def jqdownloadMinuteBarBySymbol(symbol, startDate, endDate):
"""下载某一合约的分钟线数据"""
start = time()
cl = dbMinute[symbol]
cl.ensure_index([('datetime', ASCENDING)], unique=True) # 添加索引
df = jq.get_price(setting[symbol],
start_date=startDate,
end_date=endDate,
frequency='1m',
fields=FIELDS,
skip_paused=True)
for ix, row in df.iterrows():
bar = generateVtBar(row, symbol)
d = bar.__dict__
flt = {'datetime': bar.datetime}
cl.replace_one(flt, d, True)
end = time()
cost = (end - start) * 1000
print(u'合约%s的分钟K线数据下载完成%s - %s，耗时%s毫秒' %
(symbol, df.index[0], df.index[-1], cost))
print(jq.get_query_count())
def download_minute_bar(name, start_date, end_date, ac):
"""下载某一合约的分钟线数据"""
print(f"开始下载合约数据{name}")
symbol_info = ac[ac.name == name]
symbol = name
vt_symbol = symbol_info.index[0]
exchange = ex_jq2vn.get(vt_symbol[-4:])
print(f"{symbol, exchange}")
symbol_info = ac[ac.name == name]
vt_symbol = symbol_info.index[0]
start = time()
df = jq.get_price(
vt_symbol,
start_date=start_date,
end_date=end_date,
frequency="1m",
fields=FIELDS,
)
bars = []
for ix, row in df.iterrows():
bar = generateVtBar(row, symbol, exchange)
bars.append(bar)
database_manager.save_bar_data(bars)
end = time()
cost = (end - start) * 1000
print("合约%s的分钟K线数据下载完成%s - %s，耗时%s毫秒" %
(symbol, df.index[0], df.index[-1], cost))
print(jq.get_query_count())
def downloadAllMinuteBar(self, days=0):
"""下载所有配置中的合约的分钟线数据"""
if days == 0:
days = self.setting["days"]
startDt = datetime.today() - days * timedelta(1)
enddt = datetime.today()
print('-' * 50)
print(u'开始下载合约分钟线数据')
print('-' * 50)
if 'Bar.Min' in self.setting:
l = self.setting["Bar.Min"]
for VNSymbol in l:
dt0 = time.process_time()
symbol = VNSymbol.split(".")[0]
exchange = Exchange(VNSymbol.split(".")[1])
self.query_history(symbol,
exchange,
startDt,
enddt,
interval='1m')
cost = (time.process_time() - dt0)
print(u'合约%s的分钟K线数据下载完成%s - %s，耗时%s秒' %
(symbol, startDt, enddt, cost))
print(jq.get_query_count())
print('-' * 50)
print
u'合约分钟线数据下载完成'
print('-' * 50)
return None
def login():
print("login JQDATA servering")
account = "15210089516"
password = "******"
jq.auth(account, password)
print("login successed!")
remain_query = jq.get_query_count()
print("remain query times for jqdata: ", remain_query)
def get_data():
import jqdatasdk
jqdatasdk.auth('18374980021', 'asdfasdf1')
codes = ['000905.XSHG', '000001.XSHE', '600001.XSHG', '600547.XSHG']
df = jqdatasdk.get_price(codes,
start_date='2020-01-01',
end_date='2020-03-01')
df.to_csv('sample_data.csv')
print(jqdatasdk.get_query_count())
def check_api_quote():
quote = jq.get_query_count()
spare = quote["spare"]
if spare < 500:
#流量不足，为了避免API抛错，我们等到明天
today = datetime.now()
this_day = today.day
while datetime.now().day == this_day:
time.sleep(30) # 30 seconds
print "."
def initJq(self, ):
print("login JQDATA servering")
account = "15210089516" #"15210089516"
password = "******"
account = "13132110856" # "18620156503"
password = "******" #"3DKkypzN6uPj"
account = "18620156503"
password = "******"
jq.auth(account, password)
print("login successed!")
remain_query = jq.get_query_count()
print("remain query times for jqdata: ", remain_query)
def login():
print("login JQDATA servering")
jq.auth(account, password)
print("login successed!")
remain_query = jq.get_query_count()
print("remain query times for jqdata: ", remain_query)
def remainder():
login()
count = jq.get_query_count()
print(count)
pass
if __name__ == '__main__':
#password = pd.DataFrame({'host':['jqdatasdk'],
# 'username':['13981813381'],
# 'password':['813381']}, columns=['host',
# 'username',
# 'password'])
# 用上面的代码保存密码到用户配置 .GolemQ 目录下面
password = load_settings('password.hdf5')
auth(password.username[0], password.password[0]) #ID是申请时所填写的手机号；Password为聚宽官网登录密码，新申请用户默认为手机号后6位
asset = u'000001.XSHE'
is_auth = is_auth()
# 查询当日剩余可调用条数
print(get_query_count())
print(is_auth)
codelist = ['000876']
code = codelist[0]
# 创建数据下载目录
index_path = mkdirs(os.path.join(mkdirs('datastore'), 'kline', 'index'))
print(index_path)
for year in range(2005, 2017):
start_date = '{}-01-01'
end_date = '{}-01-02'
for code in indexlist:
#his = jqapi.get_price(asset, start_date, end_date, frequency='60m')
data_day = QA.QA_fetch_stock_min_adv(codelist,
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 2 13:52:25 2020
@author: lenovo
"""
import pandas as pd
import numpy as np
from factor_analyzer import data, analyze, prepare, utils
from datetime import datetime
import statsmodels.api as sm
import jqdatasdk as jd
jd.auth('17854120489', 'shafajueduan28')
print(jd.get_query_count())
#%%
'''
获取数据，并处理一下
'''
factor_data = pd.read_csv('D:\\spyder_code\\jqfactor_analyzer01\\华泰单因子测试之估值类因子\\处理后因子pe.csv',\
index_col='Unnamed: 0')
factor_data.index = pd.to_datetime(factor_data.index)
#减少数据量
factor_data = factor_data.loc[datetime(2018, 1, 1):, ]
#%%
api = data.DataApi(fq='pre', industry='jq_l1', weight_method='ln_mktcap')
api.auth('17854120489', 'shafajueduan28')
pe_factor = analyze.FactorAnalyzer(factor_data,prices=api.get_prices,groupby=api.get_groupby,\
weights=api.get_weights,bins=5,periods=(1,10,20))
listDate = jq.get_trade_days(start_date=sdate, end_date=edate, count=None)
#filePath= path.join(cfg.jqDataPath, 'trade_days.csv')
listDate = pd.Series(listDate)
#pd.Series(listDate).to_csv(filePath,header= 0, index= 0)
listDate = listDate.map(lambda x: x.strftime("%Y%m%d"))
return list(listDate)
def stimeToStr(stime):
return stime.strftime('%Y-%m-%d %H:%M:%S')
if __name__ == '__main__':
t0 = ti.time()
jq.auth(cfg.jqUser, cfg.jqPwd)
jq.get_query_count()
localSQL = cfg.MSSQL(cfg.host, cfg.user, cfg.pwd, cfg.lcdb)
localSQL.Connect()
if not localSQL.isConnect:
print(localSQL.host + ' not connect')
else:
#sdate= '20200806'
#jqID= listJQID[0]
edate = cfg.getStrToday()
listDate = syncListTradeDay('20190101', edate)
for sdate in listDate:
print('deal date: ' + sdate)
for jqID in listJQID:
if len(localSQL.getDateData(jqID, int(sdate))) < 10:
#if len(localSQL.getDateData(jqID, int(sdate)))== 0:
# -*- coding: utf-8 -*-
"""
Created on Tue May 5 20:05:55 2020
@author: 17854
"""
import jqdatasdk as jd
jd.auth('17854120489','shafajueduan28')
count=jd.get_query_count()
print(count)
import pandas as pd
import matplotlib.pyplot as plt
'''
申万指数在2014-02-21有一次大改,删除了6个一级行业，并增加了11个一级行业。故：
date < 2014-02-21 申万一级行业有23个
date = 2014-02-21 申万一级行业有34个
date > 2014-02-21 申万一级行业有28个
#date='2014-02-20'，23个
code1 = jd.get_industries(name='sw_l1', date='2013-01-20')
#date='2014-02-21'有34个
code2 = jd.get_industries(name='sw_l1', date='2014-02-21')
#date='2014-02-22'有28个
code3 = jd.get_industries(name='sw_l1', date='2015-02-22')
'''
def get_sw1_valuation(start_date=None, end_date=None):
def remain_count(self):
count = jq.get_query_count()
return count
def jq_get_query_count():
if is_auth():
count = get_query_count()
return count['spare']
return 0
import jqdatasdk as jq
import datetime
from pymongo import MongoClient
jq.auth('15804268950','3834259hanK')
print(jq.get_query_count())
"""
**Table: share_flo**
---
##### stock_a_db['share_flo'].find_one()
```python
{'_id': ObjectId('5d5f4f3492600ddf9285d516'),
'index': '000001.XSHE',
'share_flow': 8258245083.0, # 流通股(单位 股)
'day': '2019-08-01'}
```
"""
client = MongoClient('127.0.0.1', 27017)
stock_a_db = client["stock_a"]
def get_real_share(stock):
# 10大流通股东加和
#print(stock._name)
merge_df = jq.finance.run_query(jq.query(jq.finance.STK_SHAREHOLDER_FLOATING_TOP10).filter(
jq.finance.STK_SHAREHOLDER_FLOATING_TOP10.code == stock._name).limit(10))
def __local_run():
return jq.get_query_count()
def _get_count(self):
self._count = jq.get_query_count() if jq.is_auth() else None
password = load_settings('jqdatasdk_pwd.pickle')
else:
password = input_settings(
pattern=password,
filename='jqdatasdk_pwd.pickle',
)
save_settings('jqdatasdk_pwd.pickle', password)
#ID是申请时所填写的手机号；Password为聚宽官网登录密码，新申请用户默认为手机号后6位
auth(password.username[0], password.password[0])
asset = u'000001.XSHE'
is_auth = is_auth()
# 查询当日剩余可调用条数
print(get_query_count())
print(is_auth)
# 创建数据下载目录
frequence = '60min'
stock_path = mkdirs(
os.path.join(mkdirs('datastore'), 'kline', 'stock', frequence))
print(stock_path)
frequence = '60min'
blockname = [
'MSCI中国', 'MSCI成份', 'MSCI概念', '三网融合', '上证180', '上证380', '沪深300',
'上证380', '深证300', '上证50', '上证电信', '电信等权', '上证100', '上证150', '沪深300',
'中证100', '中证500', '全指消费', '中小板指', '创业板指', '综企指数', '1000可选', '国证食品',
'深证可选', '深证消费', '深成消费', '中证酒', '中证白酒', '行业龙头', '白酒', '证券', '消费100',
'消费电子', '消费金融', '富时A50', '银行', '中小银行', '证券', '军工', '白酒', '啤酒', '医疗器械',
def valid(self):
# 如果该账号没有可用数据条数了就返回False
if jq.get_query_count()['spare'] > 0:
return True
else:
return False
# In[1]:
"""
使用JQData调取金融数据
"""
from datetime import datetime
import jqdatasdk as jq
jq.auth('username', 'password')
# In[2]:
count=jq.get_query_count()
print("当日可调用数据总条数为：",count['total'])
print("当日剩余可调用条数为：",count['spare'])
# In[3]:
# 获取平安银行股票数据，XSHE 深圳交易所 XSHG 上海交易所
jq.get_price("000001.XSHE", start_date="2021-01-01", end_date="2021-03-05")
# In[4]:
# valuation是内置市值数据对象(这里表示查询valuation下所有的属性)
def login():
# jqdatasdk.logout()
jqdatasdk.auth(jq_config.account, jq_config.password)
print(jqdatasdk.get_query_count())
import jqdatasdk as jq
account = "18620156503"
password = "******"
jq.auth(account, password)
print("login successed!")
remain_query = jq.get_query_count()
print("remain query times for jqdata: ", remain_query)
def p_perday_var(start_date, end_date):
tradeable_days = get_trade_days(start_date=start_date, end_date=end_date)
for trade_day in tradeable_days:
stock_code_list = jq.get_all_securities(date=trade_day)
result = jq.get_price(list(stock_code_list),
start_date=trade_day,
end_date=trade_day,
frequency='daily',
fields=['close'],
skip_paused=False,
fq='pre',
count=None,
panel=True,
fill_paused=True)
def login():
# jqdatasdk.logout()
jqdatasdk.auth(Config.jq_account, Config.jq_password)
# print(jqdatasdk.__version__)
print(jqdatasdk.get_query_count())
return msg
if util.isFutureTradingTime(nowTimeString=nowTimeString
) is False or util.isFutureCommonTradingTime(
security=security,
nowTimeString=nowTimeString) is False:
return msg
msg = loop(security=security,
frequency=frequency,
nowTimeString=nowTimeString,
msg=msg)
return msg
removeTreses()
jqdatasdk.auth('13824472562', '472562')
print(jqdatasdk.get_query_count())
for nowTimeString in timeArr:
msg = action(msg=msg, nowTimeString=nowTimeString)
#
# class MyStrategy(CtaTemplate):
# author = 'nobody'
#
# fast_window = 10
# slow_window = 20
#
# fast_ma0 = 0.0
# fast_ma1 = 0.0
#
# slow_ma0 = 0.0
# slow_ma1 = 0.0
from pyecharts.globals import CurrentConfig, NotebookType
CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB
# ID是申请时所填写的手机号；Password为聚宽官网登录密码，新申请用户默认为手机号后6位
jqdatasdk.auth('18605712083', 'sbbuzhidaomima')
# # In[]
# all_securities = jqdatasdk.get_all_securities(['stock', 'fund', 'index', 'futures', 'options', 'etf', 'lof',
# 'fja', 'fjb', 'open_fund', 'bond_fund', 'stock_fund', 'QDII_fund', 'money_market_fund', 'mixture_fund'])
# In[]
a = pd.read_csv("./data/csv/all_securities.csv")
jqdatasdk.get_query_count()
# In[]
all_index = a[a["type"] == "index"]
for security in all_index.iloc[:, 0]:
if os.path.exists(
"./data/csv/daily/{security}.csv".format(security=security)):
continue
security_bar = jqdatasdk.get_bars(
"000016.XSHG",
3650,
unit='1d',
fields=['date', 'open', 'close', 'high', 'low', 'volume', 'money'],
'avg': 0
}
for x in range(0, num):
if (dictValue['high'] < values['high'][x]):
dictValue['high'] = values['high'][x]
if (dictValue['low'] > values['low'][x]):
dictValue['low'] = values['low'][x]
dictValue['volume'] += values['volume'][x]
dictValue['money'] += values['money'][x]
dictValue['avg'] = dictValue['money'] / dictValue['volume']
return dictValue
#缓存全局对象
gol._Set_Value('quoteSource_API_JqData', Source_JQData_API()) #实例 行情对象
myDebug.Debug(jqdatasdk.get_query_count()) #打印当日可请求条数
#主启动程序
if __name__ == "__main__":
# 提取标的信息
pSource = gol._Get_Value('quoteSource_API_JqData', None)
print(pSource.getOptInfos())
print(pSource.getSecurities())
print(pSource.getSecurities('index'))
values = pSource.getPrice(security="600332.XSHG",
frequency='1m',
start_date='2020-02-05 09:00:00',
end_date='2020-02-05 15:00:00')
lstPrices = []
strList = ""
import pandas as pd
import numpy as np
import jqdatasdk as jq
from jqdatasdk import auth, get_query_count, get_price, opt, query, get_fundamentals, finance, get_trade_days, \
valuation, get_security_info, get_index_stocks, get_bars, bond
from tqdm import tqdm
from datetime import datetime, time, timedelta
import matplotlib.pyplot as plt
import os
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from basic_funcs.basic_function import *
import talib
auth('15951961478', '961478')
get_query_count()
import warnings
warnings.filterwarnings('ignore')
def get_short_ma_order(close, n1, n2, n3):
ma1 = close.rolling(n1).mean()
ma2 = close.rolling(n2).mean()
ma3 = close.rolling(n3).mean()
return (ma1 < ma2) & (ma2 < ma3) & (ma1 < ma3)
dateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d')
plt.rcParams['font.sans-serif'] = ['Songti SC']
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
def connect_jq(id, pw):
jq.auth(id, pw)
print(jq.get_query_count())