266 lines
10 KiB
Python
266 lines
10 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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import sqlite3
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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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DB = "/home/hmo/MoFin/data/mofin.db"
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COST = 0.0015 # 港股往返费率近似（佣金+印花税）
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# 港股温区映射（组合按温区调度用）
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_REGIME_CACHE = None
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def load_regime_map():
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global _REGIME_CACHE
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if _REGIME_CACHE is None:
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conn = sqlite3.connect(DB)
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_REGIME_CACHE = dict(conn.execute(
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"SELECT date, regime FROM market_regime WHERE market='hk'").fetchall())
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conn.close()
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return _REGIME_CACHE
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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_defensive(panel, strat, strike=3, cooldown_days=15):
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"""带三振出局防守的信号→trades：连续 strike 笔亏损 → 暂停 cooldown_days（自动避持续下跌被埋）"""
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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 "mcap_q_max" in e:
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cond &= panel["mcap_q"] < e["mcap_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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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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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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last_loss_dates = {} # code -> last loss date（个股级三振）
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consecutive = {} # code -> 连续亏损数
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for _, s in sig.iterrows():
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code = s["code"]
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# 三振出局：个股连续亏损 strike 次 → 暂停 cooldown_days
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if consecutive.get(code, 0) >= strike:
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if (pd.Timestamp(s["date"]) - pd.Timestamp(last_loss_dates.get(code, "1900-01-01"))).days < cooldown_days:
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continue
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consecutive[code] = 0 # 冷却期后恢复
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df = bycode.get(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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pnl = (exit_p - ep) / ep * 100
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exit_date = fut.iloc[min(hold - 1, len(fut) - 1)]["date"] if hold > 0 else s["date"]
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# 更新三振状态
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if pnl < 0:
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consecutive[code] = consecutive.get(code, 0) + 1
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last_loss_dates[code] = exit_date
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else:
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consecutive[code] = 0
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trades.append({
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"code": code, "name": code, "entry_date": s["date"], "exit_date": exit_date,
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"entry_price": round(ep, 2), "exit_price": round(exit_p, 2),
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"profit_pct": round(pnl, 2), "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 gen_trades(panel, strat):
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"""兼容入口：无防守的信号生成"""
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return gen_trades_defensive(panel, strat, strike=999, cooldown_days=0)
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def portfolio_nav(trades, capital=1000000, slots=8):
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"""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。
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返回 (nav_series: dict date->nav, stats)"""
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if not trades:
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return {}, {}
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dates = sorted({t["entry_date"] for t in trades} | {t.get("exit_date", t["entry_date"]) for t in trades})
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if not dates:
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return {}, {}
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# 用真实日历（stock_daily 港股日K日期）
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conn = sqlite3.connect(DB)
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cal = [r[0] for r in conn.execute(
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"SELECT DISTINCT date FROM stock_daily WHERE date>=? AND date<=? AND length(code)=5 ORDER BY date",
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(dates[0], dates[-1])).fetchall()]
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conn.close()
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if not cal:
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cal = dates
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cal_idx = {d: i for i, d in enumerate(cal)}
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alloc = capital / slots
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open_pos = [] # {exit_date, alloc, pnl}
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cash = capital
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nav_series = {}
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skipped = 0
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by_entry = collections.defaultdict(list)
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for t in trades:
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by_entry[t["entry_date"]].append(t)
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for day in cal:
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# 结算到期
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still = []
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for p in open_pos:
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if p["exit_date"] <= day:
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cash += p["alloc"] * (1 + p["pnl"] / 100)
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else:
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still.append(p)
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open_pos = still
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# 入场
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for t in by_entry.get(day, []):
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if len(open_pos) >= slots or cash < alloc:
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skipped += 1
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continue
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open_pos.append({"exit_date": t.get("exit_date", day), "alloc": alloc,
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"pnl": t["profit_pct"]})
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cash -= alloc
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# 净值
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held_val = sum(p["alloc"] * (1 + p["pnl"] / 100) for p in open_pos)
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nav_series[day] = cash + held_val
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nav_series = {d: v for d, v in sorted(nav_series.items())}
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return nav_series, {"skipped": skipped}
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def window_returns(nav_series):
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"""从净值曲线算窗口收益（近1年/6月/3月，对照最后日期）"""
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if not nav_series:
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return {"year1": None, "month6": None, "month3": None}
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items = sorted(nav_series.items())
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last_d, last_v = items[-1]
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last_dt = datetime.strptime(last_d, "%Y-%m-%d")
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out = {}
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for label, days in [("year1", 365), ("month6", 182), ("month3", 91)]:
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cutoff = (last_dt - timedelta(days=days)).strftime("%Y-%m-%d")
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# 取 cutoff 后最近的净值点
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base = None
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for d, v in items:
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if d >= cutoff:
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base = v
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break
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out[label] = (last_v / base - 1) * 100 if base else None
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return out
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def portfolio_metrics(trades, capital=1000000, slots=8):
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"""资金模拟 + 窗口收益（净值曲线）"""
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nav, stats = portfolio_nav(trades, capital, slots)
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win = window_returns(nav)
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# 年化（用首末净值）
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cagr = None
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if nav:
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items = sorted(nav.items())
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d0, v0 = items[0]
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d1, v1 = items[-1]
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yrs = max((datetime.strptime(d1, "%Y-%m-%d") - datetime.strptime(d0, "%Y-%m-%d")).days / 365.0, 0.5)
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cagr = (((v1 / v0) ** (1 / yrs)) - 1) * 100 if v0 > 0 else None
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return {
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"cagr": cagr,
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"year1": win.get("year1"),
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"month6": win.get("month6"),
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"month3": win.get("month3"),
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"trades": len(trades),
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"skipped": stats.get("skipped", 0),
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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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rm = load_regime_map() # 港股温区映射（组合温区调度）
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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_defensive(panel, strat, strike=3, cooldown_days=15)
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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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# 温区调度：只保留策略适用温区的交易（组合正确性关键）
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reg = strat.get("regime", "all")
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if reg != "all" and args.version is None:
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trades = [t for t in trades if rm.get(t["entry_date"]) == reg]
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print(f" 温区调度({reg}): 保留 {len(trades)} 笔", flush=True)
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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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