趋势量化基础（持续更新）

Get historical data of specific option

from ib_insync import *
import pandas as pd
from datetime import datetime
import pytz

util.startLoop()
ib = IB()
ib.connect("127.0.0.1", 7496, clientId=1)

contract = Option(
    symbol='QQQ', lastTradeDateOrContractMonth='20231004',
    strike=360, right='C', exchange='CBOE',
    multiplier=100, currency='USD')

# Use pytz library to define timezone
est = pytz.timezone('America/New_York')

endDateTime = datetime(2023, 9, 29, 16, 0, 0, tzinfo=est)

bars = ib.reqHistoricalData(
            contract=contract, endDateTime=endDateTime, durationStr='1 D', barSizeSetting='1 min',
            whatToShow='TRADES', useRTH=True)

# transform data to DataFrame
df = util.df(bars)

# print DataFrame
print(df)

The pytz library is a Python library for handling timezones, and it also utilizes the util module from the pandas library. The loop begins with util.startLoop(), establishing a continuous loop. Then, an IB (Interactive Brokers) connection is created. Following this, an Option contract is instantiated, using QQQ as an example, with an expiration date of 2023-10-04, a target price of 360, and a Call option on the CBOE exchange, denominated in US dollars.

Next, a variable est is created to represent the Eastern Standard Time (EST) timezone. Another variable is set for the desired end time of the data retrieval, which, in this case, is the closing time on 2023-09-29 at 4:00 PM. The timezone information is specified using tzinfo=est.

The ib.reqHistoricalData function is then used to retrieve the data with a duration of one day (durationStr='1 D') and a time interval of one minute (barSizeSetting='1 min'). Finally, the obtained data is printed using util.df(bars).

(vnpy) mikecaptain@CVN testspace % python ib_get_option_data_3.py
                         date  open  high   low  close  volume  average  barCount
0   2023-09-29 08:47:00-05:00  4.13  4.13  4.13   4.13     1.0     4.13         1
1   2023-09-29 08:48:00-05:00  4.13  4.13  4.13   4.13     0.0     4.13         0
2   2023-09-29 08:49:00-05:00  4.13  4.13  4.13   4.13     0.0     4.13         0
3   2023-09-29 08:50:00-05:00  4.13  4.13  4.13   4.13     0.0     4.13         0
4   2023-09-29 08:51:00-05:00  4.13  4.13  4.13   4.13     0.0     4.13         0
..                        ...   ...   ...   ...    ...     ...      ...       ...
383 2023-09-29 15:10:00-05:00  2.49  2.49  2.49   2.49     0.0     2.49         0
384 2023-09-29 15:11:00-05:00  2.49  2.49  2.49   2.49     0.0     2.49         0
385 2023-09-29 15:12:00-05:00  2.49  2.49  2.49   2.49     0.0     2.49         0
386 2023-09-29 15:13:00-05:00  2.49  2.49  2.49   2.49     0.0     2.49         0
387 2023-09-29 15:14:00-05:00  2.49  2.49  2.49   2.49     0.0     2.49         0

[388 rows x 8 columns]

Get historical data of specific stock and output it to a file

from ib_insync import *
from datetime import datetime, timedelta
import pytz

util.startLoop()
ib = IB()
ib.connect("127.0.0.1", 7496, clientId=1)

# 定义GOOG的股票合约
contract = Stock('GOOG', 'SMART', 'USD')

# 使用pytz库来定义美国东部时区
est = pytz.timezone('America/New_York')

# 获取当前日期
endDateTime = datetime.now(est)

# 计算5年前的日期
startDateTime = endDateTime - timedelta(days=5*365)

bars = ib.reqHistoricalData(
            contract=contract, endDateTime=endDateTime, durationStr='5 Y', barSizeSetting='1 day',
            whatToShow='TRADES', useRTH=True, formatDate=2)

print(util.df(bars))