学习目标：根据交易信号Signal评估回测结果

学习内容：

df的格式要包含收盘价（Close），交易持仓信号（Signal）买入后为1，卖出后为-1
形式如下：

1：提取交易记录，和交易概览信息的统计。

# 交易概览信息的统计
def log_trade_info(stock_dat):signal_shift = stock_dat.Signal.shift(1)signal_shift.fillna(value=-1, inplace=True)  # 序列最前面的NaN值用-1填充list_signal = np.sign(stock_dat.Signal - signal_shift)#print(list_signal)buy_singal = stock_dat[list_signal.isin([1])]sell_singal = stock_dat[list_signal.isin([-1])]trade_info = pd.DataFrame({'BuyTime': buy_singal.index.strftime("%y.%m.%d"),'SellTime': sell_singal.index.strftime("%y.%m.%d"),'BuyPrice': buy_singal.Close.values,'SellPrice': sell_singal.Close.values})trade_info['DiffPrice'] = trade_info.SellPrice - trade_info.BuyPricetrade_info['PctProfit'] = np.round(trade_info.DiffPrice/trade_info.BuyPrice*100, 2)win_count = (trade_info.DiffPrice >= 0).sum()loss_count = (trade_info.DiffPrice < 0).sum()win_profit = trade_info[trade_info.PctProfit >= 0].PctProfit.sum()loss_profit = trade_info[trade_info.PctProfit < 0].PctProfit.sum()print(f'亏损次数:{loss_count}, 盈利次数:{win_count}, 胜率:{round(win_count / (win_count + loss_count)*100, 2)}%')print(f'平均亏损:{round((loss_profit / loss_count), 2)}% 平均盈利:{round((win_profit / win_count), 2)}%')return trade_info
print(log_trade_info(df))

2：# 度量策略绝对收益和相对收益，初始资金100000，有滑点和手续费，df的格式要包含收盘价（Close），交易持仓信号（Signal）买入后为1，卖出后为-1。返回的df包含资金线（total）。

# 度量策略绝对收益和相对收益
def draw_absolute_profit(stock_dat):cash_hold = 100000 # 初始资金posit_num = 0 # 持股数目skip_days = False # 持股/持币状态slippage = 0.01 # 滑点，默认为0.01c_rate = 5.0 / 10000  # 手续费，commission，默认万分之5t_rate = 1.0 / 1000  # 印花税，tax，默认千分之1# 绝对收益—资金的度量for kl_index, today in stock_dat.iterrows():# 买入/卖出执行代码if today.Signal == 1 and skip_days == False: # 买入skip_days = Trueposit_num = int(cash_hold / (today.Close + slippage)) # 资金转化为股票posit_num = int(posit_num / 100) * 100 # 买入股票最少100股，对posit_num向下取整百buy_cash = posit_num * (today.Close + slippage) # 计算买入股票所需现金# 计算手续费，不足5元按5元收，并保留2位小数commission = round(max(buy_cash * c_rate, 5), 2)cash_hold = cash_hold - buy_cash - commissionelif today.Signal == -1 and skip_days == True: # 卖出，避免未买先卖skip_days = Falsesell_cash = posit_num * (today.Close - slippage) # 计算卖出股票得到的现金 卖出股票可以不是整百。# 计算手续费，不足5元按5元收，并保留2位小数commission = round(max(sell_cash * c_rate, 5), 2)# 计算印花税，保留2位小数tax = round(sell_cash * t_rate, 2)cash_hold = cash_hold + sell_cash - commission - tax # 剩余现金if skip_days == True: # 持股stock_dat.loc[kl_index,'total'] = posit_num * today.Close+ cash_holdelse: # 空仓stock_dat.loc[kl_index,'total'] = cash_holdline_key = "资金总体收益%d；上涨幅度 %.2f%%" % (stock_dat['total'][-1], (stock_dat['total'][-1] - 100000) / 100000*100)print(line_key)return stock_dat
print(draw_absolute_profit(df))

3:交易与不交易对比，df的格式要包含收盘价（Close），交易持仓信号（Signal）买入后为1，卖出后为-1。返回的df包含每日涨跌幅（trend_profit_pct）和波动的幅度（benchmark_profit_pct）。

def draw_relative_profit(stock_dat):# 相对收益—策略VS基准pct_close = stock_dat['Close'].pct_change()# 交易信号与股票每日涨跌幅相乘，即可得策略的每日涨跌幅,累乘后计算策略累计收益trend_profit_pct = (1 + stock_dat['Signal'] * pct_close).cumprod()# 把股票价格转换成从1开始，即波动的幅度benchmark_profit_pct = stock_dat['Close']/stock_dat['Close'][0]stock_dat['benchmark_profit_log'] = np.log(stock_dat.Close/stock_dat.Close.shift(1))stock_dat.loc[stock_dat.Signal == -1, 'Signal'] = 0stock_dat['trend_profit_log'] = stock_dat['Signal'] * stock_dat.benchmark_profit_logline_trend_key = "策略收益%.2f" % (stock_dat['trend_profit_log'].cumsum()[-1])line_bench_key = "基准收益%.2f" % (stock_dat['benchmark_profit_log'].cumsum()[-1])print("资金相对收益：%s VS %s" % (line_trend_key, line_bench_key))return stock_dat
print(draw_relative_profit(df))

4.度量策略最大风险回撤——收盘价最大回撤,df同上

def draw_closemax_risk(stock_dat):# 度量策略最大风险回撤——收盘价最大回撤# 计算收盘价曲线当前的滚动最高值stock_dat['max_close'] = stock_dat['Close'].expanding().max()# 计算收盘价曲线在滚动最高值之后所回撤的百分比stock_dat['per_close'] = stock_dat['Close'] / stock_dat['max_close']# 寻找出收盘价最大回撤率交易日min_point_df = stock_dat.sort_values(by=['per_close'])[0:1]min_point_close = min_point_df.per_close#min_point_close = stock_dat.sort_values(by=['per_close']).iloc[[0], stock_dat.columns.get_loc('per_close')]# 寻找出收盘价最高值交易日max_point_df = stock_dat[stock_dat.index <= min_point_close.index[0]].sort_values(by=['Close'], ascending=False)[0:1]max_point_close = max_point_df.Close#max_point_close = stock_dat[stock_dat.index <= min_point_close.index[0]].sort_values \#    (by=['Close'], ascending=False).iloc[[0], stock_dat.columns.get_loc('Close')]# 打印收盘价的最大回撤率与所对应的最高值交易日和最大回撤交易日print("股价最大回撤%5.2f%% 从%s开始至%s结束" % ((1 - min_point_close.values)*100,\max_point_close.index[0], min_point_close.index[0]))return stock_dat
print(draw_closemax_risk(get_trade_signal(pro_daily_stock("000651.SZ", '20180601', '20190601'))))

5.度量策略最大风险回撤——资金最大回撤，df同上

def draw_profitmax_risk(stock_dat):# 度量策略最大风险回撤——资金最大回撤cash_hold = 100000 # 初始资金posit_num = 0 # 持股数目skip_days = False # 持股/持币状态slippage = 0.01 # 滑点，默认为0.01c_rate = 5.0 / 10000  # 手续费，commission，默认万分之5t_rate = 1.0 / 1000  # 印花税，tax，默认千分之1# 绝对收益—资金的度量for kl_index, today in stock_dat.iterrows():# 买入/卖出执行代码if today.Signal == 1 and skip_days == False: #买入skip_days = Trueposit_num = int(cash_hold / (today.Close + slippage)) # 资金转化为股票posit_num = int(posit_num / 100) * 100  # 买入股票最少100股，对posit_num向下取整百buy_cash = posit_num * (today.Close + slippage) # 计算买入股票所需现金# 计算手续费，不足5元按5元收，并保留2位小数commission = round(max(buy_cash * c_rate, 5), 2)cash_hold = cash_hold - buy_cash - commissionelif today.Signal == -1 and skip_days == True: # 卖出 避免未买先卖skip_days = Falsesell_cash = posit_num * (today.Close - slippage) # 计算卖出股票得到的现金 卖出股票可以不是整百。# 计算手续费，不足5元按5元收，并保留2位小数commission = round(max(sell_cash * c_rate, 5), 2)# 计算印花税，保留2位小数tax = round(sell_cash * t_rate, 2)cash_hold = cash_hold + sell_cash - commission - tax # 剩余现金if skip_days == True: # 持股stock_dat.loc[kl_index,'total'] = posit_num * today.Close + cash_holdelse: # 空仓stock_dat.loc[kl_index,'total'] = cash_hold# expanding()计算资金曲线当前的滚动最高值stock_dat['max_total'] = stock_dat['total'].expanding().max()# 计算资金曲线在滚动最高值之后所回撤的百分比stock_dat['per_total'] = stock_dat['total'] / stock_dat['max_total']min_point_df = stock_dat.sort_values(by=['per_total'])[0:1]min_point_total = min_point_df.per_totalmax_point_df = stock_dat[stock_dat.index <= min_point_total.index[0]].sort_values(by=['total'], ascending=False)[0:1]max_point_total = max_point_df.total#min_point_total = stock_dat.sort_values(by=['per_total']).iloc[[0], stock_dat.columns.get_loc('per_total')]#max_point_total = stock_dat[stock_dat.index <= min_point_total.index[0]].sort_values \#    (by=['total'], ascending=False).iloc[[0], stock_dat.columns.get_loc('total')]print("资金最大回撤%5.2f%% 从%s开始至%s结束" % ((1 - min_point_total.values)*100, \max_point_total.index[0], min_point_total.index[0]))return stock_dat# 最大资金回撤 7.49%从2018-07-13 00:00:00开始至2018-09-12 00:00:00结束
print(draw_profitmax_risk(df))

6.回测界面化一体程序，df如上（只需改动函数最后一行的df）

class MultiTraceIf(MplTypesDraw):app = DefTypesPool()##########################回测分析界面###############################@app.route_types(u"cash_profit")# cash profit and retracementdef cash_profit_graph(stock_dat, sub_graph, cash_hold = 100000,slippage = 0.01,c_rate = 5.0 / 10000,t_rate = 1.0 / 1000):posit_num = 0  # 持股数目skip_days = False  # 持股/持币状态# 最大风险回撤——资金最大回撤# 绝对收益—资金的度量for kl_index, today in stock_dat.iterrows():# 买入/卖出执行代码if today.Signal == 1 and skip_days == False:  # 买入skip_days = Trueposit_num = int(cash_hold / (today.Close + slippage))  # 资金转化为股票posit_num = int(posit_num / 100) * 100  # 买入股票最少100股，对posit_num向下取整百buy_cash = posit_num * (today.Close + slippage)  # 计算买入股票所需现金# 计算手续费，不足5元按5元收，并保留2位小数commission = round(max(buy_cash * c_rate, 5), 2)cash_hold = cash_hold - buy_cash - commissionelif today.Signal == -1 and skip_days == True:  # 卖出 避免未买先卖skip_days = Falsesell_cash = posit_num * (today.Close - slippage) # 计算卖出股票得到的现金 卖出股票可以不是整百。# 计算手续费，不足5元按5元收，并保留2位小数commission = round(max(sell_cash * c_rate, 5), 2)# 计算印花税，保留2位小数tax = round(sell_cash * t_rate, 2)cash_hold = cash_hold + sell_cash - commission - tax # 剩余现金if skip_days == True:  # 持股stock_dat.loc[kl_index, 'total'] = posit_num * today.Close + cash_holdelse:  # 空仓stock_dat.loc[kl_index, 'total'] = cash_hold# expanding()计算资金曲线当前的滚动最高值stock_dat['max_total'] = stock_dat['total'].expanding().max()# 计算资金曲线在滚动最高值之后所回撤的百分比stock_dat['per_total'] = stock_dat['total'] / stock_dat['max_total']min_point_df = stock_dat.sort_values(by=['per_total'])[0:1]min_point_total = min_point_df.per_totalmax_point_df = stock_dat[stock_dat.index <= min_point_total.index[0]].sort_values(by=['total'],ascending=False)[0:1]max_point_total = max_point_df.total# min_point_total = stock_dat.sort_values(by=['per_total']).iloc[[0], stock_dat.columns.get_loc('per_total')]# max_point_total = stock_dat[stock_dat.index <= min_point_total.index[0]].sort_values \#    (by=['total'], ascending=False).iloc[[0], stock_dat.columns.get_loc('total')]print("资金最大回撤%5.2f%% 从%s开始至%s结束" % ((1 - min_point_total.values) * 100, \max_point_total.index[0], min_point_total.index[0]))# 最大资金回撤 7.53%从2018-07-13 00:00:00开始至2018-09-12 00:00:00结束line_total = "资金总体收益%d；上涨幅度 %.2f%%" % (stock_dat['total'][-1], (stock_dat['total'][-1] - 100000) / 100000 * 100)print(line_total)max_total = "资金滚动最高值"type_dict = {line_total: stock_dat.total,max_total: stock_dat.max_total,}view_function = MplTypesDraw.mpl.route_output(u"line")view_function(stock_dat.index, type_dict, sub_graph)type_dict = {u"资金最大回撤\n{}".format(1 - min_point_total.values):{'andata': min_point_df,'va': 'top','xy_y': 'total','xytext': (0,stock_dat['High'].mean()),'fontsize': 8,'arrow': dict(facecolor='green', shrink=0.1)},}view_function = MplTypesDraw.mpl.route_output(u"annotate")view_function(stock_dat.index, type_dict, sub_graph)@app.route_types(u"cmp_profit")  # relative_profitdef cmp_profit_graph(stock_dat, sub_graph, para_dat):# 相对收益—策略VS基准stock_dat['benchmark_profit_log'] = np.log(stock_dat.Close / stock_dat.Close.shift(1))stock_dat.loc[stock_dat.Signal == -1, 'Signal'] = 0stock_dat['trend_profit_log'] = stock_dat['Signal'] * stock_dat.benchmark_profit_logline_trend_key = "策略收益%.2f" % stock_dat['trend_profit_log'].cumsum()[-1]line_bench_key = "基准收益%.2f" % stock_dat['benchmark_profit_log'].cumsum()[-1]print("资金相对收益：%s VS %s" % (line_trend_key, line_bench_key))type_dict = {line_bench_key: stock_dat['benchmark_profit_log'].cumsum(),line_trend_key: stock_dat['trend_profit_log'].cumsum()}view_function = MplTypesDraw.mpl.route_output(u"line")view_function(stock_dat.index, type_dict, sub_graph)@app.route_types(u"close_retrace")  # relative_profitdef close_retrace_graph(stock_dat, sub_graph, para_dat):# 度量策略最大风险回撤——收盘价最大回撤# 计算收盘价曲线当前的滚动最高值stock_dat['max_close'] = stock_dat['Close'].expanding().max()# 计算收盘价曲线在滚动最高值之后所回撤的百分比stock_dat['per_close'] = stock_dat['Close'] / stock_dat['max_close']# 计算并打印收盘价的最大回撤率min_point_df = stock_dat.sort_values(by=['per_close'])[0:1]min_point_close = min_point_df.per_close# min_point_close = stock_dat.sort_values(by=['per_close']).iloc[[0], stock_dat.columns.get_loc('per_close')]# 寻找出最大回撤率所对应的最高值交易日和最大回撤交易日，并打印显示max_point_df = stock_dat[stock_dat.index <= min_point_close.index[0]].sort_values(by=['Close'],ascending=False)[0:1]max_point_close = max_point_df.Close# max_point_close = stock_dat[stock_dat.index <= min_point_close.index[0]].sort_values \#    (by=['Close'], ascending=False).iloc[[0], stock_dat.columns.get_loc('Close')]print("股价最大回撤%5.2f%% 从%s开始至%s结束" % ((1 - min_point_close.values) * 100, \max_point_close.index[0], min_point_close.index[0]))##最大股价回撤 29.21% 从2018-06-12 00:00:00开始至2018-12-27 00:00:00结束type_dict = {'最大收盘价': stock_dat.max_close,'收盘价': stock_dat.Close}view_function = MplTypesDraw.mpl.route_output(u"line")view_function(stock_dat.index, type_dict, sub_graph)type_dict = {u"股价最大回撤\n{}".format(1 - min_point_close.values):{'andata': min_point_df,'va': 'top','xy_y': 'Close','xytext': (0,stock_dat['High'].mean()),'fontsize': 8,'arrow': dict(facecolor='green', shrink=0.1)},}view_function = MplTypesDraw.mpl.route_output(u"annotate")view_function(stock_dat.index, type_dict, sub_graph)@app.route_types(u"trade")def trade_graph(stock_dat, sub_graph, para_dat):# 交易获利/亏损区间可视化type_dict = {'signal': stock_dat.Signal,'jdval': stock_dat.Close,'va': 'top','xy_y': 'Close','xytext': (0,stock_dat['High'].mean()),'fontsize': 8,'arrow': dict(facecolor='yellow', shrink=0.1)}view_function = MplTypesDraw.mpl.route_output(u"filltrade")view_function(stock_dat.index, type_dict, sub_graph)def __init__(self, **kwargs):MplTypesDraw.__init__(self)self.fig = plt.figure(figsize=kwargs['figsize'], dpi=100, facecolor="white")#创建fig对象self.graph_dict = {}self.graph_curr = []try:gs = gridspec.GridSpec(kwargs['nrows'], kwargs['ncols'],left = kwargs['left'], bottom = kwargs['bottom'], right = kwargs['right'], top = kwargs['top'],wspace = kwargs['wspace'], hspace = kwargs['hspace'], height_ratios = kwargs['height_ratios'])except:raise Exception("para error")else:for i in range(0, kwargs['nrows'], 1):self.graph_dict[kwargs['subplots'][i]] = self.fig.add_subplot(gs[i, :])def log_trade_info(self, stock_dat):signal_shift = stock_dat.Signal.shift(1)signal_shift.fillna(value=-1, inplace=True)  # 序列最前面的NaN值用-1填充list_signal = np.sign(stock_dat.Signal - signal_shift)buy_singal = stock_dat[list_signal.isin([1])]sell_singal = stock_dat[list_signal.isin([-1])]trade_info = pd.DataFrame({'BuyTime': buy_singal.index.strftime("%y.%m.%d"),'SellTime': sell_singal.index.strftime("%y.%m.%d"),'BuyPrice': buy_singal.Close.values,'SellPrice': sell_singal.Close.values})trade_info['DiffPrice'] = trade_info.SellPrice - trade_info.BuyPricetrade_info['PctProfit'] = np.round(trade_info.DiffPrice / trade_info.BuyPrice*100, 2)win_count = (trade_info.DiffPrice >= 0).sum()loss_count = (trade_info.DiffPrice < 0).sum()win_profit = trade_info[trade_info.PctProfit >= 0].PctProfit.sum()loss_profit = trade_info[trade_info.PctProfit < 0].PctProfit.sum()print(trade_info)print(f'亏损次数:{loss_count}, 盈利次数:{win_count}, 胜率:{round(win_count / (win_count + loss_count)*100, 2)}%')print(f'平均亏损:{round((loss_profit / loss_count), 2)}% 平均盈利:{round((win_profit / win_count), 2)}%')def graph_run(self, stock_data, **kwargs):# 绘制子图self.df_ohlc = stock_data# 临时把标准输出重定向到一个文件，然后再恢复正常with open('logtrade.txt', 'w') as f:oldstdout = sys.stdoutsys.stdout = ftry:self.log_trade_info(self.df_ohlc)for key in kwargs:self.graph_curr = self.graph_dict[kwargs[key]['graph_name']]for path, val in kwargs[key]['graph_type'].items():view_function = MultiTraceIf.app.route_output(path)view_function(self.df_ohlc, self.graph_curr, val)self.graph_attr(**kwargs[key])plt.show()finally:sys.stdout = oldstdout"""print("kwargs %s-->%s" % (key, kwargs[key]))#globals().get('self.%s' % key)(**kwargs[key])eval('self.%s' % key)()#self.kline_draw(**kwargs[key])"""def graph_attr(self, **kwargs):if 'title' in kwargs.keys():self.graph_curr.set_title(kwargs['title'])if 'legend' in kwargs.keys():self.graph_curr.legend(loc=kwargs['legend'], shadow=True)if 'xlabel' in kwargs.keys():self.graph_curr.set_xlabel(kwargs['xlabel'])self.graph_curr.set_ylabel(kwargs['ylabel'])self.graph_curr.set_xlim(0, len(self.df_ohlc.index))  # 设置一下x轴的范围self.graph_curr.set_xticks(range(0, len(self.df_ohlc.index), kwargs['xticks']))  # X轴刻度设定 每15天标一个日期if 'xticklabels' in kwargs.keys():self.graph_curr.set_xticklabels([self.df_ohlc.index.strftime(kwargs['xticklabels'])[index] for index inself.graph_curr.get_xticks()])  # 标签设置为日期# X-轴每个ticker标签都向右倾斜45度for label in self.graph_curr.xaxis.get_ticklabels():label.set_rotation(45)label.set_fontsize(10)  # 设置标签字体else:for label in self.graph_curr.xaxis.get_ticklabels():label.set_visible(False)
if  __name__ == '__main__':if True:layout_dict = {'figsize': (14, 8),'nrows': 3,'ncols': 1,'left': 0.08,'bottom': 0.15,'right': 0.95,'top': 0.95,'wspace': None,'hspace': 0,'height_ratios': [1.5, 1, 1],'subplots': ['kgraph', 'cashgraph', 'cmppfgraph']}subplots_dict = {'graph_fst': {'graph_name': 'kgraph','graph_type': {'trade': None,'close_retrace': None},'title': u"000651 格力电器-回测分析",'ylabel': u"价格",'xticks': 15,'legend': 'best'},'graph_sec': {'graph_name': 'cashgraph','graph_type': {'cash_profit': 100000  # 初始资金},'ylabel': u"资金收益和回撤",'xticks': 15,'legend': 'best',},'graph_fth': {'graph_name': 'cmppfgraph','graph_type': {'cmp_profit': None},'ylabel': u"策略收益VS基准收益",'xlabel': u"日期",'xticks': 15,'legend': 'best','xticklabels': '%Y-%m-%d'},}draw_stock = MultiTraceIf(**layout_dict)draw_stock.graph_run(df, **subplots_dict)#只需给一个df即可

策略评价1:根据交易信号Signal，1.提取交易记录，2.绝对收益和相对收益，3.交易与不交易对比，4.收盘价最大回撤，5.资金最大回撤。6.可视化图形回测。相关推荐

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策略评价1:根据交易信号Signal，1.提取交易记录，2.绝对收益和相对收益，3.交易与不交易对比，4.收盘价最大回撤，5.资金最大回撤。6.可视化图形回测。

学习目标：根据交易信号Signal评估回测结果

学习内容：

策略评价1:根据交易信号Signal，1.提取交易记录，2.绝对收益和相对收益，3.交易与不交易对比，4.收盘价最大回撤，5.资金最大回撤。6.可视化图形回测。相关推荐

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