Backtrader 重采样到每日的问题
Backtrader problems in resampling to daily
我想使用 backtrader 以五分钟为单位检索报价并以日为单位重新采样。
我可以重新采样五分钟到六十分钟，但不能每天都做。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()
我有联系，但没有几天。这是输出（没有打印条）：
Server Version: 76
TWS Time at connection:20210303 09:39:17 EST
***** DATA Notification: DELAYED
***** DATA Notification: DISCONNECTED
但是，如果我进行 60 分钟的重新采样，一切都很好。代码
cerebro.resampledata(data0, timeframe=bt.TimeFrame.Minutes, compression=60)
# cerebro.resampledata(data0, timeframe=bt.TimeFrame.Days, compression=1)
结果
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
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
长话短说；博士
我做了其他几个实验。
getData timeframe
getData compression
resample timeframe
resample compression
outcome
Minutes
5
Minutes
60
can see hourly, as expected
Minutes
5
Days
1
(empty)
(blank)
(blank)
Days
1
(empty)
Days
1
Days
1
(empty)
Days
1
(commented out)
(commented out)
(empty)
你是对的，几年前有一个已知问题。不过有一个简单的解决方法。不要使用 backtrader 重采样工具，而是使用 Interactive Brokers。
您可以绕过重采样并直接从 IB 调用数据。请记住，您必须先将最短时间范围添加到 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)
您可以在第一行的元组中调整时间范围和压缩率。我会把它调整到 5 和 15 分钟，这样你就可以看到输出了。
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
编辑：回应 OP 评论
我不敢苟同。 Interactive Brokers 对这些天数进行抽样，因此您将获得每日信息。在同时使用 5 分钟数据时，您必须确保不要将开始日期倒退太远。 IB 不是历史数据提供商。
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()
这是删除了中间日期的输出。
请注意，backtrader 将在前一天的最后一根柱线结束时设置第二天的值。
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验证===
这是 data0 和 data1 图的样子，在 cerebro.run() 之后添加了一个 cerebro.plot()。
每天的数据很好看。
我想使用 backtrader 以五分钟为单位检索报价并以日为单位重新采样。
我可以重新采样五分钟到六十分钟，但不能每天都做。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()
我有联系，但没有几天。这是输出（没有打印条）：
Server Version: 76
TWS Time at connection:20210303 09:39:17 EST
***** DATA Notification: DELAYED
***** DATA Notification: DISCONNECTED
但是，如果我进行 60 分钟的重新采样，一切都很好。代码
cerebro.resampledata(data0, timeframe=bt.TimeFrame.Minutes, compression=60)
# cerebro.resampledata(data0, timeframe=bt.TimeFrame.Days, compression=1)
结果
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
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
长话短说；博士 我做了其他几个实验。
| getData timeframe | getData compression | resample timeframe | resample compression | outcome |
|---|---|---|---|---|
| Minutes | 5 | Minutes | 60 | can see hourly, as expected |
| Minutes | 5 | Days | 1 | (empty) |
| (blank) | (blank) | Days | 1 | (empty) |
| Days | 1 | Days | 1 | (empty) |
| Days | 1 | (commented out) | (commented out) | (empty) |
你是对的，几年前有一个已知问题。不过有一个简单的解决方法。不要使用 backtrader 重采样工具，而是使用 Interactive Brokers。
您可以绕过重采样并直接从 IB 调用数据。请记住，您必须先将最短时间范围添加到 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)
您可以在第一行的元组中调整时间范围和压缩率。我会把它调整到 5 和 15 分钟，这样你就可以看到输出了。
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
编辑：回应 OP 评论
我不敢苟同。 Interactive Brokers 对这些天数进行抽样，因此您将获得每日信息。在同时使用 5 分钟数据时，您必须确保不要将开始日期倒退太远。 IB 不是历史数据提供商。
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()
这是删除了中间日期的输出。
请注意，backtrader 将在前一天的最后一根柱线结束时设置第二天的值。
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验证===
这是 data0 和 data1 图的样子，在 cerebro.run() 之后添加了一个 cerebro.plot()。
每天的数据很好看。