157 lines
6.7 KiB
Python
157 lines
6.7 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""build_panel_hk_v2.py — 港股 12维面板 v2（补行业动量+估值分位）
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在 v1（大盘+个股技术）基础上补：
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- sec_ret20/sec_above：港股行业动量（行业归属 stock_sectors + 日K等权均值）
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- pe_q/pb_q/mcap_q：估值分位（stock_fundamentals_history，每日截面分位）
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输出：/tmp/panel_12d_hk.pkl（覆盖 v1）
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"""
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import sys, sqlite3
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import numpy as np
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import pandas as pd
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sys.path.insert(0, "/home/hmo/MoFin/deploy/profile-scripts")
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DB = "/home/hmo/MoFin/data/mofin.db"
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def calc_rsi(closes, n=14):
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if len(closes) < n + 1:
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return [None] * len(closes)
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out = [None] * len(closes)
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for i in range(n, len(closes)):
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gains, losses = [], []
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for j in range(i - n + 1, i + 1):
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ch = closes[j] - closes[j - 1]
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gains.append(max(ch, 0)); losses.append(max(-ch, 0))
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ag, al = sum(gains) / n, sum(losses) / n
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out[i] = 100 if al == 0 else 100 - 100 / (1 + ag / al)
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return out
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def main():
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conn = sqlite3.connect(DB)
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hsi = pd.read_sql("SELECT date, close FROM stock_daily WHERE code='hkHSI' ORDER BY date", conn)
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codes = [r[0] for r in conn.execute(
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"SELECT code FROM hk_connect_stocks WHERE is_active=1 ORDER BY code").fetchall()]
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sector = dict(conn.execute(
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"SELECT code, sector_name FROM stock_sectors WHERE source='hk_em'").fetchall())
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conn.close()
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# 大盘指标（同v1）
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hsi = hsi.sort_values("date").reset_index(drop=True)
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hsi["mkt_ret20"] = hsi["close"].pct_change(20) * 100
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ma20 = hsi["close"].rolling(20).mean()
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hsi["mkt_above"] = (hsi["close"] > ma20).astype(float)
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hsi["mkt_rsi"] = calc_rsi(list(hsi["close"].values))
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hsi["mkt_adx"] = hsi["close"].rolling(14).apply(
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lambda x: abs(x.iloc[-1]-x.iloc[0])/(x.max()-x.min()+1e-9)*100 if len(x)>1 else 0, raw=False)
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mkt = hsi.set_index("date")[["mkt_above","mkt_adx","mkt_rsi","mkt_ret20"]]
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# 行业指数（等权日收益 → 20日动量）
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print("构建港股行业指数...", flush=True)
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sec_daily = {} # sector -> DataFrame(date, ret1)
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for idx, code in enumerate(codes):
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sec = sector.get(code)
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if not sec:
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continue
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conn = sqlite3.connect(DB)
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df = pd.read_sql("SELECT date, close FROM stock_daily WHERE code=? ORDER BY date",
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conn, params=(code,))
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conn.close()
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if len(df) < 90:
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continue
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df = df.sort_values("date").reset_index(drop=True)
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df["ret1"] = df["close"].pct_change() * 100
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sec_daily.setdefault(sec, []).append(df[["date","ret1"]])
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if (idx+1) % 150 == 0:
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print(f" 行业收集 {idx+1}/{len(codes)}", flush=True)
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sec_index = {}
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for sec, parts in sec_daily.items():
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allp = pd.concat(parts, ignore_index=True)
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g = allp.groupby("date")["ret1"].mean().reset_index() # 等权行业日收益
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g["sec_ret20"] = g["ret1"].rolling(20).sum() # 20日累计
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sec_index[sec] = g.set_index("date")["sec_ret20"]
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print(f"行业指数: {len(sec_index)} 个行业", flush=True)
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# 估值分位（每日截面）
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print("加载历史估值...", flush=True)
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conn = sqlite3.connect(DB)
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hist = pd.read_sql(
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"SELECT code, date, pe_ttm, pb FROM stock_fundamentals_history WHERE length(code)=5", conn)
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conn.close()
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if len(hist) > 0:
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hist["pe_q"] = hist.groupby("date")["pe_ttm"].rank(pct=True)
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hist["pb_q"] = hist.groupby("date")["pb"].rank(pct=True)
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hist = hist[["code","date","pe_q","pb_q"]]
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else:
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hist = pd.DataFrame(columns=["code","date","pe_q","pb_q"])
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print(f"估值历史: {len(hist)} 条", flush=True)
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# 资金流（当日主力净流入，万元）
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print("加载资金流...", flush=True)
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conn = sqlite3.connect(DB)
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flow = pd.read_sql(
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"SELECT code, date, flow_in, flow_out FROM hk_flow_daily", conn)
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conn.close()
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if len(flow) > 0:
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flow["flow1"] = (flow["flow_in"] - flow["flow_out"]) / 10000.0 # 万元→亿
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flow = flow[["code","date","flow1"]]
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else:
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flow = pd.DataFrame(columns=["code","date","flow1"])
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print(f"资金流: {len(flow)} 条", flush=True)
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# 构建面板
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rows = []
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for idx, code in enumerate(codes):
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sec = sector.get(code)
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conn = sqlite3.connect(DB)
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df = pd.read_sql(
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"SELECT date, open, close, high, low, volume FROM stock_daily WHERE code=? ORDER BY date",
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conn, params=(code,))
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conn.close()
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if len(df) < 90:
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continue
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df = df.sort_values("date").reset_index(drop=True)
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c = df["close"].values
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df["rsi"] = calc_rsi(list(c))
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ma60 = df["close"].rolling(60).mean()
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ma20 = df["close"].rolling(20).mean()
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df["bias60"] = (df["close"]/ma60 - 1) * 100
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df["bias20"] = (df["close"]/ma20 - 1) * 100
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df["ret1"] = df["close"].pct_change(1) * 100
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df["ret5"] = df["close"].pct_change(5) * 100
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df["ret20"] = df["close"].pct_change(20) * 100
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lo20 = df["low"].rolling(20).min()
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df["dist_lo20"] = (df["close"]/lo20 - 1) * 100
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df["hi20_new"] = (df["close"] >= df["high"].rolling(20).max()).astype(int)
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v5 = df["volume"].rolling(5).mean()
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v20 = df["volume"].rolling(20).mean()
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df["vol_ratio"] = (v5/v20).round(3)
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df["code"] = code
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df = df.merge(mkt, left_on="date", right_index=True, how="left")
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# 行业动量
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if sec and sec in sec_index:
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df = df.merge(sec_index[sec].rename("sec_ret20"), left_on="date", right_index=True, how="left")
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else:
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df["sec_ret20"] = np.nan
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# 估值分位
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df = df.merge(hist[hist["code"]==code][["date","pe_q","pb_q"]], on="date", how="left")
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# 资金流
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df = df.merge(flow[flow["code"]==code][["date","flow1"]], on="date", how="left")
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rows.append(df[["code","date","close","mkt_above","mkt_adx","mkt_rsi","mkt_ret20",
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"sec_ret20","rsi","bias60","bias20","dist_lo20","ret1","ret5","ret20",
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"vol_ratio","hi20_new","pe_q","pb_q","flow1"]])
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if (idx+1) % 100 == 0:
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print(f" 面板 {idx+1}/{len(codes)}", flush=True)
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panel = pd.concat(rows, ignore_index=True)
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for col in ["sec_above","news3","flow5","mcap_q","limit_up"]:
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panel[col] = np.nan
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panel.to_pickle("/tmp/panel_12d_hk.pkl")
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print(f"\n港股面板v2: {len(panel)} 行 × {len(panel.columns)} 列", flush=True)
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print("新增: sec_ret20(行业动量) + pe_q/pb_q(估值分位) + flow1(当日资金流)", flush=True)
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print("仍缺: 资金流flow5(港股无历史)/新闻news3/mcap_q/行业above(后续补)", flush=True)
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if __name__ == "__main__":
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main()
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