diff --git a/qlib/backtest/account.py b/qlib/backtest/account.py
index 13213c344..03e51c740 100644
--- a/qlib/backtest/account.py
+++ b/qlib/backtest/account.py
@@ -73,6 +73,18 @@ class Account:
         pos_type: str = "Position",
         port_metr_enabled: bool = True,
     ):
+        """the trade account of backtest.
+
+        Parameters
+        ----------
+        init_cash : float, optional
+            initial cash, by default 1e9
+        position_dict : Dict[stock_id, {"amount": int, "price"(optional): float}], optional
+            initial stocks with amount and price,
+            if there is no price key in the dict of stocks, it will be filled by latest close price from qlib.
+            by default {}.
+        """
+
         self._pos_type = pos_type
         self._port_metr_enabled = port_metr_enabled
         self.init_vars(init_cash, position_dict, freq, benchmark_config)
@@ -93,6 +105,8 @@ class Account:
                 "kwargs": {
                     "cash": init_cash,
                     "position_dict": position_dict,
+                    "start_time": benchmark_config["start_time"],
+                    "freq": freq,
                 },
                 "module_path": "qlib.backtest.position",
             }
diff --git a/qlib/backtest/position.py b/qlib/backtest/position.py
index 7c32edc81..92b66a342 100644
--- a/qlib/backtest/position.py
+++ b/qlib/backtest/position.py
@@ -4,10 +4,14 @@
 
 import copy
 import pathlib
-from typing import Dict, List
+from typing import Dict, List, Union
+
 import pandas as pd
+from datetime import timedelta
 import numpy as np
+
 from .order import Order
+from ..data.data import D
 
 
 class BasePosition:
@@ -199,14 +203,72 @@ class Position(BasePosition):
     }
     """
 
-    def __init__(self, cash=0, position_dict={}):
+    def __init__(self, start_time, freq, cash: float = 0, position_dict: Dict[str, Dict[str, float]] = {}):
+        """Init position by cash and position_dict.
+
+        Parameters
+        ----------
+        start_time :
+            the start time of backtest. It's for filling the initial value of stocks.
+        cash : float, optional
+            initial cash in account, by default 0
+        position_dict : Dict[stock_id, {"amount": int, "price"(optional): float}], optional
+            initial stocks with parameters amount and price,
+            if there is no price key in the dict of stocks, it will be filled by _fill_stock_value.
+            by default {}.
+        """
+
         # NOTE: The position dict must be copied!!!
         # Otherwise the initial value
         self.init_cash = cash
-        self.position = position_dict.copy()
+        self.position = self._fill_stock_value(position_dict.copy(), start_time, freq)
         self.position["cash"] = cash
         self.position["now_account_value"] = self.calculate_value()
 
+    def _fill_stock_value(
+        self, position_dict: dict, start_time: Union[str, pd.Timestamp], freq: str, last_days: int = 30
+    ):
+        """fill the stock value by the close price of latest last_days from qlib.
+
+        Parameters
+        ----------
+        position_dict : Dict[stock_id, {"amount": int, "price": float}]
+            initial holding stocks.
+        start_time :
+            the start time of backtest.
+        last_days : int, optional
+            the days to get the latest close price, by default 30.
+
+        Return
+        ----------
+        Dict[stock_id, {"amount": int, "price": float}]
+            initial holding stocks with filled price.
+        """
+
+        stock_list = []
+        for stock in position_dict:
+            if ("price" not in position_dict[stock]) or (position_dict[stock]["price"] is None):
+                stock_list.append(stock)
+
+        if len(stock_list) == 0:
+            return position_dict
+
+        start_time = pd.Timestamp(start_time)
+        # note that start time is 2020-01-01 00:00:00 if raw start time is "2020-01-01"
+        price_end_time = start_time
+        price_start_time = start_time - timedelta(days=last_days)
+        price_df = D.features(
+            stock_list, ["$close"], price_start_time, price_end_time, freq=freq, disk_cache=True
+        ).dropna()
+        price_dict = price_df.groupby(["instrument"]).tail(1).reset_index(level=1, drop=True)["$close"].to_dict()
+
+        if len(price_dict) < len(stock_list):
+            raise ValueError(f"there is no close price in qlib")
+
+        for stock in stock_list:
+            position_dict[stock]["price"] = price_dict[stock]
+        return position_dict
+
     def _init_stock(self, stock_id, amount, price=None):
         """
         initialization the stock in current position