diff --git a/omega/fetchers/tushare.py b/omega/fetchers/tushare.py
index baf27ea..5e7fc8e 100644
--- a/omega/fetchers/tushare.py
+++ b/omega/fetchers/tushare.py
@@ -1,3 +1,113 @@
+import datetime
+import logging
+from functools import cache
+from itertools import product
+from typing import List
+
+import cfg4py
+import pandas as pd
 import tushare as ts
-from coretypes import SecurityInfoSchema
+from coretypes import DayBarsSchema, FrameType, SecurityInfoSchema
+from omicron import tf
+from omicron.extensions import chunkify
 from pandera.typing import DataFrame
+from tushare.pro.client import DataApi
+
+logger = logging.getLogger(__name__)
+
+@cache
+def pro_api() -> DataApi:
+    """获取登录后的pro_api接口"""
+    cfg = cfg4py.get_instance()
+    ts.set_token(cfg.tushare.token)
+    return ts.pro_api()
+
+
+def fetch_stock_bars_daily(codes: List[str], start: datetime.date, end: datetime.date):
+    """获取日线行情数据
+
+    本API将获取日线行情(frame, OHLC, vol, money, factor, turnover)。
+    本API获得的复权因子，在计算上遵循[复权方法](https://tushare.pro/document/2?doc_id=146)：当日收盘价 × 当日复权因子 / 最新复权因子
+    """
+    batch = 6000
+
+    fields = ["trade_date", "ts_code", "open", "high", "low", "close", "vol", "amount"]
+    extend_fields = [
+        "ts_code",
+        "trade_date",
+        "turnover_rate",
+        "turnover_rate_f",
+        "volume_ratio",
+        "pe",
+        "pe_ttm",
+        "pb",
+        "ps",
+        "ps_ttm",
+        "dv_ratio",
+        "dv_ttm",
+        "total_share",
+        "float_share",
+        "free_share",
+        "total_mv",
+        "circ_mv",
+    ]
+
+    ohlc = []
+    factors = []
+    limits = []
+    suspend = []
+    extended = []
+    # 获取OHLC数据和复权数据
+    for frame in tf.get_frames(start, end, FrameType.DAY):
+        sframe = str(frame)
+        logger.info(f"fetching data for frame %s", frame)
+        for sub in chunkify(codes, batch):
+            ts_codes = ",".join(sub)
+            df = pro_api().daily(
+                ts_code=ts_codes, start_date=sframe, end_date=sframe, fields=fields
+            )
+            ohlc.append(df)
+
+        # 复权数据，据tushare文档，此调用方法可以获得单日全部股票复权因子，未提及限制
+        df = pro_api().adj_factor(trade_date=sframe)
+        factors.append(df)
+
+        # 获取涨跌停限价
+        df = pro_api().stk_limit(trade_date=sframe)
+        limits.append(df)
+
+        # 获取停牌
+        df = pro_api().suspend_d(
+            suspend_type="S",
+            trade_date=sframe,
+            fields=["ts_code", "trade_date", "suspend_type"],
+        )
+        suspend.append(df)
+
+        # 换手率及其它
+        df = pro_api().daily_basic(trade_date=sframe, fields=extend_fields)
+        extended.append(df)
+
+    ohlc = pd.concat(ohlc)
+    factors = pd.concat(factors)
+    limits = pd.concat(limits)
+    suspend = pd.concat(suspend)
+    suspend["suspend_type"] = suspend.suspend_type.apply(lambda x: x == "S")
+    extended = pd.concat(extended)
+
+    left = ohlc
+    for right in (factors, limits, suspend, extended):
+        left = pd.merge(left, right, on=["ts_code", "trade_date"], how="left")
+
+    df = left.rename({
+        "ts_code": "code",
+        "trade_date": "frame",
+        "vol": "volume",
+        "adj_factor": "factor",
+        "up_limit": "hlimit",
+        "down_limit": "llimit",
+        "suspend_type": "suspended",
+        "turnover_rate": "turnover"
+    })
+
+    df["suspended"] = df.suspended.astype(int)