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filter_stock.py 9.80 KB
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Rockyzsu 提交于 2019-09-24 13:45 . update
# -*-coding=utf-8-*-
__author__ = 'Rocky'
'''
email: weigesysu@qq.com
'''
import datetime
import tushare as ts
import os
from settings import get_engine, get_mysql_conn
import pandas as pd
import numpy as np
from collections import OrderedDict
import matplotlib
matplotlib.use("Pdf")
pd.set_option('display.max_rows', None)
import matplotlib.pyplot as plt
# 过滤器,剔除不想要的个股
class Filter_Stock():
def __init__(self):
current = os.path.dirname(__file__)
work_space = os.path.join(current, 'data')
if os.path.exists(work_space) == False:
os.mkdir(work_space)
os.chdir(work_space)
self.today = datetime.datetime.now().strftime("%Y-%m-%d")
def get_location(self):
df = ts.get_area_classified()
print(df)
# df.to_excel('location.xls')
self.save_to_excel(df, 'location.xls')
def get_ST(self):
# 暂停上市
zt = ts.get_suspended()
print(zt)
# 终止上市
zz = ts.get_terminated()
print(zz)
def get_achievement(self):
fc = ts.forecast_data(2016, 4)
print(fc)
def daily_market(self):
'''
保存每天收盘后的市场行情
:return:
'''
df = ts.get_today_all()
print(df)
try:
df.to_sql(self.today, daily_engine, if_exists='replace')
except Exception as e:
print(e)
print("Save {} data to MySQL".format(self.today))
def break_low(self, date):
'''
筛选出一年内创新低的股票
:param date: 某一天的日期 ‘'2017-11-11
:return:
'''
# cmd = 'select * from `{}`'.format(date)
df = pd.read_sql_table(date, daily_engine, index_col='index')
# **** 这里的index需要删除一个
low_db = get_mysql_conn('db_selection')
low_cursor = low_db.cursor()
for i in range(len(df)):
code = df.loc[i]['code']
cur_low = df.loc[i]['low']
mins_date, mins = self.get_lowest(code, '2017', date)
if not mins_date:
continue
if mins and float(cur_low) <= float(mins) and float(cur_low) != 0.0:
print(code, )
print(df.loc[i]['name'])
print('year mins {} at {}'.format(mins, mins_date))
print('curent mins ', cur_low)
create_cmd = 'create table if not exists break_low' \
'(`index` int primary key auto_increment,datetime datetime,code text,name text,low_price float,last_price float, last_price_date datetime);'
low_cursor.execute(create_cmd)
insert_cmd = 'insert into break_low (datetime,code,name,low_price,last_price,last_price_date) values (%s,%s,%s,%s,%s,%s);'
insert_data = (date, code, df.loc[i]['name'], cur_low, mins, mins_date)
low_cursor.execute(insert_cmd, insert_data)
low_db.commit()
def get_lowest(self, code, date, current_date):
'''
返回个股某一年最低价
:param code: 股票代码
:param date: 年份
:return:
'''
date = date + '-01-01'
cmd = 'select * from `{}` where datetime > \'{}\' and datetime <\'{}\''.format(code, date, current_date)
try:
df = pd.read_sql(cmd, history_engine, index_col='index')
except Exception as e:
print(e)
return None, None
# print(df.dtypes)
# 不知道为啥,这里的类型发生改变
if len(df) < 1:
return None, None
df['low'] = df['low'].astype('float64')
idx = df['low'].idxmin()
min_date = df.loc[idx]
return min_date['datetime'], min_date['low']
def get_highest(self, code, date):
'''
返回个股某一年最高价
:param code: 股票代码
:param date: 年份
:return:
'''
date = date + '-01-01'
cmd = 'select high from `{}` where datetime > \'{}\''.format(code, date)
df = pd.read_sql(cmd, history_engine)
return df['high'].max()
def save_to_excel(self, df, filename, encoding='gbk'):
try:
df.to_csv('temp.csv', encoding=encoding, index=False)
df = pd.read_csv('temp.csv', encoding=encoding, dtype={'code': str})
df.to_excel(filename, encoding=encoding)
return True
except Exception as e:
print("Save to excel faile")
print(e)
return None
# 专门用来存储数据,数据保存为excel,不必每次都要从网络读取
def store_data(self):
# 预测
# year_2016=ts.forecast_data(2016, 4)
# self.save_to_excel(year_2016,'2016-profit.xls')
# year_2017=ts.forecast_data(2017, 4)
# self.save_to_excel(year_2017,'2017-profit.xls')
# 盈利能力
# profit_2016=ts.get_profit_data(2016,4)
# profit_2017=ts.get_profit_data(2017,3)
# self.save_to_excel(profit_2016, '2016-profit.xls')
# self.save_to_excel(profit_2017, '2017-3rdprofit.xls')
# 股票基本信息
# basic=ts.get_stock_basics()
# basic.to_csv('temp.xls',encoding='gbk')
# df=pd.read_csv('temp.xls',encoding='gbk',dtype={'code':str})
# # print(df)
# self.save_to_excel(df,'Markets.xls')
# 基本面 每股净资产<1
df = ts.get_report_data(2017, 3)
self.save_to_excel(df, '2017-3rd-report.xls')
def to_be_ST(self):
'''
df_2016=pd.read_excel('2016-profit.xls',dtype={'code':str})
df_2017=pd.read_excel('2017-3rdprofit.xls',dtype={'code':str})
loss_2016=set(df_2016[df_2016['net_profits']<0]['code'])
loss_2017=set(df_2017[df_2017['net_profits']<0]['code'])
st= list(loss_2016 & loss_2017)
basic=pd.read_excel('Markets.xls',dtype={'code':str})
# print(basic.head(5))
# for x in st:
# print(x)
# print(basic[basic['code']==st])
for i in st:
print(basic[basic['code']==i][['code','name']])
'''
# 每股净资产小于0
df_bpvs = pd.read_excel('2017-3rd-report.xls', dtype={'code': str})
# print(df_bpvs.head())
print(df_bpvs[df_bpvs['bvps'] < 0][['code', 'name']])
# 返回新股信息
def get_new_stock(self, start='2010', end='2011'):
'''
:param start: 开始年份 如 '2010'
:param end: 结束年份 如 '2011'
:return:
'''
df = pd.read_sql('tb_basic_info', get_engine('db_stock'), index_col='index')
df = df[df['timeToMarket'] != 0]
df['timeToMarket'] = pd.to_datetime(df['timeToMarket'], format='%Y%m%d')
df = df.set_index('timeToMarket', drop=True)
years = OrderedDict()
values = []
# for year in range(1994, 2019):
# years[year] = len(df[str(year)])
# values.append(len(df[str(year)]))
# x = np.arange(1994, 2019)
# plt.figure(figsize=(10, 9))
# rect = plt.bar(x, values)
# self.rect_show(rect)
# plt.xticks(x[::2])
# plt.show()
new_stock = df[start:end] # 返回df格式
return new_stock
def rect_show(self, rects):
for rect in rects:
height = rect.get_height()
plt.text(rect.get_x(), 1.05 * height, '%s' % int(height))
# 只是用于测试,展示数据
def show(self):
df = self.get_new_stock()
# print(df)
# 返回黑名单的代码
def get_blacklist(self):
conn=get_mysql_conn('db_stock','local')
cursor = conn.cursor()
query = 'select CODE from tb_blacklist'
cursor.execute(query)
ret = cursor.fetchall()
return [i[0] for i in ret]
# 可转债过滤
class Filter_CB(object):
def __init__(self):
self.engine = get_engine('db_stock','local')
self.bonds = pd.read_sql('tb_bond_jisilu',con=self.engine)
# 获取新股的可转债,一般比较猛
def get_new_stock_bond(self,start='2017',end='2019'):
'''
:return: 返回新股对应的转债数据 df
'''
obj=Filter_Stock()
new_stock_df=obj.get_new_stock(start,end)
# index是timeToMarket
code_list = list(new_stock_df['code'].values)
new_stock_bond_df = self.bonds[self.bonds['正股代码'].isin(code_list)]
for code in new_stock_bond_df['正股代码'].values:
print(code)
t_market=new_stock_df[new_stock_df['code']==code].index.values[0]
return new_stock_bond_df
def show(self):
df = self.get_new_stock_bond()
print(df)
def run(self):
df = pd.read_sql('tb_bond_jisilu',con=self.engine)
want_cb_df = df[((df['可转债价格']<=125) & (df['溢价率']<=15))]
want_cb_df=want_cb_df[['可转债代码','可转债名称','可转债价格','溢价率']]
# want_cb_df.rename(columns={'可转债代码':''})
want_cb_df.loc[:,'优先级']=0 # 默认都为0
want_cb_df.loc[:,'当前日期']=datetime.date.today()
try:
want_cb_df.to_sql('tb_stock_candidates',con=self.engine,if_exists='replace')
except Exception as e:
print(e)
def main():
# obj = Filter_Stock()
# obj.show()
# obj.get_blacklist()
# obj.get_ST()
# obj.get_achievement()
# obj.get_location('深圳')
# obj.break_low()
# obj.break_low('2017-11-17')
# print(type(obj.get_lowest('300333','2017')))
# print(obj.get_lowest('300333', '2017'))
# print(obj.get_highest('300333', '2017'))
# obj.break_low('2017-11-17')
# obj.store_data()
# obj.to_be_ST()
# obj.get_location()
# print(obj.get_new_stock())
# obj.get_location()
obj_cb = Filter_CB()
obj_cb.show()
if __name__ == '__main__':
main()
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