Here is the code that print out the data. However i don't see how to collect these data into a pandas dataframe. I used reqHistoricalData imported from ibapi (interactive broker) to request the data from TestApp class function which inherit EClient and EWrapper.
from ibapi.client import EClient
from ibapi.wrapper import EWrapper
from ibapi.contract import Contract
from ibapi.order import Order
from ibapi.ticktype import TickTypeEnum
import pandas as pd
import numpy as np
import os.path # To manage paths
import sys # To find out the script name (in argv[0])
from datetime import datetime
from time import sleep, strftime, localtime
from socket import error as SocketError
import errno
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 (bar.date, bar.open, bar.high, bar.low, bar.close, bar.volume)
def create_contract(symbol, sec_type, exch, prim_exch, curr):
contract = Contract()
contract.symbol = symbol
contract.secType = sec_type
contract.exchange = exch
contract.currency = curr
contract.primaryExchange = prim_exch
return contract
def create_order(order_type, quantity, action):
order = Order()
order.orderType = order_type
order.totalQuantity = quantity
order.action = action
return order
app = TestApp()
app.connect('127.0.0.1', 7497, 0)
contract = create_contract('AAPL', 'STK', 'SMART', 'NASDAQ', 'USD')
app.reqHistoricalData( reqId = 0,
contract = contract,
endDateTime = '',
durationStr = '1 Y',
barSizeSetting = '1 month',
whatToShow = 'TRADES',
useRTH = 1, # =1 for RTH data
formatDate = 1,
keepUpToDate = False,
chartOptions = []
)
app.run()
and the output is:
20181031 222.52 224.23 206.09 218.86 1752000
20181130 219.07 222.36 170.26 178.58 7249186
20181231 184.39 184.94 146.6 157.74 6851826
20190131 154.89 169.0 142.0 166.44 6383564
20190228 166.93 175.87 165.93 173.15 3478346
20190329 174.28 197.69 169.5 189.95 4956586
20190430 191.64 208.48 188.38 200.67 3812115
20190531 209.88 215.31 174.99 175.07 5642571
20190628 175.58 201.57 170.27 197.92 3592406
20190731 203.28 221.37 198.41 213.04 3418242
20190830 213.82 218.03 192.58 208.74 5078104
20190930 206.42 226.42 204.22 223.97 3768842
20191023 225.13 243.18 215.13 242.51 3253952
What i am looking for:
Open High Low Close Volume
Date
20181031 222.52 224.23 206.09 218.86 1752000
20181130 219.07 222.36 170.26 178.58 7249186
20181231 184.39 184.94 146.6 157.74 6851826
20190131 154.89 169.0 142.0 166.44 6383564
20190228 166.93 175.87 165.93 173.15 3478346
20190329 174.28 197.69 169.5 189.95 4956586
20190430 191.64 208.48 188.38 200.67 3812115
20190531 209.88 215.31 174.99 175.07 5642571
20190628 175.58 201.57 170.27 197.92 3592406
20190731 203.28 221.37 198.41 213.04 3418242
20190830 213.82 218.03 192.58 208.74 5078104
20190930 206.42 226.42 204.22 223.97 3768842
20191023 225.13 243.18 215.13 242.51 3253952
What I do is create a queue from the module queue.
So that goes something like...
def create_queue(self):
my_queue = queue.Queue()
self.my_hist_queue = my_queue
return my_queue
Then, when I define historicalData in the wrapper, I have it add it to the queue. It goes like...
def historicalData(self, reqId, bar):
print("HistoricalData. ", reqId,
"Date:", bar.date,
"Open:", bar.open,
"High:", bar.high,
"Low:", bar.low,
"Close:", bar.close,
"Volume:", bar.volume,
"Count:", bar.barCount,
"WAP:", bar.average)
self.my_hist_queue.put({'Request ID': reqId,
'Date': bar.date,
'Open': bar.open,
'High': bar.high,
'Low': bar.low,
'Close': bar.close,
'Volume': bar.volume,
'Count': bar.barCount,
'WAP': bar.average})
Then, it is relatively straight forward to iterate through the queue and put the historical data into a list of dictionaries. This way, pandas can easily convert it into a data frame. Here's how I do it...
def create_dataframe:
ticker_list = []
hist_storage = self.create_queue()
num_of_days = 5 #put however many days you want
data = self.reqHistoricalData(101, Contract, '', '{} D'.format(num_of_days), '1 day', "TRADES", 1, 1, False, [])
for i in range(number_of_days):
ticker_list.append(hist_storage.get())
df = pd.DataFrame(ticker_list)
print(df)
I hope this helps! Cheers!