发帖
雷达卡
策略逻辑
使用布林带指标，构建布林带的上轨，中轨，下轨
开仓
当没有持仓的时候，收盘价突破上轨，下个开盘价开多；
当没有持仓的时候，收盘价跌破下轨，下个开盘价开空。
平仓
当持有多头仓位的时候，收盘价跌破中轨，平多。
当持有空头仓位的时候，收盘价升破中轨，平空。
数据
使用了5分钟的每个品种的后复权的连续合约
交易费用
按照当前的交易费用设置，每手收取固定金额或者按照百分比；每次交易收取一个滑点(开平都收)；由于保证金、交易费用、合约乘数可能会随着政策的变动而调整，另外交易产生的滑点可能多于一个点，也有一小部分可能小于一个点，甚至可能是负的，所以回测的结果和真实情况是存在差距的。
交易手数
按照当前资金的1倍杠杆进行下单。我们结果只需要看夏普率就好，在满足条件的情况下，夏普率不随杠杆而变动。
测试结果
#### 策略代码
策略代码回复帖子可见。
完整数据、测试结果在原文中。
本帖隐藏的内容
from __future__ import (absolute_import, division, print_function,unicode_literals)
import os,sys
import numpy as np
import pandas as pd
import backtrader as bt # backtrader
from backtrader.comminfo import ComminfoFuturesPercent,ComminfoFuturesFixed # 期货交易的手续费用，按照比例或者按照金额
# from backtrader.plot.plot import run_cerebro_and_plot # 个人编写，非backtrader自带
import pyfolio as pf
from multiprocessing import Pool
from itertools import product
### 编写相应的策略,每个策略逻辑需要单独编写，回测和实盘直接运行策略类就行
class Abbration(bt.Strategy):
# 策略作者
author = 'yunjinqi'
# 策略的参数
params = ( ("boll_period",200),
("boll_mult",2),
)
# log相应的信息
def log(self, txt, dt=None):
''' Logging function fot this strategy'''
dt = dt or bt.num2date(self.datas[0].datetime[0])
print('{}, {}'.format(dt.isoformat(), txt))
# 初始化策略的数据
def __init__(self):
# 计算布林带指标，大名鼎鼎的布林带策略
self.boll_indicator = bt.indicators.BollingerBands(self.datas[0],period=self.p.boll_period,devfactor=self.p.boll_mult)
# 保存交易状态
self.marketposition = 0
def prenext(self):
# 由于期货数据有几千个，每个期货交易日期不同，并不会自然进入next
# 需要在每个prenext中调用next函数进行运行
# self.next()
pass
# 在next中添加相应的策略逻辑
def next(self):
# 每次运行一次，bar_num自然加1,并更新交易日
self.current_datetime = bt.num2date(self.datas[0].datetime[0])
self.current_hour = self.current_datetime.hour
self.current_minute = self.current_datetime.minute
# 数据
data = self.datas[0]
# 指标值
# 布林带上轨
top = self.boll_indicator.top
# 布林带下轨
bot = self.boll_indicator.bot
# 布林带中轨
mid = self.boll_indicator.mid
# 开多
if self.marketposition == 0 and data.close[0] > top[0] and data.close[-1]<top[-1]:
# 获取一倍杠杆下单的手数
info = self.broker.getcommissioninfo(data)
symbol_multi = info.p.mult
close = data.close[0]
total_value = self.broker.getvalue()
lots = total_value/(symbol_multi*close)
self.buy(data,size = lots)
self.marketposition = 1
# 开空
if self.marketposition == 0 and data.close[0] < bot[0] and data.close[-1]>bot[-1]:
# 获取一倍杠杆下单的手数
info = self.broker.getcommissioninfo(data)
symbol_multi = info.p.mult
close = data.close[0]
total_value = self.broker.getvalue()
lots = total_value/(symbol_multi*close)
self.sell(data,size = lots)
self.marketposition = -1
# 平多
if self.marketposition == 1 and data.close[0] < mid[0] and data.close[-1]>mid[-1]:
self.close()
self.marketposition = 0
# 平空
if self.marketposition == -1 and data.close[0] > mid[0] and data.close[-1]<mid[-1]:
self.close()
self.marketposition = 0
# def notify_order(self, order):
# if order.status in [order.Submitted, order.Accepted]:
# return
# if order.status == order.Rejected:
# self.log(f"Rejected : order_ref:{order.ref} data_name:{order.p.data._name}")
# if order.status == order.Margin:
# self.log(f"Margin : order_ref:{order.ref} data_name:{order.p.data._name}")
# if order.status == order.Cancelled:
# self.log(f"Concelled : order_ref:{order.ref} data_name:{order.p.data._name}")
# if order.status == order.Partial:
# self.log(f"Partial : order_ref:{order.ref} data_name:{order.p.data._name}")
# if order.status == order.Completed:
# if order.isbuy():
# self.log(f" BUY : data_name:{order.p.data._name} price : {order.executed.price} , cost : {order.executed.value} , commission : {order.executed.comm}")
# else: # Sell
# self.log(f" SELL : data_name:{order.p.data._name} price : {order.executed.price} , cost : {order.executed.value} , commission : {order.executed.comm}")
# def notify_trade(self, trade):
# # 一个trade结束的时候输出信息
# if trade.isclosed:
# self.log('closed symbol is : {} , total_profit : {} , net_profit : {}' .format(
# trade.getdataname(),trade.pnl, trade.pnlcomm))
# # self.trade_list.append([self.datas[0].datetime.date(0),trade.getdataname(),trade.pnl,trade.pnlcomm])
# if trade.isopen:
# self.log('open symbol is : {} , price : {} ' .format(
# trade.getdataname(),trade.price))
# def stop(self):
# pass
def run(params):
print(f"开始运行{params}")
# 准备配置策略
cerebro = bt.Cerebro()
# 参数设置
data_kwargs = dict(
# fromdate = datetime.datetime(2012,12, 3),
# todate = datetime.datetime(2021,7,31),
timeframe = bt.TimeFrame.Minutes,
compression = 1,
dtformat=('%Y-%m-%d %H:%M:%S'), # 日期和时间格式
tmformat=('%H:%M:%S'), # 时间格式
datetime=0,
high=3,
low=4,
open=1,
close=2,
volume=5,
openinterest=6)
# 分解参数
name ,boll_period,boll_mult = params
# 获取当前的已经优化的文件名
file_list = list(os.listdir("./72讲/result/参数优化结果/"))
param_list=["__".join(i.split("__")[:3]) for i in file_list]
if "__".join([str(i) for i in params]) in param_list:
print("__".join([str(i) for i in params]),"has run")
# 严谨一些可以从文件里面获取原来的运行结果，这里省略
return [name,boll_period,boll_mult,0,0,0,0]
else:
# 后复权数据
data_path = "/home/yun/data/期货/5m/rq_5min_after/"+name+"889.csv"
df = pd.read_csv(data_path)
# 只要数据里面的这几列
df = df[['datetime','open','high','low','close','volume','open_interest']]
df.columns = ['datetime','open','high','low','close','volume','openinterest']
# 修改列的名字
df = df.sort_values("datetime")
df = df.drop_duplicates("datetime")
df.index = pd.to_datetime(df['datetime'])
# 如果对数据的时间顺序比较确定是从小到大的，可以不用排序，否则最好做下排序
df = df[['open','high','low','close','volume','openinterest']]
# 删除部分收盘价为0的错误数据，大部分数据都有问题哈，免费的，付费的，需要专门对数据进行清洗
df = df.astype("float")
df = df[(df["open"]>0)&(df['close']>0)]
# df = df[(df.index<=data_kwargs['todate'])&(df.index>=data_kwargs['fromdate'])]
feed = bt.feeds.PandasDirectData(dataname = df)
# feed = bt.feeds.GenericCSVData(dataname = data_path,**data_kwargs)
cerebro.adddata(feed, name = name)
# 设置合约的交易信息
# 获取该品种的交易手续费、保证金率、乘数，由于保证金率可能会随着政策调控改变，不可控性很大，但是又不影响期货的盈利，默认为10%。
data = pd.read_csv("./72讲/优矿上期货合约数据.csv",index_col=0)
data['symbol']=["".join([j for j in i.split(".")[0] if j.isalpha()]) for i in data['secID']]
data = data[["secID","symbol","lastTradeDate","minChgPriceNum","contMultNum","tradeCommiNum","tradeCommiUnit"]]
symbol_info = data[data['symbol']==name]
symbol_info = symbol_info.sort_values("lastTradeDate")
multi = list(symbol_info['contMultNum'])[-1]
comm_type = list(symbol_info['tradeCommiUnit'])[-1]
comm = list(symbol_info["tradeCommiNum"])[-1]
slippage = list(symbol_info['minChgPriceNum'])[-1]
# 配置手续费
if "元" in comm_type:
comm =ComminfoFuturesFixed(commission=comm,margin=0.10, mult=multi)
else:
comm = ComminfoFuturesPercent(commission=comm,margin=0.10, mult=multi)
cerebro.broker.addcommissioninfo(comm, name= name)
# 配置滑点费用,2跳
cerebro.broker.set_slippage_fixed(slippage*1)
cerebro.broker.setcash(1000000.0)
# 添加策略
kwags={"boll_period":boll_period,
"boll_mult":boll_mult,
}
# 添加策略
cerebro.addstrategy(Abbration,**kwags)
cerebro.addanalyzer(bt.analyzers.TotalValue, _name='my_value')
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='my_sharpe')
cerebro.addanalyzer(bt.analyzers.Returns, _name='my_returns')
cerebro.addanalyzer(bt.analyzers.DrawDown, _name='my_drawdown')
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='my_trade_analyzer')
# cerebro.addanalyzer(bt.analyzers.PyFolio)
# 运行回测
results = cerebro.run()
sharpe_ratio = results[0].analyzers.my_sharpe.get_analysis()['sharperatio']
annual_return = results[0].analyzers.my_returns.get_analysis()['rnorm']
max_drawdown = results[0].analyzers.my_drawdown.get_analysis()["max"]["drawdown"]/100
trade_num = results[0].analyzers.my_trade_analyzer.get_analysis()['total']['total']
value_df = pd.DataFrame([results[0].analyzers.my_value.get_analysis()]).T
value_df.columns=['value']
value_df['datetime']=pd.to_datetime(value_df.index)
value_df['date']=[i.date() for i in value_df['datetime']]
value_df= value_df.drop_duplicates("date",keep="last")
value_df = value_df[['value']]
# print(value_df.head())
try:
file_name = name+"__"+str(boll_period)+"__"+str(boll_mult)+"__"+str(round(sharpe_ratio,2))+"__"+str(round(annual_return,2))+"__"+str(round(max_drawdown,2))+"__"+str(trade_num)
except:
file_name = name+"__"+str(boll_period)+"__"+str(boll_mult)+"__"+str(0)+"__"+str(0)+"__"+str(0)+"__"+str(0)
value_df.to_csv("./72讲/result/参数优化结果/"+file_name+".csv")
return [name,boll_period,boll_mult,sharpe_ratio,annual_return,max_drawdown,trade_num]
if __name__=="__main__":
symbol_list = ['A', 'JD', 'J', 'JM', 'MA', 'Y', 'IF', 'BU', 'SF', 'CU', 'RB', 'HC', 'OI', 'ZC', 'P', 'RU', 'RM', 'C', 'IC', 'SR', 'T', 'AU', 'M',
'FG', 'CY', 'SM', 'SS', 'AP', 'CF', 'AG', 'L', 'TS', 'NI', 'ZN', 'EG', 'B', 'PB', 'V', 'TA', 'FU', 'CS', 'SP', 'PP', 'TF', 'SC', 'AL']
k1_list = [i for i in range(100,1000,100)]
k2_list = [i/10 for i in range(5,41,5)]
params_list = product(symbol_list,k1_list,k2_list)
with Pool(8) as p:
results = p.map(run,params_list)
df = pd.DataFrame(results,columns=["name",'boll_period',"boll_mult","sharpe_ratio","annual_return","max_drawdown","trade_num"])
df.to_csv("./72讲/result/all_参数优化结果.csv")
# run(["A",500,2])
————————————————
版权声明：本文为CSDN博主「云金杞」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/qq_26948675/article/details/120795693
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