diff --git a/data_platform/import_vnpy_daily_fast.py b/data_platform/import_vnpy_daily_fast.py
new file mode 100644
index 00000000..f97caab2
--- /dev/null
+++ b/data_platform/import_vnpy_daily_fast.py
@@ -0,0 +1,126 @@
+#!/usr/bin/env python3
+"""
+高效导入NAS日线Parquet到vnpy SQLite DB
+用pandas向量化代替逐行迭代，速度快10x+
+"""
+import sqlite3
+import pandas as pd
+import numpy as np
+import os
+import re
+import sys
+import time
+from pathlib import Path
+
+DB_PATH = '/tmp/quant_trading_import.db'
+DAILY_DIR = '/Volumes/stock/A股数据/日线数据/daily/'
+
+
+def parse_filename(filename):
+    m = re.match(r'(sh|sz)(\d{6})_daily\.parquet', filename)
+    if not m:
+        return None, None
+    prefix, code = m.groups()
+    return code, 'SSE' if prefix == 'sh' else 'SZSE'
+
+
+def import_year(conn, year):
+    year_dir = Path(DAILY_DIR) / str(year)
+    if not year_dir.exists():
+        return 0, 0
+    
+    files = sorted(year_dir.glob('*.parquet'))
+    if not files:
+        return 0, 0
+    
+    c = conn.cursor()
+    all_dfs = []
+    
+    for f in files:
+        code, exchange = parse_filename(f.name)
+        if code is None:
+            continue
+        try:
+            df = pd.read_parquet(f, columns=['date', 'open', 'high', 'low', 'close', 'volume', 'amount'])
+            if df.empty:
+                continue
+            df['symbol'] = code
+            df['exchange'] = exchange
+            all_dfs.append(df)
+        except Exception:
+            pass
+    
+    if not all_dfs:
+        return 0, 0
+    
+    combined = pd.concat(all_dfs, ignore_index=True)
+    
+    # Vectorized conversion
+    combined['datetime'] = combined['date'].astype(str)
+    combined['interval'] = 'd'
+    combined['open_interest'] = 0.0
+    combined = combined.rename(columns={
+        'open': 'open_price', 'high': 'high_price', 
+        'low': 'low_price', 'close': 'close_price', 'amount': 'turnover'
+    })
+    
+    # Fill NaN
+    for col in ['volume', 'turnover', 'open_price', 'high_price', 'low_price', 'close_price']:
+        combined[col] = combined[col].fillna(0.0).astype(float)
+    
+    values = combined[['symbol','exchange','datetime','interval','volume','turnover',
+                        'open_interest','open_price','high_price','low_price','close_price'
+                       ]].values.tolist()
+    
+    # Batch insert
+    BATCH = 50000
+    for i in range(0, len(values), BATCH):
+        c.executemany('''INSERT OR REPLACE INTO dbbardata 
+            (symbol,exchange,datetime,interval,volume,turnover,open_interest,
+             open_price,high_price,low_price,close_price) 
+            VALUES (?,?,?,?,?,?,?,?,?,?,?)''', values[i:i+BATCH])
+    conn.commit()
+    
+    return len(all_dfs), len(combined)
+
+
+def main():
+    start_year = 2017
+    for i, arg in enumerate(sys.argv):
+        if arg == '--start-year' and i+1 < len(sys.argv):
+            start_year = int(sys.argv[i+1])
+    
+    print(f'Importing from {start_year} to local DB: {DB_PATH}')
+    
+    conn = sqlite3.connect(DB_PATH)
+    total_rows = 0
+    t_start = time.time()
+    
+    for year in range(start_year, 2027):
+        t0 = time.time()
+        files, rows = import_year(conn, year)
+        t1 = time.time()
+        total_rows += rows
+        print(f'{year}: {files} files, {rows} rows ({t1-t0:.1f}s) total={total_rows}')
+    
+    elapsed = time.time() - t_start
+    
+    # Update overview
+    c = conn.cursor()
+    c.execute('''INSERT OR REPLACE INTO dbbaroverview (symbol,exchange,interval,count,start,end)
+        SELECT symbol,exchange,interval,COUNT(*),MIN(datetime),MAX(datetime)
+        FROM dbbardata GROUP BY symbol,exchange,interval''')
+    conn.commit()
+    
+    c.execute('SELECT COUNT(*) FROM dbbardata')
+    final = c.fetchone()[0]
+    c.execute('SELECT COUNT(*) FROM dbbaroverview')
+    overview = c.fetchone()[0]
+    
+    print(f'\nDone in {elapsed:.1f}s ({elapsed/60:.1f}min)')
+    print(f'Total rows: {final}, Overview entries: {overview}')
+    conn.close()
+
+
+if __name__ == '__main__':
+    main()