hmo
a293119a31
- market_watch.py: 新增 init_db() 建表 + write_snapshot() 双写 SQLite - market_snapshots: 每次采集的元信息（时间、来源、涨跌比、情绪） - sector_snapshots: 每个板块的涨跌幅、资金流向、领涨股等 - JSON 写入保留不变，SQLite 写入失败不影响 JSON 管道 - mofin_query.py: 通用查询工具 - 板块趋势查询：「半导体最近5次采集的涨跌幅」 - 资金流向排行：「净流入最多的5个板块」 - 连续净流入检测：「最近3天连续净流入的板块」 - 市场情绪趋势 + 数据库概览 - 支持直接 SQL 查询
266 lines
8.8 KiB
Python
266 lines
8.8 KiB
Python
#!/usr/bin/env python3
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"""market_watch.py — 行業熱點數據採集，寫入 dashboard data/market.json
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數據源優先級：
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後端A：東方財富 push2 API（首選，有板塊代碼+實時指數）
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後端B：同花順 THS / akshare（降級，有漲跌家數+資金流向）
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注意：當前服務器無法連通東方財富API（已被封禁/域名不可達），
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實際運行時自動降級到同花順 THS 後端。THS 提供90+行業板塊的
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實時漲跌、上漲/下跌家數、淨流入資金等數據，足以滿足需求。
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輸出：data/market.json → MoFin Dashboard 市場數據展示
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"""
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import json
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import sqlite3
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from datetime import datetime
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from pathlib import Path
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DATA_DIR = Path(__file__).parent / "data"
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DB_PATH = DATA_DIR / "mofin.db"
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# ── 数据库初始化 ──────────────────────────────────────
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def init_db():
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"""创建 mofin.db 及所有表（幂等，已存在则跳过）"""
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DATA_DIR.mkdir(parents=True, exist_ok=True)
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conn = sqlite3.connect(str(DB_PATH))
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conn.execute("PRAGMA journal_mode=WAL")
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conn.execute("PRAGMA foreign_keys=ON")
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conn.executescript("""
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CREATE TABLE IF NOT EXISTS market_snapshots (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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timestamp TEXT NOT NULL,
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source TEXT NOT NULL DEFAULT 'ths',
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up_ratio REAL,
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mood TEXT,
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created_at TEXT DEFAULT (datetime('now','localtime'))
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);
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CREATE INDEX IF NOT EXISTS idx_snapshots_time ON market_snapshots(timestamp);
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CREATE TABLE IF NOT EXISTS sector_snapshots (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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snapshot_id INTEGER NOT NULL REFERENCES market_snapshots(id),
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name TEXT NOT NULL,
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change_pct REAL,
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up_count INTEGER,
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down_count INTEGER,
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net_inflow REAL,
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lead_stock TEXT,
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lead_stock_change REAL,
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volume REAL,
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turnover REAL
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);
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CREATE INDEX IF NOT EXISTS idx_sector_name ON sector_snapshots(name);
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CREATE INDEX IF NOT EXISTS idx_sector_snapshot ON sector_snapshots(snapshot_id);
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CREATE INDEX IF NOT EXISTS idx_sector_name_time ON sector_snapshots(name, snapshot_id);
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""")
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conn.commit()
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return conn
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def write_snapshot(conn, market_data: dict):
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"""将一次采集结果双写 SQLite（JSON 写入由 main 负责）"""
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try:
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# 1. INSERT market_snapshots
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cur = conn.execute(
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"""INSERT INTO market_snapshots (timestamp, source, up_ratio, mood)
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VALUES (?, ?, ?, ?)""",
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(
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market_data["timestamp"],
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market_data.get("source", "unknown"),
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market_data.get("up_ratio", 0),
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market_data.get("mood", "unknown"),
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),
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)
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snapshot_id = cur.lastrowid
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# 2. INSERT sector_snapshots（逐板块）
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sectors = market_data.get("sectors", [])
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rows = []
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for s in sectors:
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rows.append((
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snapshot_id,
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s.get("name", ""),
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s.get("change", 0),
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s.get("up_count"),
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s.get("down_count"),
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s.get("net_inflow"),
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s.get("lead_stock"),
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s.get("lead_stock_change"),
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s.get("volume"),
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s.get("turnover"),
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))
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if rows:
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conn.executemany(
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"""INSERT INTO sector_snapshots
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(snapshot_id, name, change_pct, up_count, down_count,
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net_inflow, lead_stock, lead_stock_change, volume, turnover)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""",
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rows,
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)
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conn.commit()
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return snapshot_id, len(rows)
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except Exception as e:
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print(f"[DB] SQLite 写入失败（JSON 不受影响）: {e}", flush=True)
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try:
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conn.rollback()
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except Exception:
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pass
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return None, 0
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# ── 後端A：東方財富 push2 API（首選，有板塊代碼+實時指數） ──
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def _fetch_em(url):
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"""通用 EM API 請求"""
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import urllib.request
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req = urllib.request.Request(
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url,
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headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"}
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)
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resp = urllib.request.urlopen(req, timeout=10)
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return json.loads(resp.read().decode("utf-8"))
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def fetch_sector_em():
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"""東方財富行業板塊"""
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try:
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data = _fetch_em(
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"https://push2.eastmoney.com/api/qt/clist/get?"
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"pn=1&pz=60&po=1&np=1&fields=f2,f3,f4,f12,f14&fs=m:90+t:2"
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)
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return [{
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"name": i["f14"],
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"code": i["f12"],
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"price": i.get("f2", 0),
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"change": i.get("f3", 0),
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} for i in data.get("data", {}).get("diff", [])]
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except Exception:
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return None
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def fetch_concept_em():
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"""東方財富概念板塊"""
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try:
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data = _fetch_em(
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"https://push2.eastmoney.com/api/qt/clist/get?"
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"pn=1&pz=30&po=1&np=1&fields=f2,f3,f4,f12,f14&fs=m:90+t:3"
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)
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return [{
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"name": i["f14"],
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"code": i["f12"],
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"change": i.get("f3", 0),
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} for i in data.get("data", {}).get("diff", [])]
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except Exception:
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return None
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# ── 後端B：同花順 THS / akshare（降級） ──
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def fetch_sector_ths():
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"""THS 行業板塊（含漲跌家數、資金流向、領漲股）"""
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try:
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import akshare as ak
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df = ak.stock_board_industry_summary_ths()
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return [{
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"name": r["板块"],
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"code": "",
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"price": 0,
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"change": float(r.get("涨跌幅", 0)),
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"volume": float(r.get("总成交量", 0)),
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"turnover": float(r.get("总成交额", 0)),
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"net_inflow": float(r.get("净流入", 0)),
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"up_count": int(r.get("上涨家数", 0)),
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"down_count": int(r.get("下跌家数", 0)),
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"avg_price": float(r.get("均价", 0)),
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"lead_stock": r.get("领涨股", ""),
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"lead_stock_change": float(r.get("领涨股-涨跌幅", 0)),
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} for _, r in df.iterrows()]
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except Exception as e:
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print(f"THS行業失敗: {e}", flush=True)
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return []
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def fetch_concept_ths():
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"""THS 概念板塊（僅名稱，無實時漲跌）"""
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try:
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import akshare as ak
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df = ak.stock_board_concept_name_ths()
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return [{
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"name": r["name"],
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"code": str(r.get("code", "")),
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"change": 0,
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} for _, r in df.iterrows()]
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except Exception as e:
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print(f"THS概念失敗: {e}", flush=True)
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return []
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# ── 輔助函數 ──
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def get_market_mood(sectors):
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if not sectors:
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return "unknown"
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ratio = sum(1 for s in sectors if s.get("change", 0) > 0) / len(sectors)
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return "bullish" if ratio > 0.7 else "neutral" if ratio > 0.4 else "bearish"
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# ── 主流程 ──
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def main():
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# 行業板塊：EM → THS → 兜底
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sectors = fetch_sector_em()
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source = "eastmoney"
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if sectors is None:
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sectors = fetch_sector_ths()
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source = "ths"
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# 概念板塊：EM → THS → 空
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concepts = fetch_concept_em()
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concept_source = "eastmoney"
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if concepts is None:
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concepts = fetch_concept_ths()
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concept_source = "ths"
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if not concepts:
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concepts = []
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concept_source = "unavailable"
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# 排序
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sorted_sectors = sorted(sectors, key=lambda s: s.get("change", 0), reverse=True)
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top_gainers = [s for s in sorted_sectors if s.get("change", 0) > 0][:5]
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top_losers = [s for s in reversed(sorted_sectors) if s.get("change", 0) < 0][:3]
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market_data = {
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"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M"),
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"source": source,
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"concept_source": concept_source,
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"total_sectors": len(sectors),
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"up_ratio": round(
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sum(1 for s in sectors if s.get("change", 0) > 0) / max(len(sectors), 1) * 100, 1
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),
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"mood": get_market_mood(sectors),
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"top_gainers": top_gainers,
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"top_losers": top_losers,
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"sectors": sectors,
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"concepts": concepts,
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}
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DATA_DIR.mkdir(parents=True, exist_ok=True)
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with open(DATA_DIR / "market.json", "w", encoding="utf-8") as f:
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json.dump(market_data, f, ensure_ascii=False, indent=2)
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# ── SQLite 双写 ──
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conn = init_db()
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sid, count = write_snapshot(conn, market_data)
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if sid:
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print(f"[DB] snapshot_id={sid}, sectors={count}", flush=True)
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conn.close()
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# 靜默：只寫文件，不輸出到stdout，避免cron推送
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if __name__ == "__main__":
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main()
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