Backtrader 重采样到每日的问题
[英]Backtrader problems in resampling to daily
问题描述
Using backtrader, I would like to retrieve quotes at a five minute scale and resample at a daily scale.
使用 backtrader，我想以五分钟的规模检索报价并以每日的规模重新采样。
I am able to resample five minutes to sixty minutes, but can't do daily.我可以重新采样五分钟到六十分钟，但不能每天都这样做。 Here is the code:这是代码：
from __future__ import absolute_import, division, print_function, unicode_literals
import backtrader as bt
import backtrader.stores.ibstore as ibstore
import datetime
import logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
_CLIENTID = 100
class St(bt.Strategy):
def __init__(self):
self.sma = bt.indicators.SMA(self.data)
def logdata(self):
txt = []
txt.append('{}'.format(len(self)))
txt.append('{}'.format(self.data.datetime.datetime(0).isoformat()))
txt.append('{:.2f}'.format(self.data.open[0]))
txt.append('{:.2f}'.format(self.data.high[0]))
txt.append('{:.2f}'.format(self.data.low[0]))
txt.append('{:.2f}'.format(self.data.close[0]))
txt.append('{:.2f}'.format(self.data.volume[0]))
logger.debug(','.join(txt))
data_live = False
def notify_data(self, data, status, *args, **kwargs):
print('*' * 5, 'DATA Notification:', data._getstatusname(status), *args)
if status == data.LIVE:
self.data_live = True
def next(self):
self.logdata()
if not self.data_live:
return
_TICKER = "TSLA-STK-SMART-USD"
_FROMDATE = datetime.datetime(2021,1,4)
_TODATE = datetime.datetime(2021,1,29)
_HAS_STATS = False
def run(args=None):
cerebro = bt.Cerebro(stdstats=_HAS_STATS)
store = ibstore.IBStore(host="127.0.0.1", port=7497, clientId= _CLIENTID )
cerebro.broker = store.getbroker()
stockkwargs = dict(
timeframe=bt.TimeFrame.Minutes,
compression=10,
rtbar=False,
historical=True,
qcheck=0.5,
fromdate=_FROMDATE,
todate=_TODATE,
latethrough=False,
tradename=None
)
data0 = store.getdata(dataname=_TICKER, **stockkwargs)
# cerebro.resampledata(data0, timeframe=bt.TimeFrame.Minutes, compression=60)
cerebro.resampledata(data0, timeframe=bt.TimeFrame.Days, compression=1)
cerebro.run()
if __name__ == "__main__":
run()
I get a connection, but no days.我得到一个连接，但没有天。 Here is the output (with no bars printed):这是 output（没有打印条）：
Server Version: 76
TWS Time at connection:20210303 09:39:17 EST
***** DATA Notification: DELAYED
***** DATA Notification: DISCONNECTED
However, if I go on a 60-minute resample, all is well.但是，如果我对 go 进行 60 分钟的重新采样，一切都很好。 The code编码
cerebro.resampledata(data0, timeframe=bt.TimeFrame.Minutes, compression=60)
# cerebro.resampledata(data0, timeframe=bt.TimeFrame.Days, compression=1)
results in结果是
Server Version: 76
TWS Time at connection:20210303 09:36:27 EST
***** DATA Notification: DELAYED
DEBUG:__main__:30,2021-01-05T17:00:00,749.65,754.40,735.11,753.21,6011.00
DEBUG:__main__:31,2021-01-05T18:00:00,753.39,754.18,751.60,753.34,1126.00
DEBUG:__main__:32,2021-01-05T19:00:00,753.32,753.32,750.49,752.90,1179.00
DEBUG:__main__:33,2021-01-06T04:00:00,748.00,752.55,746.66,751.88,331.00
DEBUG:__main__:34,2021-01-06T05:00:00,751.60,753.00,749.26,750.50,137.00
(omitted)
DEBUG:__main__:286,2021-01-28T17:00:00,833.00,833.25,831.36,831.61,116.00
DEBUG:__main__:287,2021-01-28T18:00:00,831.60,833.96,830.11,831.00,175.00
DEBUG:__main__:288,2021-01-28T19:00:00,830.51,832.00,829.45,829.45,358.00
***** DATA Notification: DISCONNECTED
I am using these versions:我正在使用这些版本：
Python 3.7.4
ib 0.8.0
IbPy2 0.8.0
numpy 1.19.2
pandas 1.1.3
TL;DR I did several other experiments. TL; DR 我做了其他几个实验。
| getData timeframe获取数据时间范围 |
getData compression获取数据压缩 |
resample timeframe重新采样时间范围 |
resample compression重采样压缩 |
outcome结果 |
|---|---|---|---|---|
| Minutes分钟 |
5 5 |
Minutes分钟 |
60 60 |
can see hourly, as expected正如预期的那样，可以每小时查看一次 |
| Minutes分钟 |
5 5 |
Days天 |
1 1 |
(empty) （空的） |
| (blank) （空白的） |
(blank) （空白的） |
Days天 |
1 1 |
(empty) （空的） |
| Days天 |
1 1 |
Days天 |
1 1 |
(empty) （空的） |
| Days天 |
1 1 |
(commented out) （注释掉） |
(commented out) （注释掉） |
(empty) （空的） |
1 个解决方案
解决方案1
1 已采纳 2021-03-26 23:18:04
You are right there was a known issue a few years a back with this.你说得对，几年前就有一个已知问题。 There is an easy work-around though.不过，有一个简单的解决方法。 Instead of using the backtrader resampling facility, use Interactive Brokers.不要使用反向交易者重采样工具，而是使用盈透证券。
You can bypass resample and call the data directly from IB.您可以绕过重采样并直接从 IB 调用数据。 Just remember that you must add the shortest timeframe first to Backtrader.请记住，您必须首先将最短的时间范围添加到 Backtrader。
for tf_com in [(bt.TimeFrame.Minutes, 1), (bt.TimeFrame.Days, 1)]:
stockkwargs = dict(
timeframe=tf_com[0],
compression=tf_com[1],
rtbar=False,
historical=True,
qcheck=0.5,
fromdate=_FROMDATE,
todate=_TODATE,
latethrough=False,
tradename=None
)
data0 = store.getdata(dataname=_TICKER, **stockkwargs)
cerebro.adddata(data0)
You can adjust your timeframe and compression in the tuples in the first line.您可以在第一行的元组中调整时间范围和压缩。 I will adjust it to 5 and 15 minutes so you can see the output.我将其调整为 5 和 15 分钟，以便您可以看到 output。
for tf_com in [(bt.TimeFrame.Minutes, 5), (bt.TimeFrame.Minutes, 15)]:
************ OUTPUT ************
2021-03-25 12:00:00 636.77 632.71
2021-03-25 12:05:00 634.69 632.71
2021-03-25 12:10:00 632.71 632.71
2021-03-25 12:15:00 640.39 643.00
2021-03-25 12:20:00 640.68 643.00
2021-03-25 12:25:00 643.00 643.00
2021-03-25 12:30:00 641.33 640.00
2021-03-25 12:35:00 637.84 640.00
2021-03-25 12:40:00 640.00 640.00
2021-03-25 12:45:00 640.34 633.95
2021-03-25 12:50:00 636.15 633.95
2021-03-25 12:55:00 633.95 633.95
2021-03-25 13:00:00 633.50 637.43
2021-03-25 13:05:00 634.95 637.43
2021-03-25 13:10:00 637.43 637.43
EDIT: IN RESPONSE TO OP COMMENT编辑：回应 OP 评论
I beg to differ.我不敢苟同。 The days are sampled by Interactive Brokers so you will have daily information.天数由盈透证券抽样，因此您将获得每日信息。 You must be sure when using 5 minute data at the same time not to go back too far with your start date.您必须确保在同时使用 5 分钟数据时不要将 go 与您的开始日期相差太远。 IB is not a historical data provider. IB不是历史数据提供者。
Here is the entire code for your reference.这是完整的代码供您参考。
import backtrader as bt
import backtrader.stores.ibstore as ibstore
import datetime
import os
from dotenv import load_dotenv
load_dotenv()
class St(bt.Strategy):
def __init__(self):
self.data_live = False
self.timeframes = {4: "minute", 5: "day"}
def next(self):
time = self.data.datetime.time()
start_day = datetime.time(4, 20, 0)
end_day = datetime.time(19, 40, 0)
if time < start_day or time > end_day:
print_date = True
else:
print_date = False
if not self.data_live and print_date:
print(
f"{self.data.datetime.datetime()} "
f"data0: tf: {self.timeframes[self.datas[0]._timeframe]} "
f"comp: {self.datas[0]._compression}, "
f"{self.datas[0].close[0]:5.2f} "
f"data1: tf: {self.timeframes[self.datas[1]._timeframe]} "
f"comp: {self.datas[1]._compression} "
f"{self.datas[1].close[0]:5.2f} "
)
return
_TICKER = "TSLA-STK-SMART-USD"
_FROMDATE = datetime.datetime(2021, 3, 10)
_TODATE = datetime.datetime(2021, 3, 24)
_HAS_STATS = False
_CLIENTID = os.getenv("CLIENTID")
_PORT = os.getenv("SOCKET_PORT")
def run():
cerebro = bt.Cerebro(stdstats=_HAS_STATS)
cerebro.addstrategy(St)
store = ibstore.IBStore(host="127.0.0.1", port=int(_PORT), clientId=_CLIENTID)
cerebro.broker = store.getbroker()
for tf_com in [(bt.TimeFrame.Minutes, 5), (bt.TimeFrame.Days, 1)]:
stockkwargs = dict(
timeframe=tf_com[0],
compression=tf_com[1],
rtbar=False,
historical=True,
qcheck=0.5,
fromdate=_FROMDATE,
todate=_TODATE,
latethrough=False,
tradename=None,
)
data = store.getdata(dataname=_TICKER, **stockkwargs)
cerebro.adddata(data)
cerebro.run()
if __name__ == "__main__":
run()
Here is the output with the middle of the days removed.这是删除了中间日期的 output。
Please note that backtrader will set the next day value at the end of the last bar of the previous day.请注意，反向交易者将在前一天最后一根柱线的末尾设置下一天的值。
2021-03-10 19:55:00 data0: tf: minute comp: 5, 664.56 data1: tf: day comp: 1 664.56
2021-03-11 04:00:00 data0: tf: minute comp: 5, 699.10 data1: tf: day comp: 1 664.56
2021-03-11 04:05:00 data0: tf: minute comp: 5, 701.50 data1: tf: day comp: 1 664.56
2021-03-11 04:10:00 data0: tf: minute comp: 5, 699.00 data1: tf: day comp: 1 664.56
2021-03-11 04:15:00 data0: tf: minute comp: 5, 701.00 data1: tf: day comp: 1 664.56
2021-03-11 19:45:00 data0: tf: minute comp: 5, 698.05 data1: tf: day comp: 1 664.56
2021-03-11 19:50:00 data0: tf: minute comp: 5, 698.09 data1: tf: day comp: 1 664.56
2021-03-11 19:55:00 data0: tf: minute comp: 5, 698.50 data1: tf: day comp: 1 664.56
2021-03-11 19:55:00 data0: tf: minute comp: 5, 698.50 data1: tf: day comp: 1 698.50
2021-03-12 04:00:00 data0: tf: minute comp: 5, 674.25 data1: tf: day comp: 1 698.50
2021-03-12 04:05:00 data0: tf: minute comp: 5, 676.00 data1: tf: day comp: 1 698.50
2021-03-12 04:10:00 data0: tf: minute comp: 5, 669.00 data1: tf: day comp: 1 698.50
2021-03-12 04:15:00 data0: tf: minute comp: 5, 669.46 data1: tf: day comp: 1 698.50
2021-03-12 19:45:00 data0: tf: minute comp: 5, 693.30 data1: tf: day comp: 1 698.50
2021-03-12 19:50:00 data0: tf: minute comp: 5, 692.80 data1: tf: day comp: 1 698.50
2021-03-12 19:55:00 data0: tf: minute comp: 5, 692.99 data1: tf: day comp: 1 698.50
2021-03-12 19:55:00 data0: tf: minute comp: 5, 692.99 data1: tf: day comp: 1 692.99
2021-03-15 04:00:00 data0: tf: minute comp: 5, 689.00 data1: tf: day comp: 1 692.99
2021-03-15 04:05:00 data0: tf: minute comp: 5, 692.13 data1: tf: day comp: 1 692.99
2021-03-15 04:10:00 data0: tf: minute comp: 5, 692.12 data1: tf: day comp: 1 692.99
2021-03-15 04:15:00 data0: tf: minute comp: 5, 692.31 data1: tf: day comp: 1 692.99
2021-03-15 19:45:00 data0: tf: minute comp: 5, 702.00 data1: tf: day comp: 1 692.99
2021-03-15 19:50:00 data0: tf: minute comp: 5, 702.00 data1: tf: day comp: 1 692.99
2021-03-15 19:55:00 data0: tf: minute comp: 5, 702.00 data1: tf: day comp: 1 692.99
2021-03-15 19:55:00 data0: tf: minute comp: 5, 702.00 data1: tf: day comp: 1 702.00
2021-03-16 04:00:00 data0: tf: minute comp: 5, 704.01 data1: tf: day comp: 1 702.00
2021-03-16 04:05:00 data0: tf: minute comp: 5, 704.98 data1: tf: day comp: 1 702.00
2021-03-16 04:10:00 data0: tf: minute comp: 5, 704.99 data1: tf: day comp: 1 702.00
2021-03-16 04:15:00 data0: tf: minute comp: 5, 706.20 data1: tf: day comp: 1 702.00
2021-03-16 19:45:00 data0: tf: minute comp: 5, 673.65 data1: tf: day comp: 1 702.00
2021-03-16 19:50:00 data0: tf: minute comp: 5, 674.00 data1: tf: day comp: 1 702.00
2021-03-16 19:55:00 data0: tf: minute comp: 5, 674.10 data1: tf: day comp: 1 702.00
2021-03-16 19:55:00 data0: tf: minute comp: 5, 674.10 data1: tf: day comp: 1 674.10
2021-03-17 04:00:00 data0: tf: minute comp: 5, 672.00 data1: tf: day comp: 1 674.10
2021-03-17 04:05:00 data0: tf: minute comp: 5, 675.80 data1: tf: day comp: 1 674.10
2021-03-17 04:10:00 data0: tf: minute comp: 5, 677.19 data1: tf: day comp: 1 674.10
2021-03-17 04:15:00 data0: tf: minute comp: 5, 676.03 data1: tf: day comp: 1 674.10
2021-03-17 19:45:00 data0: tf: minute comp: 5, 699.66 data1: tf: day comp: 1 674.10
2021-03-17 19:50:00 data0: tf: minute comp: 5, 699.90 data1: tf: day comp: 1 674.10
2021-03-17 19:55:00 data0: tf: minute comp: 5, 699.74 data1: tf: day comp: 1 674.10
2021-03-17 19:55:00 data0: tf: minute comp: 5, 699.74 data1: tf: day comp: 1 699.74
2021-03-18 04:00:00 data0: tf: minute comp: 5, 685.00 data1: tf: day comp: 1 699.74
2021-03-18 04:05:00 data0: tf: minute comp: 5, 686.35 data1: tf: day comp: 1 699.74
2021-03-18 04:10:00 data0: tf: minute comp: 5, 688.32 data1: tf: day comp: 1 699.74
2021-03-18 04:15:00 data0: tf: minute comp: 5, 692.50 data1: tf: day comp: 1 699.74
2021-03-18 19:45:00 data0: tf: minute comp: 5, 652.10 data1: tf: day comp: 1 699.74
2021-03-18 19:50:00 data0: tf: minute comp: 5, 651.00 data1: tf: day comp: 1 699.74
2021-03-18 19:55:00 data0: tf: minute comp: 5, 650.56 data1: tf: day comp: 1 699.74
2021-03-18 19:55:00 data0: tf: minute comp: 5, 650.56 data1: tf: day comp: 1 650.56
2021-03-19 04:00:00 data0: tf: minute comp: 5, 661.00 data1: tf: day comp: 1 650.56
2021-03-19 04:05:00 data0: tf: minute comp: 5, 663.00 data1: tf: day comp: 1 650.56
2021-03-19 04:10:00 data0: tf: minute comp: 5, 663.60 data1: tf: day comp: 1 650.56
2021-03-19 04:15:00 data0: tf: minute comp: 5, 666.48 data1: tf: day comp: 1 650.56
2021-03-19 19:45:00 data0: tf: minute comp: 5, 652.50 data1: tf: day comp: 1 650.56
2021-03-19 19:50:00 data0: tf: minute comp: 5, 652.02 data1: tf: day comp: 1 650.56
2021-03-19 19:55:00 data0: tf: minute comp: 5, 652.20 data1: tf: day comp: 1 650.56
2021-03-19 19:55:00 data0: tf: minute comp: 5, 652.20 data1: tf: day comp: 1 652.20
2021-03-22 04:00:00 data0: tf: minute comp: 5, 665.97 data1: tf: day comp: 1 652.20
2021-03-22 04:05:00 data0: tf: minute comp: 5, 664.00 data1: tf: day comp: 1 652.20
2021-03-22 04:10:00 data0: tf: minute comp: 5, 665.00 data1: tf: day comp: 1 652.20
2021-03-22 04:15:00 data0: tf: minute comp: 5, 663.94 data1: tf: day comp: 1 652.20
2021-03-22 19:45:00 data0: tf: minute comp: 5, 668.39 data1: tf: day comp: 1 652.20
2021-03-22 19:50:00 data0: tf: minute comp: 5, 668.56 data1: tf: day comp: 1 652.20
2021-03-22 19:55:00 data0: tf: minute comp: 5, 669.35 data1: tf: day comp: 1 652.20
2021-03-22 19:55:00 data0: tf: minute comp: 5, 669.35 data1: tf: day comp: 1 669.35
2021-03-23 04:00:00 data0: tf: minute comp: 5, 670.25 data1: tf: day comp: 1 669.35
2021-03-23 04:05:00 data0: tf: minute comp: 5, 665.33 data1: tf: day comp: 1 669.35
2021-03-23 04:10:00 data0: tf: minute comp: 5, 664.11 data1: tf: day comp: 1 669.35
2021-03-23 04:15:00 data0: tf: minute comp: 5, 662.93 data1: tf: day comp: 1 669.35
2021-03-23 19:45:00 data0: tf: minute comp: 5, 661.00 data1: tf: day comp: 1 669.35
2021-03-23 19:50:00 data0: tf: minute comp: 5, 661.08 data1: tf: day comp: 1 669.35
2021-03-23 19:55:00 data0: tf: minute comp: 5, 663.00 data1: tf: day comp: 1 669.35
2021-03-23 19:55:00 data0: tf: minute comp: 5, 663.00 data1: tf: day comp: 1 663.00
=== OP Verification === === OP 验证 ===
Here is what the data0 and data1 plot look like, adding a cerebro.plot() after the cerebro.run() .这是 data0 和 data1 plot 的样子，在cerebro.plot()之后添加cerebro.run() 。
That's good looking daily data.这是好看的每日数据。
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