Interactive Brokers Python多符号请求

Question

我能够在这个网站上从IB的文档/示例和论坛中拼凑出一个脚本。我得到了我想要的单个符号的输出，但是，如果我使用股票列表，我无法找到将股票代码传递到DF输出文件的方法。我的解决方法是创建一个使用列表序列的字典(见下文(，但是IB的api的输出每次都会略有变化，使符号变得毫无意义。我在下面使用的列表通常有20多个名称，但可能会更改，我将其减少以便于查看。

@Brian/和其他开发人员，如果有一种方法可以为每个符号调用创建一个唯一的ID/序列，并将其标记到返回的数据上，那么我可以使用字典来应用该符号。在另一个论坛中，你输入了一行n_id=n_id+1，如果这可以应用，并链接到按列表顺序进行的每个特定呼叫，那么这可以工作吗？

from ibapi.client import EClient
from ibapi.wrapper import EWrapper
from ibapi.contract import Contract
import pandas as pd
import threading
import time
from datetime import timedelta
import datetime
class IBapi(EWrapper, EClient):
def __init__(self):
EClient.__init__(self, self)
self.data = [] #Initialize variable to store candle
def historicalData(self, reqId, bar):
#print(f'Time: {bar.date} Close: {bar.close} Volume: {bar.volume}',reqId)
self.data.append([bar.date, bar.close, bar.volume, reqId])

def run_loop():
app.run()
app = IBapi()
app.connect('127.0.0.1', 7496, 123)
#Start the socket in a thread
api_thread = threading.Thread(target=run_loop, daemon=True)
api_thread.start()
time.sleep(1) #Sleep interval to allow time for connection to server
symbols = ['SPY','MSFT','GOOG','AAPL','QQQ','IWM','TSLA']
for sym in symbols:
contract = Contract()
contract.symbol = str(sym) 
contract.secType = "STK"
contract.exchange = "SMART"
contract.currency = "USD"
#contract.primaryExchange = "ISLAND"
app.reqHistoricalData(1, contract, "", "1 D", "10 mins", "ADJUSTED_LAST", 1, 2, False, [])
time.sleep(5) #sleep to allow enough time for data to be returned
df = pd.DataFrame(app.data, columns=['DateTime', 'ADJUSTED_LAST','Volume','reqId'])
df['DateTime'] = pd.to_datetime(df['DateTime'],unit='s') #,unit='s') 
df['Count'] = df.groupby('DateTime').cumcount()+1
sym_dict = {1:'SPY',2:'MSFT',3:'GOOG',4:'AAPL',5:'QQQ',6:'IWM',7:'TSLA'}
df['Ticker'] = df['Count'].map(sym_dict)
print(df)

#编辑，添加@Brian的详细信息：

from ibapi.client import EClient
from ibapi.wrapper import EWrapper
from ibapi.contract import Contract
import pandas as pd
import time
from datetime import timedelta
import datetime
start = datetime.datetime.utcnow()
class IBapi(EWrapper, EClient):
def __init__(self):
EClient.__init__(self, self)
self.data = [] 
def error(self, reqId, errorCode, errorString):
print("Error. Id: " , reqId, " Code: " , errorCode , " Msg: " , errorString)
def historicalData(self, reqId, bar):
self.data.append([bar.date, bar.close, bar.volume, sym_dict[reqId]])
print("HistoricalData. ReqId:", sym_dict[reqId], "BarData.", bar)

# include this callback to track progress and maybe disconnectwhen all are finished
def historicalDataEnd(self, reqId: int, start: str, end: str):
print("finished", sym_dict[reqId])
def run_loop():
app.run()
app = IBapi()
app.connect('127.0.0.1', 7496, 123)
# you should wait for nextValidId instead of sleeping, what if it takes more than 1 second? @john: how do i do this?
time.sleep(5) @john: how do i do this? wait for nextValidId?
symbols = ['SPY','MSFT','GOOG','AAPL','QQQ','IWM','TSLA']
reqId = 1
sym_dict = {}
for sym in symbols:
contract = Contract()
contract.symbol = str(sym) 
sym_dict[reqId] = sym
contract.secType = "STK"
contract.exchange = "SMART"
contract.currency = "USD"
#contract.primaryExchange = "ISLAND" # you may need this for msft
app.reqHistoricalData(reqId, contract, "", "1 D", "10 mins", "ADJUSTED_LAST", 1, 2, False, [])
reqId += 1
time.sleep(5)
df = pd.DataFrame(app.data, columns=['DateTime', 'ADJUSTED_LAST','Volume','sym'])
df['DateTime'] = pd.to_datetime(df['DateTime'],unit='s') #,unit='s') 
df = df.set_index(['sym','DateTime']).sort_index()
print(df)
app.disconnect()

Answer 1

您只需要维护reqId和symbol的dict。

我不确定一个DataFrame是否是存储数据的最佳方式，但如果你这样做了，那么就设置一个多索引。决定需要多少数据以及如何将其存储在磁盘上，然后决定数据结构。我建议使用csv来提高速度，或者使用sqlite来简化。熊猫可以应付任何一种情况。

我删除了你的评论，并添加了一些我自己的评论。

from ibapi.client import EClient
from ibapi.wrapper import EWrapper
from ibapi.contract import Contract
import pandas as pd
import threading
import time
from datetime import timedelta
import datetime
# I added this code to get fake data, works wtihout tws running
from ibapi.common import BarData
from random import random
start = datetime.datetime.utcnow()
def fake_data(reqId, ib):
last = reqId*10
for i in range(60, 0, -10):
bar = BarData();
bar.date = start - timedelta(minutes=i)
last += random() - 0.5
bar.close = last
bar.volume = reqId * 1000
ib.historicalData(reqId, bar)
ib.historicalDataEnd(reqId,"","")

class IBapi(EWrapper, EClient):
def __init__(self):
EClient.__init__(self, self)
self.data = [] 
#always include this for important messages, also turn on api logging in TWS/IBG    
def error(self, reqId, errorCode, errorString):
print("Error. Id: " , reqId, " Code: " , errorCode , " Msg: " , errorString)
def historicalData(self, reqId, bar):
self.data.append([bar.date, bar.close, bar.volume, sym_dict[reqId]])

# include this callback to track progress and maybe disconnectwhen all are finished
def historicalDataEnd(self, reqId: int, start: str, end: str):
print("finished", sym_dict[reqId])

def run_loop():
app.run()
app = IBapi()
app.connect('127.0.0.1', 7496, 123)
# threading is needed only if you plan to interact after run is called
# this is a good way if you use a ui like jupyter
api_thread = threading.Thread(target=run_loop, daemon=True)
api_thread.start()
# you should wait for nextValidId instead of sleeping, what if it takes more than 1 second?
time.sleep(1)
symbols = ['SPY','MSFT','GOOG','AAPL','QQQ','IWM','TSLA']
reqId = 1
sym_dict = {}
for sym in symbols:
contract = Contract()
contract.symbol = str(sym) 
sym_dict[reqId] = sym
contract.secType = "STK"
contract.exchange = "SMART"
contract.currency = "USD"
#contract.primaryExchange = "ISLAND" # you may need this for msft
#app.reqHistoricalData(reqId, contract, "", "1 D", "10 mins", "ADJUSTED_LAST", 1, 2, False, [])
fake_data(reqId, app)
reqId += 1
#now you need to sleep(10) to make sure you don't get a pacing error for too many requests

# don't sleep, use historicalDataEnd to know when finished
time.sleep(5)
df = pd.DataFrame(app.data, columns=['DateTime', 'ADJUSTED_LAST','Volume','sym'])
df['DateTime'] = pd.to_datetime(df['DateTime'],unit='s')
#make an index and sort
df = df.set_index(['sym','DateTime']).sort_index()
# now you can use the indexes
print(df.loc[("SPY","2021")])
#don't forget to disconnect somewhere or the clientId will still be in use