def getTradeDay(d1, d2, dtype=backtest_runner.BackTestPolicy.enum.hisdat_mode):
"""确定是交易日
return: (d1, d2)"""
try:
if dtype == backtest_runner.BackTestPolicy.enum.hisdat_mode:
df = stock.getHisdatDataFrameFromRedis(code, d1, d2)
else:
df = stock.getFenshiDfUseRedis(code, d1,d2)
if len(df) == 0:
#在指定日期内没有数据，应该是新股或次新股
return d2,d2
d2 = agl.datetime_to_date(df.index[-1])
if agl.DateTimeCmp(d1, agl.datetime_to_date(df.index[0])) <0:
d1 = agl.datetime_to_date(df.index[0])
except:
pass
return d1, d2
def calc_history_syl(df, df_hisdat, period='year', dtype='pd.series'):
"""计算股票的历史市盈率
df : df 单个股票的净利润表
df_hisdat : 日线
period: str year|quarter
dtype: str pd.df|pd.series
return: df index如果是年一般指年尾，虽然使用的是2016-1-1, 因此调整为下一年年初"""
rows = df[u'基本每股收益元'].sort_index()
#df_hisdat = stock.getHisdatDataFrameFromRedis(code)
if len(df_hisdat) == 0:
return pd.Series([])
rows = rows.ix[df_hisdat.index[0]:df_hisdat.index[-1]]
if len(rows) == 0:
return rows
if str(rows.dtype) != 'float64':
rows = rows[rows.map(lambda x: x.strip() != '')]
rows = rows.astype(float)
for i in range(len(rows)):
price = np.nan
d = agl.datetime_to_date(rows.index[i])
# d is year
if period == 'year':
d = d.split('-')[0]
try:
df_last = df_hisdat[d].sort_index()
except:
return pd.Series([])
else:
df_last = df_hisdat[:d].sort_index()
if len(df_last) > 0:
price = df_last.iloc[-1]['c']
mgsy = rows.iloc[i]
if period == 'quarter':
#季度转年度
quarter = df_last.index[-1].quarter
mgsy = mgsy / quarter * 4
syl = stock.SYL(price, mgsy)
#print i, d, mgsy, price, syl
rows.iloc[i] = syl
if period == "year":
#调整index为下一年的年初
#print rows.index
last_next_year = str(int(rows.index[-1].year) + 1) + '-1-1'
new_index = rows.index.astype(str).tolist()
new_index = new_index[1:] + [last_next_year]
rows.index = pd.DatetimeIndex(new_index)
#rows.index = map(lambda x: )
if dtype == 'pd.series':
return rows
df[u'历史市盈率'] = rows
return df[[u'历史市盈率', u'净利润万元']]
def _test_fenshi_redis(self):
"""取一个个股的redis， 看下日期"""
end_day = agl.CurDay()
start_day = help.MyDate.s_Dec(end_day, -15)
code = u'600100'
#取一个值
df_fenshi = getFiveMinFenshiFromRedis(code)
df_fenshi_day = df_fenshi.resample('D').dropna()
self.assertEqual(len(df_fenshi_day), 5)
d = agl.datetime_to_date(df_fenshi_day.index[-1])
self.assertEqual(stock.getKlineLastDay(), d)
df = stock.LiveData().getFiveMinHisdat(code)
print(df)
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 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.ix[signals.positions == 1.0].index,
bars['c'][signals.positions == 1.0],
'^',
markersize=10,
color='m')
# Plot the "sell" trades against AAPL
ax1.plot(signals.ix[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)
#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()
def ExePolicy(self, code):
code = agl.unicode_to_utf8(code)
live_data = LiveData()
df_hisdat = live_data.getHisdat(code)
df_fenshi = live_data.getFenshi(code)
df_five_hisdat = live_data.getFiveMinHisdat(code)
if agl.IsNone(df_five_hisdat):
#print code ,'没有5分钟'
return
if len(df_fenshi) == 0:
return
if len(df_five_hisdat) < 30:
return
price = float(agl.FloatToStr(float(df_fenshi.tail(1)['p'])))
yclose = df_hisdat.ix[df_hisdat.index[-1]]['c']
zhangfu = stock.ZhangFu(price, yclose)
rsi = stock.RSI(df_five_hisdat['c'])
#ui.DrawClosesAndVolumes(pl, df_five_hisdat['c'], rsi)
upper, middle, lower = stock.TDX_BOLL(df_five_hisdat['c'])
#ui.DrawTs(pl, df_five_hisdat['c'],mid=middle, high=upper, low=lower)
highs, lows, closes = df_five_hisdat['h'], df_five_hisdat[
'l'], df_five_hisdat['c']
#atr = stock.ATR(highs, lows, closes)
adx = stock.TDX_ADX(highs, lows, closes)
closes = np.array(df_hisdat['c'])
if help.MyDate(agl.datetime_to_date(df_hisdat.index[-1])).d < \
help.MyDate(agl.CurDay()).d:
closes = agl.array_insert(closes, len(closes), price)
four = stock.FOUR(closes)
#print code, four[-1]
self._dict_four[code] = four[-1]
#return
#ui.DrawClosesAndVolumes(pl, df_five_hisdat['c'], adx)
boll_up = (price - upper[-1]) / upper[-1]
boll_mid = (price - middle[-1]) / middle[-1]
boll_down = (lower[-1] - price) / lower[-1]
boll_width = upper[-1] - lower[-1]
if abs(zhangfu) > 0.098 or boll_width < 0.01:
return
if code == '300033':
codename = stock.GetCodeName(code)
s = 'rsi = %d %s %s' % (rsi[-1], codename, str(price))
print s
if (rsi[-1] > 65 or rsi[-1] < 35) and adx[-1] > 60:
codename = stock.GetCodeName(code)
s = '%s %s' % (codename, str(price))
sign = False
#if code in ['300033','510050']:
#sign = True
#if adx[-1] > 55:
#s += ' ADX=%d'%(adx[-1])
#sign = True
if boll_up > -0.003:
s += ' 越过布林上轨'
#sign = True
if abs(boll_mid) < 0.003:
s += ' 布林中轨'
#sign = True
if boll_down > -0.003:
s += ' 越过布林下轨'
sign = True
if four[-1] > 0.1:
sign = False
#sign = False
if sign:
#ui.DrawTs(pl, df_five_hisdat['c'],mid=middle, high=upper, low=lower)
sInfo = self.calcInfo(code, price, zhangfu)
help.myprint(s, sInfo)
help.myprint(
'[%s,%s] %.3f,%.3f,%.3f,%.2f, four=%.2f, adx=%.2f' %
(code, stock.GetCodeName(code), boll_up, boll_mid,
boll_down, boll_width, four[-1], adx[-1]))
self.NotifyAutoxdShow(code)
self.Speak(s)
def _Report(self, policy, start_day, end_day):
policy._getAccount().Report(end_day, 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 == 1:
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['changwei'] = changwei
if self.mode == 1:
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)
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:
#有成交才发布
if len(df) > 0:
policy.pl.publish()
def Run(self): account = self._getAccount() code = self.data.get_code() hisdat = self.data.get_hisdat(code) hisdat = hisdat.dropna() closes = hisdat['c'].dropna() if len(closes)<60: return #基本面计算 date = agl.datetime_to_date(self.getCurTime()) history_quarter_syl = jbm.calc_history_syl(self.df_jll, hisdat, 'quarter') syl = history_quarter_syl.ix[:date].iloc[-1] self.jbm = history_quarter_syl #计算技术指标 four = stock.FOUR(closes) boll_up, boll_mid, boll_low ,boll_w= stock.TDX_BOLL2(closes) adx = stock.TDX_ADX(hisdat['h'], hisdat['l'], closes) #收集使用过的技术指标，为了报告使用 self.tech = closes, four, boll_up, boll_mid, boll_low ,boll_w, adx four = four[-1] price = hisdat.iloc[-1]['c'] #print self.getCurTime(), four, price, boll_low[-1], boll_w[-1] #基于价值回归及成本控制，加上技术指标择时 #缺点， 资金利用率低， 选股必须选好， 否则碰到一直下跌的情况就会造成亏损 #优点, 成本在相对安全区域 #当成本亏base_ratio%时加倍买入 df_stock_list = account.StockList() df_stock_list = df_stock_list[df_stock_list['证券代码'] == code] #第一次买 if so.assemble(four<self.base_four[0] ,len(df_stock_list) == 0 , adx[-1] > 60, #price<boll_low[-1] , #boll_w[-1]>2, #syl < 50, ): if hasattr(self, 'base_num_ratio'): total_money = ac.AccountMgr(self._getAccount(), price, code).init_money() self.base_num = ac.ShouShu(total_money*self.base_num_ratio/price) self._getAccount().Order(0, code, price, self.base_num) if len(df_stock_list) > 0: chengben = float(df_stock_list['参考盈亏成本价'].loc[0]) changwei = int(df_stock_list['库存数量'].loc[0]) yinkui = calcYinKui(price, chengben) #agl.Print( '盈亏:', chengben, yinkui, changwei, '频繁') if price < chengben*(1-self.base_ratio): #如果仓位大于总资金的70%， 那么仓位为剩余资金的一半 df_zhijing = account.ZhiJing() canuse_money = float(df_zhijing['可用'][-1]) total_money = canuse_money + changwei*price if canuse_money/total_money<self.base_rhombus_mid_ratio: num = ac.ShouShu(canuse_money/2/price) #这里开始就按上次交易的价格来作为判断, 最后一个买入 df_chengjiao = account.ChengJiao() df_chengjiao = df_chengjiao[df_chengjiao['买0卖1'] == '0'] last_buy_price = float(df_chengjiao['成交价格'][-1]) if price > last_buy_price*(1-self.base_ratio): num = 0 else: num = changwei*self.base_pramid_ratio if num>0: account.Order(0, code, price, num) #当赚base_ratio%时加倍卖出 chengben = float(df_stock_list['买入均价'].loc[0]) sell_avg_price = myredis.createRedisVal(self.key_sell_avg_price, chengben) sell_num = myredis.createRedisVal(self.key_sell_num, 0) if price>chengben*(1+self.base_ratio) and four>self.base_four[1]: #第一次卖 if sell_num.get() == 0: account.Order(1, code, price, self.base_num) sell_avg_price.set(price) sell_num.set(self.base_num) #print '--',chengben, sell_avg_price.get()*(1+self.base_ratio) if price > sell_avg_price.get()*(1+self.base_ratio): num = int(df_stock_list['可卖数量'].loc[0]) num = min(sell_num.get() * self.base_pramid_ratio, num) num = ac.ShouShu(num) if num > 0: account.Order(1, code, price, num) #更新卖出均价 new_sell_avg_price = (sell_avg_price.get() * sell_num.get()+price*num)/(sell_num.get()+num) sell_avg_price.set(new_sell_avg_price) sell_num.set(sell_num.get()+num)
