优化improvements- backtrader中文教程
- 发表于：
-
作者：
hao chen
- 分类： backtrader, 量化交易与机器学习
- 标签： args, argument, Backtrader, core, high, improvements, MA, MACD, management, optdatas, PARSER, runs, Sample, Usage, 原始, 回测系统
优化improvements
的backtrader包括如何改善data feeds和resultsmultiprocessing.
Note
两种行为已made
这些选项的行为可以通过两种新的Cerebro控制参数：
optdatas（默认值：True）如果
True和优化（并且该系统可以preload和使用runonce，数据预载将在主过程只需进行一次为了节省时间和resources.optreturn（默认：True）如果
True优化结果不会是完整的Strategy对象（和所有datas, indicators, observers…），但和对象具有以下属性（同Strategy）：params（或p）策略曾用于executionanalyzers的策略具有executed
在大多数occassions，只有analyzers并与params是事情需要评估的策略的性能。如果对于生成的值的详细分析（例如）indicators是必要的，把这个off
数据馈送Management
In了Optimization场景这是Cerebro一个可能的组合参数：
preload=True（默认）的数据Feeeds将运行的任何回溯测试code
runonce=True（默认值）之前被预加载Indicators将以批处理模式紧来计算for循环，而不是一步step.
If的两个条件都为True和optdatas=True，则：
- 的Data Feeds将在主预加载产卵前的新工艺子过程（那些负责执行的backtesting）
结果management
In了Optimization情况下两件事情应该发挥的最重要作用评价当不同的parameters with which each *Strategy是运行：
strategy.params（或strategy.p）用于backtesting
strategy.analyzersThe的实际设定值的负责提供如何Strategy评价的对象已实际执行。例如：
SharpeRatio_A的（年率SharpeRatio）
当optreturn=True，而不是返回全strategy的情况下，占位符对象将被创建携带这两个属性的前述让评估采取place.
这避免了回传大量生成的数据的像例如值期间由指标生成的backtesting
如若full strategy objects被希望，只需设置optreturn=False脑期间在instantiation源一直延伸到添加做时cerebro.run.
的一些测试runs
Theoptimization样品backtrader或控制optdatas和optreturn（实际上禁用它们）
单核Run
As参考时会发生什么CPU的数量是有限的，以1和multiprocessing模块不使用：
$ ./optimization.py --maxcpus 1 ================================================== ************************************************** -------------------------------------------------- OrderedDict([(u"smaperiod", 10), (u"macdperiod1", 12), (u"macdperiod2", 26), (u"macdperiod3", 9)]) ************************************************** -------------------------------------------------- OrderedDict([(u"smaperiod", 10), (u"macdperiod1", 13), (u"macdperiod2", 26), (u"macdperiod3", 9)]) ... ... OrderedDict([(u"smaperiod", 29), (u"macdperiod1", 19), (u"macdperiod2", 29), (u"macdperiod3", 14)]) ================================================== Time used: 184.922727833
多个Core Runs
Without限制的CPU的数量，Python的multiprocessing模块将尝试使用他们。optdatas和optreturn会disabled
Bothoptdata和optreturnactive
The默认行为：
$ ./optimization.py ... ... ... ================================================== Time used: 56.5889185394
由具有多核的总的改进和data feed和results改进装置184.92到56.58seconds.
Take到的是，样品使用252杆和指标从下降与252点的长度仅生成值。这只是一个example.
真正的问题是多少的，这是因为新型behavior.
optreturn deactivated
Let的传球全strategy对象返回给调用者：
$ ./optimization.py --no-optreturn ... ... ... ================================================== Time used: 67.056914007
的执行时间增加18.50%（或15.62%的速度时）是在place.
optdatas deactivated
Each subproccess被强制加载自己的一套价值观的data feeds：
$ ./optimization.py --no-optdatas ... ... ... ================================================== Time used: 72.7238112637
执行时间增加了28.52%（或的加速22.19%）是在使用place.
Both deactivated
Still多核但与旧的非改进的性能：
$ ./optimization.py --no-optdatas --no-optreturn ... ... ... ================================================== Time used: 83.6246643786
的执行时间增加47.79%（或一个速度 -最多的32.34%）是在place.
This表明，多核的使用是主要贡献者时间improvement.
Note
The处决已在笔记本电脑已经完成与i7-4710HQ（4核/ 8逻辑的）与在Windows 10的64位的RAM 16个千兆字节。里程可能会有所不同下优化过程中时间的减少等conditions
Concluding
样品Usage
$ ./optimization.py --help
usage: optimization.py [-h] [--data DATA] [--fromdate FROMDATE]
[--todate TODATE] [--maxcpus MAXCPUS] [--no-runonce]
[--exactbars EXACTBARS] [--no-optdatas]
[--no-optreturn] [--ma_low MA_LOW] [--ma_high MA_HIGH]
[--m1_low M1_LOW] [--m1_high M1_HIGH] [--m2_low M2_LOW]
[--m2_high M2_HIGH] [--m3_low M3_LOW]
[--m3_high M3_HIGH]
Optimization
optional arguments:
-h, --help show this help message and exit
--data DATA, -d DATA data to add to the system
--fromdate FROMDATE, -f FROMDATE
Starting date in YYYY-MM-DD format
--todate TODATE, -t TODATE
Starting date in YYYY-MM-DD format
--maxcpus MAXCPUS, -m MAXCPUS
Number of CPUs to use in the optimization
- 0 (default): use all available CPUs
- 1 -> n: use as many as specified
--no-runonce Run in next mode
--exactbars EXACTBARS
Use the specified exactbars still compatible with preload
0 No memory savings
-1 Moderate memory savings
-2 Less moderate memory savings
--no-optdatas Do not optimize data preloading in optimization
--no-optreturn Do not optimize the returned values to save time
--ma_low MA_LOW SMA range low to optimize
--ma_high MA_HIGH SMA range high to optimize
--m1_low M1_LOW MACD Fast MA range low to optimize
--m1_high M1_HIGH MACD Fast MA range high to optimize
--m2_low M2_LOW MACD Slow MA range low to optimize
--m2_high M2_HIGH MACD Slow MA range high to optimize
--m3_low M3_LOW MACD Signal range low to optimize
--m3_high M3_HIGH MACD Signal range high to optimize
Sample Code
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import argparse
import datetime
import time
from backtrader.utils.py3 import range
import backtrader as bt
import backtrader.indicators as btind
import backtrader.feeds as btfeeds
class OptimizeStrategy(bt.Strategy):
params = (("smaperiod", 15),
("macdperiod1", 12),
("macdperiod2", 26),
("macdperiod3", 9),
)
def __init__(self):
# Add indicators to add load
btind.SMA(period=self.p.smaperiod)
btind.MACD(period_me1=self.p.macdperiod1,
period_me2=self.p.macdperiod2,
period_signal=self.p.macdperiod3)
def runstrat():
args = parse_args()
# Create a cerebro entity
cerebro = bt.Cerebro(maxcpus=args.maxcpus,
runonce=not args.no_runonce,
exactbars=args.exactbars,
optdatas=not args.no_optdatas,
optreturn=not args.no_optreturn)
# Add a strategy
cerebro.optstrategy(
OptimizeStrategy,
smaperiod=range(args.ma_low, args.ma_high),
macdperiod1=range(args.m1_low, args.m1_high),
macdperiod2=range(args.m2_low, args.m2_high),
macdperiod3=range(args.m3_low, args.m3_high),
)
# Get the dates from the args
fromdate = datetime.datetime.strptime(args.fromdate, "%Y-%m-%d")
todate = datetime.datetime.strptime(args.todate, "%Y-%m-%d")
# Create the 1st data
data = btfeeds.BacktraderCSVData(
dataname=args.data,
fromdate=fromdate,
todate=todate)
# Add the Data Feed to Cerebro
cerebro.adddata(data)
# clock the start of the process
tstart = time.clock()
# Run over everything
stratruns = cerebro.run()
# clock the end of the process
tend = time.clock()
print("==================================================")
for stratrun in stratruns:
print("**************************************************")
for strat in stratrun:
print("--------------------------------------------------")
print(strat.p._getkwargs())
print("==================================================")
# print out the result
print("Time used:", str(tend - tstart))
def parse_args():
parser = argparse.ArgumentParser(
description="Optimization",
formatter_class=argparse.RawTextHelpFormatter,
)
parser.add_argument(
"--data", "-d",
default="../../datas/2006-day-001.txt",
help="data to add to the system")
parser.add_argument(
"--fromdate", "-f",
default="2006-01-01",
help="Starting date in YYYY-MM-DD format")
parser.add_argument(
"--todate", "-t",
default="2006-12-31",
help="Starting date in YYYY-MM-DD format")
parser.add_argument(
"--maxcpus", "-m",
type=int, required=False, default=0,
help=("Number of CPUs to use in the optimization"
"\n"
" - 0 (default): use all available CPUs\n"
" - 1 -> n: use as many as specified\n"))
parser.add_argument(
"--no-runonce", action="store_true", required=False,
help="Run in next mode")
parser.add_argument(
"--exactbars", required=False, type=int, default=0,
help=("Use the specified exactbars still compatible with preload\n"
" 0 No memory savings\n"
" -1 Moderate memory savings\n"
" -2 Less moderate memory savings\n"))
parser.add_argument(
"--no-optdatas", action="store_true", required=False,
help="Do not optimize data preloading in optimization")
parser.add_argument(
"--no-optreturn", action="store_true", required=False,
help="Do not optimize the returned values to save time")
parser.add_argument(
"--ma_low", type=int,
default=10, required=False,
help="SMA range low to optimize")
parser.add_argument(
"--ma_high", type=int,
default=30, required=False,
help="SMA range high to optimize")
parser.add_argument(
"--m1_low", type=int,
default=12, required=False,
help="MACD Fast MA range low to optimize")
parser.add_argument(
"--m1_high", type=int,
default=20, required=False,
help="MACD Fast MA range high to optimize")
parser.add_argument(
"--m2_low", type=int,
default=26, required=False,
help="MACD Slow MA range low to optimize")
parser.add_argument(
"--m2_high", type=int,
default=30, required=False,
help="MACD Slow MA range high to optimize")
parser.add_argument(
"--m3_low", type=int,
default=9, required=False,
help="MACD Signal range low to optimize")
parser.add_argument(
"--m3_high", type=int,
default=15, required=False,
help="MACD Signal range high to optimize")
return parser.parse_args()
if __name__ == "__main__":
runstrat()
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