diff --git a/deploy/profile-scripts/hk_backtest.py b/deploy/profile-scripts/hk_backtest.py new file mode 100644 index 00000000..64af4740 --- /dev/null +++ b/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() diff --git a/deploy/profile-scripts/hk_strategies.py b/deploy/profile-scripts/hk_strategies.py new file mode 100644 index 00000000..185c7ac7 --- /dev/null +++ b/deploy/profile-scripts/hk_strategies.py @@ -0,0 +1,74 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""hk_strategies.py — 港股策略定义（独立于A股strategy_lab，零干扰） + +港股策略库：各温区互补策略定义（单一来源）。 +面板数据：/tmp/panel_12d_hk.pkl（build_panel_hk_v2 构建，含价量/大盘/行业动量/估值分位/资金流） + +策略结构： + version: 策略名 + regime: 适用温区（trend_up/trend_down/choppy/all） + entry: 入场条件（面板因子，数据定阈值——step29 由果及因扫描） + exit: 出场（止盈/止损/最大持有，港股波动放宽） +""" +# ── 港股策略库（按温区互补）────────────────────────────── + +HK_STRATEGIES = { + # trend_up：低PE + 行业动量高（由果及因：大涨率10.28%，2.35x基线） + "hk_pe_mom": { + "version": "hk_pe_mom", + "name": "港股低PE+行业动量（trend_up主战场）", + "regime": "trend_up", + "summary": "低PE(<0.2分位)+行业20日动量>10%。由果及因：大涨率10.28%(基线4.37%)。低PE+1.24pp(step27港股扫描)、行业动量高+1.47pp", + "entry": { + "pe_q_max": 0.2, # PE分位<0.2（低估值） + "sec_ret20_min": 10, # 行业20日动量>10% + }, + "exit": {"tp_pct": 0.30, "sl_pct": 0.12, "max_hold_days": 40}, + }, + # trend_down：深度超卖反弹（由果及因：RSI<25/bias60<-15，trend_down温区55%胜率） + "hk_mr1": { + "version": "hk_mr1", + "name": "港股深度超卖反弹（trend_down主战场）", + "regime": "trend_down", + "summary": "RSI<25+bias60<-15+ret60<-25+止跌回升+放量。港股归因：深度超卖RSI<25胜率55.6%，trend_down温区55%胜率+3.59%", + "entry": { + "rsi_max": 25, + "bias60_max": -15, + "ret60_max": -25, + "rsi_delta_min": 2, + "vol_ratio_min": 1.5, + }, + "exit": {"tp_pct": 0.25, "sl_pct": 0.15, "max_hold_days": 30}, + }, + # choppy：低PE+超跌反弹（震荡市价值修复） + "hk_pe_oversold": { + "version": "hk_pe_oversold", + "name": "港股低PE+超卖反弹（choppy主战场）", + "regime": "choppy", + "summary": "低PE(<0.2分位)+RSI超卖(<45)。由果及因：低PE+超卖组合大涨率6.93%(基线4.37%)", + "entry": { + "pe_q_max": 0.2, + "rsi_max": 45, + "bias60_max": 0, + }, + "exit": {"tp_pct": 0.25, "sl_pct": 0.12, "max_hold_days": 40}, + }, +} + + +def get_hk_strategy(version): + """按版本取港股策略定义""" + return HK_STRATEGIES.get(version) + + +def list_hk_strategies(): + """港股策略列表（按温区）""" + return [{"version": v, "regime": s["regime"], "name": s["name"]} + for v, s in HK_STRATEGIES.items()] + + +def strategies_for_regime(regime): + """返回适用某温区的策略版本列表（regime 参数用于组合调度）""" + return [v for v, s in HK_STRATEGIES.items() + if s["regime"] == regime or s["regime"] == "all"]