diff --git a/finrl/meta/data_processors/processor_yahoofinance.py b/finrl/meta/data_processors/processor_yahoofinance.py
index b1abdc3cc..11c4c2da0 100644
--- a/finrl/meta/data_processors/processor_yahoofinance.py
+++ b/finrl/meta/data_processors/processor_yahoofinance.py
@@ -121,10 +121,10 @@ def download_data(
         # convert the column names to match processor_alpaca.py as far as poss
         data_df.columns = [
             "timestamp",
-            "open",
+            "close",
             "high",
             "low",
-            "close",
+            "open",
             "volume",
             "tic",
         ]
diff --git a/finrl/meta/preprocessor/ibkrdownloader.py b/finrl/meta/preprocessor/ibkrdownloader.py
new file mode 100644
index 000000000..0bee18ae1
--- /dev/null
+++ b/finrl/meta/preprocessor/ibkrdownloader.py
@@ -0,0 +1,144 @@
+"""Contains methods and classes to collect data from
+interactive broker API
+"""
+
+from __future__ import annotations
+
+import math
+from datetime import datetime
+
+import pandas as pd
+from ib_insync import *
+
+
+class ibkrdownloader:
+    """Provides methods for retrieving daily stock data from
+    IBKR API
+
+    Attributes
+    ----------
+        start_date : str
+            start date of the data (modified from neofinrl_config.py)
+        end_date : str
+            end date of the data (modified from neofinrl_config.py)
+        ticker_list : list
+            a list of stock tickers (modified from neofinrl_config.py)
+        interval: str
+            ime period of one bar. Must be one of:
+                '1 secs', '5 secs', '10 secs' 15 secs', '30 secs',
+                '1 min', '2 mins', '3 mins', '5 mins', '10 mins', '15 mins',
+                '20 mins', '30 mins',
+                '1 hour', '2 hours', '3 hours', '4 hours', '8 hours',
+                '1 day', '1 week', '1 month'.
+
+    Methods
+    -------
+    fetch_data()
+        Fetches data from IBKR API
+    """
+
+    def __init__(
+        self,
+        start_date: str,
+        end_date: str,
+        ticker_list: list,
+        interval="1 day",
+        host="127.0.0.1",
+        port=7497,
+        clientId=999,
+    ) -> None:
+        self.start_date = start_date
+        self.end_date = end_date
+        self.ticker_list = ticker_list
+        self.interval = interval
+        self.host = host
+        self.port = port
+        self.clientId = clientId
+
+    def createContract(self, ticker):
+        contract = Stock(ticker, "SMART", "USD")
+        return contract
+
+    def connect2ibkr(self):
+        self.ib = IB()
+        self.ib.connect(self.host, self.port, clientId=self.clientId)
+
+    def calculate_duration(self, start_date_str, end_date_str):
+        start_date = datetime.strptime(start_date_str, "%Y-%m-%d")
+        end_date = datetime.strptime(end_date_str, "%Y-%m-%d")
+
+        duration_days = (end_date - start_date).days
+
+        return duration_days
+
+    def fetch_data(self) -> pd.DataFrame:
+        """Fetches data from IBKR API
+        Parameters
+        ----------
+
+        Returns
+        -------
+        `pd.DataFrame`
+            7 columns: A date, open, high, low, close, volume and tick symbol
+            for the specified stock ticker
+        """
+        # Download and save the data in a pandas DataFrame:
+        self.connect2ibkr()
+        data_df = pd.DataFrame()
+        duration = self.calculate_duration(self.start_date, self.end_date)
+        if duration > 365:
+            duration = f"{math.ceil(duration / 365)} Y"
+        else:
+            duration = f"{duration} D"
+
+        end_date_time = datetime.strptime(self.end_date, "%Y-%m-%d")
+        for tic in self.ticker_list:
+            contract = self.createContract(tic)
+            self.ib.qualifyContracts(contract)
+            bars = self.ib.reqHistoricalData(
+                contract,
+                endDateTime=end_date_time,
+                durationStr=duration,
+                barSizeSetting=self.interval,
+                whatToShow="TRADES",
+                useRTH=False,
+                formatDate=1,
+            )
+            temp_df = util.df(bars)
+            print(f"************* {tic} *************")
+            temp_df["tic"] = [tic] * len(temp_df)
+            data_df = pd.concat([data_df, temp_df], ignore_index=True)
+        data_df = data_df[["date", "open", "high", "low", "close", "volume", "tic"]]
+        filter_df = data_df[data_df["date"] > pd.Timestamp(self.start_date).date()]
+        filter_df = filter_df.sort_values(by=["date", "tic"])
+        filter_df.index = filter_df["date"].factorize()[0]
+
+        self.disconnect()
+        return filter_df
+
+    def disconnect(self):
+        self.ib.disconnect()
+
+    def select_equal_rows_stock(self, df):
+        df_check = df.tic.value_counts()
+        df_check = pd.DataFrame(df_check).reset_index()
+        df_check.columns = ["tic", "counts"]
+        mean_df = df_check.counts.mean()
+        equal_list = list(df.tic.value_counts() >= mean_df)
+        names = df.tic.value_counts().index
+        select_stocks_list = list(names[equal_list])
+        df = df[df.tic.isin(select_stocks_list)]
+        return df
+
+
+if __name__ == "__main__":
+    intr = ibkrdownloader(
+        "2023-01-01", "2023-04-12", ["AAPL", "MSFT", "CSCO", "WMT", "TSLA"]
+    )
+    try:
+        df = intr.fetch_data()
+        df.to_csv("data.csv", index=False)
+    except:
+        intr.disconnect()
+
+    intr.disconnect()