的净值点
+        base = None
+        for d, v in items:
+            if d >= cutoff:
+                base = v
+                break
+        out[label] = (last_v / base - 1) * 100 if base else None
+    return out
+
+
 def portfolio_metrics(trades, capital=1000000, slots=8):
-    """资金模拟 + 时间窗收益（Ralph Loop验收标准）"""
+    """资金模拟 + 窗口收益（净值曲线）"""
-    import strategy_lab as lab
+    nav, stats = portfolio_nav(trades, capital, slots)
-    pf = lab.portfolio_sim(trades, capital, max_positions=slots)
+    win = window_returns(nav)
-    years_span = 7.5
+    # 年化（用首末净值）
-    cagr = pf.get("cagr_pct")
+    cagr = None
-    # 时间窗收益（按 entry_date 过滤 trades，8槽等权复利净值）
+    if nav:
-    def window_return(months):
+        items = sorted(nav.items())
-        cutoff = (datetime(2026, 7, 24) - timedelta(days=int(months * 30.4))).strftime("%Y-%m-%d")
+        d0, v0 = items[0]
-        wt = [t for t in trades if t["entry_date"] >= cutoff]
+        d1, v1 = items[-1]
-        if not wt:
+        yrs = max((datetime.strptime(d1, "%Y-%m-%d") - datetime.strptime(d0, "%Y-%m-%d")).days / 365.0, 0.5)
-            return None
+        cagr = (((v1 / v0) ** (1 / yrs)) - 1) * 100 if v0 > 0 else None
-        nav = 1.0
-        for t in sorted(wt, key=lambda x: x["entry_date"]):
-            nav *= (1 + t["profit_pct"] / 100 / slots)
-        return (nav - 1) * 100
     return {
         "cagr": cagr,
-        "year1": window_return(12),
+        "year1": win.get("year1"),
-        "month6": window_return(6),
+        "month6": win.get("month6"),
-        "month3": window_return(3),
+        "month3": win.get("month3"),
         "trades": len(trades),
+        "skipped": stats.get("skipped", 0),
         "win_rate": sum(1 for t in trades if t["profit_pct"] > 0) / len(trades) * 100 if trades else 0,
     }