159 lines
6.1 KiB
Python
159 lines
6.1 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""hk_backtest.py — 港股策略回测+资金模拟（独立于A股strategy_lab）
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功能：
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1. 读港股12维面板 + 港股策略库(hk_strategies.py)
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2. 生成信号 → trades（止盈/止损/最大持有出场）
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3. portfolio_sim 资金模拟（复用 strategy_lab.portfolio_sim 纯函数，不改A股框架）
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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）
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用法：
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python3 hk_backtest.py # 全部策略 + 组合
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python3 hk_backtest.py --version hk_pe_mom # 单策略
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"""
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import sys
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import argparse
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import collections
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from datetime import datetime, timedelta
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import pandas as pd
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sys.path.insert(0, "/home/hmo/MoFin/deploy/profile-scripts")
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sys.path.insert(0, "/home/hmo/MoFin") # strategy_lab.portfolio_sim（纯函数复用）
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from hk_strategies import HK_STRATEGIES, get_hk_strategy
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PANEL = "/tmp/panel_12d_hk.pkl"
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COST = 0.0015 # 港股往返费率近似（佣金+印花税）
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def load_panel():
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p = pd.read_pickle(PANEL)
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return p.sort_values(["code", "date"]).reset_index(drop=True)
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def gen_trades(panel, strat):
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"""按策略入场条件生成信号 → trades"""
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e = strat["entry"]
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cond = pd.Series(True, index=panel.index)
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if "pe_q_max" in e:
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cond &= panel["pe_q"] < e["pe_q_max"]
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if "sec_ret20_min" in e:
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cond &= panel["sec_ret20"] > e["sec_ret20_min"]
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if "rsi_max" in e:
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cond &= panel["rsi"] < e["rsi_max"]
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if "bias60_max" in e:
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cond &= panel["bias60"] < e["bias60_max"]
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if "rsi_delta_min" in e:
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# 面板无rsi_delta，用rsi与前5日差值近似（面板已有rsi）
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cond &= panel["rsi"] - panel.groupby("code")["rsi"].shift(5) >= e["rsi_delta_min"]
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if "vol_ratio_min" in e:
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cond &= panel["vol_ratio"] > e["vol_ratio_min"]
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sig = panel[cond].copy()
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sig = sig.dropna(subset=["close"])
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print(f" {strat['version']} 信号: {len(sig)}", flush=True)
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ex = strat["exit"]
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tp, sl, maxh = ex["tp_pct"], ex["sl_pct"], ex["max_hold_days"]
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bycode = {c: df for c, df in panel.groupby("code")}
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trades = []
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for _, s in sig.iterrows():
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df = bycode.get(s["code"])
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if df is None:
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continue
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idx = df.index[df["date"] == s["date"]]
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if len(idx) == 0:
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continue
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pos = df.index.get_loc(idx[0])
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fut = df.iloc[pos + 1: pos + maxh + 2]
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if len(fut) < 2:
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continue
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ep = s["close"]
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if ep <= 0:
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continue
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exit_p, reason, hold = None, None, 0
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for k, fb in enumerate(fut.itertuples()):
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if fb.close <= ep * (1 - sl):
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exit_p, reason, hold = ep * (1 - sl), "stop", k + 1
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break
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if fb.close >= ep * (1 + tp):
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exit_p, reason, hold = ep * (1 + tp), "target", k + 1
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break
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if exit_p is None:
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exit_p, reason, hold = fut.iloc[-1]["close"], "time", maxh
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trades.append({
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"code": s["code"], "name": s["code"], "entry_date": s["date"],
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"entry_price": round(ep, 2), "exit_price": round(exit_p, 2),
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"profit_pct": round((exit_p - ep) / ep * 100, 2),
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"exit_reason": reason, "hold_days": hold,
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"score": 0, "score_comp": {}, "kelly": 0,
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"stop_loss": round(ep * (1 - sl), 2), "target": round(ep * (1 + tp), 2),
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"dna": False, "factors": {},
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})
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return trades
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def portfolio_metrics(trades, capital=1000000, slots=8):
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"""资金模拟 + 时间窗收益（Ralph Loop验收标准）"""
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import strategy_lab as lab
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pf = lab.portfolio_sim(trades, capital, max_positions=slots)
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years_span = 7.5
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cagr = pf.get("cagr_pct")
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# 时间窗收益（按 entry_date 过滤 trades，8槽等权复利净值）
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def window_return(months):
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cutoff = (datetime(2026, 7, 24) - timedelta(days=int(months * 30.4))).strftime("%Y-%m-%d")
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wt = [t for t in trades if t["entry_date"] >= cutoff]
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if not wt:
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return None
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nav = 1.0
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for t in sorted(wt, key=lambda x: x["entry_date"]):
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nav *= (1 + t["profit_pct"] / 100 / slots)
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return (nav - 1) * 100
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return {
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"cagr": cagr,
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"year1": window_return(12),
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"month6": window_return(6),
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"month3": window_return(3),
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"trades": len(trades),
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"win_rate": sum(1 for t in trades if t["profit_pct"] > 0) / len(trades) * 100 if trades else 0,
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}
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--version", default=None)
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ap.add_argument("--slots", type=int, default=8)
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args = ap.parse_args()
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panel = load_panel()
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print(f"港股面板: {len(panel)} 行\n", flush=True)
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versions = [args.version] if args.version else list(HK_STRATEGIES.keys())
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all_trades = []
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for v in versions:
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strat = get_hk_strategy(v)
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if not strat:
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print(f"未知策略: {v}")
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continue
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print(f"=== {v} ({strat['name']}) ===", flush=True)
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trades = gen_trades(panel, strat)
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if not trades:
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print(" 无交易\n", flush=True)
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continue
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m = portfolio_metrics(trades, slots=args.slots)
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print(f" 交易{m['trades']} 胜率{m['win_rate']:.0f}% 组合年化{m['cagr']}% "
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f"近1年{m['year1']:+.1f}% 近6月{m['month6']:+.1f}% 近3月{m['month3']:+.1f}%\n", flush=True)
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all_trades += trades
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if all_trades and not args.version:
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print("=== 港股策略组合（全温区）===", flush=True)
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m = portfolio_metrics(all_trades, slots=args.slots)
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print(f" 组合: 交易{m['trades']} 胜率{m['win_rate']:.0f}% 组合年化{m['cagr']}% "
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f"近1年{m['year1']:+.1f}% 近6月{m['month6']:+.1f}% 近3月{m['month3']:+.1f}%", flush=True)
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ok = (m['cagr'] or 0) > 10 and (m['year1'] or 0) > 10 and (m['month6'] or 0) > 5 and (m['month3'] or 0) > 0
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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)
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if __name__ == "__main__":
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main()
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