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）
114 lines
4.5 KiB
Python
114 lines
4.5 KiB
Python
"""同步任务：市场情绪（market_regime_daily，衍生源）。
|
|
|
|
基于本地 kline_stock 数据聚合：advancers / decliners / turnover / advance_ratio。"""
|
|
from __future__ import annotations
|
|
|
|
from datetime import datetime
|
|
from typing import Any
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
from app.core.db import ops as db_ops
|
|
from app.core.sync.base import SyncTask
|
|
from app.core.utils.logging import get_logger
|
|
|
|
logger = get_logger("sync.market_regime")
|
|
|
|
DEFAULT_START = "2018-01-01"
|
|
EXTREME_PANIC_ADV = 0.2
|
|
EXTREME_PANIC_TURN = 1.5
|
|
|
|
|
|
def _is_extreme_panic(row: pd.Series) -> int:
|
|
ar = pd.to_numeric(row.get("advance_ratio"), errors="coerce")
|
|
tr = pd.to_numeric(row.get("turnover_ratio_5d"), errors="coerce")
|
|
if pd.isna(ar) or pd.isna(tr):
|
|
return 0
|
|
return int(ar < EXTREME_PANIC_ADV and tr > EXTREME_PANIC_TURN)
|
|
|
|
|
|
class SyncMarketRegime(SyncTask):
|
|
dataset_id = "market_regime"
|
|
|
|
def _run(self, *, trigger_source: str = "manual", start: str = DEFAULT_START, **kwargs) -> dict[str, Any]:
|
|
end = datetime.now().strftime("%Y-%m-%d")
|
|
self._progress(message=f"构建 market_regime {start} ~ {end}...")
|
|
|
|
# 1. 取所有非退市股票代码
|
|
codes = list(db_ops.iter_stock_codes(active_only=True))
|
|
if not codes:
|
|
return {"status": "error", "message": "stocks 表为空"}
|
|
|
|
# 2. 拉所有 K 线（按股票）- 这里用 SQL GROUP BY 一次性算 daily turnover
|
|
from app.core.db.connection import get_mysql
|
|
|
|
sql = """
|
|
SELECT
|
|
stock_code,
|
|
trade_date,
|
|
`close`,
|
|
volume
|
|
FROM kline_stock
|
|
WHERE trade_date BETWEEN %s AND %s
|
|
ORDER BY stock_code, trade_date
|
|
"""
|
|
with get_mysql().cursor() as cur:
|
|
cur.execute(sql, (start, end))
|
|
rows = cur.fetchall()
|
|
if not rows:
|
|
return {"status": "warning", "message": f"kline_stock 在 {start} ~ {end} 区间无数据"}
|
|
|
|
df = pd.DataFrame(rows)
|
|
df["trade_date"] = pd.to_datetime(df["trade_date"], errors="coerce")
|
|
df["close"] = pd.to_numeric(df["close"], errors="coerce")
|
|
df["volume"] = pd.to_numeric(df["volume"], errors="coerce").fillna(0.0)
|
|
df = df.dropna(subset=["trade_date", "close"]).sort_values(["stock_code", "trade_date"])
|
|
df["prev_close"] = df.groupby("stock_code")["close"].shift(1)
|
|
df = df.dropna(subset=["prev_close"])
|
|
df["advancers"] = (df["close"] > df["prev_close"]).astype(int)
|
|
df["decliners"] = (df["close"] < df["prev_close"]).astype(int)
|
|
df["turnover"] = df["close"] * df["volume"]
|
|
|
|
daily = df.groupby("trade_date", as_index=False)[["advancers", "decliners", "turnover"]].sum()
|
|
total = daily["advancers"] + daily["decliners"]
|
|
daily["advance_ratio"] = np.where(total > 0, daily["advancers"] / total, np.nan)
|
|
daily["turnover_avg_5d"] = daily["turnover"].rolling(5, min_periods=5).mean()
|
|
daily["turnover_ratio_5d"] = np.where(
|
|
daily["turnover_avg_5d"] > 0,
|
|
daily["turnover"] / daily["turnover_avg_5d"],
|
|
np.nan,
|
|
)
|
|
daily["source"] = "local_kline_proxy"
|
|
daily["is_extreme_panic"] = daily.apply(_is_extreme_panic, axis=1)
|
|
daily["trade_date"] = daily["trade_date"].dt.strftime("%Y-%m-%d")
|
|
|
|
rows_out = [
|
|
{
|
|
"trade_date": r["trade_date"],
|
|
"advancers": float(r["advancers"]),
|
|
"decliners": float(r["decliners"]),
|
|
"advance_ratio": float(r["advance_ratio"]) if pd.notna(r["advance_ratio"]) else 0,
|
|
"turnover": float(r["turnover"]),
|
|
"turnover_avg_5d": float(r["turnover_avg_5d"]) if pd.notna(r["turnover_avg_5d"]) else 0,
|
|
"turnover_ratio_5d": float(r["turnover_ratio_5d"]) if pd.notna(r["turnover_ratio_5d"]) else 0,
|
|
"source": r["source"],
|
|
"is_extreme_panic": int(r["is_extreme_panic"]),
|
|
}
|
|
for _, r in daily.iterrows()
|
|
]
|
|
|
|
try:
|
|
db_ops.upsert_market_regime_rows(rows_out)
|
|
except Exception as e:
|
|
return {"status": "error", "message": f"写库失败: {e}"}
|
|
|
|
msg = f"market_regime {len(rows_out)} 天 ({rows_out[0]['trade_date']} ~ {rows_out[-1]['trade_date']})"
|
|
return {
|
|
"status": "ok",
|
|
"message": msg,
|
|
"rows": len(rows_out),
|
|
"date_min": rows_out[0]["trade_date"],
|
|
"date_max": rows_out[-1]["trade_date"],
|
|
}
|