gao
05635b76b9
状态： - 9 个 sync task（stock_basic / kline_daily / kline_index / kline_5min / moneyflow / industry_sector / sector_features / share_snapshot / market_regime） - 数据源：baostock + mairui + 雪球(pysnowball) + 新浪（4 个） - 项目级约束：永远不用 akshare（已落实） - kline_5min 改用 DB 快照统一全量/增量逻辑 - 零后端 Chrome 扩展 xueqiu_sync（独立项目）
185 lines
7.9 KiB
Python
185 lines
7.9 KiB
Python
"""同步任务：行业聚合特征（衍生计算）。
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输入：kline_stock（日 K 线）+ industry（股票→行业映射）
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输出：
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- sector_indices ：行业日线（trade_date × sector_name × close × sector_amplitude）
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- sector_features_daily ：行业特征（+ sector_ret + ema10/20/200 + score）
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算法（参考 dashboard/api/services/sync/sector.py 重写，持久层改为 MySQL）：
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1) 每只股票日涨跌幅 pct_chg = (close/prev_close - 1) * 100
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2) 按行业聚合 sector_ret = 该行业所有股票当日 pct_chg 的均值
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3) 行业指数 close = 100 × ∏(1 + sector_ret/100) （基点 100，复合收益）
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4) 行业振幅 sector_amplitude = 该行业所有股票当日 (high-low)/close 的均值
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5) EMA10/20/200 = close 的指数移动平均（adjust=False）
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6) score: 0/1/2 — close>ema200 + 1，ema10>ema20 + 1
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无外部 API 调用，纯本地计算；增量逻辑以"全量重算"实现（数据量小 ~75K 行）。
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"""
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from __future__ import annotations
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import time
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from datetime import datetime, timedelta
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from typing import Any
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import pandas as pd
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from sqlalchemy import create_engine
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from app.core.config import settings
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from app.core.db import ops as db_ops
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from app.core.db.connection import get_mysql
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from app.core.sync.base import SyncTask
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from app.core.utils.logging import get_logger
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logger = get_logger("sync.sector_features")
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# 起算点：DB 里 kline_stock 最早一天 往前 5 年，或固定一个默认起点
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# （不用写死 2021-04-24，让数据自己决定；下面自动算）
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DEFAULT_START_YEARS_BACK = 5
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# 输出基点（行业指数 close 从多少开始）
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INDEX_BASE = 100.0
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class SyncSectorFeatures(SyncTask):
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dataset_id = "sector_features"
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def _run(
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self,
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*,
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trigger_source: str = "manual",
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codes: list[str] | None = None,
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max_workers: int = 1, # 本任务纯本地计算，单线程足够
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**kwargs,
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) -> dict[str, Any]:
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t0 = time.time()
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logger.info("[sector] 启动行业聚合特征计算")
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# ── 1) 拉 kline_stock（日线）只取需要的列 ──
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# 用 SQLAlchemy engine（pymysql 裸连接 + pd.read_sql 偶发只读 1 行的 bug）
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engine = create_engine(settings.mysql_url())
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df_kline = pd.read_sql(
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"SELECT stock_code, trade_date, open, high, low, `close`, volume "
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"FROM kline_stock ORDER BY stock_code, trade_date",
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engine,
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)
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if df_kline.empty:
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return {"status": "error", "message": "kline_stock 为空"}
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df_kline["stock_code"] = df_kline["stock_code"].astype(str).str.zfill(6)
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df_kline["trade_date"] = pd.to_datetime(df_kline["trade_date"], errors="coerce")
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logger.info(f"[sector] kline_stock: {len(df_kline):,} 行, {df_kline['stock_code'].nunique()} 只, "
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f"{df_kline['trade_date'].min().date()} ~ {df_kline['trade_date'].max().date()}")
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# ── 2) 拉 industry（股票→行业映射）──
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df_ind = pd.read_sql(
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"SELECT code, industry_name FROM industry WHERE industry_name IS NOT NULL",
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engine,
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)
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if df_ind.empty:
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return {"status": "error", "message": "industry 表为空"}
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df_ind["code"] = df_ind["code"].astype(str).str.zfill(6)
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df_ind = df_ind.drop_duplicates(subset=["code"], keep="first")
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logger.info(f"[sector] industry 映射: {len(df_ind):,} 行, {df_ind['industry_name'].nunique()} 个行业")
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# ── 3) 算每只股票日涨跌幅 + 振幅 ──
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df = df_kline.merge(df_ind, left_on="stock_code", right_on="code", how="inner")
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df = df.sort_values(["stock_code", "trade_date"])
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df["prev_close"] = df.groupby("stock_code")["close"].shift(1)
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df["stock_amplitude"] = (df["high"] - df["low"]) / df["close"]
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df = df.dropna(subset=["prev_close", "industry_name"])
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df["pct_chg"] = (df["close"] / df["prev_close"] - 1.0) * 100.0
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logger.info(f"[sector] 合并后: {len(df):,} 行, 行业 {df['industry_name'].nunique()} 个")
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# ── 4) 按行业 + 日期聚合 ──
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sector_daily = (
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df.groupby(["industry_name", "trade_date"], as_index=False).agg(
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sector_ret=("pct_chg", "mean"),
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sector_amplitude=("stock_amplitude", "mean"),
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)
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.rename(columns={"industry_name": "sector_name"})
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.sort_values(["sector_name", "trade_date"])
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.reset_index(drop=True)
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)
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logger.info(f"[sector] 行业日聚合: {len(sector_daily):,} 行")
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# ── 5) 算行业指数 close（基点 100，复合）──
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sector_index = self._build_index(sector_daily)
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# ── 6) EMA + score ──
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sector_index = self._calc_ema(sector_index)
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sector_index["score"] = sector_index.apply(
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lambda r: self._calc_score(r["close"], r["ema10"], r["ema20"], r["ema200"]),
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axis=1,
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)
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# 整理列名
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out_cols = ["trade_date", "sector_name", "sector_ret", "sector_amplitude",
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"close", "ema10", "ema20", "ema200", "score"]
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sector_index = sector_index[out_cols]
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sector_index["trade_date"] = sector_index["trade_date"].dt.strftime("%Y-%m-%d")
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# NaN → None（让 MySQL 接受 NULL）
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sector_index = sector_index.where(pd.notnull(sector_index), None)
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# ── 7) 写入两张表 ──
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# 7a. sector_indices (4 列)
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si_rows = sector_index[["trade_date", "sector_name", "close", "sector_amplitude"]].to_dict("records")
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db_ops.replace_all_sector_indices(si_rows)
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# 7b. sector_features_daily (9 列)
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sf_rows = sector_index.to_dict("records")
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db_ops.replace_all_sector_features(sf_rows)
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elapsed = round(time.time() - t0, 1)
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msg = (
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f"行业聚合 {sector_index['sector_name'].nunique()} 个行业 "
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f"× {sector_index['trade_date'].nunique()} 天, "
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f"共 {len(sector_index):,} 行, {elapsed}s"
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)
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logger.info(f"[sector] {msg}")
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return {
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"status": "ok",
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"message": msg,
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"sectors": int(sector_index["sector_name"].nunique()),
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"days": int(sector_index["trade_date"].nunique()),
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"rows": int(len(sector_index)),
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"elapsed_sec": elapsed,
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}
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@staticmethod
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def _build_index(sector_daily: pd.DataFrame) -> pd.DataFrame:
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"""行业指数 close = INDEX_BASE × ∏(1 + sector_ret/100)"""
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out = []
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for sector_name, group in sector_daily.groupby("sector_name", sort=False):
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g = group.sort_values("trade_date").copy()
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close_vals = []
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cur_base = INDEX_BASE
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for ret in g["sector_ret"].to_numpy(dtype=float):
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if pd.isna(ret):
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close_vals.append(cur_base)
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else:
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cur_base = cur_base * (1 + float(ret) / 100.0)
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close_vals.append(cur_base)
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g["close"] = close_vals
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out.append(g)
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return pd.concat(out, ignore_index=True)
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@staticmethod
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def _calc_ema(sector_index: pd.DataFrame) -> pd.DataFrame:
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"""每个行业分别算 EMA10/20/200"""
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out = []
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for sector_name, group in sector_index.groupby("sector_name", sort=False):
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g = group.sort_values("trade_date").copy()
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g["ema10"] = g["close"].ewm(span=10, adjust=False).mean()
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g["ema20"] = g["close"].ewm(span=20, adjust=False).mean()
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g["ema200"] = g["close"].ewm(span=200, adjust=False).mean()
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out.append(g)
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return pd.concat(out, ignore_index=True)
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@staticmethod
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def _calc_score(close, ema10, ema20, ema200) -> int:
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score = 0
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if pd.notna(close) and pd.notna(ema200) and close > ema200:
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score += 1
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if pd.notna(ema10) and pd.notna(ema20) and ema10 > ema20:
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score += 1
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return int(score)
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