feat: 港股策略独立模块——hk_strategies(三温区策略定义)+hk_backtest(独立回测资金模拟,复用portfolio_sim纯函数,零干扰A股)

deploy/profile-scripts/hk_backtest.py

@@ -0,0 +1,157 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""hk_backtest.py — 港股策略回测+资金模拟（独立于A股strategy_lab）
+
+功能：
+  1. 读港股12维面板 + 港股策略库(hk_strategies.py)
+  2. 生成信号 → trades（止盈/止损/最大持有出场）
+  3. portfolio_sim 资金模拟（复用 strategy_lab.portfolio_sim 纯函数，不改A股框架）
+  4. 输出组合指标：平均年化/近1年/近6月/近3月（Ralph Loop验收标准）
+
+用法：
+  python3 hk_backtest.py                    # 全部策略 + 组合
+  python3 hk_backtest.py --version hk_pe_mom  # 单策略
+"""
+import sys
+import argparse
+import collections
+from datetime import datetime, timedelta
+
+import pandas as pd
+
+sys.path.insert(0, "/home/hmo/MoFin/deploy/profile-scripts")
+from hk_strategies import HK_STRATEGIES, get_hk_strategy
+
+PANEL = "/tmp/panel_12d_hk.pkl"
+COST = 0.0015  # 港股往返费率近似（佣金+印花税）
+
+
+def load_panel():
+    p = pd.read_pickle(PANEL)
+    return p.sort_values(["code", "date"]).reset_index(drop=True)
+
+
+def gen_trades(panel, strat):
+    """按策略入场条件生成信号 → trades"""
+    e = strat["entry"]
+    cond = pd.Series(True, index=panel.index)
+    if "pe_q_max" in e:
+        cond &= panel["pe_q"] < e["pe_q_max"]
+    if "sec_ret20_min" in e:
+        cond &= panel["sec_ret20"] > e["sec_ret20_min"]
+    if "rsi_max" in e:
+        cond &= panel["rsi"] < e["rsi_max"]
+    if "bias60_max" in e:
+        cond &= panel["bias60"] < e["bias60_max"]
+    if "ret60_max" in e:
+        cond &= panel["ret60"] < e["ret60_max"]
+    if "rsi_delta_min" in e:
+        # 面板无rsi_delta，用rsi与前5日差值近似（面板已有rsi）
+        cond &= panel["rsi"] - panel.groupby("code")["rsi"].shift(5) >= e["rsi_delta_min"]
+    if "vol_ratio_min" in e:
+        cond &= panel["vol_ratio"] > e["vol_ratio_min"]
+    sig = panel[cond].copy()
+    sig = sig.dropna(subset=["close"])
+    print(f"  {strat['version']} 信号: {len(sig)}", flush=True)
+
+    ex = strat["exit"]
+    tp, sl, maxh = ex["tp_pct"], ex["sl_pct"], ex["max_hold_days"]
+    bycode = {c: df for c, df in panel.groupby("code")}
+    trades = []
+    for _, s in sig.iterrows():
+        df = bycode.get(s["code"])
+        if df is None:
+            continue
+        idx = df.index[df["date"] == s["date"]]
+        if len(idx) == 0:
+            continue
+        pos = df.index.get_loc(idx[0])
+        fut = df.iloc[pos + 1: pos + maxh + 2]
+        if len(fut) < 2:
+            continue
+        ep = s["close"]
+        if ep <= 0:
+            continue
+        exit_p, reason, hold = None, None, 0
+        for k, fb in enumerate(fut.itertuples()):
+            if fb.close <= ep * (1 - sl):
+                exit_p, reason, hold = ep * (1 - sl), "stop", k + 1
+                break
+            if fb.close >= ep * (1 + tp):
+                exit_p, reason, hold = ep * (1 + tp), "target", k + 1
+                break
+        if exit_p is None:
+            exit_p, reason, hold = fut.iloc[-1]["close"], "time", maxh
+        trades.append({
+            "code": s["code"], "name": s["code"], "entry_date": s["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,
+            "score": 0, "score_comp": {}, "kelly": 0,
+            "stop_loss": round(ep * (1 - sl), 2), "target": round(ep * (1 + tp), 2),
+            "dna": False, "factors": {},
+        })
+    return trades
+
+
+def portfolio_metrics(trades, capital=1000000, slots=8):
+    """资金模拟 + 时间窗收益（Ralph Loop验收标准）"""
+    import strategy_lab as lab
+    pf = lab.portfolio_sim(trades, capital, max_positions=slots)
+    years_span = 7.5
+    cagr = pf.get("cagr_pct")
+    # 时间窗收益（按 entry_date 过滤 trades 做简单等权组合）
+    def window_return(months):
+        cutoff = (datetime(2026, 7, 24) - timedelta(days=int(months * 30.4))).strftime("%Y-%m-%d")
+        wt = [t for t in trades if t["entry_date"] >= cutoff]
+        if not wt:
+            return None
+        tot = sum(t["profit_pct"] for t in wt) / slots
+        return tot
+    return {
+        "cagr": cagr,
+        "year1": window_return(12),
+        "month6": window_return(6),
+        "month3": window_return(3),
+        "trades": len(trades),
+        "win_rate": sum(1 for t in trades if t["profit_pct"] > 0) / len(trades) * 100 if trades else 0,
+    }
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--version", default=None)
+    ap.add_argument("--slots", type=int, default=8)
+    args = ap.parse_args()
+
+    panel = load_panel()
+    print(f"港股面板: {len(panel)} 行\n", flush=True)
+
+    versions = [args.version] if args.version else list(HK_STRATEGIES.keys())
+    all_trades = []
+    for v in versions:
+        strat = get_hk_strategy(v)
+        if not strat:
+            print(f"未知策略: {v}")
+            continue
+        print(f"=== {v} ({strat['name']}) ===", flush=True)
+        trades = gen_trades(panel, strat)
+        if not trades:
+            print("  无交易\n", flush=True)
+            continue
+        m = portfolio_metrics(trades, slots=args.slots)
+        print(f"  交易{m['trades']} 胜率{m['win_rate']:.0f}% 组合年化{m['cagr']}% "
+              f"近1年{m['year1']:+.1f}% 近6月{m['month6']:+.1f}% 近3月{m['month3']:+.1f}%\n", flush=True)
+        all_trades += trades
+
+    if all_trades and not args.version:
+        print("=== 港股策略组合（全温区）===", flush=True)
+        m = portfolio_metrics(all_trades, slots=args.slots)
+        print(f"  组合: 交易{m['trades']} 胜率{m['win_rate']:.0f}% 组合年化{m['cagr']}% "
+              f"近1年{m['year1']:+.1f}% 近6月{m['month6']:+.1f}% 近3月{m['month3']:+.1f}%", flush=True)
+        ok = (m['cagr'] or 0) > 10 and (m['year1'] or 0) > 10 and (m['month6'] or 0) > 5 and (m['month3'] or 0) > 0
+        print(f"  验收: {'✅ 达标' if ok else '❌ 未达标'}（年化>10/近1年>10/近6月>5/近3月>0）", flush=True)
+
+
+if __name__ == "__main__":
+    main()