def roi(self, df=None, days=1):
if df is None:
return sheep.wool2(self.data,
self.raw_data,
PRICEB=self.PRICEB,
PRICES=self.PRICES,
days=days)
return sheep.wool2(df,
self.raw_data,
PRICEB=self.PRICEB,
PRICES=self.PRICES,
days=days)
def line_stock():
raw_data=read_data('daily',start_date=start_date,end_date=end_date)
data=raw_data.loc[raw_data['low']==raw_data['high']]
print(data.shape)
list_days=basic().list_days(data)
print(list_days.shape)
data=data.merge(list_days,on=['ts_code','trade_date'])
res1=sheep.wool2(data.loc[data['pct_chg']>0],raw_data,)
res2=sheep.wool2(data.loc[data['pct_chg']<0],raw_data)
print(res1.describe())
print(res2.describe())
print()
def buy_model(red_line,raw_data):
res=[]
# 单独处理涨跌停数据
# 做切分处理，如果满足rate*pre_close>low,卖出，否则delete
pre_date=basic().pre_date(red_line[['trade_date']],days=-1)
red_line=red_line.merge(pre_date,on=['trade_date'])
red_line['trade_date']=red_line['pre_-1_date']
red_line.drop(['pre_-1_date'],axis=1,inplace=True)
print(red_line.shape)
red_line=red_line.iloc[:,:2].merge(raw_data.iloc[:,:11],on=['ts_code','trade_date'])
red_line['low_pct']=100*(red_line['low']/red_line['pre_close']-1)
for cut in (['down']+list(range(-8,10,2))+['up']):
data=raw_data.iloc[:,:11].copy()
if cut=='down':
buy_data=red_line.loc[red_line['low']==red_line['down_limit']].copy()
PB='down_limit'
elif cut=='up':
buy_data=red_line.loc[red_line['low']==red_line['up_limit']].copy()
PB='up_limit'
else:
buy_data=red_line.loc[cut>red_line['low_pct']].copy()
data['PB']=data['pre_close']*(1+0.01*cut)
PB='PB'
if buy_data.empty:
continue
print(cut)
roi=sheep.wool2(buy_data,data,PRICEB=PB)
res.append([cut,roi.shape[0],roi.iloc[-1,-1]])
res=pd.DataFrame(res,columns=['cut','n_days','pct'])
return res
def roi(data,rawdata):
summary=pd.DataFrame()
print(data.shape[0])
all_data = rawdata.copy()
for pctb in range(-10,11,2):
if pctb==-10:
all_data['price_buy']=all_data['down_limit']
elif pctb==10:
all_data['price_buy']=all_data['up_limit']
else:
all_data['price_buy']=all_data['pre_close']*(1+0.01*pctb)
for pcts in range(-10,11,2):
if pcts == -10:
all_data['price_sell'] = all_data['down_limit']
elif pcts == 10:
all_data['price_sell'] = all_data['up_limit']
else:
all_data['price_sell'] = all_data['pre_close']*(1+0.01*pctb)
all_data=all_data.loc[(all_data['price_buy']>=all_data['low'])&(all_data['price_sell']<=all_data['high'])]
res=sheep.wool2(data,all_data,PRICEB='price_buy',PRICES='price_sell')
detail=pd.DataFrame({'buy':{pctb},'sell':{pcts},'day':{res.shape[0]},'n':{res['n'].sum()},'pct_avg':{res['pct'].mean()},'pct_all':{res.iloc[-1,-1]}})
summary=pd.concat([summary,detail],ignore_index=True)
print()
def fun1(limit_type='up'):
res = pd.DataFrame()
#
data = read_data('daily', start_date=start_date, end_date=end_date)
limit = read_data('stk_limit', start_date=start_date, end_date=end_date)
data = data.merge(limit, on=['ts_code', 'trade_date'])
data['is_roof'] = data.apply(
lambda x: 99 if x['close'] == x['up_limit'] else 1
if x['close'] == x['down_limit'] else x['pct_chg'],
axis=1)
for rate in [-99] + list(range(-10, 10)) + [99]:
print(rate)
df = data.loc[(data['is_roof'] >= rate)
& (data['is_roof'] < (rate + 1))].copy()
if df.empty:
continue
# df['pct']=(df['close']/df['open']-1)*100
# res.loc[rate,'pct']=df['pct'].mean()
wool = sheep.wool2(df[['ts_code', 'trade_date']],
data,
PRICEB=PRICEB,
days=1)
res.loc[rate, 'mean'] = wool.iloc[:, -3].mean()
res.loc[rate, 'n'] = wool.iloc[-1, -2]
res.loc[rate, 'all_pct'] = wool.iloc[-1, -1]
save_data(res, 'pct_chg_cut_res%s-%s.csv' % (start_date, end_date))
def pro(start_date=None, end_date=None, limit_type='up', days=2):
data = gls.first_limit(start_date=start_date,
end_date=end_date,
limit_type=limit_type,
days=days)
data = data.loc[(data['pct_chg'] >= -11) & (data['pct_chg'] <= 11)]
fl = data.loc[(data['pre_1_is_roof'] == 1) & (data['pre_2_is_roof'] == 0)]
return sheep.wool2(fl, fl, days=0, PRICEB='open', PRICES='close')
def next_performance(self,data,PRICEBUY='close',PRICESELL='close',days=1):
res=pd.DataFrame()
for d in range(1, days):
wool=sheep.wool2(data,self.raw_data,PRICEB=PRICEBUY,PRICES=PRICESELL,days=d)
res['%spct' % d] = wool['pct']
res['%sall_pct' % d] = wool['all_pct']
res['n']=wool['n']
return res.iloc[:-days,:]
def verify(up,data):
t1 = datetime.datetime.now()
s1=sheep.wool(up,data)
t2= datetime.datetime.now()
s2=sheep.wool2(up,data)
t3 = datetime.datetime.now()
s1.to_csv('s1.csv')
s2.to_csv('s2.csv')
print()
def roi(red_line,
raw_data,
cut_left=-24,
cut_right=24,
step=2,
selling_price=selling):
res = []
section = list(range(cut_left, cut_right, 2))
# red_line['low_pct']=100*(red_line['low']/red_line['pre_close']-1)
raw_data = raw_data.loc[raw_data['ts_code'].isin(red_line['ts_code'])]
for pb in section:
if red_line.empty:
continue
PB = 'PB'
red_line.loc[:, PB] = red_line['open'] * (1 + 0.01 * pb)
raw_data.loc[:, PB] = raw_data['open'] * (1 + 0.01 * pb)
grass = red_line.loc[(red_line[PB] >= red_line['low'])].copy()
if grass.empty:
continue
raw_data.loc[:, PB] = raw_data.apply(lambda x: x['open']
if pb >= 0 else x[PB]
if x[PB] >= x['low'] else None,
axis=1)
# for ps in section:
# if ps not in raw_data.columns:
# if isinstance(ps,int):
# PS = 'PS'
# raw_data[PS] = raw_data['pre_close'] * (1 + 0.01 * ps)
# else:
# PS=ps
#
# # raw_data[PS]=raw_data.apply(lambda x:x[PS] if x[PS]<=x['high'] else x['close'],axis=1)
# # 集合竞价中，价格低于open，卖出，否则，价格小于当天最高价卖出，否则收盘价卖出
# # raw_data[PS]=raw_data.apply(lambda x:x['open'] if x[PS]<=x['open'] else x['up_limit'] if x[PS]>=x['up_limit'] else x[PS] if x[PS]<=x['high'] else x['close'],axis=1)
#
#
# meat=sheep.wool2(grass,raw_data,PRICEB=PB,PRICES=PS).dropna()
# if meat.empty:
# continue
# res.append([pb,ps,meat.shape[0],meat.iloc[-1,-1]])
meat = sheep.wool2(grass, raw_data, PRICEB=PB,
PRICES=selling_price).dropna()
if meat.empty:
continue
# grass_count=grass.groupby('trade_date')['trade_date'].count().reset_index()
res.append([pb, meat.shape[0], meat['n'].sum(), meat.iloc[-1, -1]])
res = pd.DataFrame(res, columns=['pb', 'n_days', 'num', 'pct'])
return res
disclosure=ts.pro_api().disclosure_date(end_date=need_date,fields='ts_code,ann_date,end_date,pre_date,actual_date,modify_date')
# 解禁
# share_float=ts.pro_api().share_float(ts_code='300617.SZ' )
# 分红送股
dividend=ts.pro_api().dividend()
dividend=dividend.loc['dividend'].[]
disclosure=disclosure.loc[(disclosure['pre_date']>=need_date)&(disclosure['pre_date']<=end_date)].copy()
# print(disclosure.shape,disclosure['ts_code'].unique().shape)
data=read_data('daily',start_date=buy_date,end_date=end_date)
# disclosure
res=pd.DataFrame()
for ts_code in disclosure['ts_code'].unique():
df=data.loc[data['ts_code']==ts_code].copy()
df.sort_values('trade_date',inplace=True)
df['buy_date']=df['trade_date'].shift(5)
df['buy_price']=df['close'].shift(5)
res=pd.concat([res,df],ignore_index=True)
disclosure['trade_date']=disclosure['pre_date']
disclosure.sort_values('trade_date',ascending=False,inplace=True)
print(disclosure.shape)
disclosure=disclosure[['ts_code','trade_date']].drop_duplicates()
print(disclosure.shape,disclosure['ts_code'].unique().shape)
sheep.wool2(disclosure,res,PRICEB='buy_price')
print()