refactor: 数据层重构 — 统一 SQLite 访问层 + 多脚本双写
新建 mofin_db.py 共享数据库模块: - get_conn() 统一连接管理 (WAL + Row factory + 外键) - init_all_tables() 幂等建表 (12张表: market/sector/stock/kline/fundamentals/sectors/holdings/strategies/watchlist/candidates/score_history/events/evaluations) - write_market_snapshot() 市场快照双写 - write_klines() K线数据双写 (stocks + daily/weekly/monthly + fundamentals) - write_price_event() 价格事件双写 - migrate_stock_sectors() 一次性迁移 stock_sector_map.json - query_*() 通用查询函数 (sector_trend/top_inflow/consecutive_inflow/market_mood/db_stats) 重构现有脚本: - market_watch.py: 删除内联 DB 代码，改用 mofin_db - multi_timeframe.py: _save_local_history() 加 SQLite 双写 - price_monitor.py: record_event() 加 SQLite 双写 - mofin_query.py: 改用 mofin_db 查询函数 新增: - migrate_sectors.py: 一次性迁移脚本 清理: - get_realtime_prices.py: 死代码 (只读 portfolio.json，不调API)
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@@ -6,41 +6,22 @@
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python3 mofin_query.py "今天资金净流入最多的5个板块"
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python3 mofin_query.py "最近3天连续净流入的板块"
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python3 mofin_query.py "市场情绪趋势(最近10次)"
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python3 mofin_query.py "数据库概览"
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"""
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import sqlite3
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import sys
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from pathlib import Path
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DB_PATH = Path(__file__).parent / "data" / "mofin.db"
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import re
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from mofin_db import (get_conn, query_sector_trend, query_top_inflow,
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query_consecutive_inflow, query_market_mood, query_db_stats)
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def get_conn():
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conn = sqlite3.connect(str(DB_PATH))
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conn.row_factory = sqlite3.Row
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return conn
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# ── 预定义查询 ──────────────────────────────────────
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def query_sector_trend(name: str, limit: int = 5):
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"""查询某板块最近N次采集的涨跌幅趋势"""
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def _print_sector_trend(name: str, limit: int = 5):
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conn = get_conn()
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rows = conn.execute("""
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SELECT s.timestamp, ss.change_pct, ss.net_inflow,
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ss.up_count, ss.down_count, ss.lead_stock, ss.lead_stock_change
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FROM sector_snapshots ss
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JOIN market_snapshots s ON ss.snapshot_id = s.id
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WHERE ss.name = ?
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ORDER BY s.timestamp DESC
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LIMIT ?
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""", (name, limit)).fetchall()
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rows = query_sector_trend(conn, name, limit)
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conn.close()
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if not rows:
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print(f"未找到板块「{name}」的数据")
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return
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print(f"\n{'='*60}")
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print(f" {name} 板块 — 最近 {len(rows)} 次采集")
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print(f"{'='*60}")
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@@ -51,24 +32,13 @@ def query_sector_trend(name: str, limit: int = 5):
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f"{r['up_count'] or '-':>6} {r['down_count'] or '-':>6} {r['lead_stock'] or '-':>10}")
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def query_top_inflow(limit: int = 5):
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"""最新一次采集中资金净流入最多的板块"""
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def _print_top_inflow(limit: int = 5):
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conn = get_conn()
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rows = conn.execute("""
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SELECT ss.name, ss.change_pct, ss.net_inflow, ss.lead_stock, s.timestamp
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FROM sector_snapshots ss
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JOIN market_snapshots s ON ss.snapshot_id = s.id
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WHERE s.id = (SELECT MAX(id) FROM market_snapshots)
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AND ss.net_inflow IS NOT NULL
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ORDER BY ss.net_inflow DESC
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LIMIT ?
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""", (limit,)).fetchall()
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rows = query_top_inflow(conn, limit)
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conn.close()
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if not rows:
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print("暂无数据")
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return
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print(f"\n{'='*60}")
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print(f" 资金净流入 Top {len(rows)}（{rows[0]['timestamp']}）")
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print(f"{'='*60}")
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@@ -78,26 +48,13 @@ def query_top_inflow(limit: int = 5):
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print(f"{r['name']:<12} {r['change_pct']:>8.2f} {r['net_inflow']:>10.2f} {r['lead_stock'] or '-':>10}")
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def query_consecutive_inflow(days: int = 3):
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"""最近N次采集中连续净流入的板块"""
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def _print_consecutive_inflow(days: int = 3):
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conn = get_conn()
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rows = conn.execute("""
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SELECT name, COUNT(*) as times, ROUND(AVG(net_inflow), 2) as avg_inflow,
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ROUND(AVG(change_pct), 2) as avg_change
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FROM sector_snapshots ss
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JOIN market_snapshots s ON ss.snapshot_id = s.id
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WHERE s.id > (SELECT MAX(id) - ? FROM market_snapshots)
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AND net_inflow > 0
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GROUP BY name
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HAVING COUNT(*) >= ?
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ORDER BY avg_inflow DESC
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""", (days, days)).fetchall()
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rows = query_consecutive_inflow(conn, days)
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conn.close()
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if not rows:
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print(f"没有板块连续 {days} 次净流入")
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return
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print(f"\n{'='*60}")
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print(f" 连续 {days} 次净流入的板块")
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print(f"{'='*60}")
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@@ -107,21 +64,13 @@ def query_consecutive_inflow(days: int = 3):
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print(f"{r['name']:<12} {r['times']:>4} {r['avg_inflow']:>12.2f} {r['avg_change']:>10.2f}")
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def query_market_mood(limit: int = 10):
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"""市场情绪趋势"""
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def _print_market_mood(limit: int = 10):
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conn = get_conn()
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rows = conn.execute("""
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SELECT timestamp, source, up_ratio, mood
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FROM market_snapshots
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ORDER BY timestamp DESC
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LIMIT ?
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""", (limit,)).fetchall()
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rows = query_market_mood(conn, limit)
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conn.close()
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if not rows:
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print("暂无数据")
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return
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print(f"\n{'='*60}")
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print(f" 市场情绪趋势 — 最近 {len(rows)} 次")
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print(f"{'='*60}")
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@@ -132,96 +81,69 @@ def query_market_mood(limit: int = 10):
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print(f"{r['timestamp']:<20} {r['source']:>10} {r['up_ratio']:>10.1f} {mood_emoji} {r['mood']:>8}")
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def query_stats():
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"""数据库概览统计"""
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def _print_stats():
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conn = get_conn()
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snap_count = conn.execute("SELECT COUNT(*) FROM market_snapshots").fetchone()[0]
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sector_count = conn.execute("SELECT COUNT(*) FROM sector_snapshots").fetchone()[0]
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latest = conn.execute(
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"SELECT timestamp, source FROM market_snapshots ORDER BY id DESC LIMIT 1"
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).fetchone()
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stats = query_db_stats(conn)
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conn.close()
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print(f"\n{'='*40}")
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print(f" MoFin 数据库概览")
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print(f"{'='*40}")
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print(f" 采集次数: {snap_count}")
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print(f" 板块快照: {sector_count}")
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if latest:
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print(f" 最新采集: {latest['timestamp']} ({latest['source']})")
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print(f" 采集次数: {stats['snapshots']}")
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print(f" 板块快照: {stats['sector_rows']}")
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print(f" 个股数量: {stats['stocks']}")
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print(f" 日K线数: {stats['daily_klines']}")
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print(f" 价格事件: {stats['price_events']}")
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ls = stats.get('latest_snapshot')
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if ls:
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print(f" 最新采集: {ls['timestamp']} ({ls['source']})")
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else:
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print(f" 最新采集: 暂无")
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# ── 智能路由 ──────────────────────────────────────
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def route(query: str):
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q = query.strip()
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# 板块趋势
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if "最近" in q and "次" in q and ("涨跌" in q or "趋势" in q or "采集" in q):
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# 提取板块名
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import re
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names = re.findall(r'["「]([^"」]+)["」]', q)
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if not names:
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# 尝试无引号匹配
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for word in ["半导体", "银行", "医药", "新能源", "白酒", "军工", "芯片", "房地产", "汽车"]:
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if word in q:
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names = [word]
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break
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names = [word]; break
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if names:
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limit = 5
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m = re.search(r'(\d+)\s*次', q)
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if m:
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limit = int(m.group(1))
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query_sector_trend(names[0], limit)
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if m: limit = int(m.group(1))
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_print_sector_trend(names[0], limit)
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return
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# 资金净流入排行
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if "净流入" in q and ("最多" in q or "排行" in q or "top" in q.lower()):
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limit = 5
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import re
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m = re.search(r'(\d+)', q)
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if m:
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limit = int(m.group(1))
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query_top_inflow(limit)
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if m: limit = int(m.group(1))
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_print_top_inflow(limit)
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return
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# 连续净流入
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if "连续" in q and "净流入" in q:
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days = 3
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import re
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m = re.search(r'(\d+)\s*天', q)
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if m:
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days = int(m.group(1))
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query_consecutive_inflow(days)
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if m: days = int(m.group(1))
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_print_consecutive_inflow(days)
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return
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# 市场情绪
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if "情绪" in q or "mood" in q.lower():
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limit = 10
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import re
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m = re.search(r'(\d+)\s*次', q)
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if m:
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limit = int(m.group(1))
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query_market_mood(limit)
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if m: limit = int(m.group(1))
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_print_market_mood(limit)
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return
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# 统计概览
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if "概览" in q or "统计" in q or "stats" in q.lower():
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query_stats()
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_print_stats()
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return
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# 直接 SQL（以 SELECT 开头）
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if q.upper().strip().startswith("SELECT"):
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conn = get_conn()
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try:
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rows = conn.execute(q).fetchall()
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if rows:
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cols = rows[0].keys()
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cols = [d[0] for d in conn.execute(q + " LIMIT 0").description]
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print("\t".join(cols))
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for r in rows:
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print("\t".join(str(r[c]) for c in cols))
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print("\t".join(str(c) for c in r))
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else:
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print("(empty)")
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except Exception as e:
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@@ -229,10 +151,7 @@ def route(query: str):
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finally:
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conn.close()
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return
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# 未匹配
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print(f"未识别的查询: {q}")
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print()
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print(f"未识别的查询: {q}\n")
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print("支持的查询模式:")
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print(" 「半导体」最近5次采集的涨跌幅")
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print(" 今天资金净流入最多的5个板块")
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@@ -247,5 +166,4 @@ if __name__ == "__main__":
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print("用法: python3 mofin_query.py \"查询语句\"")
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print("示例: python3 mofin_query.py \"半导体最近5次采集的涨跌幅\"")
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sys.exit(1)
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route(sys.argv[1])
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