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tradeStrategy.py
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tradeStrategy.py
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# -*- coding:utf-8 -*-
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
import os,sys,time,platform
import tushare as ts
import json
import string
#import urllib.request, urllib.error, urllib.parse
import datetime
import threading
import smtplib
from pdSql_common import get_easyhistory_df
import tradeTime as tt
import easyquotation
ISOTIMEFORMAT='%Y-%m-%d %X'
#ISODATEFORMAT='%Y%m%d'
ISODATEFORMAT='%Y-%m-%d'
#ROOT_DIR='C:/work/stockAnalyze'
ROOT_DIR="C:/中国银河证券海王星/T0002"
RAW_HIST_DIR=ROOT_DIR+'/export/'
HIST_DIR=ROOT_DIR+'/hist/'
HIST_FILE_TYPE='.csv'
#get the all file source data in certain DIR
def get_all_code(hist_dir='C:/hist/day/data/'):
all_code=[]
for filename in os.listdir(hist_dir):#(r'ROOT_DIR+/export'):
"""
code=filename[:-4]
if len(code)==6:
all_code.append(code)
"""
code = filename.split('.')
if len(code)>=1:
all_code.append(code[0])
return all_code
#get the history raw data for certain code: ['date','open','high','low','close','volume','amount']
"""data from 'export' is update from trade software everyday """
def get_raw_hist_df(code_str,latest_count=None):
file_type='csv'
file_name=RAW_HIST_DIR+code_str+'.'+file_type
column_list=['date','open','high','low','close','volume','amount']
#print('file_name=',file_name)
df_0=pd.read_csv(file_name,names=column_list, header=0,encoding='gb2312')#'utf-8') #for python3
#print df_0
#delete column 'amount' and delete the last row
del df_0['amount']
df=df_0.ix[0:(len(df_0)-2)]
#print df
""""check"""
df= df.set_index('date')
#df.index.name='date'
this_time=datetime.datetime.now()
this_time_str=this_time.strftime('%Y-%m-%d %X')
df.index.name=this_time_str
"""check"""
hist_dir=ROOT_DIR+'/hist/'
file_type='csv'
file_name=hist_dir+code_str+'.'+file_type
#print df
df.to_csv(file_name)
hist_dir=ROOT_DIR+'/update/'
file_name=hist_dir+code_str+'.'+file_type
df.to_csv(file_name)
#column_list=['date','open','high','low','close','volume']
if latest_count!=None and latest_count<len(df):
df=df.tail(latest_count)
return df
def write_hist_index():
"""
hist_sh_df=ts.get_hist_data('sh')
hist_sz_df=ts.get_hist_data('sz')
hist_hs300_df=ts.get_hist_data('hs300')
hist_sz50_df=ts.get_hist_data('sz50')
hist_zxb_df=ts.get_hist_data('zxb')
hist_cyb_df=ts.get_hist_data('cyb')
"""
#print datetime.datetime.now()
index_list=['sh','sz','zxb','cyb','hs300','sz50']
index_dir=ROOT_DIR+'/index/'
file_type='csv'
for index_str in index_list:
print('Getting hist data for %s index ...' % index_str)
file_name=index_dir+index_str+'.'+file_type
index_df=ts.get_hist_data(index_str)
index_df.to_csv(file_name)
print('Getting hist data for %s index is completed' % index_str)
#print datetime.datetime.now()
def get_hist_index(index_str):
index_dir=ROOT_DIR+'/index/'
file_type='csv'
index_list=['sh','sz','zxb','cyb','hs300','sz50']
file_name=index_dir+index_str+'.'+file_type
column_list=['date','open','high','close','low','volume','price_change','p_change','ma5','ma10','ma20','v_ma5','v_ma10','v_ma20']
index_df=pd.read_csv(file_name)#,names=column_list)
index_df= index_df.set_index('date')
return index_df
def get_file_timestamp(file_name):
file_mt_str=''
try:
file_mt= time.localtime(os.stat(file_name).st_mtime)
file_mt_str=time.strftime("%Y-%m-%d %X",file_mt)
except:
#file do not exist
pass
return file_mt_str
#get the history data for certain code: ['date','open','high','low','close','volume']
def get_hist_df(code_str,analyze_type,latest_count=None):
target_dir=''
if analyze_type=='history':
target_dir=ROOT_DIR+'/hist/'
else:
if analyze_type=='realtime':
target_dir=ROOT_DIR+'/update/'
else:
pass
file_type='csv'
code_str=str(code_str)
file_name=target_dir+code_str+'.'+ file_type
#print 'file_name:',file_name
data={}
column_list=['date','open','high','low','close','volume']#,'amount']
df=pd.DataFrame(data=data,columns=column_list)
now_time=datetime.datetime.now()
file_mt_str=now_time.strftime("%Y-%m-%d %X")
try:
hist_df=pd.read_csv(file_name,names=column_list, header=0)
#column_list=['date','open','high','low','close','volume']
""""check"""
#df= df_0.set_index('date')
#df=df_0.ix[:'2015-05-22']
#df.index.name='date'
"""check"""
default_count=10000
if latest_count!=None and latest_count<len(hist_df):
default_count=latest_count
df=hist_df.tail(min(default_count,len(hist_df)))
#df=df.set_index('date')
file_mt_str= get_file_timestamp(file_name)
except:
pass
return df,file_mt_str
def f_code_2sybol(code_f):
#print 'code_f:',code_f
s1=str(code_f)
#print 's1=',s1
zero_str={6:'',5:'0',4:'00',3:'000',2:'0000',1:'00000'} #,2:'00000',1:'00000'}
le_n=len(s1)
code_str=zero_str[le_n]+s1 #[:-2]
#print 'code_str=',code_str
return code_str
#is_trade_time=now_timestamp>start_timestamp and
def get_ma_list(close_list,ma_num):
ma_list=[]
i=0
while (i<len(close_list)):
k=0
if i<ma_num:
temp_list=close_list[0:(i+1)]
ma=round(round(sum(temp_list),2)/len(temp_list),2)
ma_list.append(ma)
else:
temp_list=close_list[(i-(ma_num-1)):(i+1)]
ma=round(round(sum(temp_list),2)/ma_num,2)
ma_list.append(ma)
i=i+1
#print ma_list,len(ma_list)
return ma_list
def find_boduan(data_list,min_range_rate=0.10):
count=len(data_list)
max=data_list[0]
min=data_list[0]
split_list=[]
indx=1
max_index=0
min_index=0
action=''
found_min=False
found_max=False
print(data_list)
while indx<(count-1) and indx>=1:
value=data_list[indx]
print(max,min,value)
up_bord=min*(1+min_range_rate)
if min<0:
up_bord=min*(1-min_range_rate)
down_bord=max*(1-min_range_rate)
if max<0:
down_bord=max*(1+min_range_rate)
print(up_bord,down_bord)
if value>up_bord:
action='find_max'
print(found_min)
if not found_min:
split_list.append(min)
min=value
found_min=True
found_max=False
else:
if value>max:
max=value
max_index=indx
found_max=False
if value<min:
min=value
min_index=indx
found_min=False
elif value<down_bord:
action='find_min'
if not found_max:
split_list.append(max)
max=value
found_max=True
found_min=False
else:
if value>max:
max=value
max_index=indx
found_max=False
if value<min:
min=value
min_index=indx
found_min=False
else:
if value>max:
max=value
max_index=indx
found_max=False
if value<min:
min=value
min_index=indx
#found_min=False
#found_max=False
indx=indx+1
#split_list.append(lst_value)
return split_list
def find_boduan0(data_list):
indx=0
count=len(data_list)
max=data_list[0]
min=data_list[0]
split_list=[]
split_list.append(max)
indx=1
action=''
if data_list[0]>data_list[1]:
action='find_min'
else:
action='find_max'
lst_value=data_list[(count-1)]
while indx<(count-1) and indx>=1:
value=data_list[indx]
#print value,action,indx
if action=='find_max':
#print min,max
if value>=max:
max=value
action='find_max'
else:
last_value=data_list[indx-1]
min=value
max=last_value
split_list.append(last_value)
action='find_min'
pass
else:
if action=='find_min':
#print min,max
if value<=min:
min=value
action='find_min'
else:
last_value=data_list[indx-1]
max=value
min=last_value
split_list.append(last_value)
action='find_max'
pass
else:
pass
indx=indx+1
split_list.append(lst_value)
return split_list
def specify_rate_range(init_rate=-1.5,rate_interval=0.5,range_num=10):
"""
return the range to seperate the rate in class
"""
rate_list=[]
for i in range(0,range_num):
rate=round(init_rate+i*rate_interval,1)
rate_list.append(rate)
if rate>10.95 or rate <-9.0:
break
return rate_list
def get_today_df():
this_time=datetime.datetime.now()
this_time_str=this_time.strftime('%Y-%m-%d %X')
"""
latest_trade_day=tradeTime.get_latest_trade_date()
latest_trade_end=latest_trade_day + ' 15:00:00'
latest_trade_before_start=latest_trade_day + ' 09:15:00'
"""
latest_trade_day_str=tradeTime.get_latest_trade_date()
latest_trade_time_str=latest_trade_day_str + ' 15:00:00'
pre_name='all'
#if profix_name != None:
# pre_name=profix_name
file_name=ROOT_DIR+'/data/%s.csv'%(pre_name+latest_trade_day_str)
file_time_str=get_file_timestamp(file_name)
data={}
column_list=['code','changepercent','trade','open','high','low','settlement','volume','turnoverratio']
today_df=pd.DataFrame(data,columns=column_list)#,index=['
if not tradeTime.is_trade_time_now():
if file_time_str:
#if file_time_str>=latest_trade_day_str+' 13:00:00':
#print '---------------1'
today_df=read_today_df(file_name)
today_df.index.name=latest_trade_time_str
#return today_df,file_time_str
else:
#print '---------------2'
today_df=ts.get_today_all()
del today_df['name']
today_df=write_today_df(file_name,today_df)
today_df= today_df.set_index('code')
file_time_str=get_file_timestamp(file_name)
today_df.index.name=latest_trade_time_str
#return today_df,this_time_str
else:
# read the real time update data on today
#print '---------------3'
today_df=ts.get_today_all()
del today_df['name']
today_df= today_df.set_index('code')
today_df.index.name=this_time_str
#return today_df,this_time_str
today_df=today_df.astype(float)
today_df.insert(6, 'h_change', (100*(today_df.high-today_df.settlement)/today_df.settlement).round(2))
today_df.insert(7, 'l_change', (100*(today_df.low-today_df.settlement)/today_df.settlement).round(2))
today_df['atr']=np.where(today_df['high']-today_df['low']<today_df['high']-today_df['settlement'],(today_df['high']-today_df['settlement'])/today_df['settlement']*100.0,(today_df['high']-today_df['low'])/today_df['settlement']*100.0) #temp_df['close'].shift(1)-temp_df['low'])
today_df['atr']=np.where(today_df['atr']<today_df['settlement']-today_df['low'],(today_df['settlement']-today_df['low'])/today_df['settlement']*100.0,today_df['atr'])
return today_df,this_time_str
def write_today_df(file_name,today_df):
#latest_trade_day_str=tradeTime.get_latest_trade_date()
#today_df=ts.get_today_all()
#del today_df['name']
#today_df1=today_df.set_index('code')
#pre_name='all'
#file_name=ROOT_DIR+'/data/%s.csv'%(pre_name+latest_trade_day_str)
this_time=datetime.datetime.now()
this_time_str=this_time.strftime('%Y-%m-%d %X')
today_df.index.name=this_time_str
today_df.to_csv(file_name) #,encoding='GB18030')
return today_df
def read_today_df(file_name):
column_list=['code','changepercent','trade','open','high','low','settlement','volume','turnoverratio']
today_df=pd.read_csv(file_name,names=column_list)# header=1)
#all_codes_f=today_df['code'].values.tolist()
#print all_codes_f
"""
all_codes=[]
if all_codes_f:
for code_f in all_codes_f:
code=f_code_2sybol(code_f)
all_codes.append(code)
all_codes=all_codes[1:]
today_df['code']=pd.Series(all_codes)
"""
df= today_df.set_index('code')
#this_time=datetime.datetime.now()
#this_time_str=this_time.strftime('%Y-%m-%d %X')
#print 'read_today_df,index.name',df.index.name
# df.index.name=this_time_str
df=df.ix[1:]
#print df
return df
def update_one_hist(code_sybol,today_df,today_df_update_time):
#latest_trade_day_str=tradeTime.get_latest_trade_date()
#print latest_trade_day_str
#today_df_update_time=today_df.index.name
#print 'today_df_update_time=',today_df_update_time
#print today_df
today_str=today_df_update_time[:10]
#print 'today_str=',today_str
hist_df,file_mt=get_hist_df(code_sybol,'history')
#print 'hist_df0=',hist_df
#print 'file_mt=',file_mt
date_list=hist_df['date'].values.tolist()
#print date_list
last_hist_date=date_list[-1]
#print 'last_hist_date=',last_hist_date
if file_mt>today_df_update_time:
#print 'update_one_hist1, if1'
pass
else:
#print 'update_one_hist, else1'
if last_hist_date==today_str:
hist_df=hist_df.head(len(hist_df)-1)
#print 'update_one_hist1,hist_df1=',hist_df
print(last_hist_date)
else:
#print 'update_one_hist1, else2'
pass
today_df=today_df.astype(float)
code_value=today_df.ix[code_sybol].values.tolist()
open_price=today_df.ix[code_sybol].open
high_price=today_df.ix[code_sybol].high
low_price=today_df.ix[code_sybol].low
current_price=today_df.ix[code_sybol].trade
last_price=today_df.ix[code_sybol].settlement
volume=today_df.ix[code_sybol].volume
"""
open_price=round(today_df.ix[code_sybol]['open'].mean(),2)
high_price=round(today_df.ix[code_sybol]['high'].mean(),2)
low_price=round(today_df.ix[code_sybol]['low'].mean(),2)
current_price=round(today_df.ix[code_sybol]['trade'].mean(),2)
last_price=round(today_df.ix[code_sybol]['settlement'].mean(),2)
volume=round(today_df.ix[code_sybol]['volume'].mean(),2)
"""
#hist_value=[code_value[3],code_value[4],code_value[5],code_value[2],code_value[7]]
data={'date':[today_str],'open':open_price,'high':high_price,'low':low_price,'close':current_price,'volume':volume}
column_list=['date','open','high','low','close','volume']
hist_today_df=pd.DataFrame(data,columns=column_list)#,index=['2015-05-15'])
hist_df=hist_df.append(hist_today_df,ignore_index=True)
#print hist_today_df
#print hist_df
#print hist_df_updated
hist_df= hist_df.set_index('date')
#hist_df_updated.index.name='date'
hist_df.index.name=today_df_update_time
#print hist_df.index.name
#print hist_df
#print 'update_one_hist:',hist_df_updated
update_file_name=ROOT_DIR+'/update/%s.csv'% code_sybol
hist_df.to_csv(update_file_name)
hist_file_name=ROOT_DIR+'/hist/%s.csv'% code_sybol
if tradeTime.is_trade_time_now0(tradeTime.get_latest_trade_date()):
pass
else:
hist_df.to_csv(hist_file_name)
return hist_df
def update_all_hist(today_df,today_df_update_time):
print('Star update history stock data...')
hist_all_code=get_all_code(ROOT_DIR+'/hist')
#print 'update_all_hist:',hist_all_code
all_codes=today_df.index.values.tolist()
#for code_sybol in hist_all_code:
latest_trade_date=tradeTime.get_latest_trade_date()
#all_codes=['000001']
#hist_all_code=['000001']
print('update_all_hist,all_codes=', all_codes)
print('update_all_hist,hist_all_code=', hist_all_code)
for code_sybol in all_codes:
if code_sybol in hist_all_code:
updated_df=update_one_hist(code_sybol, today_df,today_df_update_time)
"""
file_name=ROOT_DIR+'/update/%s.csv'% code_sybol
hist_file_name=ROOT_DIR+'/hist/%s.csv'% code_sybol
#updated_df.to_csv(file_name) #,encoding='GB18030')
if tradeTime.is_trade_time_now0(latest_trade_date):
pass
else:
#updated_df.to_csv(hist_file_name)
pass
"""
else:
#to get hist data update
print('update_all_hist----else')
pass
print('Completed history stock data update!')
return
#update all hist code from export
def init_all_hist_from_export():
raw_hist_code=get_all_code(RAW_HIST_DIR)
hist_code=get_all_code(HIST_DIR)
except_code_list=list(set(raw_hist_code).difference(set(hist_code)))
print(except_code_list)
print(len(hist_code))
print(len(raw_hist_code))
"""update all hist data from export"""
if len(hist_code)==0 or (len(hist_code)!=0 and len(hist_code)!=(len(raw_hist_code))):
print('Begin pre-processing the hist data')
for code_sybol in raw_hist_code:
get_raw_hist_df(code_sybol)
print('pre-processing completed')
else:
pass
"""update all hist data based on the new 'today' data"""
#update_all_hist(get_today_df())
def get_top_list():
update_file_name=ROOT_DIR+'/result/top_2015-07-08.csv'
df=ts.top_list('2015-07-08')
df.to_csv(update_file_name,encoding='GB18030')
return
def get_timestamp(date_time_str):
#date_time_str='2015-07-20 13:20:01'
return time.mktime(time.strptime(date_time_str, ISOTIMEFORMAT))
def get_delta_seconds(start_time, end_time):
#start_time,end_time: datetime.datetime Object
delta=end_time-start_time
delta_second=delta.days*24*3600+delta.seconds+delta.microseconds*10**(-6)
return delta_second
def send_mail(alarm_list):
# alarm_list=[stock_code,this_date_time,alarm_type,alarm_category,alarm_content]
if alarm_list:
mailto_list=['104450966@qq.com']
mail_host='smtp.163.com'
mail_user='zgx20022002@163.com'
mail_pass='821853Zgx'
mail_postfix="qq.com"
me=mail_user+"<"+mail_user+"@"+mail_postfix+">"
"""
print 'Setting MIMEText'
CT=open('content.txt','r') #read the txt
content=CT.read().decode('utf-8')
msg=MIMEText(content.encode('utf8'),_subtype='html')
CT.close()#close tst
"""
sub=alarm_list[2]+ ' for '+ alarm_list[0]+' :' +alarm_list[3]
#date_time='%s'%alarm_list[1]
content=alarm_list[1] + '\n ' + alarm_list[4]
msg = MIMEText(content)
msg['Subject'] = sub
msg['From'] = mail_user
msg['To'] = ";".join(mailto_list)
try:
#s = smtplib.SMTP()
s=smtplib.SMTP_SSL(mail_host,465)
s.login(mail_user,mail_pass)
s.sendmail(mail_user, mailto_list, msg.as_string())
s.close()
except Exception as e:
print(str(e))
else:
pass
def get_interval(interval):
this_time=datetime.datetime.now()
this_time_str=this_time.strftime('%Y-%m-%d %X')
latest_trade_day=tradeTime.get_latest_trade_date()
morning_time0=datetime.datetime.strptime(latest_trade_day+' 09:30:00','%Y-%m-%d %X')
morning_time1=datetime.datetime.strptime(latest_trade_day+' 11:30:00','%Y-%m-%d %X')
noon_time0=datetime.datetime.strptime(latest_trade_day+' 13:00:00','%Y-%m-%d %X')
noon_time1=datetime.datetime.strptime(latest_trade_day+' 15:00:00','%Y-%m-%d %X')
next_morning_time0=morning_time0+datetime.timedelta(days=1)
#print 'now_time=',this_time
#this_time=datetime.datetime(2015,7,21,13,25,20,0)
#print my_df
if this_time>morning_time1 and this_time<noon_time0 :
interval_time=noon_time0-this_time
interval=interval_time.days*24*3600+interval_time.seconds
print('Have a lest druing the noon, sleep %s seconds...'%interval)
else:
if this_time<=morning_time0:
interval_time=morning_time0-this_time
interval=interval_time.days*24*3600+interval_time.seconds
print('Market does not start yet in the morning, sleep %s seconds...'%interval)
else:
if this_time>=noon_time1:
interval_time=next_morning_time0-this_time
interval=interval_time.days*24*3600+interval_time.seconds
market_analyze_today()
#market_analyze_today()
write_hist_index()
print('Market will start in next morning, sleep %s seconds...'%interval)
else:
if (this_time>=morning_time0 and this_time<=morning_time1) or (this_time>=noon_time0 and this_time<=noon_time1):
interval=interval
"""
def filter_df_by_date(raw_df,from_date_str,to_date_str): #index of df should be date:
#from_date_str: '2015-05-16'
if from_date_str>to_date_str:
temp_date_str=from_date_str
from_date_str=to_date_str
to_date_str=temp_date_str
else:
pass
#self.h_df.set_index('date')
date_index=raw_df.index.values.tolist()
#print date_index
df=raw_df[from_date_str:to_date_str]
return df
"""
def filter_df_by_date(raw_df,from_date_str,to_date_str): #index of df should not be date:
#from_date_str: '2015-05-16'
if from_date_str>to_date_str:
temp_date_str=from_date_str
from_date_str=to_date_str
to_date_str=temp_date_str
else:
pass
#self.h_df.set_index('date')
"""
date_index=raw_df.index.values.tolist()
#print date_index
if date_index[0]>from_date_str:
from_date_str=date_index[0]
else:
pass
if date_index[-1]<to_date_str:
to_date_str=date_index[-1]
else:
pass
"""
crit1=raw_df.date>=from_date_str
crit2=raw_df.date<=to_date_str
df=raw_df[crit1 & crit2]
return df
def market_analyze_today():
#init_all_hist_from_export()
latest_trade_day=tradeTime.get_latest_trade_date()
today_df,df_time_stamp=get_today_df()
out_file_name=ROOT_DIR+'/result/result-' + latest_trade_day + '.txt'
output=open(out_file_name,'w')
sys.stdout=output
market=Market(today_df)
#update_all_hist(today_df,df_time_stamp)
#actual_101_list,success_101_rate=market.get_101()
market.get_hist_cross_analyze()
market.get_realtime_cross_analyze()
actual_101_list,success_101_rate=market.get_101('realtime')
t_df=market.today_df
df_101=t_df[t_df.index.isin(actual_101_list)]
#print 'df_101=',df_101
star_rate=0.25
star_df=market.get_star_df(star_rate)
#print star_df
star_list=star_df.index.values.tolist()
code_10,rate=market.get_10('history', star_list)
#print code_10
t_df=market.today_df
df_10=t_df[t_df.index.isin(code_10)]
#print df_10
filename=ROOT_DIR+'/data/is10-%s.csv' % latest_trade_day
df_10.to_csv(filename)
#code_10= ['002579', '002243', '002117', '000970', '600654', '000533', '600377', '300080', '600382', '600423', '600208', '601188', '002338', '002237', '002234', '000666', '600858', '601678', '300104', '002487', '600581', '600580', '002242', '600616', '600618', '002412', '002148', '600320', '000409', '600978', '600405', '600819', '600816', '002201', '002207', '002562', '000637', '601390', '000593', '600094', '600146', '600668', '000785', '601718', '300018', '002585', '600449', '600565', '600219', '300342', '600282', '002323', '002328', '300347', '600825', '000673', '601100', '300115', '002551', '002490', '002495', '002392', '600741', '600621', '002597', '002073', '000004', '600133', '601339', '000419', '000555', '600570', '603100', '600419', '000955', '000952', '000789', '300155', '002213', '601999', '600707', '600680', '600686', '600159', '601002', '002668', '002503', '600052', '002006', '002501', '600513', '600222', '600225', '300349', '600350', '300291', '600358', '600292', '000888', '601116', '300122', '300125', '601800', '002387', '002386', '002389', '002263', '601231', '600633', '601600', '002042', '600495', '002169', '600499', '600643', '600640', '600308', '000548', '300317', '300314', '300091', '600396', '000726', '000729', '002227', '603166', '603167', '600393', '600636', '002121', '002125', '600695', '002087', '603008', '600169', '000509', '000501', '601519', '601518', '002409', '600360', '000698', '600506', '600332', '600330', '002103', '002651', '300286', '002083', '603001', '000897', '600802']
#print 'potential_101_list=',potential_101_list
realtime_101_list,success_101_rate=market.get_101('realtime',code_10)
sys.stdout=sys.__stdout__
output.close()
print('market_analyze completed for today.')
class Stockhistory:
def __init__(self,code_str,ktype, test_num=0,source='easyhistory',rate_to_confirm=0.01):
self.code=code_str
self.ktype=ktype
self.DEBUG_ENABLED=False
#self.h_df=pds.get_raw_hist_df(code_str) #the history data frame data set
"""
self.h_df = None #ta_lib indicator
if source=='yh' or source=='YH':
self.h_df = pds.get_yh_raw_hist_df(code_str)
self.h_df['amount'] = self.h_df['rmb']
else:
self.h_df=pds.get_easyhistory_df(code_str) #ta_lib indicator
"""
self.h_df= get_easyhistory_df(code_str,source) #ta_lib
#print(self.h_df)
self.alarm_trigger_timestamp=0
self.max_price=-1
self.min_price=1000
self.alarm_category='normal'
self.realtime_stamp=0
self.temp_hist_df=self._form_temp_df()
self.test_num = test_num
self.rate_to_confirm = rate_to_confirm
if test_num ==0 and isinstance(test_num, int):
self.test_num = len(self.h_df)
#self.average_high=0
#self.average_low=0
def set_code(self,code_str):
self.code = code_str
def data_feed(self, k_data, feed_type='hist'):
if feed_type=='hist':
self.h_df = k_data
elif feed_type=='temp':
self.temp_hist_df = k_data
else:
pass
"""
if k_data==None:
self.h_df = pds.get_raw_hist_df(code_str)
if feed_type=='temp':
self.temp_hist_df = self._form_temp_df()
else:
pass
else:
if feed_type=='hist':
self.h_df = k_data
elif feed_type=='temp':
self.temp_hist_df = k_data
else:
pass
"""
def get_all_symbols(self):
return
def get_all_on_trade_symbols(self):
return
def filter(self, filter_type, symbols=None):
target_symbols=[]
if symbols:
if isinstance(symbols, str):
target_symboles = [symbols]
elif isinstance(symbols, str):
target_symboles = symbols
else:
pass
else:
target_symboles = self.get_all_symbols()
stock_hist_anlyse = tds.Stockhistory(code_str='000001',ktype='D')
for symbol in target_symboles:
pass
return
def is_island_reverse_up(self,gap_rate=0.005):
"""
temp_df = self.temp_hist_df
temp_df['jump_max']=np.where(temp_df['close']>temp_df['open'],temp_df['close'],temp_df['open'])
temp_df['jump_min']=np.where(temp_df['close']<temp_df['open'],temp_df['close'],temp_df['open'])
temp_df['jump_up']=np.where(temp_df['jump_min']>(1+gap_rate)*temp_df['jump_max'].shift(1),
temp_df['jump_min']/temp_df['jump_max'].shift(1)-1,0)
temp_df['jump_down']=np.where(temp_df['jump_max']<(1-gap_rate)*temp_df['jump_min'].shift(1),
1-temp_df['jump_min'].shift(1)/temp_df['jump_max'],0)
temp_df['gap'] = (temp_df['jump_up'] + temp_df['jump_down']).round(3)
temp_df['island'] = np.where(temp_df['gap']*temp_df['gap'].shift(1)<0,temp_df['gap'],0)
island_df=temp_df[['date','p_change','gap','star','island','atr_in']]
print(island_df[island_df.island!=0])
print(island_df.tail(50))
del temp_df['jump_max']
del temp_df['jump_min']
del temp_df['jump_up']
del temp_df['jump_down']
"""
return
def get_realtime_k_data(self):
#https://github.com/shidenggui/easyquotation
quotation=easyquotation.use('sina') # 新浪 ['sina'] 腾讯 ['tencent', 'qq']
#quotation = easyquotation.use('lf') # ['leverfun', 'lf'] #免费十档行情
index_code=['999999','399001','399002','399003','399005']
print(quotation.stocks(self.code))
k_data=quotation.stocks(self.code)
is_stop_trade = False
if k_data:
k_data=k_data[self.code]
is_stop_trade = not k_data['volume'] and not k_data['high']
"""
{'000680': {'bid4_volume': 19000, 'high': 5.76, 'bid2_volume': 119096, 'sell': 5.7, 'bid2': 5.68, 'volume': 202358001.01,
'ask4_volume': 143800, 'ask5_volume': 153400, 'ask1': 5.7, 'bid1_volume': 110500, 'bid3_volume': 20817,
'ask3_volume': 337408, 'open': 5.41, 'ask3': 5.72, 'turnover': 36100590, 'ask2': 5.71, 'ask1_volume': 210213,
'ask2_volume': 217367, 'bid4': 5.66, 'ask5': 5.74, 'date': '2016-04-22', 'low': 5.37, 'time': '15:05:56',
'bid3': 5.67, 'name': '山推股份', 'now': 5.69, 'ask4': 5.73, 'bid5': 5.65, 'buy': 5.69, 'bid1': 5.69,
'close': 5.44, 'bid5_volume': 31000}
}
"""
return k_data
def update_realtime_hist_df(self,k_data=None):
"""
实时更新当前K线到历史数据
"""
#k_data=[date,open,high,low,close,volume,amount]
k_data = self.get_realtime_k_data()
#print(k_data)
if self.h_df.empty:
return
latest_day=self.h_df.tail(1)['date'].values.tolist()[0]
latest_index=self.h_df.tail(1).index.values.tolist()[0]
#print('latest_day=',latest_day)
#print(self.h_df.tail(10))
if k_data and len(k_data)>=7:
this_trade_time = k_data['date'] + ' ' + k_data['time']
pass_time_rate = tt.get_pass_trade_time()
predict_volume_rate = 1.0
if pass_time_rate:
predict_volume_rate = pass_time_rate
column_list=['date','open','high','low','close','volume','amount']
#this_k_data={'date': k_data[0],'open': k_data[1],'high': k_data[2],'low': k_data[3],'close': k_data[4],'volume': k_data[5],'amount': k_data[6]}
this_k_data={'date': k_data['date'],'open': k_data['open'],'high': k_data['high'],'low': k_data['low'],'close': k_data['now'],'volume': k_data['turnover']*predict_volume_rate,'amount': k_data['volume']*predict_volume_rate}
this_k_df=pd.DataFrame(data=this_k_data,columns=column_list,index=[latest_index+1])
#print(this_k_df)
#print(k_data['date'])
if k_data['date']>latest_day:
pass
elif k_data['date']==latest_day:
this_k_df=pd.DataFrame(data=this_k_data,columns=column_list,index=[latest_index])
self.h_df=self.h_df.head(len(self.h_df)-1)
else:
return
self.h_df=self.h_df.append(this_k_data,ignore_index=True)
#print(self.h_df.tail(10))
return
def get_last_k_data(self,last_num=None):
if self.temp_hist_df.empty:
return
last_n=1
if last_num!=None:
last_n=last_num
temp_hist_df_len=len(self.temp_hist_df)
last_df=self.temp_hist_df.head(temp_hist_df_len-last_n+1).tail(1)
ma5=last_df.iloc[0].ma5
ma10=last_df.iloc[0].ma10
ma20=last_df.iloc[0].ma20
ma30=last_df.iloc[0].ma30
ma60=last_df.iloc[0].ma60
ma120=last_df.iloc[0].ma120
change=last_df.iloc[0].p_change
open=last_df.iloc[0].open
close=last_df.iloc[0].close
high=last_df.iloc[0].high
low=last_df.iloc[0].low
volume=last_df.iloc[0].volume
atr=last_df.iloc[0].atr
score=last_df.iloc[0].score
v_ma5=last_df.iloc[0].v_ma5
v_ma10=last_df.iloc[0].v_ma10
v_ma30=last_df.iloc[0].v_ma30
v_ma60=last_df.iloc[0].v_ma60
return open,high,low,close,volume,change,ma5,ma10,ma30,ma60,ma120,v_ma5,v_ma10,v_ma30,v_ma60
def is_great_drop_then_high_open(self):
great_drop_rate=-3.0
if self.temp_hist_df.empty:
return 0.0
temp_df=self.temp_hist_df.tail(2)
ma5_0=temp_df.tail(1).iloc[0].ma5
ma10_0=temp_df.tail(1).iloc[0].ma10
ma20_0=temp_df.tail(1).iloc[0].ma20
last_change=temp_df.tail(1).iloc[0].p_change
ma5_1=temp_df.tail(1).iloc[1].ma5
ma10_1=temp_df.tail(1).iloc[1].ma10
ma20_1=temp_df.tail(1).iloc[1].ma20
change=temp_df.tail(1).iloc[1].p_change
close=temp_df.tail(1).iloc[1].close
open=temp_df.tail(1).iloc[1].open
return 0.0
def get_price_score(self):
if self.temp_hist_df.empty:
return 0.0
temp_df=self.temp_hist_df.tail(2)
ma5_0=temp_df.tail(1).iloc[0].ma5
ma10_0=temp_df.tail(1).iloc[0].ma10
ma20_0=temp_df.tail(1).iloc[0].ma20
ma5_1=temp_df.tail(1).iloc[1].ma5
ma10_1=temp_df.tail(1).iloc[1].ma10
ma20_1=temp_df.tail(1).iloc[1].ma20
close=temp_df.tail(1).iloc[1].close
open=temp_df.tail(1).iloc[1].open
return 0.0
def get_market_score0(self,short_turn_weight=None,k_data=None):
ma_type='ma5'
temp_df=self.temp_hist_df
if temp_df.empty:
return 0,0,0
if k_data!=None:
update_one_hist(code_sybol, today_df, today_df_update_time)
temp_df=self._form_temp_df()
if len(temp_df)<5:
return 0.0,0.0
ma_offset=0.002
WINDOW=3
ma_type_list=['ma5','ma10','ma20','ma30','ma60','ma120']
ma_weight=[0.3,0.2,0.2,0.1,0.1,0.1]
short_weight=0.7
ma_seq_score={'ma5_o_ma10':0.5,'ma10_o_ma30':0.3,'ma30_o_ma60':0.2}
ma_cross_score={'ma5_c_ma10':0.75,'ma10_c_ma30':0.5,'ma30_c_ma60':0.3}
if short_turn_weight!=None:
short_weight=short_turn_weight
"""Get MA score next"""
for ma_type in ma_type_list:
ma_sum_name='sum_o_'
temp_df['c_o_ma']=np.where((temp_df['close']-temp_df[ma_type])>ma_offset*temp_df['close'].shift(1),1,0) #1 as over ma; 0 for near ma but unclear
temp_df['c_o_ma']=np.where((temp_df['close']-temp_df[ma_type])<-ma_offset*temp_df['close'].shift(1),-1,temp_df['c_o_ma']) #-1 for bellow ma
ma_sum_name=ma_sum_name+ma_type
#temp_df[ma_sum_name] = pd.rolling_sum(temp_df['c_o_ma'], window=WINDOW).round(2)
if '3.5' in platform.python_version():
temp_df[ma_sum_name] = temp_df['c_o_ma'].rolling(window=5,center=False).mean().round(2)
else:
temp_df[ma_sum_name] = np.round(pd.rolling_sum(temp_df['c_o_ma'], window=WINDOW), 2)
del temp_df['c_o_ma']
temp_df[ma_sum_name]=temp_df[ma_sum_name]+(temp_df[ma_sum_name]-temp_df[ma_sum_name].shift(1))
temp_df['ma_score0']=(short_weight*ma_weight[0]+(1-short_weight)*ma_weight[5])*temp_df['sum_o_ma5']\
+(short_weight*ma_weight[1]+(1-short_weight)*ma_weight[4])*temp_df['sum_o_ma10']\
+(short_weight*ma_weight[2]+(1-short_weight)*ma_weight[3])*temp_df['sum_o_ma20']\
+(short_weight*ma_weight[3]+(1-short_weight)*ma_weight[2])*temp_df['sum_o_ma30']\
+(short_weight*ma_weight[4]+(1-short_weight)*ma_weight[1])*temp_df['sum_o_ma60']\
+(short_weight*ma_weight[5]+(1-short_weight)*ma_weight[1])*temp_df['sum_o_ma120']
#temp_df['ma_s_score']=0.1*temp_df['sum_o_ma5']+0.1*temp_df['sum_o_ma10']+0.1*temp_df['sum_o_ma20']+0.2*temp_df['sum_o_ma30']+0.2*temp_df['sum_o_ma60']+0.3*temp_df['sum_o_ma120']
del temp_df['sum_o_ma5']
del temp_df['sum_o_ma10']
del temp_df['sum_o_ma20']
del temp_df['sum_o_ma30']
del temp_df['sum_o_ma60']
del temp_df['sum_o_ma120']
"""Get MA trend score next"""
ma_trend_score=0.0
for ma_o_name in ma_seq_score.keys():
ma_name_list=ma_o_name.split('_')
s_ma_name=ma_name_list[0]
l_ma_name=ma_name_list[2]
ma_cross_name=ma_o_name.replace('o','c')
temp_df[ma_o_name]=np.where(temp_df[s_ma_name]>temp_df[l_ma_name],ma_seq_score[ma_o_name],-ma_seq_score[ma_o_name])
temp_df[ma_cross_name+'_d']=np.where((temp_df[s_ma_name]<=temp_df[l_ma_name]) &
(temp_df[s_ma_name].shift(1)>temp_df[l_ma_name].shift(1)),-ma_cross_score[ma_cross_name],0)
temp_df[ma_cross_name+'_u']=np.where((temp_df[s_ma_name]>=temp_df[l_ma_name]) &
(temp_df[s_ma_name].shift(1)<temp_df[l_ma_name].shift(1)),ma_cross_score[ma_cross_name],0)
temp_df[ma_cross_name]=temp_df[ma_cross_name+'_d']+temp_df[ma_cross_name+'_u']
del temp_df[ma_cross_name+'_d']
del temp_df[ma_cross_name+'_u']
ma_o_name_list=ma_seq_score.keys()
ma_c_name_list=ma_cross_score.keys()
temp_df['ma_trend_score']=temp_df['ma5_o_ma10']+temp_df['ma10_o_ma30']+temp_df['ma30_o_ma60']\
+temp_df['ma5_c_ma10']+temp_df['ma10_c_ma30']+temp_df['ma30_c_ma60']
temp_df['ma_score']=temp_df['ma_score0']+temp_df['ma_trend_score']
del temp_df['ma5_o_ma10']
del temp_df['ma10_o_ma30']
del temp_df['ma30_o_ma60']
"""
del temp_df['ma5_c_ma10']
del temp_df['ma10_c_ma30']
del temp_df['ma30_c_ma60']
"""
"""Get K data trend score next"""
great_high_open_rate = 1.0
great_low_open_rate = -1.5
recent_k_num = 120
temp_df1=temp_df.fillna(0).tail(recent_k_num)
extreme_rate = 0.8
temp_df1['o_change']=(temp_df1['o_change']).round(1)
o_change_list=temp_df1['o_change'].values.tolist()
great_high_open_rate,great_low_open_rate=self.get_extreme_change(o_change_list,rate=extreme_rate)
print('great_high_open_rate=%s,great_low_open_rate=%s' %(great_high_open_rate,great_low_open_rate))
temp_df['gt_o_h']=np.where(temp_df['o_change']>great_high_open_rate,(temp_df['o_change']/great_high_open_rate-1),0)
temp_df['gt_o_l']=np.where(temp_df['o_change']<great_low_open_rate,-(temp_df['o_change']/great_low_open_rate-1),0)
temp_df['gt_open']=temp_df['gt_o_h']+temp_df['gt_o_l']
del temp_df['gt_o_h']
del temp_df['gt_o_l']
great_increase_rate=3.0
great_descrease_rate=-3.0
temp_df1['p_change']=(temp_df1['p_change']).round(1)
p_change_list=temp_df1['p_change'].values.tolist()
great_increase_rate,great_descrease_rate=self.get_extreme_change(p_change_list,rate=extreme_rate)
print('great_increase_rate=%s,great_descrease_rate=%s' %(great_increase_rate,great_descrease_rate))
temp_df['gt_c_h']=np.where(temp_df['p_change']>great_increase_rate,(temp_df['p_change']/great_increase_rate-1),0)
temp_df['gt_c_l']=np.where(temp_df['p_change']<great_descrease_rate,-(temp_df['p_change']/great_descrease_rate-1),0)
temp_df['gt_close']=temp_df['gt_c_h']+temp_df['gt_c_l']