def day_year3():
"""
获取三年内的交易天数
:return: 三年内的交易天数
"""
# 判断到最近一个交易日
d_end = today_get()
while 1:
rs = bs.query_trade_dates(start_date=d_end, end_date=d_end)
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
if data_list[0][1] == '1':
break
else:
day_change = datetime.timedelta(days=1)
d_end = d_end - day_change
# 获取今天和一周前日期
d_start = year3_get(d_end)
# 获取交易日信息
rs = bs.query_trade_dates(start_date=d_start, end_date=d_end)
# print('query_trade_dates respond error_code:' + rs.error_code)
# print('query_trade_dates respond error_msg:' + rs.error_msg)
# 打印结果集
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
# 获取一会走交易日数
i = 0
number_trade_day = 0
for data in data_list:
if data_list[i][1] == '1':
number_trade_day += 1
i += 1
# 判断一周前是不是交易日
if data_list[0][1] == '0':
number_trade_day += 1
# 消除baostock交易日计算错误
number_trade_day += 3
return number_trade_day
def init_trade_date():
settings.init()
# 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
# 获取交易日信息 ####
rs = bs.query_trade_dates(start_date=settings.START_DATE, end_date=settings.END_DATE)
print('query_trade_dates respond error_code:' + rs.error_code)
print('query_trade_dates respond error_msg:' + rs.error_msg)
# 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
# 结果集输出到csv文件 ####
result.to_csv(settings.STOCKS_DATE, encoding="utf-8", index=False, header=True)
print(result)
# 登出系统 ####
bs.logout()
def get_his_trade_date(start_date="2018-01-27", end_date="2019-05-01"):
#### 登陆系统 ####
lg = bs.login(user_id="anonymous", password="******")
# 显示登陆返回信息
print('login respond error_code:'+lg.error_code)
print('login respond error_msg:'+lg.error_msg)
#### 获取交易日信息 ####
rs = bs.query_trade_dates(start_date=start_date, end_date=end_date)
print('query_trade_dates respond error_code:'+rs.error_code)
print('query_trade_dates respond error_msg:'+rs.error_msg)
#### 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
#### 登出系统 ####
bs.logout()
#### 结果集输出到csv文件 ####
#result.to_csv("/data/trade_datas.csv", encoding="gbk", index=False)
return result
def prepare_fetch_data():
# 获取当前年/季度
current_date = datetime.date.today().strftime('%Y-%m-%d')
current_year = datetime.date.today().year
current_month = datetime.date.today().month
current_quarter = (current_month - 1) // 3 + 1
hour = datetime.datetime.now().hour
# baostock data maybe not update right after the trade market close.
if hour < 20:
dd = datetime.date.today() + datetime.timedelta(-1)
current_date = dd.strftime('%Y-%m-%d')
# 计算最后一个交易日时间
start_date = "2006-01-01"
data_list = []
bs.login()
rs = bs.query_trade_dates(start_date=start_date, end_date=current_date)
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
bs.logout()
lasttradedate = current_date
for i in range(len(data_list) - 1, -1, -1):
if data_list[i][1] == '1':
lasttradedate = data_list[i][0]
break
print('start query day:' + start_date + ', last query data:' + lasttradedate)
bs.login()
stock_rs = bs.query_all_stock(day=lasttradedate)
stock_df = stock_rs.get_data()
bs.logout()
return stock_df, current_year, current_quarter
def query_trade_dates(start_date, end_date):
"""
交易日查询：query_trade_dates()
方法说明：查询股票交易日信息信息，可以通过参数设置获取起止年份数据，提供2014-2018年数据。
返回类型：pandas的DataFrame类型。
"""
#### 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
#print('login respond error_code:'+lg.error_code)
#print('login respond error_msg:'+lg.error_msg)
#### 获取交易日信息 ####
rs = bs.query_trade_dates(start_date=start_date, end_date=end_date)
#print('query_trade_dates respond error_code:'+rs.error_code)
#print('query_trade_dates respond error_msg:'+rs.error_msg)
#### 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
#### 结果集输出到csv文件 ####
result.to_csv(path + "trade_dates.csv", encoding="gbk", index=False)
#### 登出系统 ####
bs.logout()
return
def getTradeDays(start_date, end_date):
tradeDays = bs.query_trade_dates(start_date, end_date)
# print('query_trade_dates respond error_code:'+tradeDays.error_code)
# print('query_trade_dates respond error_msg:'+tradeDays.error_msg)
tradeDays_data_list = []
while (tradeDays.error_code == '0') & tradeDays.next():
tradeDays_data_list.append(tradeDays.get_row_data())
return pd.DataFrame(tradeDays_data_list, columns=tradeDays.fields)
def download_trade_cal():
filename = FILE_PATH + 'trade_cal.csv'
rs = bs.query_trade_dates()
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
result.to_csv(filename, encoding="gbk", index=False)
return result
def query_trade_dates(self, start_date = None, end_date = None):
'''
start_date：开始日期，为空时默认为2015-01-01。
end_date：结束日期，为空时默认为当前日期。
'''
rs = bs.query_trade_dates(start_date=start_date, end_date=end_date)
self.log('query_trade_dates respond error_msg:', rs)
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
return result
def record(self, entity, start, end, size, timestamps):
df = pd.DataFrame()
dates = query_trade_dates(start_date=start).get_data()
dates = dates[dates.is_trading_day == '1']
self.logger.info(f'add dates:{dates}')
df['timestamp'] = pd.to_datetime(dates['calendar_date'])
df['id'] = [to_time_str(date) for date in dates['calendar_date']]
df['entity_id'] = 'stock_sz_000001'
df_to_db(df=df,
data_schema=self.data_schema,
provider=self.provider,
force_update=self.force_update)
logout()
def get_trading_time(self, s_time, e_time):
rs = bs.query_trade_dates(start_date=s_time, end_date=e_time)
# print('query_trade_dates respond error_code:' + rs.error_code)
print('query_trade_dates respond error_msg:' + rs.error_msg)
#### 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
result.columns = ['date', 'is_trading_day']
# print(result)
return result
def collector(self) -> Iterable[pd.Timestamp]:
lg = bs.login()
if lg.error_code != "0":
raise ValueError(f"login respond error_msg: {lg.error_msg}")
rs = bs.query_trade_dates(
start_date=self._format_datetime(self.start_date), end_date=self._format_datetime(self.end_date)
)
if rs.error_code != "0":
raise ValueError(f"query_trade_dates respond error_msg: {rs.error_msg}")
data_list = []
while (rs.error_code == "0") & rs.next():
data_list.append(rs.get_row_data())
calendar = pd.DataFrame(data_list, columns=rs.fields)
calendar["is_trading_day"] = calendar["is_trading_day"].astype(int)
return pd.to_datetime(calendar[calendar["is_trading_day"] == 1]["calendar_date"]).to_list()
def tradedate():
'''
获取最近交易日
'''
import datetime
import time
yestoday30=(datetime.date.today() + datetime.timedelta(-10)).strftime("%Y-%m-%d")
today=time.strftime('%Y-%m-%d',time.localtime(time.time()))
rs = bs.query_trade_dates(start_date=yestoday30, end_date=today)
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
result=result.loc[result.is_trading_day=='1','calendar_date'].values[-2]
return result
def trading_orlast(date):
rs = bs.query_trade_dates(start_date=date, end_date=date)
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
if result.iloc[0]['is_trading_day'] == '1':
return date
else:
year = int(date[:4])
month = int(date[5:7])
day = int(date[8:])
dt = datetime.date(year, month, day)
dt = dt - datetime.timedelta(days=1)
dt = dt.strftime('%Y-%m-%d')
return trading_orlast(dt)
def get_trade_dates(start_date, end_date=today, is_trading_day=1):
# 获取交易日信息
rs = bs.query_trade_dates(start_date=start_date, end_date=end_date)
if rs.error_code != '0':
print('query_trade_dates respond error_code::' + rs.error_msg)
login()
return get_trade_dates(start_date, end_date)
# 打印结果集
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
result['is_trading_day'] = pd.to_numeric(result['is_trading_day'])
result = result[result['is_trading_day'] == is_trading_day]
return result
def get_trade_dates(start_date, end_date):
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
#### 获取交易日信息 ####
rs = bs.query_trade_dates(start_date, end_date)
print('query_trade_dates respond error_code:' + rs.error_code)
print('query_trade_dates respond error_msg:' + rs.error_msg)
#### 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
bs.logout()
return result
def future_calendar_collector(qlib_dir: [str, Path], freq: str = "day"):
"""get future calendar
Parameters
----------
qlib_dir: str or Path
qlib data directory
freq: str
value from ["day", "1min"], by default day
"""
qlib_dir = Path(qlib_dir).expanduser().resolve()
if not qlib_dir.exists():
raise FileNotFoundError(str(qlib_dir))
lg = bs.login()
if lg.error_code != "0":
logger.error(f"login error: {lg.error_msg}")
return
# read daily calendar
daily_calendar = read_calendar_from_qlib(qlib_dir)
end_year = pd.Timestamp.now().year
if daily_calendar.empty:
start_year = pd.Timestamp.now().year
else:
start_year = pd.Timestamp(daily_calendar.iloc[-1, 0]).year
rs = bs.query_trade_dates(start_date=pd.Timestamp(f"{start_year}-01-01"),
end_date=f"{end_year}-12-31")
data_list = []
while (rs.error_code == "0") & rs.next():
_row_data = rs.get_row_data()
if int(_row_data[1]) == 1:
data_list.append(_row_data[0])
data_list = sorted(data_list)
date_list = generate_qlib_calendar(data_list, freq=freq)
date_list = sorted(
set(daily_calendar.loc[:, 0].values.tolist() + date_list))
write_calendar_to_qlib(qlib_dir, date_list, freq=freq)
bs.logout()
logger.info(
f"get trading dates success: {start_year}-01-01 to {end_year}-12-31")
def latestTradeDate():
customLogin()
# set_trace()
rs = bs.query_trade_dates(start_date=date.today().replace(day=1), end_date = date.today())
if rs.error_code != '0':
raise RuntimeError("交易日api调用失败了:" + rs.error_code)
tradeDates = []
while (rs.error_code == '0') & rs.next():
row = rs.get_row_data()
if row[1] == "1":
tradeDates.append(row[0])
# set_trace()
if len(tradeDates) == 0:
raise RuntimeError("取不到最新的交易日")
now = datetime.now()
set_trace()
tradeDatesCount = len(tradeDates)
## 因为baoStock的日K数据更新时间是 17：30， 所以如果在18点前启动，可能取不到当天数据，交易日向前推一天
if (now.hour < 18 and tradeDatesCount > 1):
return tradeDates[tradeDatesCount - 2]
else:
return tradeDates[len(tradeDates) - 1]
def getLatestTradeDay(inDate):
tmp = inDate.split('-')
if tmp[1] == '01':
date1 = str(int(tmp[0]) - 1) + '-12-' + tmp[2]
else:
month = int(tmp[1]) - 1
if month < 10:
month = '0' + str(month)
else:
month = str(month)
date1 = tmp[0] + '-' + month + '-' + tmp[2]
rs = bs.query_trade_dates(start_date=date1, end_date=inDate)
if rs.error_code != '0':
print('从指定日期返回最近一个交易日: %s' % rs.error_msg)
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
data_list.pop()
data_list.reverse()
for i in data_list:
if i[1] == '1':
return i[0]
def query_trade_dates(start_date=None, end_date=None):
"""
交易日查询
方法说明：通过API接口获取股票交易日信息，可以通过参数设置获取起止年份数据，提供上交所1990-今年数据。 返回类型：pandas的DataFrame类型。
:param start_date:开始日期，为空时默认为2015-01-01。
:param end_date:结束日期，为空时默认为当前日期。
"""
lg = bs.login()
if lg.error_code != '0':
logger.error('login respond error_msg:' + lg.error_msg)
rs = bs.query_trade_dates(start_date=start_date, end_date=end_date)
if rs.error_code != '0':
logger.error('query_trade_dates respond error_msg:' +
rs.error_msg)
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
bs.logout()
return result
def init_trade_date():
# 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
# 获取交易日信息 ####
st = ConfigUtils.get_stock("START_DATE")
et = ConfigUtils.get_stock("END_DATE")
print(st, et)
rs = bs.query_trade_dates(start_date=st, end_date=et)
print('query_trade_dates respond error_code:' + rs.error_code)
print('query_trade_dates respond error_msg:' + rs.error_msg)
# 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
result.to_csv(ConfigUtils.get_stock("STOCKS_DATE"), index=False)
# 结果集输出到csv文件 ####import ujson
bs.logout()
import baostock as bs
import pandas as pd
def today():
import datetime
print (datetime.datetime.now().strftime("%Y-%m-%d"))
#### 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:'+lg.error_code)
print('login respond error_msg:'+lg.error_msg)
#### 获取交易日信息 ####
rs = bs.query_trade_dates(start_date="2000-01-01", end_date=today())
print('query_trade_dates respond error_code:'+rs.error_code)
print('query_trade_dates respond error_msg:'+rs.error_msg)
#### 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
result.to_csv("/home/toby/data/datasource/baostock/trade_dates.csv", encoding="gbk", index=False )
# print(result)
#### 登出系统 ####
bs.logout()
if __name__ == '__main__':
createDB.createDB()
current_date = datetime.date.today().strftime('%Y-%m-%d')
hour = datetime.datetime.now().hour
#baostock data maybe not update right after the trade market close.
if hour < 20:
dd = datetime.date.today() + datetime.timedelta(-1)
current_date = dd.strftime('%Y-%m-%d')
start_date = "2017-01-01"
data_list = []
bs.login()
rs = bs.query_trade_dates(start_date=start_date, end_date=current_date)
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
bs.logout()
lastTradeDate = current_date
for i in range(len(data_list) - 1, -1, -1):
if data_list[i][1] == '1':
lastTradeDate = data_list[i][0]
break
if start_date <= lastTradeDate:
print("start to refresh all stock")
parse_stock_data.refresh_all_stock(lastTradeDate)
print("start to refresh all stock day K")
parse_stock_data.refresh_all_stock_day_k(start_date, lastTradeDate)
print("start to calculate sh")
def _bao_get_trade_days(start_date=None, end_date=None):
k_rs = bs.query_trade_dates(start_date=start_date, end_date=end_date)
return k_rs.get_data()
else:
return False
# 返回每年交易日数据框
lg = bs.login()
trading_log = pd.DataFrame()
for year in range(2010, 2021):
date_ori1 = '{}-01-01'.format(year)
date1 = trading_ornext(date_ori1)
if year != 2020:
date_ori2 = '{}-12-31'.format(year)
date2 = trading_orlast(date_ori2)
else:
date2 = '2020-03-31'
rs2 = bs.query_trade_dates(start_date=date1, end_date=date2)
data_list = []
while (rs2.error_code == '0') & rs2.next():
data_list.append(rs2.get_row_data())
result2 = pd.DataFrame(data_list, columns=rs2.fields)
trading_date = result2[result2['is_trading_day'] == '1']['calendar_date']
df = trading_date.to_frame()
trading_log = pd.concat([trading_log, df], axis=0)
trading_log = trading_log.reset_index(drop=True)
trading_log['capital'] = None
#获取大盘数据
from openpyxl import load_workbook
def trade_dates(self, start_date, end_date):
self.getStockInstance()
rs = bs.query_trade_dates(start_date, end_date)
self._test_rs(rs)
return rs
return lszhibiao
lg = bs.login()
if datetime.datetime.now().hour >= 17:
nowday = datetime.datetime.now().date()
else:
nowday = datetime.datetime.now().date() - datetime.timedelta(days=1)
newday = models.Jiaoyiday.objects.last().date
print('最新数据为', newday, '今天是', nowday, '准备开始更新')
if newday >= nowday:
togmail.send('*****@*****.**', '股票更新报错',
f'最新日期是{newday}，现在需要更新的日期是{nowday}，不需要更新，直接退出')
raise Exception(f'最新日期是{newday}，现在需要更新的日期是{nowday}，不需要更新，直接退出')
rs = bs.query_trade_dates(start_date=str(newday), end_date=str(nowday))
date_list = []
while (rs.error_code == '0') & rs.next():
date_list.append(rs.get_row_data())
for daylist in date_list[1:]:
if daylist[1] == '1':
newday = daylist[0]
print(newday, '是交易日，开始更新。。。')
zhibiaodf = zhibiao(newday)
c = models.Jiaoyiday(date=newday, isover=False)
c.save()
n = 0
gupiaolist = models.Gupiaolist.objects.all()
tiaoguolist = []
for ii in gupiaolist:
# -*- coding: utf-8 -*-
# @File : get_trading_calender.py
# @Author: Lizi
# @Date : 2021/4/13
import baostock as bs
import pandas as pd
#### 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
#### 获取交易日信息 ####
rs = bs.query_trade_dates(start_date="2020-01-01", end_date="2022-01-01")
print('query_trade_dates respond error_code:' + rs.error_code)
print('query_trade_dates respond error_msg:' + rs.error_msg)
#### 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
#### 结果集输出到csv文件 ####
result.to_csv("../trade_datas.csv", encoding="gbk", index=False)
print(result)
#### 登出系统 ####
def computeAllRSIDataIntegrate(period, specifiedDateStr, includeST):
BaoStockUtil.customLogin()
specifiedDate = datetime.datetime.strptime(specifiedDateStr, "%Y-%m-%d")
today = datetime.date.today()
#如果把时间设成未来，自动调成今天
if specifiedDate > datetime.datetime.today():
specifiedDate = datetime.date.today()
#避免跨年问题，直接从去年开始取
startDate = specifiedDate - datetime.timedelta(days=365)
#取交易日列表，用作倒推周期使用
rs = bs.query_trade_dates(start_date=datetime.datetime.strftime(
startDate, "%Y-%m-%d"),
end_date=specifiedDate)
BaoStockUtil.customLogout()
if rs.error_code != '0':
raise RuntimeError("交易日api调用失败了:" + rs.error_code)
tradeDates = []
while (rs.error_code == '0') & rs.next():
row = rs.get_row_data()
if row[1] == "1":
tradeDates.append(row[0])
if len(tradeDates) == 0:
raise RuntimeError("取不到最新的交易日")
#若期望计算的日期比库里RSI最新日期还晚，数据不全待补齐
rsiLatestDate = findLatestRSIDate(period)
rsiLatestDateStr = datetime.datetime.strftime(rsiLatestDate, "%Y-%m-%d")
if rsiLatestDate < specifiedDate:
raise RuntimeError(specifiedDateStr + " 的 " + period +
" RSI的数据不存在，待补齐数据")
#找到指定日期以及rsi存量数据最近日期在交易日周期的序号
specifiedDateIndex = tradeDates.index(specifiedDateStr)
if specifiedDateIndex == -1:
raise RuntimeError(specifiedDateStr + " 可能不是交易日")
daysBefore = computeRSIDataStartTradeDateRange(period, specifiedDateStr)
startDateIndex = specifiedDateIndex - daysBefore
#起始日期index负数，说明rsi数据不够
if startDateIndex < 0:
raise RuntimeError(period + " rsi数据不够")
startDateStr = tradeDates[startDateIndex]
print("compute rsi tradeDates from ", startDateStr, "to", specifiedDateStr)
processCount = 0
failCount = 0
startDateIndex = -1
dictStocks = KlineService.allStocks()
klineDataFrame = KlineService.readAllStockKline(period, specifiedDateStr,
specifiedDateStr)
klineDataFrame = klineDataFrame.set_index("code")
klineDict = klineDataFrame.to_dict('index')
jobTotal = len(dictStocks)
rsiValueArrs = []
for i in range(0, 6):
rsiValueArrs.append([])
for key, stock in dictStocks.items():
processCount = processCount + 1
#指数没有分钟线，跳过指数的RSI分钟线计算
if period.endswith("m") and stock.stockType != 1:
continue
#如果不计算ST，跳过
if not includeST and stock["isST"]:
continue
#退市股就不要算了
if "退" in stock["name"]:
continue
#科创板不达门槛没法买，不看
if key.startswith("sh.68"):
continue
try:
rsiDF = readRSI(period, key, startDateStr, specifiedDateStr)
rsiCount = len(rsiDF)
if rsiCount < INTEGRATE_CALC_RANGE:
raise RuntimeError("积分计算节点不够")
rsiValueArrs[0].append(key)
rsiValueArrs[1].append(stock["name"])
rsiValueArrs[2].append(klineDict[key]["closePrice"])
#取最近的数据用于计算积分
rsiValueArrs[3].append(
rsiDF["rsi_6"][rsiCount - INTEGRATE_CALC_RANGE:rsiCount])
rsiValueArrs[4].append(
rsiDF["rsi_12"][rsiCount - INTEGRATE_CALC_RANGE:rsiCount])
rsiValueArrs[5].append(
rsiDF["rsi_24"][rsiCount - INTEGRATE_CALC_RANGE:rsiCount])
except BaseException as e:
failCount = failCount + 1
print("compute rsi integrate " + key + " error:" + str(e))
if processCount % 100 == 0 and processCount > 0:
print("compute rsi integrate process:", processCount, " of ",
jobTotal, " failed:", failCount)
rsi6Arr = np.array(rsiValueArrs[3]).reshape(-1, INTEGRATE_CALC_RANGE)
rsi6InteArr = integrateValues(rsi6Arr)
rsi12Arr = np.array(rsiValueArrs[4]).reshape(-1, INTEGRATE_CALC_RANGE)
rsi12InteArr = integrateValues(rsi12Arr)
rsi24Arr = np.array(rsiValueArrs[5]).reshape(-1, INTEGRATE_CALC_RANGE)
rsi24InteArr = integrateValues(rsi24Arr)
rsiInteDF = pd.DataFrame()
rsiInteDF["code"] = rsiValueArrs[0]
rsiInteDF["name"] = rsiValueArrs[1]
rsiInteDF["closePrice"] = rsiValueArrs[2]
rsiInteDF["rsi_inte_6"] = rsi6InteArr
rsiInteDF["rsi_inte_12"] = rsi12InteArr
rsiInteDF["rsi_inte_24"] = rsi24InteArr
return rsiInteDF
import sys
import baostock as bs
import pandas as pd
lg = bs.login()
rs = bs.query_trade_dates(start_date=sys.argv[1], end_date=sys.argv[2])
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
print(result.to_json(orient='records'))
bs.logout()
import baostock as bs
import pandas as pd
#### 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
#### 获取交易日信息 ####
rs = bs.query_trade_dates(start_date="2017-01-01", end_date="2017-06-30")
print('query_trade_dates respond error_code:' + rs.error_code)
print('query_trade_dates respond error_msg:' + rs.error_msg)
#### 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
#### 结果集输出到csv文件 ####
result.to_csv("D:\\trade_datas.csv", encoding="gbk", index=False)
print(result)
#### 登出系统 ####
bs.logout()
