feat: step49_save_to_research——v_oversold v5定稿回测保存到strategy_research(支持10y/5y/2y窗口), 复现年化18.57%

2026-08-12 01:31:40 +08:00
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+#!/usr/bin/env python3
+"""step49_save_to_research.py — 复现 v5 定稿（年化18.57%）并保存到 strategy_research
+在 step49_final_v5.py 基础上：记录逐笔 trades + 保存 results_json（研究Tab 展示）
+输出：strategy_research 表 version='v_oversold' period_tag='10y'
+"""
+import numpy as np
+import pandas as pd
+import sqlite3
+import sys, json
+from datetime import datetime
+
+sys.path.insert(0, "/tmp")
+sys.path.insert(0, "/home/hmo/MoFin")
+from sr_calculator import SRCalculator
+import strategy_lab as lab
+
+print("=== 加载面板 ===", flush=True)
+panel = pd.read_pickle("/tmp/panel_12d.pkl")
+panel = panel.sort_values(["code", "date"]).reset_index(drop=True)
+panel["_key"] = panel["code"] + "_" + panel["date"]
+pos_map = {k: i for i, k in enumerate(panel["_key"])}
+dates = sorted(panel["date"].unique())
+
+# 大盘指标（mkt_dd60 + mkt_down_days）
+conn = sqlite3.connect("file:/home/hmo/MoFin/data/mofin.db?mode=ro", uri=True)
+idx_df = pd.read_sql("SELECT date, close, high FROM stock_daily WHERE code='sh000001' ORDER BY date", conn)
+idx_df["date"] = idx_df["date"].astype(str)
+idx_df["hi60"] = idx_df["high"].rolling(60).max()
+idx_df["mkt_dd60"] = (idx_df["close"] / idx_df["hi60"] - 1) * 100
+close_arr = idx_df["close"].values
+down_days, streak, prev = [], 0, None
+for c in close_arr:
+    streak = streak + 1 if (prev is not None and c < prev) else 0
+    down_days.append(streak)
+    prev = c
+idx_df["mkt_down_days"] = down_days
+dd_map = dict(zip(idx_df["date"], idx_df["mkt_dd60"]))
+down_map = dict(zip(idx_df["date"], idx_df["mkt_down_days"]))
+panel["mkt_dd60"] = panel["date"].map(dd_map)
+panel["mkt_down_days"] = panel["date"].map(down_map)
+
+# 信号（v5 定稿条件）
+sig_cond = (
+    (panel["mkt_rsi"] < 50) & (panel["mcap_q"] < 0.2) & (panel["pe_q"] < 0.2) &
+    (panel["news3"] >= 1) & (panel["sec_ret20"] < 0) & (panel["bias60"] < -20) &
+    (panel["mkt_dd60"] <= -5)
+)
+yin_die = (panel["mkt_down_days"] >= 2) & (panel["mkt_adx"] <= 55) & (panel["mkt_rsi"] >= 33)
+final_cond = sig_cond & ~yin_die
+
+print("基础信号:", sig_cond.sum(), "| 阴跌跳过:", (sig_cond & yin_die).sum(), "| 最终:", final_cond.sum(), flush=True)
+
+def build_cand(cond):
+    cand = panel[cond][["code", "date"]].copy()
+    cand = cand.sort_values(["code", "date"])
+    cand["prev"] = cand.groupby("code")["date"].shift(1)
+    cand["gap"] = (pd.to_datetime(cand["date"]) - pd.to_datetime(cand["prev"])).dt.days
+    cand = cand[(cand["prev"].isna()) | (cand["gap"] > 30)]
+    return cand
+
+def prep(sigdf):
+    codes = sigdf["code"].unique().tolist()
+    ph = ",".join("?" * len(codes))
+    df = pd.read_sql("SELECT code, date, close, high, low FROM stock_daily WHERE code IN ({}) ORDER BY code, date".format(ph), conn, params=codes)
+    df["date"] = df["date"].astype(str)
+    df["code"] = df["code"].astype(str).str.zfill(6)
+    df = df.sort_values(["code", "date"]).reset_index(drop=True)
+    df["_key"] = df["code"] + "_" + df["date"]
+    dpos = {k: i for i, k in enumerate(df["_key"])}
+    sigdf["kidx"] = (sigdf["code"] + "_" + sigdf["date"]).map(dpos)
+    sigdf = sigdf.dropna(subset=["kidx"]).copy()
+    sigdf["kidx"] = sigdf["kidx"].astype(int)
+    sr = SRCalculator()
+    out = []
+    for r in sigdf.itertuples():
+        bars_code = sr.get_bars(r.code)
+        d_idx = bars_code.index[bars_code["date"] == r.date]
+        if len(d_idx) == 0:
+            continue
+        sr_full = sr.sr_full(r.code, d_idx[0])
+        pv = sr_full["pivot"]
+        chip = sr_full["chip"]
+        if not pv:
+            continue
+        sig_close = df["close"].values[r.kidx]
+        support = pv["s2"]
+        if chip and chip["chip_ss"] < sig_close:
+            support = max(pv["s2"], chip["chip_ss"])
+        resist = pv["r2"]
+        if chip and chip["chip_sr"] > sig_close:
+            resist = min(pv["r2"], chip["chip_sr"])
+        if support >= sig_close or resist <= sig_close:
+            continue
+        out.append({"code": r.code, "date": r.date, "kidx": r.kidx,
+                    "sig_close": sig_close, "support": support, "resist": resist})
+    return pd.DataFrame(out), df
+
+def run_sim(sigdf, df, max_daily=5, slots=10, hold=40, pos_frac=0.15, stop_buf=0.05):
+    """同 step49 run_sim，但记录逐笔 trades"""
+    dpos = {k: i for i, k in enumerate(df["_key"])}
+    closes = df["close"].values
+    highs = df["high"].values
+    lows = df["low"].values
+    sig_by_date = {}
+    for r in sigdf.itertuples():
+        sig_by_date.setdefault(r.date, []).append(r)
+    INIT_CAP = 1_000_000
+    positions = {}
+    cash = INIT_CAP
+    navs = []
+    trades = []
+    for di, d in enumerate(dates):
+        for code in list(positions.keys()):
+            pos = positions[code]
+            k = dpos.get(code + "_" + d)
+            if k is None:
+                continue
+            hi, lo, cl = highs[k], lows[k], closes[k]
+            exited = False
+            if hi >= pos["tp"]:
+                sell_qty = pos["qty"] // 2
+                if sell_qty > 0:
+                    cash += sell_qty * pos["tp"]
+                    pos["qty"] -= sell_qty
+                    pos["sold_parts"].append({"pct": sell_qty / pos["init_qty"], "price": pos["tp"]})
+                if pos["qty"] <= 0:
+                    del positions[code]
+                    exited = True
+            if not exited and lo <= pos["stop"]:
+                cash += pos["qty"] * lo
+                pos["exit_price"] = lo
+                pos["exit_reason"] = "stop"
+                pos["exit_date"] = d
+                pos["hold_days"] = di - pos["entry_di"]
+                trades.append(pos)
+                del positions[code]
+                exited = True
+            if not exited and di - pos["entry_di"] >= hold:
+                cash += pos["qty"] * cl
+                pos["exit_price"] = cl
+                pos["exit_reason"] = "time"
+                pos["exit_date"] = d
+                pos["hold_days"] = di - pos["entry_di"]
+                trades.append(pos)
+                del positions[code]
+        if d in sig_by_date:
+            bought = 0
+            for r in sig_by_date[d]:
+                if bought >= max_daily or len(positions) >= slots:
+                    break
+                if r.code in positions:
+                    continue
+                cur_nav = cash
+                for c, p in positions.items():
+                    k = dpos.get(c + "_" + d)
+                    px = closes[k] if k is not None else p["avg_cost"]
+                    cur_nav += p["qty"] * px
+                pos_val = cur_nav * pos_frac
+                price = r.sig_close
+                if price <= 0:
+                    continue
+                qty = int(pos_val / price)
+                if qty <= 0 or qty * price > cash:
+                    continue
+                cash -= qty * price
+                positions[r.code] = {
+                    "code": r.code, "entry_date": r.date, "entry_price": price,
+                    "entry_di": di, "qty": qty, "init_qty": qty, "avg_cost": price,
+                    "stop": r.support * (1 - stop_buf), "tp": r.resist,
+                    "support": r.support, "resist": r.resist,
+                    "sold_parts": [], "exit_price": None, "exit_reason": None,
+                    "exit_date": None, "hold_days": None,
+                }
+                bought += 1
+        nav = cash
+        for c, p in positions.items():
+            k = dpos.get(c + "_" + d)
+            px = closes[k] if k is not None else p["avg_cost"]
+            nav += p["qty"] * px
+        navs.append(nav)
+    # 收盘时未平仓的强制平仓
+    for code in list(positions.keys()):
+        pos = positions[code]
+        k = dpos.get(code + "_" + dates[-1])
+        px = closes[k] if k is not None else pos["avg_cost"]
+        pos["exit_price"] = px
+        pos["exit_reason"] = "end"
+        pos["exit_date"] = dates[-1]
+        pos["hold_days"] = len(dates) - 1 - pos["entry_di"]
+        trades.append(pos)
+    nav_arr = np.array(navs)
+    final = nav_arr[-1]
+    years = len(navs) / 250
+    cagr = ((final / INIT_CAP) ** (1 / years) - 1) * 100 if final > 0 else -100
+    cummax = np.maximum.accumulate(nav_arr)
+    dd = ((nav_arr - cummax) / cummax).min() * 100
+    return cagr, dd, nav_arr, trades
+
+# ── 支持 period_tag 参数（10y/5y/2y）──
+PERIOD_TAG = sys.argv[1] if len(sys.argv) > 1 else '10y'
+PERIODS = {
+    '10y': ('2016-07-01', '2026-07-24'),
+    '5y':  ('2021-07-01', '2026-07-24'),
+    '2y':  ('2024-07-01', '2026-07-24'),
+}
+BT_START, BT_END = PERIODS.get(PERIOD_TAG, PERIODS['10y'])
+print(f"=== v5 定稿（阴跌判定+限流5）复现 + 保存 [{PERIOD_TAG}] {BT_START}~{BT_END} ===", flush=True)
+# 按回测窗口过滤信号（同股30日去重保持）
+bt_mask = (panel["date"] >= BT_START) & (panel["date"] <= BT_END)
+s1, df1 = prep(build_cand(final_cond & bt_mask))
+# run_sim 里 dates 也要限窗口
+dates = sorted(d for d in dates if BT_START <= d <= BT_END)
+c1, d1, nav1, trades = run_sim(s1, df1)
+print("年化={:.2f}% 回撤={:.1f}% 信号={} trades={}".format(c1, d1, len(s1), len(trades)), flush=True)
+
+# 构造 result dict（对齐 run_mr_backtest 的 result 格式）
+trade_list = []
+for t in trades:
+    profit_pct = (t["exit_price"] / t["entry_price"] - 1) * 100 if t["entry_price"] > 0 else 0
+    trade_list.append({
+        "code": t["code"], "name": t["code"],
+        "entry_date": t["entry_date"], "entry_price": round(t["entry_price"], 2),
+        "exit_price": round(t["exit_price"], 2), "profit_pct": round(profit_pct, 2),
+        "exit_reason": t["exit_reason"], "hold_days": t["hold_days"],
+        "score": 0, "score_comp": {}, "kelly": 0,
+        "stop_loss": round(t["stop"], 2), "target": round(t["tp"], 2),
+        "dna": False,
+        "factors": {"bias60": None, "mkt_rsi": None, "mcap_q": None, "pe_q": None,
+                    "news3": None, "sec_ret20": None, "support": round(t["support"], 2),
+                    "resist": round(t["resist"], 2)},
+    })
+
+wins = [t for t in trade_list if t["profit_pct"] > 0]
+losses = [t for t in trade_list if t["profit_pct"] <= 0]
+summary = {
+    "total_trades": len(trade_list),
+    "win_rate": round(len(wins) / len(trade_list) * 100, 1) if trade_list else 0,
+    "avg_profit_pct": round(sum(t["profit_pct"] for t in trade_list) / len(trade_list), 2) if trade_list else 0,
+    "avg_win_pct": round(sum(t["profit_pct"] for t in wins) / len(wins), 2) if wins else 0,
+    "avg_loss_pct": round(sum(t["profit_pct"] for t in losses) / len(losses), 2) if losses else 0,
+    "avg_hold_days": round(sum(t["hold_days"] or 0 for t in trade_list) / len(trade_list), 1) if trade_list else 0,
+    "wins": len(wins), "losses": len(losses),
+    "portfolio": {"cagr_pct": round(c1, 1), "total_return_pct": round((nav1[-1] / 1000000 - 1) * 100, 1),
+                  "portfolio_max_dd_pct": round(abs(d1), 1)},
+    "portfolio_full": {"cagr_pct": round(c1, 1), "total_return_pct": round((nav1[-1] / 1000000 - 1) * 100, 1),
+                       "portfolio_max_dd_pct": round(abs(d1), 1)},
+}
+print("summary:", json.dumps(summary, ensure_ascii=False), flush=True)
+
+# 保存到 strategy_research（先删旧的同版本同周期记录）
+strat = lab.STRATEGIES["v_oversold"]
+result = {
+    "strategy": "v_oversold",
+    "strategy_name": strat["name"],
+    "market": "a",
+    "period": f"{BT_START} ~ {BT_END}",
+    "period_tag": PERIOD_TAG,
+    "capital": 1000000,
+    "total_stocks_screened": len(df1["code"].unique()),
+    "scored_events": len(trade_list),
+    "trades": trade_list,
+    "summary": summary,
+}
+conn2 = sqlite3.connect("/home/hmo/MoFin/data/mofin.db")
+conn2.execute("DELETE FROM strategy_research WHERE version='v_oversold' AND period_tag=?", (PERIOD_TAG,))
+conn2.commit()
+conn2.close()
+lab.save_result(strat, result)
+print(f"已保存 v_oversold {PERIOD_TAG} 到 strategy_research", flush=True)
+print("=== 完成 ===", flush=True)