def savfig(figureObj, fn_prefix1='backtest8', fn_prefix2='_1_'):

     import datetime

     fmt= '%Y_%m_%d_%H_%M_%S'

     now = datetime.datetime.now()

     fname_savfig = fn_prefix1 + fn_prefix2 + now.strftime(fmt)+ '.png'

     figureObj.savefig(fname_savfig, facecolor=fig.get_facecolor())

 def backtest8(ohlc=ohlc, SD=1.0, n_short=2, n_long=20, f_savfig=False):

     u'''

     双均线策略回测函数

     signature: backtest8(ohlc=ohlc, SD=1.0, n_short=2, n_long=20, f_savfig=False)

     param::

     ohlc      - dohlcva 数据, dataFrame结构的

     SD        - MA1/MA2 > SD 触发多头买入的快均线/慢均线的阀值

     f_savefig - flag for saving Matplot output figures

     '''

     import matplotlib

     #import seaborn as sns

     #sns.set_style('white')

     myfontprops = matplotlib.font_manager.FontProperties(

                         fname='C:/Windows/Fonts/msyh.ttf')#微软雅黑

     maShort = pd.Series.rolling(ohlc.C, n_short).mean()

     maLong  = pd.Series.rolling(ohlc.C, n_long).mean()

     fig=plt.figure() # create new figure

     ohlc.C.plot(grid=True, figsize=(8,4))

     maShort.plot(label='MA'+str(n_short))

     maLong.plot(grid=True,label='MA'+str(n_long))

 #    ohlc.iloc[:,[0,1,2,3]].plot(grid=False, figsize=(8,4))

 #    ohlc.iloc[:,[0,1,2,3]].plot(grid=True,figsize=(8,4))

     plt.legend(loc='best')

     plt.title( s=u'历史股价', fontproperties=myfontprops)

     if f_savfig:

         savfig(fig, 'backtest8', '_0_')

 #    SD=1.0

     regime = np.where( maShort/maLong > SD, 1, 0)

     regime = pd.Series(regime, index=maShort.index)

     print ('Regime Length = %s'%regime.size)

     fig=plt.figure() # create new figure

     regime[:].plot(lw=1.5, ylim=(-0.1, 1.1), figsize=(8,4), title=u'Regime')

     if f_savfig:

         savfig(fig, 'backtest8', '_1_')

     fig=plt.figure() # create new figure

     regime[-100:].plot(lw=1.5, ylim=(-0.1, 1.1), figsize=(8,4), title=u'Regime')

     if f_savfig:

         savfig(fig, 'backtest8', '_2_')

     pp_ratio_bnh = np.log(ohlc.C / ohlc.C.shift(1) )

     pp_ratio_strategy = regime.shift(1) * pp_ratio_bnh

     #最后我们把每天的收益率求和就得到了最后的累计收益率

     #（这里因为我们使用的是指数收益率，所以将每日收益累加是合理的），

     #这个累加的过程也可以通过DataFrame的内置函数cumsum轻松完成：

     norm_return_bnh      = pp_ratio_bnh     .cumsum().apply(np.exp)

     norm_return_strategy = pp_ratio_strategy.cumsum().apply(np.exp)

     fig=plt.figure() # create a new figure

     norm_return_strategy. plot(lw=1.5,  figsize=(8,4), label=u'Strategy')

     norm_return_bnh.      plot(lw=1.5, label=u'BnH')

     plt.legend(loc='best')

     plt.title(s=u'策略收益率与历史价格对比', fontproperties=myfontprops)

     if f_savfig:

         savfig(fig, 'backtest8', '_3_')

     assert (regime.index == ohlc.C.index).all()==True # 'signal index not equals price index'

     # assert用来判断语句的真假，如果为假的话将触发AssertionError错误, 为开发人员提示出错的表达式

     return norm_return_strategy, n_short, n_long, SD