前言
- T+0限制实现思路
一、调整买卖比例并统计pnl
- 1 - 在main中添加统计pnl
- 2 - 调整买入比例0.98，卖出比例1.02
- 3 - 获取pnl值
二、策略添加T+0限制
- 1 - T+0实现
- 2 - 获取T+0限制后pnl值
三、盈亏柱状图对比
- 1 - 无T+0限制柱状图
- 2 - T+0限制柱状图
四、k线图对比
- 1 - 无T+0限制k线图
- 2 - T+0限制k线图
五、完整源码

前言

之前我们已经完成了回测，但是我们策略是ma20的单均线策略，这种策略太单一收益也不高
我们需要对回归策略的单均线策略进行升级
- 添加T+0交易限制
- 买入需要升级为2次判断，在均线下方做一些累计计算

T+0限制实现思路

其实就是在sell方法中添加上日期的判断即可

一、调整买卖比例并统计pnl

1 - 在main中添加统计pnl

if __name__ == '__main__':# 省略。。。# T = transposeorders_df = pd.DataFrame(orders).Torders_df.loc[:, 'pnl'].plot.bar()plt.show()# print sum of pnlprint('sum of pnl is: ' + str(orders_df.loc[:, 'pnl'].sum()))# 使用 mplfinance 绘制k线图：订单交易价格与时间bar5 = pd.read_csv(bar_path, parse_dates=['datetime'])bar5.loc[:, 'datetime'] = [date2num(x) for x in bar5.loc[:, 'datetime']]# 省略。。。

2 - 调整买入比例0.98，卖出比例1.02

    def strategy(self):# last < 0.95 *ma20 ,long position(仓位), last > ma20 *1.05, sellif self._is_new_bar:sum_ = 0for item in self._Close[1:21]:sum_ = sum_ + itemself._ma20 = sum_ / 20if 0 == len(self._current_orders):if self._Close[0] < 0.98 * self._ma20:# 100000/44.28 = 2258   44.28是当前价格，10万指的你拥有的钱# 2258 -> 2200 sharesvolume = int(100000 / self._Close[0] / 100) * 100self._buy(self._Close[0] + 0.01, volume)  # 这里的0.01是为了防止挂单，我们需要即可买入elif 1 == len(self._current_orders):if self._Close[0] > self._ma20 * 1.02:key = list(self._current_orders.keys())[0]self._sell(key, self._Close[0] - 0.01)# 省略。。。

3 - 获取pnl值

二、策略添加T+0限制

1 - T+0实现

    def strategy(self):# last < 0.95 *ma20 ,long position(仓位), last > ma20 *1.05, sellif self._is_new_bar:sum_ = 0for item in self._Close[1:21]:sum_ = sum_ + itemself._ma20 = sum_ / 20if 0 == len(self._current_orders):if self._Close[0] < 0.98 * self._ma20:# 100000/44.28 = 2258   44.28是当前价格，10万指的你拥有的钱# 2258 -> 2200 sharesvolume = int(100000 / self._Close[0] / 100) * 100self._buy(self._Close[0] + 0.01, volume)  # 这里的0.01是为了防止挂单，我们需要即可买入elif 1 == len(self._current_orders):if self._Close[0] > self._ma20 * 1.02:key = list(self._current_orders.keys())[0]if self._Dt[0].date() != self._current_orders[key]['open_datetime'].date():self._sell(key, self._Close[0] - 0.01)print('open date is %s, close date is: %s.'% (self._history_orders[key]['open_datetime'].date(), self._Dt[0].date()))else:# if sam dates, sell order aborted due to T+0 limitprint('sell order aborted due to T+0 limit')else:  # len() = 2raise ValueError("we have more then 1 current orders")# Close[0] in between 0.95*ma20 and 1.05*ma20,do nothing

2 - 获取T+0限制后pnl值

三、盈亏柱状图对比

1 - 无T+0限制柱状图

2 - T+0限制柱状图

四、k线图对比

1 - 无T+0限制k线图

2 - T+0限制k线图

五、完整源码

import requests
from time import sleep
from datetime import datetime, time, timedelta
from dateutil import parser
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
# import mplfinance as mpf
from mplfinance.original_flavor import candlestick_ohlc
from matplotlib.dates import date2numdef get_ticks_for_backtesting(tick_path, bar_path):""":func: get ticks for backtesting, need two params:param1 tick_path: 生成的回测数据路径csv file with tick data,when there is not tick data,use bat_path to create tick dataexample: "E:\\Downloads\\600036_data\\600036_ticks.csv":param2 bar_path: 历史数据的tick路径csv file with bar data,used in creating tick dataexample: "E:\\Downloads\\600036_data\\600036_5m.csv":return: ticks in list with tuples in it, such as[(datetime, last_price), (datetime, last_price)]"""if os.path.exists(tick_path):  # 如果已存在回测数据，直接读取回测数据ticksticks = pd.read_csv(tick_path,parse_dates=['datetime'],index_col='datetime')tick_list = []for index, row in ticks.iterrows():tick_list.append((index, row[0]))# ticks = np.array(tick_list)ticks = tick_listelse:bar_5m = pd.read_csv(bar_path)  # 使用pandas读取csv数据ticks = []for index, row in bar_5m.iterrows():  # 根据不同的开盘价设置步长if row['open'] < 30:step = 0.01elif row['open'] < 60:step = 0.03elif row['open'] < 90:step = 0.05else:step = 0.1# in case of np.arrange(30, 30.11, 0.02), (open, high, step)# we will not have 30.11 as the highest price,# we might not catch high when step is more than 0.01# that is why me need: arr = np.append(arr, row['high']) and#   arr = np.append(arr, row['low'])arr = np.arange(row['open'], row['high'], step)  # 按步长生成从open到high的数据arr = np.append(arr, row['high'])  # 这个是为了弥补步长的不对等会漏掉higharr = np.append(arr, np.arange(row['open'] - step, row['low'], -step))  # 按步长生成从open到low的数据arr = np.append(arr, row['low'])  # 这个是为了弥补步长的不对等会漏掉lowarr = np.append(arr, row['close'])i = 0dt = parser.parse(row['datetime']) - timedelta(minutes=5)for item in arr:ticks.append((dt + timedelta(seconds=0.1 * i), item))  # 将数据时间模拟到0.1秒递进i += 1tick_df = pd.DataFrame(ticks, columns=['datetime', 'price'])tick_df.to_csv(tick_path, index=0)  # 保存到csv回测数据中return ticks# __init__，构造，初始化，实例化
class AstockTrading(object):""":class: A stock trading platform, needs one param,It has backtesting, paper trading, and real trading.:param1: strategy_name: strategy_name"""def __init__(self, strategy_name):self._strategy_name = strategy_nameself._Dt = []  # 交易时间self._Open = []  # 开盘价self._High = []  # 最高价self._Low = []  # 最低价self._Close = []  # 最新价self._Volume = []self._tick = []  # 数据self._last_bar_start_minute = None  # 最后一次更新bar的时间self._is_new_bar = False  # 是否有新barself._ma20 = None# 当前订单，dict, 字典self._current_orders = {}# 历史订单self._history_orders = {}self._order_number = 0self._init = False  # for backtestingdef get_tick(self):""":func: for paper trading or real trading, not for backtestingIt goes to sina to get last tick info,address is: https://hq.sinajs.cn/list=sh600519,sh600519 can be changedneed to set headers Referer to: https://finance.sina.com.cnA股的开盘时间是9:15，9:15-9:25是集合竞价 -> 开盘价，9:259:25-9:30不交易，时间>9:30，交易开始start this method after 9:25tick info is organized in tuple,such as (trade_datetime, last_price),tick info is save in self._tick.:param: no param:return: None"""headers = {'Referer': "https://finance.sina.com.cn"}page = requests.get("https://hq.sinajs.cn/list=sh600519", headers=headers)stock_info = page.textmt_info = stock_info.replace("\"", "").split("=")[1].split(",")# 最新价last = float(mt_info[1])trade_datetime = mt_info[30] + ' ' + mt_info[31]self._tick = (trade_datetime, last)def get_history_data_from_local_machine(self):""":not done yet:return:"""# tushare 数据来源# self.Open = [1, 2, 3]# self.High = [2, 3, 4]self._Open = []self._High = []self._Low = []self._Close = []self._Dt = []def bar_generator(self):""":not done yet:how save and import history data?:return:"""# assume we have history data already# 1、update bars，calculate 5 minutes ma20 , not daily data# 2、compare last and ma20  -> buy or sell or pass# assume we have history data,Open,High,Low,Close,Dt# 这里可以是5minutes、10minutes、15minutes、20minutes、30minutesif self._tick[0].minute % 5 == 0 and self._tick[0].minute != self._last_bar_start_minute:self._last_bar_start_minute = self._tick[0].minuteself._Open.insert(0, self._tick[1])self._High.insert(0, self._tick[1])self._Low.insert(0, self._tick[1])self._Close.insert(0, self._tick[1])self._Dt.insert(0, self._tick[0])self._is_new_bar = Trueelse:# update current barself._High[0] = max(self._High[0], self._tick[1])self._Low[0] = max(self._Low[0], self._tick[1])self._Close[0] = self._tick[1]self._Dt[0] = self._tick[0]self._is_new_bar = Falsedef _buy(self, price, volume):""":method: create am order:param1 price: buying price:param2 volume: buying volume:return: none"""self._order_number += 1key = "order" + str(self._order_number)self._current_orders[key] = {"open_datetime": self._Dt[0],"open_price": price,"volume": volume  # 股数}passdef _sell(self, key, price):""":method: close a long order, It needs two params:param1 key: long order's key:param2 price: selling price:return:"""self._current_orders[key]['close_price'] = priceself._current_orders[key]['close_datetime'] = self._Dt[0]self._current_orders[key]['pnl'] = \(price - self._current_orders[key]['open_price']) \* self._current_orders[key]['volume'] \- price * self._current_orders[key]['volume'] * 1 / 1000 \- (price - self._current_orders[key]['open_price']) \* self._current_orders[key]['volume'] * 3 / 10000# move order from current orders to history ordersself._history_orders[key] = self._current_orders.pop(key)def strategy(self):# last < 0.95 *ma20 ,long position(仓位), last > ma20 *1.05, sellif self._is_new_bar:sum_ = 0for item in self._Close[1:21]:sum_ = sum_ + itemself._ma20 = sum_ / 20if 0 == len(self._current_orders):if self._Close[0] < 0.98 * self._ma20:# 100000/44.28 = 2258   44.28是当前价格，10万指的你拥有的钱# 2258 -> 2200 sharesvolume = int(100000 / self._Close[0] / 100) * 100self._buy(self._Close[0] + 0.01, volume)  # 这里的0.01是为了防止挂单，我们需要即可买入elif 1 == len(self._current_orders):if self._Close[0] > self._ma20 * 1.02:key = list(self._current_orders.keys())[0]if self._Dt[0].date() != self._current_orders[key]['open_datetime'].date():self._sell(key, self._Close[0] - 0.01)print('open date is %s, close date is: %s.'% (self._history_orders[key]['open_datetime'].date(), self._Dt[0].date()))else:# if sam dates, sell order aborted due to T+0 limitprint('sell order aborted due to T+0 limit')else:  # len() = 2raise ValueError("we have more then 1 current orders")# Close[0] in between 0.95*ma20 and 1.05*ma20,do nothingdef bar_generator_for_backtesting(self, tick):""":method: for backtesting only, used to update _Open, _ High, etc, It needs just one param:param tick: tick info in tuple, (datetime, price):return:"""if tick[0].minute % 5 == 0 and tick[0].minute != self._last_bar_start_minute:self._last_bar_start_minute = tick[0].minuteself._Open.insert(0, tick[1])self._High.insert(0, tick[1])self._Low.insert(0, tick[1])self._Close.insert(0, tick[1])self._Dt.insert(0, tick[0])self._is_new_bar = Trueelse:# update current barself._High[0] = max(self._High[0], tick[1])self._Low[0] = max(self._Low[0], tick[1])self._Close[0] = tick[1]self._Dt[0] = tick[0]self._is_new_bar = Falsedef run_backtestting(self, ticks):""":method: ticks will be used to generate bars,when bars is long enough, call strategy():param ticks: list with (datetime, price) in the list:return: none"""for tick in ticks:self.bar_generator_for_backtesting(tick)if self._init:self.strategy()else:if len(self._Open) >= 100:self._init = Trueself.strategy()# ma = AstockTrading('600036')  # 类实例化
# ma.get_history_data_from_local_machine()
#
# # 交易时间是9：30-11:30,13：00-15：00
# while time(9, 26) < datetime.now().time() < time(11, 32) \
#         or time(13) < datetime.now().time() < time(15, 2):
#     ma.get_tick()
#     ma.bar_generator()
#     ma.strategy()
#     # trade_time = parser.parse(ma._tick[0]).time()
#     # sleep(3)if __name__ == '__main__':tick_path = "E:\\Downloads\\600036_data\\600036_ticks.csv"bar_path = "E:\\Downloads\\600036_data\\600036_5m.csv"ticks = get_ticks_for_backtesting(tick_path, bar_path)  # 获取回测数据ast = AstockTrading('ma')ast.run_backtestting(ticks)  # 运行回测数据print('ast._current_orders:')print(ast._current_orders)print("-------------------------------------")print('ast._history_orders:')print(ast._history_orders)# 使用matplotlib绘制盈亏柱状图profit_orders = 0  # 盈利的交易数loss_orders = 0  # 亏损的交易数orders = ast._history_ordersfor key in orders.keys():if orders[key]['pnl'] >= 0:profit_orders += 1else:loss_orders += 1win_rate = profit_orders / len(orders)loss_rate = loss_orders / len(orders)# T = transposeorders_df = pd.DataFrame(orders).Torders_df.loc[:, 'pnl'].plot.bar()plt.show()# print sum of pnlprint('sum of pnl is: ' + str(orders_df.loc[:, 'pnl'].sum()))# 使用 mplfinance 绘制k线图：订单交易价格与时间bar5 = pd.read_csv(bar_path, parse_dates=['datetime'])bar5.loc[:, 'datetime'] = [date2num(x) for x in bar5.loc[:, 'datetime']]fig, ax = plt.subplots()candlestick_ohlc(ax,quotes=bar5.values,width=0.2,colorup="r",colordown='g',alpha=1.0,)# put orders in candle sticksfor index, row in orders_df.iterrows():ax.plot([row['open_datetime'], row['close_datetime']],[row['open_price'], row['close_price']],color='darkblue',marker='o',)plt.show()

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6、Python量化交易-单均线策略升级1：T+0限制

目录

前言

T+0限制实现思路

一、调整买卖比例并统计pnl

1 - 在main中添加统计pnl

2 - 调整买入比例0.98，卖出比例1.02

3 - 获取pnl值

二、策略添加T+0限制

1 - T+0实现

2 - 获取T+0限制后pnl值

三、盈亏柱状图对比

1 - 无T+0限制柱状图

2 - T+0限制柱状图

四、k线图对比

1 - 无T+0限制k线图

2 - T+0限制k线图

五、完整源码

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