Storing API Data Into A DataFrame

Question

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?

Answer 1

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]