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delivery_order.py
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# -*-coding=utf-8-*-
import re
import sys
__author__ = 'Rocky'
'''
http://30daydo.com
Contact: weigesysu@qq.com
'''
# 交割单处理 保存交割单到数据库
import os
import datetime
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from setting import get_engine, get_mysql_conn
engine = get_engine('db_stock', local=True)
pd.set_option('display.max_rows', None)
class Delivery_Order():
def __init__(self):
path = os.path.join(os.getcwd(), 'private/2019/GJ')
if os.path.exists(path) == False:
os.mkdir(path)
os.chdir(path)
# 合并一年的交割单
def years_ht(self):
df_list = []
for i in range(1, 2):
# 固定一个文件
filename = 'HT_2018-05_week4-5.xls'
# filename='2018-%s.xls' %str(i).zfill(2)
# filename='HT_2018_%s.xls' %str(i).zfill(2)
print(filename)
try:
t = pd.read_table(filename, encoding='gbk',
dtype={'证券代码': np.str})
except Exception as e:
print(e)
continue
# fee=t['手续费'].sum()+t['印花税'].sum()+t['其他杂费'].sum()
# print(i," fee: ")
# print(fee)
df_list.append(t)
# result.append(fee)
df = pd.concat(df_list)
df = df.reset_index()
# df['xxxx']=df['成交日期']+df['成交时间']
# df['成交日期']=pd.to_datetime(df['xxxx'],format='%Y%m%d %H:%M:%S')
df['成交日期'] = map(lambda x: datetime.datetime.strptime(
str(x), "%Y%m%d"), df['成交日期'])
df = df[df['摘要'] != '申购配号']
df = df[df['摘要'] != '质押回购拆出']
df = df[df['摘要'] != '拆出质押购回']
# print(df.info())
# print(df)
# print(df['2017-01'])
del df['合同编号']
del df['备注']
del df['股东帐户']
del df['结算汇率']
del df['Unnamed: 16']
df = df.sort_values(by='成交日期')
df = df.set_index('成交日期')
df.to_sql('tb_delivery_HT', engine, if_exists='append')
# df=df[(df['摘要']=='证券卖出') | (df['摘要']=='证券买入')]
# df= df.groupby(df['证券名称'])
# print(df.describe())
# print(df['手续费'].sum())
# print(df['印花税'].sum())
# df1=df[['证券名称','证券代码','成交数量', '成交均价' ,'成交金额','手续费', '印花税','发生金额','操作']]
# print(df1['证券名称'].value_counts())
# print(df.groupby(by=['证券名称'])['发生金额'].sum())
# df1.to_excel('2017-all.xls')
# print(df1.groupby(df1['证券名称']).describe())
# print(df1['2017-02'])
# df.to_excel('2016_delivery_order.xls')
# self.caculation(df)
# plt.plot(j,result)
# plt.show()
def caculation(self, df):
fee = df['手续费'].sum() + df['印花税'].sum() + df['其他杂费'].sum()
print(fee)
# 计算每个月的费用
def month(self):
pass
# 国金账户 2018-01 到 11月数据入库， 这个函数不用动了。保留csv格式
def years_gj(self):
df_list = []
k = [str(i) for i in range(1, 13)]
j = [i for i in range(1, 13)]
result = []
for i in range(2, 12):
# filename='GJ_2018_0{}.csv'.format(i)
# filename = 'GJ_2018_04.csv'
filename = 'GJ_2018_%s.csv' % str(i).zfill(2)
# filename='GJ_2018_%s.xls' %str(i).zfill(2)
print(filename)
try:
# t=pd.read_table(filename,encoding='gbk',dtype={'证券代码':np.str})
t = pd.read_csv(filename, encoding='gbk', dtype={'证券代码': np.str})
# t = pd.read_excel(filename, encoding='gbk',dtype={'证券代码': np.str})
except Exception as e:
print(e)
# return
# continue
# fee=t['手续费'].sum()+t['印花税'].sum()+t['其他杂费'].sum()
df_list.append(t)
# result.append(fee)
df = pd.concat(df_list)
df = df.reset_index(drop='True')
# df['成交时间'] = df['成交时间'].map(lambda x: x.zfill(8))
df['成交日期'] = df['成交日期'].astype(np.str) + df['成交时间']
# for i in df['成交日期'].values:
# try:
# x = datetime.datetime.strptime(
# i, "%Y%m%d%H:%M:%S").strftime('%Y-%m-%d %H:%M:%S')
# except Exception as e:
# print(e)
df['成交日期'] = df['成交日期'].map(lambda x: datetime.datetime.strptime(
x, "%Y%m%d%H:%M:%S").strftime('%Y-%m-%d %H:%M:%S'))
try:
df['成交日期'] = pd.to_datetime(df['成交日期'])
except Exception as e:
print(e)
# df=df[df['摘要']!='申购配号']
# df=df[df['摘要']!='质押回购拆出']
# df=df[df['摘要']!='拆出质押购回']
# print(df.info())
# print(df)
# print(df['2017-01'])
# del df['合同编号']
# del df['备注']
del df['股东帐户']
del df['成交时间']
# del df['结算汇率']
# del df['Unnamed: 17']
df = df.sort_values(by='成交日期', ascending=False)
df = df.set_index('成交日期')
# print(df.info())
# print(df)
#
df.to_sql('tb_delivery_gj', engine, if_exists='replace')
# df=df[(df['摘要']=='证券卖出') | (df['摘要']=='证券买入')]
# df= df.groupby(df['证券名称'])
# print(df.describe())
# print(df['手续费'].sum())
# print(df['印花税'].sum())
# df1=df[['证券名称','证券代码','成交数量', '成交均价' ,'成交金额','手续费', '印花税','发生金额','操作']]
# print(df1['证券名称'].value_counts())
# print(df.groupby(by=['证券名称'])['发生金额'].sum())
# df1.to_excel('2017-all.xls')
# print(df1.groupby(df1['证券名称']).describe())
# print(df1['2017-02'])
# df.to_excel('2016_delivery_order.xls')
# self.caculation(df)
# plt.plot(j,result)
# plt.show()
#
# 单独处理某个文件（单独一个月的数据） 文件格式：国金-保存为xls，然后另存为csv 或者按照天也可以
def years_gj_each_month_day(self,filename):
# filename = 'GJ_2019-05-11-05-16.csv'
try:
# 根据不同的格式选用不同的函数
# t=pd.read_table(filename,encoding='gbk',dtype={'证券代码':np.str})
t = pd.read_csv(filename, encoding='gbk', dtype={'证券代码': np.str})
# t = pd.read_excel(filename, encoding='gbk',dtype={'证券代码': np.str})
except Exception as e:
print(e)
# continue
# fee=t['手续费'].sum()+t['印花税'].sum()+t['其他杂费'].sum()
else:
# df_list.append(t)
# result.append(fee)
df = t
# df = pd.concat(df_list)
df = df.reset_index(drop='True')
# df['成交时间'] = df['成交时间'].map(lambda x: x.zfill(8))
df['成交日期'] = df['成交日期'].astype(np.str) + df['成交时间']
# for i in df['成交日期'].values:
# try:
# x = datetime.datetime.strptime(
# i, "%Y%m%d%H:%M:%S").strftime('%Y-%m-%d %H:%M:%S')
# except Exception as e:
# print(e)
df['成交日期'] = df['成交日期'].map(lambda x: datetime.datetime.strptime(
x, "%Y%m%d%H:%M:%S").strftime('%Y-%m-%d %H:%M:%S'))
try:
df['成交日期'] = pd.to_datetime(df['成交日期'])
except Exception as e:
print(e)
# df=df[df['摘要']!='申购配号']
# df=df[df['摘要']!='质押回购拆出']
# df=df[df['摘要']!='拆出质押购回']
# print(df.info())
# print(df)
# print(df['2017-01'])
# del df['合同编号']
# del df['备注']
del df['股东帐户']
del df['成交时间']
# del df['结算汇率']
# del df['Unnamed: 17']
df=df.fillna(0)
df=df[(df['操作']!='申购配号') & (df['操作']!='拆出质押购回') & (df['操作']!='质押回购拆出')]
df = df.sort_values(by='成交日期', ascending=False)
conn = get_mysql_conn('db_stock', 'local')
cursor = conn.cursor()
insert_cmd = '''
insert into tb_delivery_gj_django (成交日期,证券代码,证券名称,操作,成交数量,成交均价,成交金额,余额,发生金额,手续费,印花税,过户费,本次金额,其他费用,交易市场) values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)'''
check_dup = '''
select * from tb_delivery_gj_django where 成交日期=%s and 证券代码=%s and 操作=%s and 成交数量=%s and 余额=%s
'''
for index, row in df.iterrows():
date=row['成交日期']
date=date.to_pydatetime()
# print(type(date))
# print(date)
cursor.execute(check_dup, (date, row['证券代码'], row['操作'], row['成交数量'], row['余额']))
if cursor.fetchall():
print('有重复数据，忽略')
else:
cursor.execute(insert_cmd, (
date, row['证券代码'], row['证券名称'], row['操作'], row['成交数量'], row['成交均价'], row['成交金额'], row['余额'],
row['发生金额'], row['手续费'], row['印花税'], row['过户费'], row['本次金额'], row['其他费用'], row['交易市场']))
conn.commit()
conn.close()
# df.to_sql('tb_delivery_gj', engine, if_exists='append')
# df=df[(df['摘要']=='证券卖出') | (df['摘要']=='证券买入')]
# df= df.groupby(df['证券名称'])
# print(df.describe())
# print(df['手续费'].sum())
# print(df['印花税'].sum())
# df1=df[['证券名称','证券代码','成交数量', '成交均价' ,'成交金额','手续费', '印花税','发生金额','操作']]
# print(df1['证券名称'].value_counts())
# print(df.groupby(by=['证券名称'])['发生金额'].sum())
# df1.to_excel('2017-all.xls')
# print(df1.groupby(df1['证券名称']).describe())
# print(df1['2017-02'])
# df.to_excel('2016_delivery_order.xls')
# self.caculation(df)
# plt.plot(j,result)
# plt.show()
def pretty(self):
df = pd.read_sql('tb_delivery_GJ', engine, index_col='成交日期')
# print(df)
# del df['Unnamed: 17']
del df['index']
df.to_sql('tb_delivery_GJ', engine, if_exists='replace')
# 数据同步到另一个django数据库
def data_sync(self):
conn = get_mysql_conn('db_stock', 'local')
cursor = conn.cursor()
# 最新的数据库
select_cmd = '''select * from tb_delivery_gj'''
cursor.execute(select_cmd)
ret = list(cursor.fetchall())
print('new db ', len(ret))
# 旧的数据库
select_cmd2 = '''select * from tb_delivery_gj_django'''
cursor.execute(select_cmd2)
ret2 = list(cursor.fetchall())
print('old db ', len(ret2))
ret_copy = ret.copy()
for item in ret:
# print(item)
for item2 in ret2:
if item[0] == item2[0] and item[1] == item2[1] and item[2] == item2[2] and item[4] == item2[4] and item[
5] == item2[5]:
try:
ret_copy.remove(item)
except Exception as e:
# print(e)
# print()
pass
# print(ret_copy)
for i in ret_copy:
# print(i)
update_sql = '''
insert into tb_delivery_gj_django (成交日期,证券代码,证券名称,操作,成交数量,成交均价,成交金额,)
'''
print('diff len ', len(ret_copy))
# 银转证
def bank_account():
folder_path = os.path.join(os.path.dirname(__file__), 'private')
os.chdir(folder_path)
df_list = []
for file in os.listdir(folder_path):
if re.search('2', file.decode('gbk')):
df = pd.read_table(file, encoding='gbk')
# df[df['']]
# print(df)
# df_list.append(df[['日期','操作','发生金额']])
df_list.append(df)
total_df = pd.concat(df_list)
# total_df=total_df.reset_index()
# del total_df['level_0']
del total_df['货币单位']
del total_df['合同编号']
del total_df['Unnamed: 8']
del total_df['银行名称']
# print(total_df)
# f=total_df[total_df['操作']=='证券转银行']['发生金额']*-1
total_df['发生金额'] = map(lambda x, y: x * -1 if y ==
'证券转银行' else x, total_df['发生金额'], total_df['操作'])
# print(total_df.columns)
# print(total_df5)
# total_df=total_df.reset_index()
# total_df=total_df.set_index('index')
# total_df=total_df.reset_index(drop=True)
total_df['委托时间'] = map(lambda x: str(x).zfill(6), total_df['委托时间'])
total_df['日期'] = map(lambda x, y: str(x) + " " + y,
total_df['日期'], total_df['委托时间'])
total_df['日期'] = pd.to_datetime(total_df['日期'], format='%Y%m%d %H%M%S')
total_df = total_df.set_index('日期')
df = total_df[total_df['备注'] == '成功[[0000]交易成功]']
# print(df)
# print(total_df.iloc[131])
# print(total_df['备注'].values)
print(df['发生金额'].sum())
# df.dropna('')
del df['备注']
del df['委托时间']
df.to_sql('tb_bank_cash', engine, if_exists='replace')
# print(df['2018'])
def main():
filename=sys.argv[1]
obj = Delivery_Order()
# obj.data_sync()
obj.years_gj_each_month_day(filename=filename)
# obj.years_gj_each_month()
# obj.years_gj()
# obj.years_ht()
# bank_account()
# obj.pretty()
if __name__ == '__main__':
main()