def initData(self, start_day, end_day):
#数据初始化, 生成数据面板
days = (start_day, end_day)
hisdat_start_day = help.MyDate.s_Dec(start_day, -100)
self.panel_hisdat = stock.DataSources.getHisdatPanl(
self.codes, (hisdat_start_day, end_day))
if start_day == '':
start_day = hisdat_start_day
fenshi_start_day = help.MyDate.s_Dec(start_day, -5)
fenshi_days = (fenshi_start_day, help.MyDate.s_Dec(end_day, 1))
if self.mode & self.enum.hisdat_five_mode == self.enum.hisdat_five_mode:
self.panel_fiveminHisdat = stock.DataSources.getFiveMinHisdatPanl(
self.codes, fenshi_days)
if self.mode == BackTestPolicy.enum.tick_mode:
self.dict_fenshi = stock.DataSources.getFenshiPanl(
self.codes, fenshi_days)
else:
self.dict_fenshi = None
#self.panel_fiveminHisdat = None
for policy in self.policys:
policy.data.set_datasource(self.panel_hisdat, self.dict_fenshi, \
self.panel_fiveminHisdat)
#修正日期
if self.mode & self.enum.hisdat_mode == self.enum.hisdat_mode:
if self.mode & self.enum.hisdat_five_mode == self.enum.hisdat_five_mode:
df = self.panel_fiveminHisdat[self.codes[0]]
else:
df = self.panel_hisdat[self.codes[0]]
else:
df = self.dict_fenshi[self.codes[0]]
if len(df) > 0:
end_day = agl.datetime_to_date(df.index[-1])
start_day = help.MyDate.s_Dec(start_day, 2)
return start_day, end_day
def _Report(self, policy, start_day, end_day, last_close):
policy._getAccount().Report(end_day, last_close, True)
#绘制图形
if hasattr(policy, 'Report'):
policy.Report()
#end_day = help.MyDate.s_Dec(end_day, 1)
#bars = stock.CreateFenshiPd(self.code, start_day, end_day)
if self.mode == 0:
bars = self.dict_fenshi[self.codes[0]]
if len(bars) == 0:
return
bars = bars.resample('1min').mean()
bars['c'] = bars['p']
else:
#日线
bars = self.panel_hisdat[self.codes[0]]
if self.mode & self.enum.hisdat_five_mode == self.enum.hisdat_five_mode:
bars = self.panel_fiveminHisdat[self.codes[0]]
bars['positions'] = 0
bars = bars.dropna()
df = policy._getAccount().ChengJiao()
df_zhijing = policy._getAccount().ZhiJing()
init_money = df_zhijing.iloc[0]['资产']
df_zhijing = df_zhijing[bars.index[0]:]
df_changwei = policy._getAccount().ChengJiao()
cols = ['买卖标志', '委托数量']
df_flag = df_changwei[cols[0]].map(lambda x: x == '证券卖出' and -1 or 1)
df_changwei[cols[1]] *= df_flag
changwei = df_changwei[cols[1]].cumsum()
if self.mode == self.enum.hisdat_mode:
df.index = df.index.map(lambda x: agl.datetime_to_date(x))
#bars.is_copy = False
for i in range(len(df)):
index = df.index[i]
bSell = bool(df.iloc[i]['买卖标志'] == '证券卖出')
if index in bars.index:
bars.at[index, 'positions'] = agl.where(bSell, -1, 1)
#同步资金到bar
#df_zhijing.is_copy = False
df_zhijing = copy.deepcopy(df_zhijing) # 为了避免赋值警告
df_zhijing['changwei'] = changwei
if self.mode == self.enum.hisdat_mode:
df_zhijing.index = df_zhijing.index.map(
lambda x: agl.datetime_to_date(x))
bars = bars.join(df_zhijing)
bars = bars.fillna(method='pad')
#同步价格的动态总资产
bars['资产'] = bars['可用'] + bars['changwei'] * bars['c']
zhican = (bars['资产'] - init_money) / init_money * 100
zhican = zhican.fillna(0)
if sys.version > '3':
title = '%s %s' % (self.codes[0], stock.GetCodeName(self.codes[0]))
else:
title = '%s %s' % (self.codes[0], stock.GetCodeName(
self.codes[0]).decode('utf8'))
ui.TradeResult_Boll(agl.where(policy.pl, policy.pl, pl), bars, \
stock.GuiYiHua(zhican),\
stock.GuiYiHua(bars['changwei']),
title=title)
if policy.pl is not None:
#if policy.pl.explicit:
policy.pl.publish()
def ShowTradeResult2(pl, bars, signals, zhijin, changwei,signal_dependent_num=0, freq=300, title=''):
"""绘制策略的交易结果, 交易剔除了非交易时间， 资金未剔除， 因此两者的xtick看起来是不一致的
注意，bars不能有nan，否则画不出箭头
bars: pd.DateFrame 日线等数据集 index为时间 收盘为['c']
signals: pd.DateFrame 信号 包含技术指标 index同bars
zhijin: pd.Serias 资金结果
changwei: 当前仓位
signal_dependent_num: 显示signals里的依赖线
freq: 坐标的间隔
"""
# Plot two charts to assess trades and equity curve
pl.figure
#fig = pl.figure()
pl.close()
fig = pl.gcf()
fig.patch.set_facecolor('white') # Set the outer colour to white
ax1 = fig.add_subplot(211, ylabel='Price in RMB')
if title != '':
pl.title(title, fontproperties=getFont())
#思路， 把时间转整数，显示时再把整数转时间字符串
# 为了去除非交易时间， 把index 转换为整数
date_ticks = copy.deepcopy(bars.index)
indexs = np.arange(len(bars))
bars.index = indexs
signals.index = indexs
if len(bars) == len(zhijin):
if zhijin is not None:
zhijin.index = indexs
if changwei is not None:
changwei.index = indexs
# Plot the AAPL closing price overlaid with the moving averages
bars['c'].plot(ax=ax1, color='r', lw=2.)
#显示信号产生的依据, 最好是使用固定的值， 比如信号线1-n, range(n)
if signal_dependent_num>0:
signals[np.array(range(signal_dependent_num), dtype=str)].plot(ax=ax1, lw=2.)
#signals[['short_ma', 'long_ma']].plot(ax=ax1, lw=2.)
# Plot the "buy" trades against AAPL
ax1.plot(signals.loc[signals.positions == 1.0].index,
bars['c'][signals.positions == 1.0],
'^', markersize=10, color='m')
# Plot the "sell" trades against AAPL
ax1.plot(signals.loc[signals.positions == -1.0].index,
bars['c'][signals.positions == -1.0],
'v', markersize=10, color='k')
# Plot the equity curve in dollars
ax2 = fig.add_subplot(212, ylabel='Portfolio value in RMB')
if zhijin is not None and len(zhijin)>1:
zhijin.plot(ax=ax2, lw=2.)
legends = ['zhijing']
if changwei is not None and len(changwei)>1:
changwei.plot(ax=ax2)
legends.append('changwei')
ax2.legend(legends,loc=0)
#剔除非交易时间
#freq = 300
ax1.set_xticks(indexs[::freq])
date_ticks = date_ticks.map(lambda x : agl.datetime_to_date(x))
ax1.set_xticklabels(date_ticks[::freq], rotation=45, ha='right')
if len(bars) == len(zhijin):
ax2.set_xticks(indexs[::freq])
ax2.set_xticklabels(date_ticks[::freq], rotation=45, ha='right')
# Plot the figure
#fig.show()
pl.show()
pl.close()
