def job_7():
try:
print("I'm working......龙虎榜数据")
# 每日龙虎榜列表
top_list = ts.top_list(today)
data = pd.DataFrame(top_list)
data.to_sql('top_list',engine,index=True,if_exists='replace')
print("每日龙虎榜列表......done")
# 个股上榜统计
cap_tops = ts.cap_tops()
data = pd.DataFrame(cap_tops)
data.to_sql('cap_tops',engine,index=True,if_exists='replace')
print("个股上榜统计......done")
# 营业部上榜统计
broker_tops = ts.broker_tops()
data = pd.DataFrame(broker_tops)
data.to_sql('broker_tops',engine,index=True,if_exists='replace')
print("营业部上榜统计......done")
# 机构席位追踪
inst_tops = ts.inst_tops()
data = pd.DataFrame(inst_tops)
data.to_sql('inst_tops',engine,index=True,if_exists='replace')
print("机构席位追踪......done")
# 机构成交明细
inst_detail = ts.inst_detail()
data = pd.DataFrame(inst_detail)
data.to_sql('inst_detail',engine,index=True,if_exists='replace')
print("机构成交明细......done")
except Exception as e:
print(e)
def store_top_data(self, trading_date=None):
"""龙虎榜数据: 龙虎榜数据接口提供历史龙虎榜上榜股票数据"""
trading_date = self.last_trading_date if trading_date is None else trading_date
# 每日龙虎榜列表
print('top_list...')
top_df = ts.top_list(self.stock_date_format(trading_date))
self.mysqlUtils.append_data(top_df, 'top_list')
# 个股上榜统计
print('cap_tops...')
cap_tops_df = ts.cap_tops()
cap_tops_df['date'] = trading_date
self.mysqlUtils.append_data(cap_tops_df, 'cap_tops')
# 营业部上榜统计
print('broker_tops...')
broker_tops_df = ts.broker_tops()
broker_tops_df['date'] = trading_date
self.mysqlUtils.append_data(broker_tops_df, 'broker_tops')
# 龙虎榜机构席位追踪
print('inst_tops...')
inst_tops_df = ts.inst_tops()
inst_tops_df['date'] = trading_date
self.mysqlUtils.append_data(inst_tops_df, 'inst_tops')
# 龙虎榜机构席位成交明细
print('inst_detail...')
inst_detail_df = ts.inst_detail()
self.mysqlUtils.append_data(inst_detail_df, 'inst_detail')
def inst_tops(days,retry_count,pause):
try:
df = ts.inst_tops(days,retry_count,pause)
engine = create_engine('mysql://*****:*****@127.0.0.1/stock?charset=utf8')
df.to_sql('inst_tops', engine, if_exists='append')
print "message"
except Exception, e:
e.message
def getdragontigerdata():
curday = datetime.date.today()
curdate = curday.strftime('%Y%m%d')
print(curdate)
mylogger = getmylogger()
# 每日龙虎榜列表
df = ts.top_list(curday.strftime('%Y-%m-%d'))
if df is not None:
df['date'] = curdate
tosql(df, 'toplistdata', "append", "每日龙虎榜数据", mylogger)
else:
mylogger.info("没有每日龙虎榜数据。")
# 个股上榜统计
for i in [5, 10, 30, 60]:
df = ts.cap_tops(i)
logmsg = "个股上榜数据" + "%d日：" % i
if df is not None:
df['date'] = curdate
df['period'] = i
tosql(df, 'captops', "append", logmsg, mylogger)
else:
mylogger.info("没有" + logmsg)
# 营业部上榜统计
for i in [5, 10, 30, 60]:
df = ts.broker_tops(i)
logmsg = "营业部上榜数据" + "%d日：" % i
if df is not None:
df['date'] = curdate
df['period'] = i
tosql(df, 'brokertops', "append", logmsg, mylogger)
else:
mylogger.info("没有" + logmsg)
# 机构席位追踪
for i in [5, 10, 30, 60]:
df = ts.inst_tops(i)
logmsg = "机构席位追踪数据" + "%d日：" % i
if df is not None:
df['date'] = curdate
df['period'] = i
tosql(df, 'instops', "append", logmsg, mylogger)
else:
mylogger.info("没有" + logmsg)
# 机构成交明细
df = ts.inst_detail()
logmsg = "机构成交明细："
if df is not None:
df['date'] = curdate
tosql(df, 'instdetail', "append", logmsg, mylogger)
else:
mylogger.info("没有机构成交明细。")
def test():
ts.get_sz50s()
ts.get_hs300s()
ts.get_zz500s()
ts.realtime_boxoffice()
ts.get_latest_news()
ts.get_notices(tk)
ts.guba_sina()
ts.get_cpi()
ts.get_ppi()
ts.get_stock_basics()
ts.get_concept_classified()
ts.get_money_supply()
ts.get_gold_and_foreign_reserves()
ts.top_list() #每日龙虎榜列表
ts.cap_tops() #个股上榜统计
ts.broker_tops() #营业部上榜统计
ts.inst_tops() # 获取机构席位追踪统计数据
ts.inst_detail()
def download_org_top(time ):
list=[5,10,30]
for i in list:
Datas_b= ts.inst_tops(days= i)
files_path = '../report/Brokerage/%s'%time
if os.path.exists(files_path) == False: # 判断文件是不是存在
os.mkdir(files_path) # 创建目录
Datas_b.to_csv(files_path+'/%s_org_top%sD_csv.csv'%(time,i),encoding='gbk')
with pd.ExcelWriter(files_path+'/%s_org_top%sD_xlx.xlsx'%(time,i)) as writer:
Datas_b.to_excel(writer, sheet_name='Sheet1')
print('\n%s %s机构席位 have been saved'%(time,i))
def get_lbh_inst_tops(day):
"""
机构席位追踪
获取机构近5、10、30、60日累积买卖次数和金额等情况。
"""
df = ts.inst_tops(day)
print(df)
if df is not None:
res = df.to_sql(lbh_inst_tops, engine, if_exists='replace')
msg = 'ok' if res is None else res
print('获取机构近 {0} 日累积买卖次数和金额等情况：{1}'.format(day, msg) + '\n')
else:
print('获取机构近 {0} 日累积买卖次数和金额等情况：{1}'.format(day, 'None') + '\n')
def stk_inst_tops(days=5,table = 'stk_inst_tops_ma5'):
print("\n插入数据：" + table)
try:
conn = connMysql().createconn()
cur = conn.cursor()
sql = 'truncate table ' + table + ';'
cur.execute(sql)
cur.close()
conn.close()
df = ts.inst_tops(days=days)
df.to_sql(table,engine,if_exists='append')
except:
print("\n收集机构席位追踪失败")
def get_top_inst_count(self, days=5):
"""
获取个股在近5、10、30、60日的龙虎榜上榜次数、累积购买额、累积卖出额、净额、买入席位数和卖出席位数。
获取机构的近5、10、30、60日累积买入次数
具体返回参数参考tushare.cap_tops和tushare.inst_tops
Parameters：
----------
days:5、10、30、60,表示统计周期
"""
top_count_df = ts.cap_tops(days=days, retry_count=5, pause=1)
with codecs.open(u"{}/cap_tops_count_{}_{}.csv".format(data_path, days, self.today), 'w+', "utf-8") as f:
top_count_df.to_csv(f, header=True, sep="\t", index=True)
inst_tops_df = ts.inst_tops(days=days, retry_count=5, pause=1)
with codecs.open(u"{}/inst_tops_count_{}_{}.csv".format(data_path, days, self.today), 'w+', "utf-8") as f:
top_count_df.to_csv(f, header=True, sep="\t", index=True)
def custom_stocks_streage():
streage = {
'机构五日内买卖数据按买入 - 卖出倒序\n字段：代码 名称 累积买入额(万) 买入次数 累积卖出额(万) 卖出次数 净额(万)':
institutional_seat_tracking,
'成交明细':
institutional_seat_tracking_detail,
'5天之内公布的分配预案\n字段：股票代码 股票名称 分配年份 公布日期 分红金额（每10股）:转增和送股数（每10股)':
profit,
'今日机构买卖数据按买入 - 卖出倒序\n字段：代码 股票名称 交易日期 机构席位买入额(万) 机构席位卖出额(万) type 差额':
today_institutional_seat_tracking_detail
}
data = ts.inst_tops().sort_values(by='net', ascending=False).head(5)
for item, fuc in streage.items():
mec = fuc(data)
for i in mec:
testnotice.notify('"{0}"\n{1}'.format(item, i))
def get_institution_tops(days=5, retry_count=RETRY_COUNT, pause=PAUSE):
"""机构成交明细"""
logger.info('Begin get InstitutionTops. Days is: %s.' % days)
try:
data_df = ts.inst_tops(days, retry_count, pause)
except Exception as e:
logger.exception('Error get InstitutionTops. Days is: %s.' % days)
return None
else:
data_dicts = []
if data_df is None or data_df.empty:
logger.warn('Empty get InstitutionTops. Days is: %s.' % days)
else:
data_dicts = [{'code': row[0], 'name': row[1],
'bamount': row[2], 'bcount': row[3], 'samount': row[4],
'scount': row[5], 'net': row[6],
'days_type': days, 'insert_date': today_line}
for row in data_df.values]
logger.info('Success get InstitutionTops. Days is: %s.' % days)
return data_dicts
def top_type(top_type):
today = datetime.datetime.today().strftime('%Y-%m-%d')
if top_type == 'top_list':
top_list = ts.top_list(today)
if top_list is not None:
top_list.to_sql('top_list',
engine,
flavor='mysql',
if_exists='append')
elif top_type == 'cap_tops':
cap_tops = ts.cap_tops()
if cap_tops is not None:
cap_tops['date'] = today
cap_tops.to_sql('top_cap_tops',
engine,
flavor='mysql',
if_exists='append')
elif top_type == 'broker_tops':
broker_tops = ts.broker_tops()
if broker_tops is not None:
broker_tops['date'] = today
broker_tops.to_sql('top_broker_tops',
engine,
flavor='mysql',
if_exists='append')
elif top_type == 'inst_tops':
inst_tops = ts.inst_tops()
if inst_tops is not None:
inst_tops['date'] = today
inst_tops.to_sql('top_inst_tops',
engine,
flavor='mysql',
if_exists='append')
elif top_type == 'inst_detail':
inst_detail = ts.inst_detail()
if inst_detail is not None:
inst_detail.to_sql('top_inst_detail',
engine,
flavor='mysql',
if_exists='append')
def inst_tops(days=5, retry_count=5, pause=1):
df = ts.inst_tops(days=days, retry_count=retry_count, pause=pause)
return df
def ChairTotal():
df=ts.inst_tops()
df=df.sort_values('bcount',ascending=False)
return [u"机构席位追踪:",df[:20]]
from sqlalchemy import create_engine
import tushare as ts
#engine = create_engine('mysql+pymysql://root:[email protected]/packageing?charset=utf8')
engine = create_engine(
'mysql+pymysql://root:[email protected]/gupiao?charset=utf8')
df = ts.inst_detail()
de = ts.inst_tops()
broker = ts.broker_tops()
#df.to_sql('inst_datail',engine)
#de.to_sql('inst_tops',engine)
#broker.to_sql('broker_tops',engine)
df.to_sql('inst_datail', engine, if_exists='append')
de.to_sql('inst_tops', engine, if_exists='append')
#broker.to_sql('broker_tops',engine,if_exists='append')
参数说明：
days：统计周期5、10、30和60日，默认为5日
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:代码
name:名称
bamount:累积买入额(万)
bcount:买入次数
samount:累积卖出额(万)
scount:卖出次数
net:净额(万)'''
print('机构席位追踪')
print(ts.inst_tops())
'''
获取最近一个交易日机构席位成交明细统计数据
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:代码
name:名称
date:交易日期
bamount:机构席位买入额(万)
samount:机构席位卖出额(万)
type:类型
#! D:/Python27/python
# coding=utf-8
__ = 'Administrator'
from datetime import date
import numpy as np
import sys
import tushare as ts
today = date.today()
today1 = str(today)
start = '%s-%s-%s' % (today.year - 1, today.month, today.day)
data = ts.get_hist_data('600567', start=start, end=today1)
close = data['close']
# print close
print ts.inst_detail(retry_count=5, pause=0.001)
print ts.inst_tops()
import pymongo
import json
# ts.get_cash_flow : get specialed Code Cash_flow in the History .
# 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 = "TopRanks"
coll = "InstTop"
date = '2017-12-22'
tops = 5
conn = pymongo.MongoClient('127.0.0.1', port=27017)
df = ts.inst_tops(tops)
# 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] + "-" + date + "-" + str(tops),
'Date': date,
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]
}
try:
def getInstTops(self):
file_name = 'inst_tops.csv'
path = self.index + self.index_inst_tops + file_name
data = ts.inst_tops()
data.to_csv(path, encoding='utf-8')
print(file_name)
def Getjigouxiwei():
df = ts.inst_tops()
df = df.to_json(force_ascii=False)
print(df)
return df
def get_inst_tops():
# 统计周期5、10、30和60日，默认为5日
df = ts.inst_tops(days=5)
df.to_sql('inst_tops', engine, if_exists='append', index=False, index_label='code')
def capture_stock_data():
capture_date = datetime.datetime.now().strftime("%Y%m%d")
save_dir = "/home/dandelion/stock_data/" + capture_date
if not os.path.exists(save_dir):
os.mkdir(save_dir)
print("The save directory is created successfully!\n", save_dir)
print("The save directory is already exist!\n", save_dir)
# ======================Daily Command================================================================
# get the boxoffcie data of the last day and save as csvfile named as the capture command
ts.day_boxoffice().to_csv(
save_dir + "/" + capture_date + "_day_boxoffice.csv",
header=True,
sep=",",
index=False,
)
print("day_boxoffice data capture completed!")
# get the cinema data of the last day and save as csvfile named as the capture command
ts.day_cinema().to_csv(
save_dir + "/" + capture_date + "_day_cinema.csv",
header=True,
sep=",",
index=False,
)
print("day_cinema data capture completed!")
ts.month_boxoffice().to_csv(
save_dir + "/" + capture_date + "_month_boxoffice.csv",
header=True,
sep=",",
index=False,
)
print("month_boxoffice data capture completed!")
ts.realtime_boxoffice().to_csv(
save_dir + "/" + capture_date + "_realtime_boxoffice.csv",
header=True,
sep=",",
index=False,
)
print("realtime_boxoffice data capture completed!")
# get the stock data index of the last day and save as csvfile named as the capture command
ts.get_index().to_csv(
save_dir + "/" + capture_date + "_get_index.csv",
header=True,
sep=",",
index=False,
)
print("get_index data capture completed!")
# get the history cpi data and save as csvfile named as the capture command
ts.get_cpi().to_csv(
save_dir + "/" + capture_date + "_get_cpi.csv",
header=True,
sep=",",
index=False,
)
print("get_cpi data capture completed!")
# get the history gdp data by month and save as csvfile named as the capture command
ts.get_gdp_year().to_csv(
save_dir + "/" + capture_date + "_get_gdp_year.csv",
header=True,
sep=",",
index=False,
)
print("get_gdp_year data capture completed!")
# get today all stock data and save as csvfile named as the capture command
# ts.get_today_all().to_csv(save_dir+'/'+capture_date+'_get_today_all.csv',header=True,sep=',',index=False)
# get detail information of the top brokers today and save as csvfile named as the capture command
ts.broker_tops().to_csv(
save_dir + "/" + capture_date + "_broker_tops.csv",
header=True,
sep=",",
index=False,
)
print("broker_tops data capture completed!")
# get detail information of the top brokers today and save as csvfile named as the capture command
ts.cap_tops().to_csv(
save_dir + "/" + capture_date + "_cap_tops.csv",
header=True,
sep=",",
index=False,
)
print("cap_tops data capture completed!")
ts.get_area_classified().to_csv(
save_dir + "/" + capture_date + "_get_area_classified.csv",
header=True,
sep=",",
index=False,
)
print("get_area_classified data capture completed!")
# ts.get_balance_sheet(code='').to_csv(save_dir+'/'+capture_date+'_get_balance_sheet.csv',header=True,sep=',',index=False)
# print('get_balance_sheet data capture completed!')
# ts.get_cash_flow(code='').to_csv(save_dir+'/'+capture_date+'_get_cash_flow.csv',header=True,sep=',',index=False)
# print('get_cash_flow data capture completed!')
ts.get_day_all().to_csv(
save_dir + "/" + capture_date + "_get_day_all.csv",
header=True,
sep=",",
index=False,
)
print("get_day_all data capture completed!")
ts.get_cashflow_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_cashflow_data.csv",
header=True,
sep=",",
index=False,
)
print("get_cashflow_data data capture completed!")
ts.get_concept_classified().to_csv(
save_dir + "/" + capture_date + "_get_concept_classified.csv",
header=True,
sep=",",
index=False,
)
print("get_concept_classified data capture completed!")
ts.get_debtpaying_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_debtpaying_data.csv",
header=True,
sep=",",
index=False,
)
print("get_debtpaying_data data capture completed!")
ts.get_deposit_rate().to_csv(
save_dir + "/" + capture_date + "_get_deposit_rate.csv",
header=True,
sep=",",
index=False,
)
print("get_deposit_rate data capture completed!")
ts.get_gdp_contrib().to_csv(
save_dir + "/" + capture_date + "_get_gdp_contrib.csv",
header=True,
sep=",",
index=False,
)
ts.get_gdp_for().to_csv(
save_dir + "/" + capture_date + "_get_gdp_for.csv",
header=True,
sep=",",
index=False,
)
ts.get_gdp_pull().to_csv(
save_dir + "/" + capture_date + "_get_gdp_pull.csv",
header=True,
sep=",",
index=False,
)
ts.get_gdp_quarter().to_csv(
save_dir + "/" + capture_date + "_get_gdp_quarter.csv",
header=True,
sep=",",
index=False,
)
print("get_gdp_ data capture completed!")
# ts.get_gdp_year().to_csv(save_dir+'/'+capture_date+'_get_gdp_year.csv',header=True,sep=',',index=False)
ts.get_gem_classified().to_csv(
save_dir + "/" + capture_date + "_get_gem_classified.csv",
header=True,
sep=",",
index=False,
)
ts.get_gold_and_foreign_reserves().to_csv(
save_dir + "/" + capture_date + "_get_gold_and_foreign_reserves.csv",
header=True,
sep=",",
index=False,
)
ts.get_growth_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_growth_data.csv",
header=True,
sep=",",
index=False,
)
ts.get_industry_classified().to_csv(
save_dir + "/" + capture_date + "_get_industry_classified.csv",
header=True,
sep=",",
index=False,
)
ts.get_hs300s().to_csv(
save_dir + "/" + capture_date + "_get_hs300s.csv",
header=True,
sep=",",
index=False,
)
ts.get_sz50s().to_csv(
save_dir + "/" + capture_date + "_get_sz50s.csv",
header=True,
sep=",",
index=False,
)
ts.get_zz500s().to_csv(
save_dir + "/" + capture_date + "_get_zz500s.csv",
header=True,
sep=",",
index=False,
)
ts.get_operation_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_operation_data.csv",
header=True,
sep=",",
index=False,
)
ts.get_stock_basics().to_csv(
save_dir + "/" + capture_date + "_get_stock_basics.csv",
header=True,
sep=",",
index=False,
)
ts.get_report_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_report_data.csv",
header=True,
sep=",",
index=False,
)
ts.inst_detail().to_csv(
save_dir + "/" + capture_date + "_inst_detail.csv",
header=True,
sep=",",
index=False,
)
ts.inst_tops().to_csv(
save_dir + "/" + capture_date + "_inst_tops.csv",
header=True,
sep=",",
index=False,
)
print("inst_tops data capture completed!")
ts.new_stocks().to_csv(
save_dir + "/" + capture_date + "_new_stocks.csv",
header=True,
sep=",",
index=False,
)
print("new_stocks data capture completed!")
ts.top_list().to_csv(
save_dir + "/" + capture_date + "_top_list.csv",
header=True,
sep=",",
index=False,
)
print("top_list data capture completed!")
from sqlalchemy import create_engine
import tushare as ts
engine = create_engine('mysql+pymysql://root:[email protected]/packageing?charset=utf8')
df = ts.inst_detail()
de = ts.inst_tops()
broker = ts.broker_tops()
df.to_sql('inst_datail',engine,if_exists = 'append')
de.to_sql('inst_tops',engine,if_exists = 'append')
broker.to_sql('broker_tops',engine,if_exists = 'append')
for idx in df.index:
temp = df.ix[idx]
sql = "insert into broker_tops(broker,count,bamount,bcount,samount,scount,top3) values(%s,%s,%s,%s,%s,%s,%s)"
param = (temp['broker'],temp['count'],temp['bamount'],temp['bcount'],temp['samount'],temp['scount'],temp['top3'])
cursor.execute(sql, param)
conn.commit()
except:
f=open("errors/"+today+".log",'a')
traceback.print_exc(file=f)
f.flush()
f.close()
# 机构席位追踪
try:
df = idx = temp = sql = param = None
df = ts.inst_tops()
if df is not None:
for idx in df.index:
temp = df.ix[idx]
sql = "insert into inst_tops(code,name,bamount,bcount,samount,scount,net) values(%s,%s,%s,%s,%s,%s,%s)"
param = (temp['code'],temp['name'],temp['bamount'],temp['bcount'],temp['samount'],temp['scount'],temp['net'])
cursor.execute(sql,param)
conn.commit()
except:
f=open("errors/"+today+".log",'a')
traceback.print_exc(file=f)
f.flush()
f.close()
# 机构成交明细
try:
try:
df = ts.broker_tops(freq)
df['date'] = today
df['freq'] = freq
df = df.set_index('date', drop='true')
df.to_sql('tops_broker',engine,if_exists='append')
print
tsl.log("tops_broker done")
except BaseException, e:
print
print e
tsl.log("tops_broker error")
tsl.log("tops_inst_seat start...")
try:
df = ts.inst_tops(freq)
df['date'] = today
df['freq'] = freq
df = df.set_index('code', drop='true')
df.to_sql('tops_inst_seat',engine,if_exists='append')
print
tsl.log("tops_inst_seat done")
except BaseException, e:
print
print e
tsl.log("tops_inst_seat error")
# TBD
# no data, should be a bug
'''
if freq == 5:
import tushare as ts
import util
import pandas
from sqlalchemy import create_engine
engine = create_engine(util.db_url)
# 每日龙虎榜列表
# top_daily = ts.top_list('2019-05-06')
# pandas.io.sql.to_sql(top_daily, "top_daily_20190506", engine, schema='StockPick', if_exists='append')
# 个股上榜统计
# individual = ts.cap_tops(days=10)
# pandas.io.sql.to_sql(individual, "individual_20190507_10", engine, schema='StockPick', if_exists='append')
# 营业部上榜统计
# broker_tops = ts.broker_tops(days=10)
# pandas.io.sql.to_sql(broker_tops, "broker_tops_20190507_10", engine, schema='StockPick', if_exists='append')
# 机构席位追踪
institution_tops = ts.inst_tops(days=10)
pandas.io.sql.to_sql(institution_tops, "institution_tops_20190507_10", engine, schema='StockPick', if_exists='append')
days：统计周期5、10、30和60日，默认为5日
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:代码
name:名称
bamount:累积买入额(万)
bcount:买入次数
samount:累积卖出额(万)
scount:卖出次数
net:净额(万)
'''
ts.inst_tops()
# 机构成交明细
'''
获取最近一个交易日机构席位成交明细统计数据
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:代码
name:名称
date:交易日期
def cn_main_loop(mode):
the_ticks, info, flags = interact_choose_ticks(mode)
# print the_ticks
# print info
exec_func = get_one_ticker_k_data
if 'realtime' in flags:
exec_func = real_time_ticks
elif 'onestock' in flags:
exec_func = real_time_ticks
elif 'news_sina' in flags:
df = get_latest_news()
idxs, nflags = cli_select_menu(df['title'], menu_columns=1)
for rowid in idxs:
url = df.iloc[rowid]['url']
texts, html = get_article_detail(url, 'p')
print texts.encode(ENCODE, 'ignore')
elif 'news_wscn' in flags or 'hot_wscn' in flags:
wscn = StockNewsWSCN()
if 'hot_wscn' in flags:
df = wscn.mode_run('hot_article')
else:
df = wscn.mode_run('info_flow')
idxs, nflags = cli_select_menu(df['title'], menu_columns=1)
# pdb.set_trace()
for rowid in idxs:
url = df.iloc[rowid]['uri']
res = wscn.mode_run('article', stocks=[url])
print res[0].encode(ENCODE, 'ignore')
print ''
elif 'index' in flags:
df = ts.get_index()
print df
elif 'futu_news' in flags:
_ftnn = StockNewsFUTUNN()
df = _ftnn.get_news()
df.index = pd.RangeIndex(df.shape[0])
idxs, nflags = cli_select_menu(df['content'], menu_columns=1)
for rowid in idxs:
url = df.iloc[rowid]['detail_url']
texts, tags = get_article_detail(url, 'div', '#content')
print texts
# print (u'\n'.join(texts[:-5])).encode('gbk','ignore')
elif 'wscn_loop' in flags:
wscn_loop()
elif 'top' in flags:
df = ts.top_list()
print df.sort_values('amount', ascending=False)
elif 'inst' in flags:
df = ts.inst_tops()
print df.sort_values('net', ascending=False)
raw_input('[pause]')
df = ts.inst_detail()
print df.sort_values('bamount', ascending=False)
elif 'quit' in flags:
sys.exit()
if Pool is None:
for tk in the_ticks:
results = [exec_func(tk, info)]
else:
pool = Pool(8)
jobs = []
for tk in the_ticks:
job = pool.spawn(exec_func, tk, info, flags)
jobs.append(job)
# pool.close()
pool.join()
# jobs = [gevent.spawn(get_one_ticker_k_data,tk,info,flags) for tk in the_tks]
# gevent.joinall(jobs)
results = [job.value for job in jobs]
## 读取分析结果
# fname = 'results.%s.json'%exec_func.func_name
# print fname
# json.dump(results,open(fname,'w'),indent=2)
########### 打印技术分析
print '\n\n' + analyse_res_to_str(results) + '\n'
if 'graph' in flags and exec_func.func_name == 'get_one_ticker_k_data':
cols = len(results)
fig, ax = plt.subplots(nrows=3, ncols=cols, sharex=False)
for i, onestk in enumerate(results):
tick = onestk['code']
name = onestk['info'].get('name')
# fname = FNAME_PAT_HIST%tick
# df = pd.read_csv(fname,encoding='utf8',index_col='date')
df = pd.DataFrame.from_dict(onestk['df'])
df = df[-50:]
title = '%s' % (tick)
df['atr'] = talib.ATR(df['high'], df['low'], df['close'])
df['sma10'] = talib.SMA(df['close'], 10)
df['ema10'] = talib.EMA(df['close'], 10)
df['ema_dif'] = df['ema10'] - df['sma10']
if cols > 1:
aax = [ax[0, i], ax[1, i], ax[2, i]]
else:
aax = [ax[0], ax[1], ax[2]]
df[['close', 'sma10', 'ema10']].plot(title=title, ax=aax[0])
df[['ema_dif']].plot(title=title, ax=aax[1])
df[['volume']].plot(title=title, ax=aax[2])
plt.show()
if 'pause' in flags:
raw_input('pause')
return flags
def get_tiger():
file2 = './data/jigou.xlsx'
df = ts.inst_tops(5)
df2 = df.sort_values(by='bcount', ascending=False)
df2.to_excel(file2, index=False)
print "import jigou done!"
def cb(**kw):
return ts.inst_tops()
def get_td_inst_list(days=5, retry=3, pause=0):
"""
上榜票列表
"""
data = ts.inst_tops(days=days, retry_count=retry, pause=pause)
return data
def GetInsttops(self):
datateees = ts.inst_tops()
self.SaveCSV(datateees, 'insttops.csv')
def findAgent(self):
df = ts.inst_tops(10)
df.sort_values(by='bamount')
print (df[df.net > 5000])
return df[df.net > 5000]
