def Report(self, start_day, end_day):
"""回測报告"""
self._getAccount().Report(end_day)
#return
#绘制图形
#end_day = help.MyDate.s_Dec(end_day, 1)
bars = stock.CreateFenshiPd(self.code, start_day, end_day)
if len(bars) == 0:
return
bars = bars.resample('1min').mean()
bars['positions'] = 0
bars['c'] = bars['p']
bars = bars.dropna()
df = self._getAccount().ChengJiao()
df_zhijing = self._getAccount().ZhiJing()
df_zhijing = df_zhijing[bars.index[0]:]
df_changwei = self._getAccount().ChengJiao()
cols = ['买卖标志','委托数量']
df_flag = df_changwei[cols[0]].map(lambda x: agl.where(int(x), -1, 1))
df_changwei[cols[1]] *= df_flag
changwei = stock.GuiYiHua(df_changwei[cols[1]].cumsum())
for i in range(len(df)):
index = df.index[i]
bSell = bool(df.iloc[i]['买卖标志']=='1')
if index in bars.index:
#bars.ix[index]['positions'] = agl.where(bSell, -1, 1)
bars.set_value(index, 'positions', agl.where(bSell, -1, 1))
trade_positions = np.array(bars['positions'])
ui.TradeResult_Boll(self.code, bars, trade_positions, \
stock.GuiYiHua(df_zhijing['资产']), changwei)
def recog_history_boll(pl, x, p, code):
"""在历史数据中找到相似的曲线
x : np.ndarray 基准曲线
p : float pearson相似度
"""
df = stock.getFiveHisdatDf(code, method='local')
upper, middle, lower = stock.TDX_BOLL(df['c'].values)
df['upper'] = upper
df = df[-1000:]
#upper = upper[np.isnan(upper) == False]
upper = df['upper'].values
n = 30
report_list = []
x = stock.GuiYiHua(x - np.min(x))
for i in range(0, len(upper) - 30, 2):
y = upper[i:i + n]
assert (len(y) == 30)
y = stock.GuiYiHua(y - np.min(y))
pearson_v = pearson(x, y)
if pearson_v > p:
df2 = pd.DataFrame(x)
df2['2'] = y
df2.plot()
pl.show()
pl.close()
fimg = pl.get_CurImgFname()
report_list.append([code, pearson_v, df.index[i], fimg])
return report_list
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]]
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 Report(self):
"""报告技术指标"""
if not hasattr(self, 'tech'):
return
closes, four, adx = self.tech
assert (len(closes) == len(four))
cur_pl = self.pl
#adx = stock.GuiYiHua(adx)
df = pd.DataFrame(adx, columns=['adx'])
#df['adx'] = adx
df = stock.GuiYiHua(df)
df['four'] = four * 10
df['close'] = stock.GuiYiHua(closes - np.min(closes))
#df.index = pd.DatetimeIndex(df.index)
df.plot()
cur_pl.show()
cur_pl.close()
def Report(self): """报告技术指标""" closes, four, boll_up, boll_mid, boll_low ,boll_w, adx = self.tech assert(len(closes) == len(four)) df = pd.DataFrame(closes) df['boll_w'] = boll_w df = stock.GuiYiHua(df) df['four'] = four df[df.columns[0]] = (df[df.columns[0]]-1)*2 df.plot() self.pl.show()
def recog_boll(pl, report_list):
import stock_pinyin as jx
codes = [jx.HCGD, jx.HYGY]
codes = stock.get_codes(stock.myenum.randn, 2)
#import tushare_handle as th
up1 = get_upper(codes[0])
up2 = get_upper(codes[1])
#print(len(up1), len(up2))
n = 30
a = up1[-n:]
b = up2[-n:]
a = stock.GuiYiHua(a - np.min(a))
b = stock.GuiYiHua(b - np.min(b))
v = pearson(a, b)
df = pd.DataFrame(a)
df['2'] = b
df.plot()
pl.show()
fimg = pl.get_CurImgFname()
report_list.append([v, fimg])
def Report(self):
"""报告技术指标"""
if not hasattr(self, 'tech'):
return
closes, four, boll_up, boll_mid, boll_low ,boll_w, adx = self.tech
assert(len(closes) == len(four))
cur_pl = agl.where(self.pl, self.pl, pl)
df = pd.DataFrame(closes)
df['boll_w'] = boll_w
df = stock.GuiYiHua(df)
df['four'] = four
#df[df.columns[0]] = (df[df.columns[0]]-1)*2
#index转日期
df.index = pd.DatetimeIndex(df.index)
df.plot()
cur_pl.show()
cur_pl.close()
def Report(self): """报告技术指标""" if not hasattr(self, 'tech'): return closes, four, boll_up, boll_mid, boll_low ,boll_w, adx = self.tech assert(len(closes) == len(four)) cur_pl = agl.where(self.pl, self.pl, pl) if hasattr(self, 'jbm'): syl = self.jbm df = pd.DataFrame(syl) df.columns = ['市盈率'] df.plot() cur_pl.show() cur_pl.close() df = pd.DataFrame(closes) df['boll_w'] = boll_w df = stock.GuiYiHua(df) df['four'] = four #df[df.columns[0]] = (df[df.columns[0]]-1)*2 df.plot() cur_pl.show() cur_pl.close()