455 lines
20 KiB
Python
455 lines
20 KiB
Python
'''
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以下是代码的详细说明：
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#公众号：松鼠Quant
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#主页：www.quant789.com
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#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
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#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
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1.
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导入必要的模块和库：
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backtrader 用于回测功能
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datetime 用于处理日期和时间
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GenericCSVData 用于从CSV文件加载数据
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numpy 用于数值操作
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time 用于时间相关操作
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matplotlib.pyplot 用于绘图
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2. 定义自定义手续费模板MyCommission
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继承自bt.CommInfoBase
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3.
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定义自定义数据源类 GenericCSV_SIG：
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继承自 GenericCSVData，并添加了两个额外的行：'sig'和'delta'
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定义了参数 'sig'和'delta'
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4.
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定义 MyStrategy_固定止损_跟踪止盈 类：
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继承自 bt.Strategy（backtrader的基础策略类）
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定义了两个参数：trailing_stop_percent 和 fixed_stop_loss_percent
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初始化策略并设置各种变量和指标
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实现了 next 方法，该方法在数据源的每个新的K线出现时被调用
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根据当前K线数据更新跟踪止盈价格
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实现了跟踪止盈出场和固定止损出场
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根据信号处理多头和空头仓位
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在策略执行过程中打印调试信息
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5.
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if __name__ == "__main__": 代码块：
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使用 Cerebro 实例设置回测环境
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使用 GenericCSV_SIG 数据源从CSV文件加载数据
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将数据源和策略添加到 Cerebro 实例中
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添加观察者和分析器以评估性能
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设置初始资金和经纪人参数
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运行回测并获取结果
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打印回测报告，包括收益率、回撤、胜率和交易统计数据
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使用 matplotlib 绘制回测结果
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使用说明：使用前需要调整的相关参数如下
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1.确定python到csv文件夹下运行,修改csv文件为需要运行的csv
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2.MyStrategy_固定止损_跟踪止盈：可以修改跟踪百分比和移动周期均线。
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3.__init__函数中：可以修改lost手数，，
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4.next函数：一、修改清仓时间参数，每个品种不一致；二、window_size和window_size_delta的周期暂为10；三、修改“开多组合”和“开空组合”
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5.__main__函数:一、修改回测时间段fromdate和todate；二、根据交易平中设置初始资金、手续费，单手保证金，合约倍数；
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'''
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# 需要进一步了解windows_size的计算规则，日线？
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import backtrader as bt
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from datetime import datetime
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from datetime import time as s_time
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from backtrader.feeds import GenericCSVData
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import numpy as np
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import pandas as pd
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import time
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import matplotlib.pyplot as plt
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import os
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# 导入表头解决图标中中文显示问题
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from pylab import mpl
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手续费汇总=0
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class GenericCSV_SIG(GenericCSVData):
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# 从基类继承，添加一个 'sig'，‘delta’行
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lines = ('sig','delta')
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# 添加参数为从基类继承的参数
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params = (('sig',6),('delta', 8))
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class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
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params = (
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('trailing_stop_percent', 0.02), # 跟踪止盈百分比
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('fixed_stop_loss_percent', 0.01), # 固定止损百分比
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# ('sma1_period', 60), # 移动平均线周期
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# ('sma2_period',120),
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)
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def __init__(self):
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self.Lots=1 #下单手数
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self.signal = self.datas[0].sig # 使用sig字段作为策略的信号字段
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self.delta= self.datas[0].delta
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# 获取数据序列别名列表
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line_aliases = self.datas[0].getlinealiases()
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self.pos=0
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print(line_aliases)
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self.high=self.datas[0].high
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self.low=self.datas[0].low
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self.closes=self.datas[0].close
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self.open=self.datas[0].open
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self.trailing_stop_percent = self.params.trailing_stop_percent
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self.short_trailing_stop_price = 0
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self.long_trailing_stop_price = 0
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self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
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self.sl_long_price=0
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self.sl_shor_price=0
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#240884432
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self.out_long=0
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self.out_short=0
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self.rinei_ma=[]
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self.rinei_mean=0
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self.datetime_list= []
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self.high_list = []
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self.low_list = []
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self.close_list = []
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self.opens_list = []
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self.deltas_list = []
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self.delta_cumsum=[]
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self.barN = 0
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# self.sma1 = bt.indicators.SimpleMovingAverage(
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# self.data, period=self.params.sma1_period
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# )
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# self.sma2 = bt.indicators.SimpleMovingAverage(
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# self.data, period=self.params.sma2_period
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# )
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self.df = pd.DataFrame(columns=['datetime', 'high', 'low', 'close', 'open', 'delta', 'delta_cumsum'])
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self.trader_df=pd.DataFrame(columns=['open', 'high', 'low', 'close', 'volume', 'openInterest','delta'])
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def log(self, txt, dt=None):
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'''可选，构建策略打印日志的函数：可用于打印订单记录或交易记录等'''
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dt = dt or self.datas[0].datetime.date(0)
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print('%s, %s' % (dt.isoformat(), txt))
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def notify_order(self, order):
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# 未被处理的订单
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if order.status in [order.Submitted, order.Accepted]:
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return
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# 已经处理的订单
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if order.status in [order.Completed, order.Canceled, order.Margin]:
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global 手续费汇总
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if order.isbuy():
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手续费汇总 +=order.executed.comm
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self.log(
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'BUY EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f' %
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(order.ref, # 订单编号
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order.executed.price, # 成交价
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order.executed.comm, # 佣金
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order.executed.size, # 成交量
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order.data._name,# 品种名称
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手续费汇总))
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else: # Sell
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手续费汇总 +=order.executed.comm
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self.log('SELL EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f' %
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(order.ref,
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order.executed.price,
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order.executed.comm,
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order.executed.size,
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order.data._name,
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手续费汇总))
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def next(self):
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#公众号：松鼠Quant
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#主页：www.quant789.com
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#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
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#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
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#bar线计数初始化
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self.barN += 1
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position = self.getposition(self.datas[0]).size
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#时间轴
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dt = bt.num2date(self.data.datetime[0])
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#更新跟踪止损价格
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def 每日重置数据():
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# 获取当前时间
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current_time = dt.time()
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#print(current_time)
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# 设置清仓操作的时间范围1：14:55到15:00
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clearing_time1_start = s_time(14, 55)
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clearing_time1_end = s_time(15, 0)
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# 设置清仓操作的时间范围2：00:55到01:00
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clearing_time2_start = s_time(22, 55)
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clearing_time2_end = s_time(23, 0)
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# 创建一个标志变量
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clearing_executed = False
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if clearing_time1_start <= current_time <= clearing_time1_end and not clearing_executed :
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clearing_executed = True # 设置标志变量为已执行
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self.rinei_ma=[]
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self.delta_cumsum=[]
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self.deltas_list=[]
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elif clearing_time2_start <= current_time <= clearing_time2_end and not clearing_executed :
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clearing_executed = True # 设置标志变量为已执行
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self.rinei_ma=[]
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self.delta_cumsum=[]
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self.deltas_list=[]
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# 如果不在任何时间范围内，可以执行其他操作
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else:
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self.rinei_ma.append(self.closes[0])
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self.rinei_mean = np.mean(self.rinei_ma)
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#self.delta_cumsum=[]
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#self.deltas_list=[]
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#print('rinei_ma',self.rinei_ma)
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clearing_executed = False
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pass
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return clearing_executed
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run_kg=每日重置数据()
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#过滤成交量为0或小于0
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if self.data.volume[0] <= 0 :
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return
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#print(f'volume,{self.data.volume[0]}')
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if self.long_trailing_stop_price >0 and self.pos>0:
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#print('datetime+sig: ',dt,'旧多头出线',self.long_trailing_stop_price,'low',self.low[0])
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self.long_trailing_stop_price = self.low[0] if self.long_trailing_stop_price<self.low[0] else self.long_trailing_stop_price
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#print('datetime+sig: ',dt,'多头出线',self.long_trailing_stop_price)
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if self.short_trailing_stop_price >0 and self.pos<0:
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#print('datetime+sig: ',dt,'旧空头出线',self.short_trailing_stop_price,'high',self.high[0])
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self.short_trailing_stop_price = self.high[0] if self.high[0] <self.short_trailing_stop_price else self.short_trailing_stop_price
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#print('datetime+sig: ',dt,'空头出线',self.short_trailing_stop_price)
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self.out_long=self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
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self.out_short=self.short_trailing_stop_price*(1 + self.trailing_stop_percent)
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#print('datetime+sig: ',dt,'空头出线',self.out_short)
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#print('datetime+sig: ',dt,'多头出线',self.out_long)
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# 跟踪出场
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if self.out_long >0:
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if self.low[0] < self.out_long and self.pos>0 and self.sl_long_price>0 and self.low[0]>self.sl_long_price:
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print('--多头止盈出场datetime+sig: ',dt,'Trailing stop triggered: Closing position','TR',self.out_long,'low', self.low[0])
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self.close(data=self.data, price=self.data.close[0],size=self.Lots, exectype=bt.Order.Market)
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self.long_trailing_stop_price = 0
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self.sl_long_price=0
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self.out_long=0
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self.pos = 0
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if self.out_short>0:
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if self.high[0] > self.out_short and self.pos<0 and self.sl_shor_price>0 and self.high[0]<self.sl_shor_price:
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print('--空头止盈出场datetime+sig: ',dt,'Trailing stop triggered: Closing position: ','TR',self.out_short,'high', self.high[0])
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self.close(data=self.data, price=self.data.close[0],size=self.Lots, exectype=bt.Order.Market)
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self.short_trailing_stop_price = 0
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self.sl_shor_price=0
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self.out_shor=0
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self.pos = 0
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# 固定止损
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self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
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if self.sl_long_price>0 and self.fixed_stop_loss_L>0 and self.pos > 0 and self.closes[0] < self.fixed_stop_loss_L:
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print('--多头止损datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_L, 'close', self.closes[0])
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self.close(data=self.data, price=self.data.close[0],size=self.Lots, exectype=bt.Order.Market)
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self.long_trailing_stop_price = 0
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self.sl_long_price=0
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self.out_long = 0
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self.pos = 0
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self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
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if self.sl_shor_price>0 and self.fixed_stop_loss_S>0 and self.pos < 0 and self.closes[0] > self.fixed_stop_loss_S:
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print('--空头止损datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_S, 'close', self.closes[0])
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self.close(data=self.data, price=self.data.close[0], size=self.Lots,exectype=bt.Order.Market)
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self.short_trailing_stop_price = 0
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self.sl_shor_price=0
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self.out_short = 0
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self.pos = 0
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# 更新最高价和最低价的列表
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self.datetime_list.append(dt)
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self.high_list.append(self.data.high[0])
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self.low_list.append(self.data.low[0])
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self.close_list.append(self.data.close[0])
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self.opens_list.append(self.data.open[0])
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self.deltas_list.append(self.data.delta[0])
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# 计算delta累计
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self.delta_cumsum.append(sum(self.deltas_list))
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# 将当前行数据添加到 DataFrame
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# new_row = {
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# 'datetime': dt,
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# 'high': self.data.high[0],
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# 'low': self.data.low[0],
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# 'close': self.data.close[0],
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# 'open': self.data.open[0],
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# 'delta': self.data.delta[0],
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# 'delta_cumsum': sum(self.deltas_list)
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# }
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# # 使用pandas.concat代替append
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# self.df = pd.concat([self.df, pd.DataFrame([new_row])], ignore_index=True)
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# # 检查文件是否存在
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# csv_file_path = f"output.csv"
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# if os.path.exists(csv_file_path):
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# # 仅保存最后一行数据
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# self.df.tail(1).to_csv(csv_file_path, mode='a', header=False, index=False)
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# else:
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# # 创建新文件并保存整个DataFrame
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# self.df.to_csv(csv_file_path, index=False)
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#
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if run_kg==False : #
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# # 构建delta的正数和负数
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# positive_nums = [x for x in self.data.delta if x > 0]
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# negative_nums = [x for x in self.data.delta if x < 0]
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# positive_sums = [x for x in self.delta_cumsum if x > 0]
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# negative_sums = [x for x in self.delta_cumsum if x < 0]
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# #
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# # 开多组合= self.rinei_mean>0 and self.closes[0]>self.rinei_mean and self.signal[0] >1 and self.data.delta[0]>1000 and self.delta_cumsum[-1]>1500
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# # 开空组合= self.rinei_mean>0 and self.closes[0]<self.rinei_mean and self.signal[0] <-1 and self.data.delta[0]<-1000 and self.delta_cumsum[-1]<-1500
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# 开多组合= self.rinei_mean>0 and self.closes[0]>self.rinei_mean and self.signal[0] > 1 and self.data.delta[0]>max(self.data.delta[-60:-1]) #and self.delta_cumsum[-1] > np.max(self.delta_cumsum[-61:-2]) #np.mean(self.data.delta_cumsum[-61:-2])
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# 开空组合= self.rinei_mean>0 and self.closes[0]<self.rinei_mean and self.signal[0] <-1 and self.data.delta[0]<min(self.data.delta[-60:-1]) #and self.delta_cumsum[-1] < np.min(self.delta_cumsum[-61:-2]) #np.mean(self.data.delta_cumsum[-61:-2])
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#print(self.delta_cumsum)
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开多组合= self.rinei_mean>0 and self.closes[0]>self.rinei_mean and self.signal[0] >1 and self.data.delta[0]>1500 and self.delta_cumsum[-1]>2000
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开空组合= self.rinei_mean>0 and self.closes[0]<self.rinei_mean and self.signal[0] <-1 and self.data.delta[0]<-1500 and self.delta_cumsum[-1]<-2000
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平多条件=self.pos<0 and self.signal[0] >1
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平空条件=self.pos>0 and self.signal[0] <-1
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if self.pos !=1 : #
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if 平多条件:
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#print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_S, 'close', self.closes[0])
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self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
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self.short_trailing_stop_price = 0
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self.sl_shor_price=0
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self.out_short = 0
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self.pos = 0
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if 开多组合 : #
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self.buy(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
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self.pos=1
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self.long_trailing_stop_price=self.low[0]
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self.sl_long_price=self.data.open[0]
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#print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存多头价格: ',self.long_trailing_stop_price)
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if self.pos !=-1 : #
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if 平空条件:
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#print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_L, 'close', self.closes[0])
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self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
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self.long_trailing_stop_price = 0
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self.sl_long_price=0
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self.out_long = 0
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self.pos = 0
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if 开空组合: #
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self.sell(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
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self.pos=-1
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self.short_trailing_stop_price=self.high[0]
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self.sl_shor_price=self.data.open[0]
|
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#print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存空头价格: ',self.short_trailing_stop_price)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
#公众号：松鼠Quant
|
||
#主页：www.quant789.com
|
||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||
# 创建Cerebro实例
|
||
|
||
cerebro = bt.Cerebro()
|
||
#数据
|
||
csv_file='./tick生成的OF数据-own/back_ofdata_dj.csv' #
|
||
# 从CSV文件加载数据
|
||
data = GenericCSV_SIG(
|
||
dataname=csv_file,
|
||
fromdate=datetime(2023,1,1),
|
||
todate=datetime(2023,12,29),
|
||
timeframe=bt.TimeFrame.Minutes,
|
||
nullvalue=0.0,
|
||
dtformat='%Y-%m-%d %H:%M:%S',
|
||
datetime=0,
|
||
high=3,
|
||
low=4,
|
||
open=2,
|
||
close=1,
|
||
volume=5,
|
||
openinterest=None,
|
||
sig=6,
|
||
delta=8
|
||
)
|
||
|
||
# 添加数据到Cerebro实例
|
||
cerebro.adddata(data)
|
||
|
||
# 添加策略到Cerebro实例
|
||
cerebro.addstrategy(MyStrategy_固定止损_跟踪止盈)
|
||
|
||
# 添加观察者和分析器到Cerebro实例
|
||
#cerebro.addobserver(bt.observers.BuySell)
|
||
cerebro.addobserver(bt.observers.Value)
|
||
cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')
|
||
cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')
|
||
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='trades')
|
||
初始资金=10000
|
||
cerebro.broker.setcash(初始资金) # 设置初始资金
|
||
|
||
#手续费，单手保证金，合约倍数
|
||
cerebro.broker.setcommission(commission=14, margin=5000.0,mult=10)#回测参数
|
||
|
||
# 运行回测
|
||
result = cerebro.run()
|
||
|
||
# 获取策略分析器中的结果
|
||
analyzer = result[0].analyzers
|
||
total_trades = analyzer.trades.get_analysis()['total']['total']
|
||
winning_trades = analyzer.trades.get_analysis()['won']['total']
|
||
# 获取TradeAnalyzer分析器的结果
|
||
trade_analyzer_result = analyzer.trades.get_analysis()
|
||
# 获取总收益额
|
||
total_profit = trade_analyzer_result.pnl.net.total
|
||
|
||
if total_trades > 0:
|
||
win_rate = winning_trades / total_trades
|
||
else:
|
||
win_rate = 0.0
|
||
# 打印回测报告
|
||
print('回测报告:')
|
||
print('期初权益', 初始资金)
|
||
print('期末权益', 初始资金+round(total_profit))
|
||
print('盈亏额', round(total_profit))
|
||
print('最大回撤率,', round(analyzer.drawdown.get_analysis()['drawdown'],2),'%')
|
||
print('胜率,', round(win_rate*100,2),'%')
|
||
print("交易次数,", total_trades)
|
||
print("盈利次数,", winning_trades)
|
||
print("亏损次数,", total_trades - winning_trades)
|
||
print('总手续费+滑点,', 手续费汇总)
|
||
手续费汇总=0
|
||
|
||
# 设置中文显示
|
||
mpl.rcParams["font.sans-serif"] = ["SimHei"]
|
||
mpl.rcParams["axes.unicode_minus"] = False
|
||
|
||
# 保存回测图像文件
|
||
plot = cerebro.plot()[0][0]
|
||
plot_filename = os.path.splitext(os.path.basename(csv_file))[0] +'ss'+ '_plot.png'
|
||
plot_path = os.path.join('部分回测报告', plot_filename)
|
||
plot.savefig(plot_path)
|
||
|
||
|
||
|
||
#公众号：松鼠Quant
|
||
#主页：www.quant789.com
|
||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|