def __init__(self,retry=5,local=False):

if local:

for i in range(retry):

try:

self.bases_save = ts.get_stock_basics()

# print(self.bases_save)

self.bases_save=self.bases_save.reset_index()

self.bases_save.to_csv('bases.csv')

self.bases_save.to_sql('bases',engine,if_exists='replace')

if self.bases_save:

break

except Exception as e:

if i>=4:

self.bases_save=pd.DataFrame()

exit()

continue

else:

self.bases_save = pd.read_sql('bases',engine,index_col='index')

self.base=self.bases_save

# 因为网速问题，手动从本地抓取

self.today = time.strftime("%Y-%m-%d", time.localtime())

# self.base = pd.read_csv('bases.csv', dtype={'code': np.str})

self.all_code = self.base['code'].values

self.working_count = 0

self.mystocklist = Toolkit.read_stock('mystock.csv')

# 保存为excel 文件 这个时候csv 乱码,excel正常.

def save_data_excel(self):

df = ts.get_stock_basics()

df.to_csv(self.today + '.csv', encoding='gbk')

df_x = pd.read_csv(self.today + '.csv', encoding='gbk')

df_x.to_excel(self.today + '.xls', encoding='gbk')

os.remove(self.today + '.csv')

def insert_garbe(self):

print('*' * 30)

print('\n')

def showInfo(self, df):

print('*' * 30)

print('\n')

print(df.info())

print('*' * 30)

print('\n')

print(df.dtypes)

self.insert_garbe()

print(df.describe())

# 计算每个地区有多少上市公司

def count_area(self, writeable=False):

count = self.base['area'].value_counts()

print(count)

print(type(count))

if writeable:

count.to_csv('各省的上市公司数目.csv')

return count

# 显示你要的某个省的上市公司

def get_area(self, area, writeable=False):

user_area = self.base[self.base['area'] == area]

user_area.sort_values('timeToMarket', inplace=True, ascending=False)

if writeable:

filename = area + '.csv'

user_area.to_csv(filename)

return user_area

# 获取所有地区的分类个股

def get_all_location(self):

series = self.count_area()

index = series.index

for i in index:

name = unicode(i)

self.get_area(name, writeable=True)

# 找出指定日期后的次新股

def fetch_new_ipo(self, start_time, writeable=False):

# 需要继续转化为日期类型

df = self.base.loc[self.base['timeToMarket'] > start_time]

df.sort_values('timeToMarket', inplace=True, ascending=False)

if writeable == True:

df.to_csv("New_IPO.csv")

# sum_a=df['pe'].sum()

pe_av = df[df['pe'] != 0]['pe'].mean()

pe_all_av = self.base[self.base['pe'] != 0]['pe'].mean()

print(u"平均市盈率为 ", pe_av)

print('A股的平均市盈率为 ', pe_all_av)

return df

# 获取成分股

def get_chengfenggu(self, writeable=False):

s50 = ts.get_sz50s()

if writeable == True:

s50.to_excel('sz50.xls')

list_s50 = s50['code'].values.tolist()

# print(type(s50))

# print(type(list_s50))

# 返回list类型

return list_s50

# 计算一个票从最高位到目前 下跌多少 计算跌幅

def drop_down_from_high(self, start, code):

end_day = datetime.date(datetime.date.today().year, datetime.date.today().month, datetime.date.today().day)

end_day = end_day.strftime("%Y-%m-%d")

# print(e)nd_day

# print(start)

total = ts.get_k_data(code=code, start=start, end=end_day)

# print(total)

high = total['high'].max()

high_day = total.loc[total['high'] == high]['date'].values[0]

print(high)

print(high_day)

current = total['close'].values[-1]

print(current)

percent = round((current - high) / high * 100, 2)

print(percent)

return percent

def loop_each_cixin(self):

df = self.fetch_new_ipo(20170101, writeable=False)

all_code = df['code'].values

print(all_code)

# exit()

percents = []

for each in all_code:

print(each)

# print(type(each))

percent = self.drop_down_from_high('2017-01-01', each)

percents.append(percent)

df['Drop_Down'] = percents

# print(df)

df.sort_values('Drop_Down', ascending=True, inplace=True)

# print(df)

df.to_csv(self.today + '_drop_Down_cixin.csv')

# 获取所有的ma5>ma10

def macd(self):

# df=self.fetch_new_ipo(writeable=True)

# all_code=df['code'].values

# all_code=self.get_all_code()

# print(all_code)

result = []

for each_code in self.all_code:

print(each_code)

try:

df_x = ts.get_k_data(code=each_code, start='2017-03-01')

# 只找最近一个月的，所以no item的是停牌。

except:

print("Can't get k_data")

continue

if len(df_x) < 11:

# return

print("no item")

continue

ma5 = df_x['close'][-5:].mean()

ma10 = df_x['close'][-10:].mean()

if ma5 > ma10:

# print("m5>m10: ",each_code," ",self.base[self.base['code']==each_code]['name'].values[0], "ma5: ",ma5,' m10: ',ma10)

temp = [each_code, self.base[self.base['code'] == each_code]['name'].values[0]]

print(temp)

result.append(temp)

print(result)

print("Done")

return result

# 返回所有股票的代码

def get_all_code(self):

return self.all_code

# 获取成交量的ma5 或者10

def volume_calculate(self, codes):

delta_day = 180 * 7 / 5

end_day = datetime.date(datetime.date.today().year, datetime.date.today().month, datetime.date.today().day)

start_day = end_day - datetime.timedelta(delta_day)

start_day = start_day.strftime("%Y-%m-%d")

end_day = end_day.strftime("%Y-%m-%d")

print(start_day)

print(end_day)

result_m5_large = []

result_m5_small = []

for each_code in codes:

# print(e)ach_code

try:

df = ts.get_k_data(each_code, start=start_day, end=end_day)

print(df)

except Exception as e:

print("Failed to get")

print(e)

continue

if len(df) < 20:

# print("not long enough")

continue

print(each_code)

all_mean = df['volume'].mean()

m5_volume_m = df['volume'][-5:].mean()

m10_volume_m = df['volume'][-10:].mean()

last_vol = df['volume'][-1] # 这里会不会有问题？？？

# 在这里分几个分支，放量 180天均量的4倍

if m5_volume_m > (4.0 * all_mean):

print("m5 > m_all_avg ")

print(each_code,)

temp = self.base[self.base['code'] == each_code]['name'].values[0]

print(temp)

result_m5_large.append(each_code)

# 成交量萎缩

if last_vol < (m5_volume_m / 3.0):

result_m5_small.append(each_code)

return result_m5_large, result_m5_large

def turnover_check(self):

delta_day = 60 * 7 / 5

end_day = datetime.date(datetime.date.today().year, datetime.date.today().month, datetime.date.today().day)

start_day = end_day - datetime.timedelta(delta_day)

start_day = start_day.strftime("%Y-%m-%d")

end_day = end_day.strftime("%Y-%m-%d")

print(start_day)

print(end_day)

for each_code in self.all_code:

try:

df = ts.get_hist_data(code=each_code, start=start_day, end=end_day)

except:

print("Failed to get data")

continue

mv5 = df['v_ma5'][-1]

mv20 = df['v_ma20'][-1]

mv_all = df['volume'].mean()

# 写入csv文件

def write_to_text(self):

print("On write")

r = self.macd()

filename = self.today + "-macd.csv"

f = open(filename, 'w')

for i in r:

f.write(i[0])

f.write(',')

f.write(i[1])

f.write('\n')

f.close()

def saveList(self, l, name):

f = open(self.today + name + '.csv', 'w')

if len(l) == 0:

return False

for i in l:

f.write(i)

f.write(',')

name = self.base[self.base['code'] == i]['name'].values[0]

f.write(name)

f.write('\n')

f.close()

return True

# 读取自己的csv文件

def read_csv(self):

filename = self.today + "-macd.csv"

df = pd.read_csv(filename)

print(df)

# 持股从高点下跌幅度

def own_drop_down(self):

for i in self.mystocklist:

print(i)

self.drop_down_from_high(code=i, start='2017-01-01')

print('\n')

# 持股跌破均线

def _break_line(self, codes, k_type):

delta_day = 60 * 7 / 5

end_day = datetime.date(datetime.date.today().year, datetime.date.today().month, datetime.date.today().day)

start_day = end_day - datetime.timedelta(delta_day)

start_day = start_day.strftime("%Y-%m-%d")

end_day = end_day.strftime("%Y-%m-%d")

print(start_day)

print(end_day)

all_break = []

for i in codes:

try:

df = ts.get_hist_data(code=i, start=start_day, end=end_day)

if len(df) == 0:

continue

except Exception as e:

print(e)

continue

else:

self.working_count = self.working_count + 1

current = df['close'][0]

ma5 = df['ma5'][0]

ma10 = df['ma10'][0]

ma20 = df['ma20'][0]

ma_dict = {'5': ma5, '10': ma10, '20': ma20}

ma_x = ma_dict[k_type]

# print(ma_x)

if current < ma_x:

print('破位')

print(i, " current: ", current)

print(self.base[self.base['code'] == i]['name'].values[0], " ")

print("holding place: ", ma_x)

print("Break MA", k_type, "\n")

all_break.append(i)

return all_break

# 检查自己的持仓或者市场所有破位的

def break_line(self, code, k_type='20', writeable=False, mystock=False):

all_break = self._break_line(code, k_type)

l = len(all_break)

beaking_rate = l * 1.00 / self.working_count * 100

print("how many break: ", l)

print("break Line rate ", beaking_rate)

if mystock == False:

name = '_all_'

else:

name = '_my__'

if writeable:

f = open(self.today + name + 'break_line_' + k_type + '.csv', 'w')

f.write("Breaking rate: %f\n\n" % beaking_rate)

f.write('\n'.join(all_break))

f.close()

def _break_line_thread(self, codes, k_type='5'):

delta_day = 60 * 7 / 5

end_day = datetime.date(datetime.date.today().year, datetime.date.today().month, datetime.date.today().day)

start_day = end_day - datetime.timedelta(delta_day)

start_day = start_day.strftime("%Y-%m-%d")

end_day = end_day.strftime("%Y-%m-%d")

print(start_day)

print(end_day)

all_break = []

for i in codes:

try:

df = ts.get_hist_data(code=i, start=start_day, end=end_day)

if len(df) == 0:

continue

except Exception as e:

print(e)

continue

else:

self.working_count = self.working_count + 1

current = df['close'][0]

ma5 = df['ma5'][0]

ma10 = df['ma10'][0]

ma20 = df['ma20'][0]

ma_dict = {'5': ma5, '10': ma10, '20': ma20}

ma_x = ma_dict[k_type]

# print(ma_x)

if current > ma_x:

print(i, " current: ", current)

print(self.base[self.base['code'] == i]['name'].values[0], " ")

print("Break MA", k_type, "\n")

all_break.append(i)

q.put(all_break)

def multi_thread_break_line(self, ktype='20'):

total = len(self.all_code)

thread_num = 10

delta = total / thread_num

delta_left = total % thread_num

t = []

i = 0

for i in range(thread_num):

sub_code = self.all_code[i * delta:(i + 1) * delta]

t_temp = Thread(target=self._break_line_thread, args=(sub_code, ktype))

t.append(t_temp)

if delta_left != 0:

sub_code = self.all_code[i * delta:i * delta + delta_left]

t_temp = Thread(target=self._break_line_thread, args=(sub_code, ktype))

t.append(t_temp)

for i in range(len(t)):

t[i].start()

for j in range(len(t)):

t[j].join()

result = []

print("working done")

while not q.empty():

result.append(q.get())

ff = open(self.today + '_high_m%s.csv' % ktype, 'w')

for kk in result:

print(kk)

for k in kk:

ff.write(k)

ff.write(',')

ff.write(self.base[self.base['code'] == k]['name'].values[0])

ff.write('\n')

ff.close()

# 计算大盘的相关系，看关系如何

def relation(self):

sh_index = ts.get_k_data('000001', index=True, start='2012-01-01')

sh = sh_index['close'].values

print(sh)

vol_close = sh_index.corr()

print(vol_close)

'''

sz_index=ts.get_k_data('399001',index=True)

sz=sz_index['close'].values

print(sz)

cy_index=ts.get_k_data('399006',index=True)

s1=Series(sh)

s2=Series(sz)

print(s1.corr(s2))

'''

# 寻找业绩两年未负的，以防要st

def profit(self):

df_2016 = ts.get_report_data(2016, 4)

# 第四季度就是年报

# df= df.sort_values('profits_yoy',ascending=False)

# df.to_excel('profit.xls')

df_2015 = ts.get_report_data(2015, 4)

df_2016.to_excel('2016_report.xls')

df_2015.to_excel('2015_report.xls')

code_2015_lost = df_2015[df_2015['net_profits'] < 0]['code'].values

code_2016_lost = df_2016[df_2016['net_profits'] < 0]['code'].values

print(code_2015_lost)

print(code_2016_lost)

two_year_lost = []

# two_year_lost_name=[]

for i in code_2015_lost:

if i in code_2016_lost:

print(i,)

# name=self.base[self.base['code']==i].values[0]

two_year_lost.append(i)

self.saveList(two_year_lost, 'st_dangours.csv')

# df_2014=ts.get_report_data(2014,4)

def mydaily_check(self):

self.break_line(self.mystocklist, k_type='5', writeable=True, mystock=True)

def all_stock(self):

base_info = ts.get_stock_basics()

current_prices = ts.get_today_all()

current_prices[current_prices['code'] == '000625']['trade'].values[0]

folder = os.path.join(os.path.dirname(__file__), 'data')

if os.path.exists(folder) == False:

os.mkdir(folder)

os.chdir(folder)

obj = filter_stock(local=True)

# 留下来的函数都是有用的

# obj.count_area(writeable=True)

# df=obj.get_area('广东',writeable=True)

# obj.showInfo(df)

# df=obj.get_area('深圳',writeable=True)

# obj.showInfo(df)

# obj.get_all_location()

# obj.fetch_new_ipo(20170101,writeable=False)

# obj.drop_down_from_high('2017-01-01','300580')

# obj.loop_each_cixin()

# df=obj.get_all_code()

# result=obj.volume_calculate(df)

# obj.saveList(result)

# df=obj.get_chengfenggu()

# large,small=obj.volume_calculate(df)

# obj.saveList(large,'large')

# obj.saveList(small,'small')

# obj.write_to_text()

# obj.read_csv()

# obj.own_drop_down()

# obj.volume_calculate()

# obj.break_line()

# obj.save_data_excel()

# obj.break_line(mine=False,k_type='5')

# obj.multi_thread()

# code=obj.get_chengfenggu()

# obj.break_line(code)

# obj.big_deal('603918',400,'2017-04-22')

# obj.current_day_ticks()

# obj.relation()

# obj.profit()

# obj.mydaily_check()