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