#!/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)