# -*- coding: utf-8 -*- # @Date: 2017-08-26 # @Original: from collections import namedtuple from collections import OrderedDict from functools import reduce import itertools class StockTradeDays(object): def __init__(self, price_array, start_date, date_array=None): # 私有价格序列 self.__price_array = price_array # 私有日期序列 self.__date_array = self._init_days(start_date, date_array) # 私有涨跌幅序列 self.__change_array = self.__init_change() # 进行OrderedDict的组装 self.stock_dict = self._init_stock_dict() def __init_change(self): """ 从price_array生成change_array :return: """ price_float_array = [float(price_str) for price_str in self.__price_array] # 通过将时间平移形成两个错开的收盘价序列，通过zip打包成为一个新的序列 # 每个元素为相邻的两个收盘价格 pp_array = [(price1, price2) for price1, price2 in zip(price_float_array[:-1], price_float_array[1:])] change_array = list(map(lambda pp: reduce(lambda a, b: round((b - a) / a, 3), pp), pp_array)) # list insert插入数据，将第一天的涨跌幅设置为0 change_array.insert(0, 0) return change_array def _init_days(self, start_date, date_array): """ protect方法， :param start_date: 初始日期 :param date_array: 给定日期序列 :return: """ if date_array is None: # 由start_date和self.__price_array来确定日期序列 date_array = [str(start_date + ind) for ind, _ in enumerate(self.__price_array)] else: # 稍后的内容会使用外部直接设置的方式 # 如果外面设置了date_array，就直接转换str类型组成新date_array date_array = [str(date) for date in date_array] return date_array def _init_stock_dict(self): """ 使用namedtuple，OrderedDict将结果合并 :return: """ stock_namedtuple = namedtuple('stock', ('date', 'price', 'change')) # 使用以被赋值的__date_array等进行OrderedDict的组装 stock_dict = OrderedDict( (date, stock_namedtuple(date, price, change)) for date, price, change in zip(self.__date_array, self.__price_array, self.__change_array)) return stock_dict def filter_stock(self, want_up=True, want_calc_sum=False): """ 筛选结果子集 :param want_up: 是否筛选上涨 :param want_calc_sum: 是否计算涨跌和 :return: """ # Python中的三目表达式的写法 filter_func = (lambda p_day: p_day.change > 0) if want_up else ( lambda p_day: p_day.change < 0) # 使用filter_func做筛选函数 want_days = list(filter(filter_func, self.stock_dict.values())) if not want_calc_sum: return want_days # 需要计算涨跌幅和 change_sum = 0.0 for day in want_days: change_sum += day.change return change_sum """ 下面的__str__，__iter__， __getitem__， __len__稍后会详细讲解作 """ def __str__(self): return str(self.stock_dict) __repr__ = __str__ def __iter__(self): """ 通过代理stock_dict的跌倒，yield元素 :return: """ for key in self.stock_dict: yield self.stock_dict[key] def __getitem__(self, ind): date_key = self.__date_array[ind] return self.stock_dict[date_key] def __len__(self): return len(self.stock_dict) price_array = '413.05,416.51,420.47,410.01,411.87,415.91,415.5,417.28,418.75,407.86,408.68,411.25,411.88,417.7,418.12,415.3,416,416.71,427.36,424.06,416,413.12,416.02,417.9,420.3,420.6,420.46,423.75,422.57,422.28,418.5,418.47,421.32,423.74,426.59,424.75,426.01,431.48,432.04,428.51,430.03,437.76,443.85,452.26,447.8,453.69,463.02,461.77,468.14,444.85,450.46,455.32,446.6,451.11,443.73,450.39,447.38,448.4,461.18,460.2,459.87,461.56,450.7,452.28,455.01,455.76,455.8,457.89,453.01,453.24,453.52,434.55,441.57,440.81,437.48,443.51,445.03,449.09,453.95,472.01,526.02,531,532.89,530.69,536.79,538.8,570.87,572.87,574.02,585.34,576.88,583.05,575.52,580.09,614.51,672,705.87,684.5,696.99,769.5,747.95,757.6,767.3,743.9,668.6,605.85,625.8,665.5,664.87,627.42,662.33,646.61,640,674.74,674.75,705.99,659.29,681.34,667.8,677.04,640.51,664.8,648.11,649.72,647.95,667.19,653.7,659.78,665.5,665.33,683.2,674.3,675,665.85,665.01,648.04,654.03,661.82,654.17,648.47,655.51,655.93,658.34,654.99,622.83,608.29,604.1,590.28,591.27,585.5,583.73,569.41,564.64,574.17,571.83,572.21,573.51,582.1,581.42,588.01,583.54,580.32,577.2,578.02,568.55,574.17,574.78,579.49,576.15,572.73,579.85,609.89,614.52,611.5,615.23,619.75,631.73,626.25,628,612.08,611.62,614.23,613.88,611.81,610.01,607.69,613.03,609.79,600.14,597.43,597.08,603.29,602.55,600.36,609.14,605.53,603.76,604.6,611.1,614.09,609.09,612.67,610.98,614.09,613.51,620.13,620.5,616.56,618.87,641.87,637.63,637.01,643,640.2,644.18,639.79,638.68,631.77,632.46,636.73,664.99,659.03,654.65,659.52,678.7,688.67,693.47,714.51,702.55,701.02,734.6,750.85,692.51,707.62,708.89,713.95,705.55,711.99,724.54,714.87,716.22,703.57,703.64,707.43,712.17,744.98,740.67,753.97,752.9,729.67,738.99,749.85,742,737.61,740.36,731.19,724.9,731.52,731.05,739,755.36,774.88,765.46,768.5,750.62,757.36,765.01,765.01,770.5,772.9,770.21,777.99,775,774.49,777.43,784.17,790.99,790.21,790.59,797.99,829.34,859.2,918.99,895.24,898,906.4,936.43,981.7,974.74,959.26,966.58,998.99,1019.3,1037.5,1139.6,1003.2,898.5,908,915.9,903,905.76,779.54,804.58,828.12,815.3,820.74,830.1,903.99,887.46,900.29,895.74,924.02,923.72,908.52,886.1,893.35,915.12,916.7,919.43,912.55,917.35,966.19,983.73,1007,1015.7,1031.1,1006.6,1022.6,1052.1,1048.8,984.97,992,1000.1,996.01,996.5,1013.3,1013.9,1038.5,1056.2,1059.7,1056.2,1091.2,1129.6,1125.5,1189.8,1185.4,1153,1178.3,1195.5,1189.1,1233.2,1258,1289.2,1267.8,1278.4,1279.2,1232.4,1150,1190.4,1115.4,1172.4,1224.4,1238.5,1245,1256.2,1168.6,1070.4,971.51,1016.5,1040.5,1115.9,1039.1,1032.7,942.13,972.17,968.9,1042.7,1044.7,1041.8,1041.2,1081.5,1093.5,1107.5,1150.1,1145.8,1140.4,1191.5,1196.6,1188.1,1215.9,1220.3,1235.6,1227.4,1186.9,1206.8,1193.3,1212,1240,1265.4,1260.5,1308.5,1327,1346.4,1355.2,1345,1371.1,1400,1440.3,1415.6,1423.6,1435,1533,1558.5,1619,1607.1,1545.1,1597,1619.9,1703.5,1760,1796.9,1853.9,1735,1819.5,1827.3,1772,1786.2,1870,1941.5,1966.5,2059.3,2026.6,2087.3,2249.6,2395.5,2268.1,2125.9,1980.2,2056.9,2207.4,2146.7,2191.8,2312,2405.9,2461,2488.2,2636.9,2844.6,2644,2781.5,2809,2806,2941.8,2569.6,2677.1,2394.3,2377.5,2437.5,2610.1,2491.4,2582,2714.5,2624.4,2672.8,2674.9,2502.6,2483.3,2393.6,2521.2,2518.2,2472.4,2420.6,2346.2,2445.1,2524,2579.9,2598.6,2593.2,2479.3,2542,2477.9,2318.3,2283.8,2375.6,2330.1,2206.5,1978.6,1925,2220,2302.8,2253.4,2865.1,2659,2844.7,2750.1,2769.7,2560.9,2527.7381,2664.6,2784.8,2713.1,2748.2,2854.3,2731.3,2702,2790.3,2860,3252.3,3232.1,3396,3415,3340.4,3405,3643.4,3866.2,4061.6,4320.8,4151.8,4386.4,4263,4090.1,4145,4063.1,3998.2,4081.9,4130.2,4322.1,4351.5,4340.4,4332.8,4385.1,4587.1,4568,4718.3,4907.7,4532.3,4598.5,4205,4375,4595.8,4613.7,4304,4315.9,4233.9,4198.7,4149.4,3849.7,3235.3,3697.1,3681.5,3666.6,4084.4,3892.2,3872.4,3596.7,3602.3,3779.6,3654.7,3930.1,3881.5,4209.7,4190,4168,4367.1,4404.3,4400.2,4310.6,4215.9,4312,4370,4435.6,4611.9,4782.3,4777.7,4824.9,5440,5636.8,5833.5,5713.9,5764.8,5597.1,5567,5694.2,5983.8,6005.1,5981.3,5907.3,5510,5724.1,5890,5759.7,5720.3,6150,6130,6455.1'.split(',') date_base = 20170118 # 从StockTradeDays类初始化一个实例对象trade_days，内部会调用__init__ trade_days = StockTradeDays(price_array, date_base) # # 打印对象信息 # print(trade_days) # print('trade_days对象长度为: {}'.format(len(trade_days))) # from collections import Iterable # # 如果是trade_days是可迭代对象，依次打印出 # if isinstance(trade_days, Iterable) : # for day in trade_days: # print(day) # print(trade_days.filter_stock()) import six from abc import ABCMeta, abstractmethod """ 交易策略抽象基类 """ class TradeStrategyBase(six.with_metaclass(ABCMeta, object)): @abstractmethod def buy_strategy(self, *args, **kwargs): # 买入策略基类 pass @abstractmethod def sell_strategy(self, *args, **kwargs): # 卖出策略基类 pass """ 交易策略1: 追涨策略，当股价上涨一个阀值默认为7%时 买入股票并持有s_keep_stock_threshold（20）天 """ class TradeStrategy1(TradeStrategyBase): s_keep_stock_threshold = 20 def __init__(self): self.keep_stock_day = 0 # 7%上涨幅度作为买入策略阀值 self.__buy_change_threshold = 0.07 def buy_strategy(self, trade_ind, trade_day, trade_days): if self.keep_stock_day == 0 and trade_day.change > self.__buy_change_threshold: # 当没有持有股票的时候self.keep_stock_day == 0 并且 # 符合买入条件上涨一个阀值，买入 self.keep_stock_day += 1 elif self.keep_stock_day > 0: # self.keep_stock_day > 0代表持有股票，持有股票天数递增 self.keep_stock_day += 1 def sell_strategy(self, trade_ind, trade_day, trade_days): if self.keep_stock_day >= TradeStrategy1.s_keep_stock_threshold: # 当持有股票天数超过阀值s_keep_stock_threshold，卖出股票 self.keep_stock_day = 0 """ property属性稍后会讲到 """ @property def buy_change_threshold(self): return self.__buy_change_threshold @buy_change_threshold.setter def buy_change_threshold(self, buy_change_threshold): if not isinstance(buy_change_threshold, float): """ 上涨阀值需要为float类型 """ raise TypeError('buy_change_threshold must be float!') # 上涨阀值只取小数点后两位 self.__buy_change_threshold = round(buy_change_threshold, 2) """ 交易回测系统 """ class TradeLoopBack(object): def __init__(self, trade_days, trade_strategy): """ 使用上一节封装的StockTradeDays类和本节编写的交易策略类 TradeStrategyBase类初始化交易系统 :param trade_days: StockTradeDays交易数据序列 :param trade_strategy: TradeStrategyBase交易策略 """ self.trade_days = trade_days self.trade_strategy = trade_strategy # 交易盈亏结果序列 self.profit_array = [] def execute_trade(self): """ 执行交易回测 :return: """ for ind, day in enumerate(self.trade_days): """ 以时间驱动，完成交易回测 """ if self.trade_strategy.keep_stock_day > 0: # 如果有持有股票，加入交易盈亏结果序列 self.profit_array.append(day.change) # hasattr: 用来查询对象有没有实现某个方法 if hasattr(self.trade_strategy, 'buy_strategy'): # 买入策略执行 self.trade_strategy.buy_strategy(ind, day, self.trade_days) if hasattr(self.trade_strategy, 'sell_strategy'): # 卖出策略执行 self.trade_strategy.sell_strategy(ind, day, self.trade_days) """ 交易策略2: 均值回复策略，当股价连续两个交易日下跌， 且下跌幅度超过阀值默认s_buy_change_threshold(-10%)， 买入股票并持有s_keep_stock_threshold（10）天 """ class TradeStrategy2(TradeStrategyBase): # 买入后持有天数 s_keep_stock_threshold = 10 # 下跌买入阀值 s_buy_change_threshold = -0.10 def __init__(self): self.keep_stock_day = 0 def buy_strategy(self, trade_ind, trade_day, trade_days): if self.keep_stock_day == 0 and trade_ind >= 1: """ 当没有持有股票的时候self.keep_stock_day == 0 并且 trade_ind >= 1, 不是交易开始的第一天，因为需要yesterday数据 """ # trade_day.change < 0 bool：今天是否股价下跌 today_down = trade_day.change < 0 # 昨天是否股价下跌 yesterday_down = trade_days[trade_ind - 1].change < 0 # 两天总跌幅 down_rate = trade_day.change + trade_days[trade_ind - 1].change if today_down and yesterday_down and down_rate < TradeStrategy2.s_buy_change_threshold: # 买入条件成立：连跌两天，跌幅超过s_buy_change_threshold self.keep_stock_day += 1 elif self.keep_stock_day > 0: # self.keep_stock_day > 0代表持有股票，持有股票天数递增 self.keep_stock_day += 1 def sell_strategy(self, trade_ind, trade_day, trade_days): if self.keep_stock_day >= TradeStrategy2.s_keep_stock_threshold: # 当持有股票天数超过阀值s_keep_stock_threshold，卖出股票 self.keep_stock_day = 0 #类方法，第一个参数为表示自身类的cls参数 @classmethod def set_keep_stock_threshold(cls, keep_stock_threshold): cls.s_keep_stock_threshold = keep_stock_threshold #静态方法 @staticmethod def set_buy_change_threshold(buy_change_threshold): TradeStrategy2.s_buy_change_threshold = buy_change_threshold def calc(keep_stock_threshold, buy_change_threshold): """ :param keep_stock_threshold: 持股天数 :param buy_change_threshold: 下跌买入阀值 :return: 盈亏情况，输入的持股天数, 输入的下跌买入阀值 """ # 实例化TradeStrategy2 trade_strategy2 = TradeStrategy2() # 通过类方法设置买入后持股天数 TradeStrategy2.set_keep_stock_threshold(keep_stock_threshold) # 通过类方法设置下跌买入阀值 TradeStrategy2.set_buy_change_threshold(buy_change_threshold) # 进行回测 trade_loop_back = TradeLoopBack(trade_days, trade_strategy2) trade_loop_back.execute_trade() # 计算回测结果的最终盈亏值profit profit = 0.0 if len(trade_loop_back.profit_array) == 0 else reduce(lambda a, b: a + b, trade_loop_back.profit_array) # 返回值profit和函数的两个输入参数 return profit, keep_stock_threshold, buy_change_threshold #1. 继续和多态 trade_loop_back = TradeLoopBack(trade_days, TradeStrategy1()) trade_loop_back.execute_trade() print('回测策略1 总盈亏为：{}%'.format(reduce(lambda a, b: a + b, trade_loop_back.profit_array) * 100)) #2. 使用@property给私有变量赋值 trade_strategy1 = TradeStrategy1() # 买入阀值从0.07上升到0.1 trade_strategy1.buy_change_threshold = 0.1 trade_loop_back = TradeLoopBack(trade_days, trade_strategy1) trade_loop_back.execute_trade() print('回测策略1 总盈亏为：{}%'.format(reduce(lambda a, b: a + b, trade_loop_back.profit_array) * 100)) trade_strategy2 = TradeStrategy2() trade_loop_back = TradeLoopBack(trade_days, trade_strategy2) trade_loop_back.execute_trade() print('回测策略2 总盈亏为：{}%'.format(reduce(lambda a, b: a + b, trade_loop_back.profit_array) * 100)) # 实例化一个新的TradeStrategy2类对象 trade_strategy2 = TradeStrategy2() # 修改为买入后持有股票20天，默认为10天 TradeStrategy2.set_keep_stock_threshold(20) # 修改股价下跌买入阀值为-0.08（下跌8%），默认为-0.10（下跌10%） TradeStrategy2.set_buy_change_threshold(-0.08) # 实例化新的回测对象trade_loop_back trade_loop_back = TradeLoopBack(trade_days, trade_strategy2) # 执行回测 trade_loop_back.execute_trade() print('回测策略2 总盈亏为：{}%'.format(reduce(lambda a, b: a + b, trade_loop_back.profit_array) * 100)) print(calc(20, -0.08)) # range集合：买入后持股天数从2天－30天，间隔两天 keep_stock_list = list(range(2, 30, 2)) print('持股天数参数组：{}'.format(keep_stock_list)) # 下跌买入阀值从-0.05到-0.15，即从下跌5%到15% buy_change_list = [buy_change / 100.0 for buy_change in range(-5, -16, -1)] print('下跌阀值参数组：{}'.format(buy_change_list)) result = [] for keep_stock_threshold, buy_change_threshold in itertools.product( keep_stock_list, buy_change_list): # 使用calc计算参数对应的最终盈利，结果加入result序列 result.append(calc(keep_stock_threshold, buy_change_threshold)) print('笛卡尔积参数集合总共结果为：{}个'.format(len(result))) print(sorted(result)[::-1][:20])