Storing API Data Into A DataFrame

user_13449937 0
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I am running a Python Script to collect financial market data from Interactive Brokers API. After connecting to the API the terminal prints out requested historical data. How do I have the data saved into a dataframe rather than streamed in terminal?

from ibapi.wrapper import EWrapper
from ibapi.client import EClient 
from ibapi.contract import Contract
from ibapi.ticktype import TickTypeEnum


class TestApp(EWrapper, EClient):
    def __init__(self):
        EClient.__init__(self,self)

    def error(self, reqId, errorCode, errorString):
        print("Error: ", reqId, " ", errorCode, " ", errorString)

    def historicalData(self, reqId, bar):
        print("historicalData. ", reqId, "Data:", bar.date, "Open:", bar.open, "High:", bar.high, "low:", bar.low, "close:", bar.close, "Volume:", bar.volume, "WAP:", bar.average)


def main():
    app = TestApp()
    app.connect("127.0.0.1", 7497, 0)

    contract = Contract()
    contract.symbol = "EUR"
    contract.secType = "CASH"
    contract.exchange = "IDEALPRO"
    contract.currency = "USD"

    app.reqHistoricalData(1, contract, "", "1 D", "1 min", "MIDPOINT", 0, 1, False, [])

    app.run()


if __name__ == "__main__":
    main()

The output of the code prints a feed of historical data, such as:

historicalData.  1 Data: 20200616  11:53:00 Open: 1.125985 High: 1.12601 low: 1.12592 close: 1.12592 Volume: -1 WAP: -1.0
historicalData.  1 Data: 20200616  11:54:00 Open: 1.12592 High: 1.125925 low: 1.12583 close: 1.125885 Volume: -1 WAP: -1.0
historicalData.  1 Data: 20200616  11:55:00 Open: 1.125885 High: 1.126045 low: 1.125865 close: 1.126045 Volume: -1 WAP: -1.0

How do I have this information stored into a dataframe rather than just printed in terminal?

interactive-brokers algorithmic-trading dataframe api python
2 Answers

You could create a dataframe in the TestApp object then add a row to it every time historicalData() is called.

def __init__(self):
    ...
    self.df = pd.DataFrame(columns=['date', 'open', 'high', 'low', 'close', 'volume'])


def historicalData(self, reqId, bar):
    self.df.loc[len(self.df)] = [bar.date, bar.open, bar.high, bar.low, bar.close, bar.volume]
user_112348340

You can use DataFrame.append() function too.

After all historical data is returned, write the whole DataFrame into database, and then clear up the original dataframe to reuse for the next cycle.

user_150486840
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