def fund(self):
fd = ts.Fund()
df = fd.FundDiv(ticker='184688', adjustedType='D', beginDate='20000101',
field='secShortName,effectDate,publishDate,dividendAfTax,dividendBfTax')
print(df)
dd = ts.fund_holdings(2015, 4)
print(dd[['name', 'nums', 'clast', 'amount']])
def _getFundStocksData(self,year,quater):
try:
res = ts.fund_holdings(year,quater)
except IOError as e:
print str(year)+' '+str(quater)+'季度没有数据。'
return None
print str(year)+' '+str(quater)+'季度数据读取成功！'
return res
def year_fund_holdings(year):
for i in range(4):
try:
preDTS = ts.fund_holdings(year, i + 1)
preDTS.to_csv('D:\\ts\\tzck\\fund_holdings_%s_%s.csv'%(year,i+1),encoding='gbk')
except :
next
def updateholds():
holdsdatalist=ts.fund_holdings(2014,2,10,1)
holdsdata=pd.DataFrame(holdsdatalist)
conn= ms.connect(host='localhost',port = 3306,user='******', passwd='123456',db ='investment',charset="utf8")
cur = conn.cursor()
values=[]
for index,row in holdsdata.iterrows():
values.append((row['code'],row['name'],row['date'],row['nums'],row['nlast'],row['count'],row['clast'],row['amount'],row['ratio']))
cur.executemany('insert into holds20142 (code,name,date,nums,nlast,count,clast,amount,ratio) values(%s,%s,%s,%s,%s,%s,%s,%s,%s)',values)
conn.commit()
cur.close()
conn.close()
def j_fund(_year, _quarter, _db):
begin = get_micro_second()
log_debug('year: %d, quarter: %d', _year, _quarter)
try:
df = ts.fund_holdings(_year, _quarter)
except Exception:
log_error("warn:error: %s/%s fund() exception!", _year, _quarter)
return -4
# calc cost time
log_info("fund [%s, %s] costs %d us", _year, _quarter, get_micro_second()-begin)
if df is None :
log_error("warn: df is None, next")
return -1
if df.empty:
log_error("warn: df is empty, next")
return -2
if len(df) <= 1:
log_error("warn: df is empty, next")
return -3
# pd.options.display.max_rows = 1000
log_debug(df)
begin = get_micro_second()
j_fund_to_db(df, _db)
log_info("to_db costs %d us", get_micro_second() - begin)
return
def stockholder():
data = ts.fund_holdings(2019, 4)
data.to_sql('stockholder', con=con, if_exists='append', index=None)
HS300S.to_csv('./ASHR/DATA/HS300S.csv', index = False)
# SZ 50
SZ50S = ts.get_sz50s()
SZ50S.to_csv('./ASHR/DATA/SZ50S.csv', index = False)
# ZZ 500
ZZ500S = ts.get_zz500s()
ZZ500S.to_csv('./ASHR/DATA/ZZ500S.csv', index = False)
#################
# Fund Holdings #
#################
# TODO Data is available quarterly
FundHolding = ts.fund_holdings(CURRENT.year, np.floor((CURRENT.month+2)/3))
####################
# Financial Report #
####################
# FinancialReport: EPS, EPS_YOY, ROE, net_profits, profits_yoy
# ProfitData: ROE, net_profit_ratio, gross_profit_rate, EPS, bips (business income per share)
# GrowthData: mbrg (main business rate growth), nprg (net profit),
# nav, targ (total asset), epsg, seg (shareholder's eqty)
# DebtPayingData: currentratio, quickratio, cashratio, icratio (interest coverage)
# TODO Data is available quarterly
# TODO Compare data for FinancialReport and ProfitData
import tushare as ts
import requests
import os
df = ts.fund_holdings(2017, 3)
df.clast = df.clast.astype("float")
df2 = df.set_index("code")
def chajing(s):
return s[:2].upper() + "#" + s[2:]
def huifushuzi(w):
return w[3:9]
def huifu(w):
return w[0:2].lower() + w[3:9]
def zhao(gpdm):
x = ts.get_k_data(gpdm, autype="hfq")
if type(x) == type(None):
return
if len(x) < 150:
return
if x.close[len(x) - 100] * 1.2 > x.close[len(x) - 1]:
return gpdm
def invoke_fund_holdings(year, quarter):
invoke_fund_holdings = ts.fund_holdings(year=year, quarter=quarter)
return invoke_fund_holdings
def getInvestData():
filepath = 'f:\\stockdata\\投资参考数据\\'
index = 0
# 分配预案
# 参数说明：
# year: 预案公布的年份，默认为2014
# top: 取最新n条数据，默认取最近公布的25条
# retry_count：当网络异常后重试次数，默认为3
# pause: 重试时停顿秒数，默认为0
# 返回值说明：
# code: 股票代码
# name: 股票名称
# year: 分配年份
# report_date: 公布日期
# divi: 分红金额（每10股）
# shares: 转增和送股数（每10股）
df = ts.profit_data(top=60)
# df.sort('shares', ascending=False)
saveCsv(df, filepath, investList, index)
index += 1
# 业绩预告
# 参数说明：
# year: int 年度 e.g: 2014
# quarter: int 季度: 1、2、3、4，只能输入这4个季度
# 返回值说明：
# code, 代码
# name, 名称
# type, 业绩变动类型【预增、预亏等】
# report_date, 发布日期
# pre_eps, 上年同期每股收益
# range, 业绩变动范围
# 获取2014年中报的业绩预告数据
df = ts.forecast_data(2014, 2)
saveCsv(df, filepath, investList, index)
index += 1
# 限售股解禁
# 参数说明：
# year: 年份, 默认为当前年
# month: 解禁月份，默认为当前月
# retry_count：当网络异常后重试次数，默认为3
# pause: 重试时停顿秒数，默认为0
# 返回值说明：
# code：股票代码
# name：股票名称
# date: 解禁日期
# count: 解禁数量（万股）
# ratio: 占总盘比率
df = ts.xsg_data()
saveCsv(df, filepath, investList, index)
index += 1
# 基金持股
# 参数说明：
# year: 年份, 默认为当前年
# quarter: 季度（只能输入1，2，3，4
# 这个四个数字）
# retry_count：当网络异常后重试次数，默认为3
# pause: 重试时停顿秒数，默认为0
# 返回值说明：
# code：股票代码
# name：股票名称
# date: 报告日期
# nums: 基金家数
# nlast: 与上期相比（增加或减少了）
# count: 基金持股数（万股）
# clast: 与上期相比
# amount: 基金持股市值
# ratio: 占流通盘比率
df = ts.fund_holdings(2014, 4)
saveCsv(df, filepath, investList, index)
index += 1
# 新股上市
# 参数说明：
# retry_count：当网络异常后重试次数，默认为3
# pause: 重试时停顿秒数，默认为0
# 返回值说明：
# code：股票代码
# name：股票名称
# ipo_date: 上网发行日期
# issue_date: 上市日期
# amount: 发行数量(万股)
# markets: 上网发行数量(万股)
# price: 发行价格(元)
# pe: 发行市盈率
# limit: 个人申购上限(万股)
# funds：募集资金(亿元)
# ballot: 网上中签率( %)
df = ts.new_stocks()
saveCsv(df, filepath, investList, index)
index += 1
# 沪市融资融券汇总数据
# 参数说明：
# start: 开始日期
# format：YYYY - MM - DD
# 为空时取去年今日
# end: 结束日期
# format：YYYY - MM - DD
# 为空时取当前日期
# retry_count：当网络异常后重试次数，默认为3
# pause: 重试时停顿秒数，默认为0
# 返回值说明：
# opDate: 信用交易日期
# rzye: 本日融资余额(元)
# rzmre: 本日融资买入额(元)
# rqyl: 本日融券余量
# rqylje: 本日融券余量金额(元)
# rqmcl: 本日融券卖出量
# rzrqjyzl: 本日融资融券余额(元)
# df = ts.sh_margins(start='2015-01-01', end='2015-04-19')
# saveCsv(df, filepath, investList, index)
index += 1
# 沪市融资融券明细数据
# 参数说明：
# date: 日期
# format：YYYY - MM - DD
# 默认为空’‘, 数据返回最近交易日明细数据
# symbol：标的代码，6
# 位数字e.g
# .600848，默认为空’‘
# start: 开始日期
# format：YYYY - MM - DD
# 默认为空’‘
# end: 结束日期
# format：YYYY - MM - DD
# 默认为空’‘
# retry_count：当网络异常后重试次数，默认为3
# pause: 重试时停顿秒数，默认为0
# 返回值说明：
# opDate: 信用交易日期
# stockCode: 标的证券代码
# securityAbbr: 标的证券简称
# rzye: 本日融资余额(元)
# rzmre: 本日融资买入额(元)
# rzche: 本日融资偿还额(元)
# rqyl: 本日融券余量
# rqmcl: 本日融券卖出量
# rqchl: 本日融券偿还量
# df = ts.sh_margin_details(start='2015-01-01', end='2015-04-19', symbol='601989')
# #如果不设symbol参数或者开始和结束日期时段设置过长，数据获取可能会比较慢，建议分段分步获取，比如一年为一个周期
# saveCsv(df, filepath, investList, index)
index += 1
# 深市融资融券汇总数据
# 参数说明：
# start: 开始日期
# format：YYYY - MM - DD
# 为空时取去年今日
# end: 结束日期
# format：YYYY - MM - DD
# 为空时取当前日期
# retry_count：当网络异常后重试次数，默认为3
# pause: 重试时停顿秒数，默认为0
# 返回值说明：
# opDate: 信用交易日期(index)
# rzmre: 融资买入额(元)
# rzye: 融资余额(元)
# rqmcl: 融券卖出量
# rqyl: 融券余量
# rqye: 融券余量(元)
# rzrqye: 融资融券余额(元)
# df = ts.sz_margins(start='2015-01-01', end='2015-04-19')
# saveCsv(df, filepath, investList, index)
index += 1
# 沪市融资融券明细数据
# 参数说明：
# date:日期 format：YYYY-MM-DD 默认为空’‘,数据返回最近交易日明细数据
# symbol：标的代码，6位数字e.g.600848，默认为空’‘
# start:开始日期 format：YYYY-MM-DD 默认为空’‘
# end:结束日期 format：YYYY-MM-DD 默认为空’‘
# retry_count：当网络异常后重试次数，默认为3
# pause:重试时停顿秒数，默认为0
# 返回值说明：
# opDate:信用交易日期
# stockCode:标的证券代码
# securityAbbr:标的证券简称
# rzye:本日融资余额(元)
# rzmre: 本日融资买入额(元)
# rzche:本日融资偿还额(元)
# rqyl: 本日融券余量
# rqmcl: 本日融券卖出量
# rqchl: 本日融券偿还量
# df = ts.sh_margin_details(start='2015-01-01', end='2015-04-19', symbol='601989')
# #如果不设symbol参数或者开始和结束日期时段设置过长，数据获取可能会比较慢，建议分段分步获取，比如一年为一个周期
# saveCsv(df, filepath, investList, index)
index += 1
import datetime
import tushare as ts
import numpy as np
import pymysql
import calendar
if __name__ == '__main__':
# 建立数据库连接,剔除已入库的部分
db = pymysql.connect(host='127.0.0.1', user='******', passwd='admin', db='stock', charset='utf8')
cursor = db.cursor()
year = 2017
for i in range(4):
temp_day = ts.fund_holdings(year,i+1)
for j in range(temp_day.shape[0]):
resu0 = list(temp_day.ix[j])
print(resu0)
try:
sql_insert = "insert into stock_fundholdings(state_dt,report_dt,stock_code,nums,nlast,count,clast,amount,ratio)values('%s','%s','%s','%i','%i','%.2f','%.2f','%.2f','%.2f')"%(str(resu0[2]),str(resu0[2]),str(resu0[0]),int(resu0[3]),int(resu0[4]),float(resu0[5]),float(resu0[6]),float(resu0[7]),float(resu0[8]))
cursor.execute(sql_insert)
db.commit()
except Exception as exp:
print(exp)
continue
#%%
import pandas as pd
import tushare as ts
# raw_data1 = pd.read_csv("Data\\Investment-Portfolio-Analysis-Data.csv", index_col='Date', parse_dates=True)
# print(pd.DataFrame(raw_data1))
fundHolding_df = pd.DataFrame(ts.fund_holdings(2019, 2))
fundHolding_df = fundHolding_df.sort_values(by='count', ascending=0)
# print(fundHolding_df)
# film_preDay_df = pd.DataFrame(ts.day_boxoffice())
#%%
import tushare as ts
re = ts.fund_holdings(2018, 3)
# print(type(re))
# re.to_excal('./tushare_out/fund_holdings.xlsx')
# print(re)
df = ts.get_hist_data('000875')
#直接保存
print(type(df))
df.to_excel('./000875.xlsx')
#设定数据位置（从第3行，第6列开始插入数据）
# df.to_excel('c:/day/000875.xlsx', startrow=2,startcol=5)
recent_k5_data = ts.get_k_data(target_stock_code, ktype='5')
'''最近5min线'''
#==============================================================================指数成分==============================================================================
'''获取指数成分'''
df_hs300 = ts.get_hs300s() #获取沪深300当前成份股及所占权重
df_sz50 = ts.get_sz50s() #获取上证50成份股
df_zz500 = ts.get_zz500s() #获取中证500成份股
#==============================================================================投资参考==============================================================================
df_dividend = ts.profit_data(top=60)
df_dividend.sort('shares',ascending=False)
'''分配预案'''
df_forecast = ts.forecast_data(2017,4)
'''业绩预告'''
df_restrict = ts.xsg_data()
'''限售股解禁'''
df_fund = ts.fund_holdings(2017, 2)
'''基金持股'''
df_sh = ts.sh_margin_details(start='2015-01-01', end='2015-04-19', symbol='601989')
'''上交所融资融券明细'''
df_sz = ts.sz_margins(start='2015-01-01', end='2015-04-19')
df_sz_detail = ts.sz_margin_details('2015-04-20')
'''深交所融资融券总额'''
#==============================================================================新闻==============================================================================
ts.get_latest_news() #默认获取最近80条新闻数据，只提供新闻类型、链接和标题
ts.get_latest_news(top=5,show_content=True) #显示最新5条新闻，并打印出新闻内容
'''即时新闻'''
ts.get_notices('600028')
'''个股新闻'''
ts.guba_sina(True)
'''新浪股吧'''
#==============================================================================海外数据源==============================================================================
df = ts.get_operation_data(year, season)
filename = 'management'
print('正在下载营运报表......\n')
df.to_excel(out_path + filename + filetype)
print('\n')
df = ts.get_growth_data(year, season)
filename = 'growth'
print('正在下载成长性报表......\n')
df.to_excel(out_path + filename + filetype)
print('\n')
df = ts.get_debtpaying_data(year, season)
filename = 'debt'
print('正在下载资产负债表......\n')
df.to_excel(out_path + filename + filetype)
print('\n')
df = ts.get_cashflow_data(year, season)
filename = 'cashflow'
print('正在下载现金流量表......\n')
df.to_excel(out_path + filename + filetype)
print('\n')
df = ts.fund_holdings(year, season)
filename = '基金持股数据'
print('正在下载基金持股数据......\n')
df.to_excel(out_path + filename + filetype)
print('\n')
import pymongo
import json
# 600547:山东黄金
# 600362:江西铜业
# 600312:平高电气
# 600499：科达洁能
# 603993：洛阳钼业
db = "InvestInfos"
coll = "FundHolds"
stock = "600547"
year = 2017
quart = 1
conn = pymongo.MongoClient('127.0.0.1', port=27017)
df = ts.fund_holdings(year, quart)
# index data columns(X columns)
dicIndex = json.loads(df.to_json(orient='split'))
for i, ind in enumerate(dicIndex['index']):
jsonstr = {
'_id': dicIndex['data'][i][0] + "-" + dicIndex['data'][i][2],
dicIndex['columns'][0]: dicIndex['data'][i][0],
dicIndex['columns'][1]: dicIndex['data'][i][1],
dicIndex['columns'][2]: dicIndex['data'][i][2],
dicIndex['columns'][3]: dicIndex['data'][i][3],
dicIndex['columns'][4]: dicIndex['data'][i][4],
dicIndex['columns'][5]: dicIndex['data'][i][5],
dicIndex['columns'][6]: dicIndex['data'][i][6],
dicIndex['columns'][7]: dicIndex['data'][i][7],
dicIndex['columns'][8]: dicIndex['data'][i][8]
}
# -*- coding:utf-8 -*-
'''
Created on 2016年5月24日
@author: 04yyl
'''
import tushare as ts
if __name__ == '__main__':
print ts.fund_holdings(2016, 1)
# ts.set_token('14a9186596c5cd0a6e74b994434d620c5ddaab9611d686f6e201768e1aa957cb')
# # 获取secid
# mt = ts.Master()
# secid = mt.SecID(assetClass="E",cnSpell="",ticker="300027")
#
# print secid
# fd = ts.Subject()
# df = fd.NewsByCompany(field='newsPublishTime,newsTitle,relatedScore,sentiment,newsPublishSite,newsInsertTime',partyID=35390,beginDate='20160524')
# # df = df.sort('insertTime', ascending=False)
# print df
def downloading_information(time=3, SAVE=True):
"""
获取全STOCK 数据
"""
year = 2018
quarter = 1
df = pd.read_csv("../list/stock_code_all.csv", encoding='gbk')
rt_df = df
df["code"] = df['code'].map(lambda x: str(x).zfill(6))
ST_basics = ts.get_stock_basics()
ST_basics = ST_basics.drop(["name"], axis=1)
ST_basics = ST_basics.reset_index()
df = pd.merge(df, ST_basics, how="left", on="code")
print("\n\n process downloading the pre-announcement datas for %s -Q%s" %
(year, quarter))
pre_announcement = ts.get_report_data(year, quarter + 1)
pre_announcement.columns = information_dict["predict"]
pre_announcement = pre_announcement.drop(["name"], axis=1)
df = pd.merge(df, pre_announcement, how="left", on="code")
print("\n\n process downloading the profit datas for %s -Q%s" %
(year, quarter))
profit_df = ts.get_profit_data(year, quarter)
profit_df = profit_df.drop(["name"], axis=1)
df = pd.merge(df, profit_df, how="left", on="code")
print("\n\n process downloading the operation datas for %s -Q%s" %
(year, quarter))
op_df = ts.get_operation_data(year, quarter)
op_df = op_df.drop(["name"], axis=1)
df = pd.merge(df, op_df, how="left", on="code")
print("\n\n process downloading the growth datas for %s -Q%s" %
(year, quarter))
growth_df = ts.get_growth_data(year, quarter)
growth_df = growth_df.drop(["name"], axis=1)
df = pd.merge(df, growth_df, how="left", on="code")
print("\n\n process downloading the debt datas for %s -Q%s" %
(year, quarter))
debt_df = ts.get_debtpaying_data(year, quarter)
debt_df = debt_df.drop(["name"], axis=1)
df = pd.merge(df, debt_df, how="left", on="code")
print("\n\n process downloading the cashflow datas for %s -Q%s" %
(year, quarter))
cashflow_df = ts.get_cashflow_data(year, quarter)
cashflow_df = cashflow_df.drop(["name"], axis=1)
df = pd.merge(df, cashflow_df, how="left", on="code")
print("\n\n process downloading the funds holding datas for %s -Q%s" %
(year, quarter))
fund_df = ts.fund_holdings(year, quarter)
fund_df = fund_df.drop(["name"], axis=1)
fund_df = fund_df.drop(["date"], axis=1)
fund_df.rename(columns={
"num": "fund_nums",
"nlast": "fund_nlast",
"count": "fund_shares",
"clast": "fund_clast",
"amount": "fund_amount",
"ratio": "fund_ratio",
},
inplace=True)
df = pd.merge(df, fund_df, how="left", on="code")
df = df.drop_duplicates("code", keep='first', inplace=False)
df = df.set_index("code")
#profit_df=profit_df.set_index("code")
#df=pd.concat([df,profit_df],axis=1,join_axes=[df.index])
#df.columns=df.columns +information_dict["predict"].keys[2:]
#print (pre_announcement)
'''
for year in range(2015,2017):
print(year)
for Q in range(1,5):
print(Q)
ST_basics=download_ACH_Q(year,Q,ST_basics)
'''
if SAVE == True:
import os
path = os.path.dirname(os.getcwd()) + '\\report\\' # 存储路径
df.to_csv(path + 'Stock_Information.csv', encoding='gbk', header=True)
return df, rt_df
import pandas as pd
import sys
import time
from mongoModel import *
# Exchange(name='沪深股市').save()
for year in range(2004, 2018):
for quarter in range(1, 5):
finance = ts.get_report_data(year, quarter)
profit = ts.get_profit_data(year, quarter)
operation = ts.get_operation_data(year, quarter)
# time.sleep(9)
debtpay = ts.get_debtpaying_data(year, quarter)
cashFlow = ts.get_cashflow_data(year, quarter)
fundHold = ts.fund_holdings(year, quarter)
forecast = ts.forecast_data(year, quarter)
growth = ts.get_growth_data(year, quarter)
result = pd.concat([
finance, profit, operation, growth, debtpay, cashFlow, fundHold,
forecast
],
axis=1)
for i in range(len(result)):
it = result.iloc[i].to_dict()
# print(it)
# print(result.loc[i]['code'].all(),'kksksksk ',it['code'])
if (type(it['code']) is type(0.9)) or (not it['code']):
# print('ssssssssssssssssssssssssssssssssss')
continue
args = []
