271 lines
12 KiB
Python
271 lines
12 KiB
Python
#!/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)
|