def QA_fetch_get_stock_day(name, startDate, endDate, if_fq='01'):
try:
from WindPy import w
except:
QA_util_log_info('No WindPY Module!')
w.start()
if(QA_util_date_valid(endDate) == False):
QA_util_log_info("wrong date")
else:
if if_fq in ['00', 'bfq']:
data = w.wsd(name, "sec_name,pre_close,open,high,low,close,volume",
startDate, endDate)
elif if_fq in ['01', 'qfq']:
data = w.wsd(name, "sec_name,pre_close,open,high,low,close,volume",
startDate, endDate, "PriceAdj=F")
elif if_fq in ['02', 'hfq']:
data = w.wsd(name, "sec_name,pre_close,open,high,low,close,volume",
startDate, endDate, "PriceAdj=B")
else:
QA_util_log_info('wrong fq factor! using qfq')
data = w.wsd(name, "sec_name,pre_close,open,high,low,close,volume",
startDate, endDate, "PriceAdj=B")
if (data.ErrorCode == 0):
QA_util_log_info("Connent to Wind successfully")
return pd.DataFrame(np.asarray(data.Data).T, columns=data.Fields, index=data.Times)
def QA_fetch_get_stock_financial(name, startDate, endDate):
try:
from WindPy import w
except:
QA_util_log_info('No WindPY Module!')
w.start()
pass
def GetDatas(a_ifId, a_begin, a_end, a_max):
client = MongoClient('mongodb://localhost:27017/')
col = client.Test[a_ifId]
colRange = client.Test["range_%s" % a_ifId]
if not col.count == 0:
#检测是否给定的时间范围
#否则重置col,colRange
if colRange.count() == 0:
col.drop()
else:
range = [ x[u'_id'] for x in colRange.find()]
if not (range[0] == a_ifId and range[1] == a_begin and range[2] == a_end):
colRange.drop()
col.drop()
if col.count() == 0:
w.start(waitTime=10)
wdatas = w.wsi(a_ifId,"open,close",a_begin,a_end, 'Fill=Previous')
datasOpen = wdatas.Data[0]
datasClose = wdatas.Data[1]
docs =[ {'_id':x[0],'open':x[1],'close':x[2] } for x in zip(wdatas.Times, datasOpen, datasClose) ]
col.insert(docs)
colRange.insert( [ {'_id':a_ifId} , {'_id':a_begin} , {'_id':a_end}] )
else:
datasClose = [ x[u'close'] for x in col.find()]
if a_max == -1:
return datasClose
return datasClose[:a_max]
def QA_fetch_get_stock_list(date):
try:
from WindPy import w
except:
QA_util_log_info('No WindPY Module!')
w.start()
if(QA_util_date_valid(date) == False):
QA_util_log_info("wrong date")
else:
awgs = 'date=' + date + ';sectorid=a001010100000000'
data = w.wset("sectorconstituent", awgs)
return pd.DataFrame(np.asarray(data.Data).T, columns=data.Fields, index=data.Times)
def QA_fetch_get_stock_list_special(date, id):
try:
from WindPy import w
except:
QA_util_log_info('No WindPY Module!')
w.start()
if(QA_util_date_valid(date) == False):
QA_util_log_info("wrong date")
else:
if id in ['big', 'small', 'cixin', 'yujing', 'rzrq', 'rq', 'yj', 'st', 'sst']:
awgs = 'date=' + date + ';sectorid=' + \
data_list.wind_stock_list_special_id[id]
data = w.wset("sectorconstituent", awgs)
return pd.DataFrame(np.asarray(data.Data).T, columns=data.Fields, index=data.Times)
def start(self):
'''the main function to perform all operations'''
print('开始程序')
w.start(60)
#if cannot connect to wind, just stop
if not w.isconnected():
print('无法连接万德')
print('程序结束')
w.stop()
return
#otherwise
print('万德已连接')
print('开始处理数据')
#期货行情
today = datetime.now()
print('今天是', today.strftime('%Y-%m-%d'))
loop = asyncio.get_event_loop()
exchanges = self.config['futures']['exchanges']
for eid in dict.keys(exchanges):
instruments = exchanges[eid]
instrument = None
for instrument in instruments:
print('处理品种 ' + instrument + ' 交易所 ' + eid)
items = []
i = 0
for i in range(today.month, today.month + 13, 1):
iid = instrument
if i > 12:
iid = iid + self.fix_instrument(str(today.year + 1)[2:] + \
pad_month(i % 12), eid)
else:
iid = iid + self.fix_instrument(str(today.year)[2:] + \
pad_month(i), eid)
iid = iid + '.' + eid
items.append(iid)
tasks = []
for item in items:
tasks.append(asyncio.ensure_future(self.process_item(w, item)))
try:
loop.run_until_complete(asyncio.wait(tasks))
except:
print('循环产生异常：处理品种' + instrument + '期货价格数据')
finally:
print('完成品种 ' + instrument + ' 交易所 ' + eid)
loop.close()
print('程序结束')
w.stop()
def __init__(self):
try:
#wind不会重复启动，也无需特别关闭
w.start()
if w.isconnected:
pass
else:
print('打开Wind失败')
except:
print('打开Wind时异常')
#启动交易帐户
if VendorWind._tradeaccid[0]==0:
self.tradelogon()
def QA_fetch_get_stock_shape(name, startDate, endDate):
try:
from WindPy import w
except:
QA_util_log_info('No WindPY Module!')
w.start()
if(QA_util_date_valid(endDate) == False):
QA_util_log_info("wrong date")
else:
# history_low近期创历史新低,stage_high近期创阶段新高,history_high近期创历史新高,stage_low近期创阶段新高,up_days连涨天数,down_days连跌天数,breakout_ma向上有效突破均线,breakdown_ma向下有效突破均线,bull_bear_ma均线多空排列看涨看跌
data = w.wsd(name, "history_low,stage_high,history_high,stage_low,up_days,down_days,breakout_ma,breakdown_ma,bull_bear_ma",
startDate, endDate, "n=3;m=60;meanLine=60;N1=5;N2=10;N3=20;N4=30;upOrLow=1")
if (data.ErrorCode == 0):
QA_util_log_info("Connent to Wind successfully")
return pd.DataFrame(np.asarray(data.Data).T, columns=data.Fields, index=data.Times)
def QA_fetch_get_stock_xueqiu(name, startDate, endDate):
try:
from WindPy import w
except:
QA_util_log_info('No WindPY Module!')
w.start()
if(QA_util_date_valid(endDate) == False):
QA_util_log_info("wrong date")
else:
data = w.wsd(name, "xq_accmfocus,xq_accmcomments,xq_accmshares,\
xq_focusadded,xq_commentsadded,xq_sharesadded,\
xq_WOW_focus,xq_WOW_comments,xq_WOW_shares", startDate, endDate, "")
if (data.ErrorCode == 0):
QA_util_log_info("Connent to Wind successfully")
return pd.DataFrame(np.asarray(data.Data).T, columns=data.Fields, index=data.Times)
def QA_fetch_get_stock_day_simple(name, startDate, endDate):
try:
from WindPy import w
except:
QA_util_log_info('No WindPY Module!')
w.start()
if(QA_util_date_valid(endDate) == False):
QA_util_log_info("wrong date")
else:
data = w.wsd(name, "sec_name,preclose,open,high,low,close,volume",
startDate, endDate, "Fill=Previous;PriceAdj=F")
#data=w.wsd("000002.SZ", "open,high,low,close,volume", "2017-03-03", "2017-04-01", "PriceAdj=B")
QA_util_log_info(data.ErrorCode)
if (data.ErrorCode == 0):
QA_util_log_info("Connent to Wind successfully")
return data.Data
def QA_fetch_get_stock_risk(name, startDate, endDate):
try:
from WindPy import w
except:
QA_util_log_info('No WindPY Module!')
w.start()
if(QA_util_date_valid(endDate) == False):
QA_util_log_info("wrong date")
else:
data = w.wsd(name, "annualyeild_100w,annualyeild_24m,annualyeild_60m,\
annualstdevr_100w,annualstdevr_24m,annualstdevr_60m,beta_100w,\
beta_24m,beta_60m,avgreturn,avgreturny,stdevry,stdcof,\
risk_nonsysrisk1,r2,alpha2,beta,sharpe,treynor,jensen,jenseny,betadf",
startDate, endDate, "period=2;returnType=1;index=000001.SH;yield=1")
if (data.ErrorCode == 0):
QA_util_log_info("Connent to Wind successfully")
return pd.DataFrame(np.asarray(data.Data).T, columns=data.Fields, index=data.Times)
def main():
w.start()
func1()
func2()
func3()
func4()
func5()
func6()
func7()
func8()
func9()
func10()
func11()
#a=w.wsd('150074.SZ', 'nav','2015-12-11','2015-12-22')
#print a
#a=w.wsd('502022.SH', 'nav','2015-12-11','2015-12-22')
#print a
'''
def QA_fetch_get_trade_date(endDate, exchange):
try:
from WindPy import w
except:
QA_util_log_info('No WindPY Module!')
w.start()
supportExchanges = ["SSE", "SZSE", "CFFEX", "SHFE", "DCE", "CZCE"]
if (exchange in supportExchanges):
#"SSE","SZSE","CFFEX","SHFE","DCE","CZCE"
# 上海股票交易所,深圳股票交易所,中国金融期货交易所,上海期货交易所,大连商品交易所,郑州期货交易所
exchanges = "TradingCalendar=" + exchange
data = w.tdays("1990-01-01", endDate, exchanges)
# QA_util_log_info(data.Data)
dates = pd.DataFrame(np.asarray(data.Data).T,
columns=data.Fields, index=data.Times)
else:
QA_util_log_info("exchange name problem")
return dates
def QA_fetch_get_stock_info(name, startDate, endDate):
try:
from WindPy import w
except:
QA_util_log_info('No WindPY Module!')
w.start()
# get the all stock list on the endDate
# judge the vaild date
if(QA_util_date_valid(endDate) is False):
QA_util_log_info("wrong date")
else:
# tempStr='date='+endDate+";sectorid=a001010100000000"
# data=w.wset("sectorconstituent",tempStr)
data = w.wsd(name, "sec_name,sec_englishname,ipo_date,exch_city,mkt,\
sec_status,delist_date,issuecurrencycode,curr,RO,parvalue,\
lotsize,tunit,exch_eng,country,concept,marginornot,SHSC,\
parallelcode,sec_type,backdoor,backdoordate,windtype",
startDate, endDate)
# QA_util_log_info(data)
if (data.ErrorCode != 0):
QA_util_log_info("Connent to Wind successfully")
return data.Data
def QA_fetch_get_stock_indicator(name, startDate, endDate):
try:
from WindPy import w
except:
QA_util_log_info('No WindPY Module!')
w.start()
if(QA_util_date_valid(endDate) == False):
QA_util_log_info("wrong date")
else:
# ADTM动态买卖气指标,ATR真实波幅,BBI多空指数,BBIBOLL多空布林线,BIAS乖离率,BOLL布林带,CCI顺势指标,CDP逆势操作,DMA平均线差,
# DMI趋向标准,DPO区间震荡线,ENV,EXPMA指数平滑移动平均,KDJ随机指标,slowKD慢速kd,MA简单移动平均,MACD指数平滑移动平均,MIKE麦克指数,
# MTM动力指标,PRICEOSC价格震荡指标,PVT量价趋势指标,RC变化率指数,ROC变动速率,RSI相对强弱指标,SAR抛物转向,SI摆动指标,SOBV能量潮,
# SRMI MI修正指标,STD 标准差,TAPI 加权指数成交值,TRIX 三重指数平滑平均,VHF纵横指标,VMA量简单移动平均,VMACD量指数平滑移动平均,
# VOSC成交量震荡,WVAD威廉变异离散量,vol_ratio量比
data = w.wsd(name, "ADTM,ATR,BBI,BBIBOLL,BIAS,BOLL,CCI,CDP,\
DMA,DMI,DPO,ENV,EXPMA,KDJ,slowKD,MA,MACD,\
MIKE,MTM,PRICEOSC,PVT,RC,ROC,RSI,SAR,SI,\
SOBV,SRMI,STD,TAPI,TRIX,VHF,VMA,VMACD,VOSC,\
WVAD,vol_ratio", startDate, endDate,
"ADTM_N1=23;ADTM_N2=8;ADTM_IO=1;ATR_N=14;ATR_IO=1;\
BBI_N1=3;BBI_N2=6;BBI_N3=12;BBI_N4=24;BBIBOLL_N=10;\
BBIBOLL_Width=3;BBIBOLL_IO=1;BIAS_N=12;BOLL_N=26;\
BOLL_Width=2;BOLL_IO=1;CCI_N=14;CDP_IO=1;DMA_S=10;\
DMA_L=50;DMA_N=10;DMA_IO=1;DMI_N=14;DMI_N1=6;\
DMI_IO=1;DPO_N=20;DPO_M=6;DPO_IO=1;ENV_N=14;ENV_IO=1;\
EXPMA_N=12;KDJ_N=9;KDJ_M1=3;KDJ_M2=3;KDJ_IO=1;SlowKD_N1=9;\
SlowKD_N2=3;SlowKD_N3=3;SlowKD_N4=5;SlowKD_IO=1;MA_N=5;\
MACD_L=26;MACD_S=12;MACD_N=9;MACD_IO=1;MIKE_N=12;MIKE_IO=1;\
MTM_interDay=6;MTM_N=6;MTM_IO=1;PRICEOSC_L=26;PRICEOSC_S=12;\
RC_N=50;ROC_interDay=12;ROC_N=6;ROC_IO=1;RSI_N=6;SAR_N=4;\
SAR_SP=2;SAR_MP=20;SRMI_N=9;STD_N=26;TAPI_N=6;TAPI_IO=1;\
TRIX_N1=12;TRIX_N2=20;TRIX_IO=1;VHF_N=28;VMA_N=5;VMACD_S=12;\
VMACD_L=26;VMACD_N=9;VMACD_IO=1;VOSC_S=12;VOSC_L=26;WVAD_N1=24;\
WVAD_N2=6;WVAD_IO=1;VolumeRatio_N=5")
if (data.ErrorCode == 0):
QA_util_log_info("Connent to Wind successfully")
return pd.DataFrame(np.asarray(data.Data).T, columns=data.Fields, index=data.Times)
""" Created on Wed Jan 2 10:18:35 2019 @author: Administrator """ # -*- coding:utf-8 -*- #Python 3.5.0 from WindPy import w import pandas as pd import datetime import matplotlib.pyplot as plt import numpy as np import csv w.start(); future_name = 'T1812' start_date = '2018-08-11' end_date = "2018-11-11" #csvfile = open(future_name + '_day.csv', 'w') #writer = csv.writer(csvfile) # 取数据的命令如何写可以用命令生成器来辅助完成 #wsd_data=w.wsi(future_name+".CFE", "open,high,low,close,chg,volume,amt,oi", "2018-07-11", end_date, "Fill=Previous","BarSize=5") wsd_data=w.wsd(future_name+".CFE", "open,high,low,close,chg", "2018-07-11", end_date, "Fill=Previous")
def post(self):
"""
json str:{"codes": "600008.SH, "fields": "ask1,bid1,asize1,bsize1,volume,amt,pre_close,open,high,low,last",
"begin_time": "2017-01-04", "end_time": "2017-02-28", "options": ""}
:return: 返回万得返回数据dict
"""
args = receive_wst_parser.parse_args()
logger.info('/wst/ args:%s', args)
# codes = args['codes']
# fields = args['fields']
# begin_time = args['begin_time']
# end_time = args['end_time']
# options = args['options']
if args['options'] == "":
args['options'] = None
if not w.isconnected():
w.start()
ret_data = None
for nth in range(2):
ret_data = w.wst(**args)
error_code = ret_data.ErrorCode
if error_code != 0:
if nth == 0 and error_code == -40521010:
w.stop()
w.start()
logger.warning('尝试重新登陆成功，再次调用函数')
continue
msg = ERROR_CODE_MSG_DIC.setdefault(error_code, "")
logger.error('wst(%s) ErrorCode=%d %s' %
(args, error_code, msg))
raise RequestError(msg, None, error_code)
else:
break
else:
if ret_data is None:
msg = 'wst(%s) ret_data is None' % args
logger.error(msg)
raise RequestError(msg, None, 0)
# 将 Data数据中所有 datetime date 类型的数据转换为 string
data_len = len(ret_data.Data)
for n_data in range(data_len):
data = ret_data.Data[n_data]
data_len2 = len(data)
if data_len2 > 0:
# 取出第一个部位None的数据
item_check = None
for item_check in data:
if item_check is not None:
break
# 进行类型检查，如果发现是 datetime, date 类型之一，则进行类型转换
if item_check is not None and type(item_check) in (datetime,
date):
ret_data.Data[n_data] = [
format_2_datetime_str(dt) for dt in data
]
logger.info('%d column["%s"] date to str', n_data,
ret_data.Fields[n_data])
# 组成 DataFrame
ret_df = pd.DataFrame(
ret_data.Data,
index=ret_data.Fields,
columns=[format_2_datetime_str(dt) for dt in ret_data.Times])
# print(ret_df)
ret_dic = ret_df.to_dict()
# print('ret_dic:\n', ret_dic)
return ret_dic
######## coding=gbk
__author__ = 'henry'
import site
import sys
reload(sys)
sys.setdefaultencoding( "utf-8" )
from WindPy import w
from Utility import str1D
from Utility import str2D
if (w.isconnected() != True):
w.start(); #pwd:607387
stock_code = sys.argv[1]
start_date = sys.argv[2]
end_date = sys.argv[3]
# res= w.wset("TradeSuspend","startdate=20150122;enddate=20150122")
fields = "sec_name,trade_status,susp_reason"
res = w.wsd(stock_code, fields, start_date, end_date, "Fill=Previous")
#1.List getStocks
print "WindStockReuslt:"
print res
from futureimporter import FutureImporter
# from spotimporter import SpotImporter
# from indeximporter import IndexImporter
# from volumeimporter import VolumeImporter
if __name__ == '__main__':
'''main function'''
print('开始程序')
config = None
with open('config.json', encoding='utf-8') as config_file:
config = json.load(config_file, encoding='utf-8')
if config is None:
print('无法读取配置文件config.json')
#move on to perform the actual job
w.start(60)
if not w.isconnected():
print('无法连接万德')
else:
print('万德已连接')
print('开始处理数据')
#grab the main loop
loop = asyncio.get_event_loop()
#future quotes upload
future_importer = FutureImporter(loop, w, config)
future_importer.run()
# spot_importer = SpotImporter(loop, w, config)
# spot_importer.run()
def __init__(self):
w.start()
def wrap(data: pd.DataFrame, group_target: str, **kwargs):
w.start()
data = data.groupby(by=group_target, group_keys=False).apply(
partial(fun, **kwargs)).reset_index(drop=True)
w.close()
return data
def __init__(self):
w.start()
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 25 14:17:37 2020
@author: 029046
"""
from WindPy import w
from DiscreteKalmanFilter import *
from Functions import *
from DynamicFactorModel import *
w.start(waitTime=60)
w.isconnected()
plt.rcParams['figure.figsize'] = (9, 6)
data = vintage_PPI
data_sm = DataInterpolation(data, 0, len(data.index),
'linear').dropna(axis=0, how='any')
data_dev = data_sm - data_sm.mean()
dfm_em = DFM_EMalgo(data_sm, 10, 2, 3)
#error = pd.DataFrame(data=rand_Matrix(len(data.index), 2),columns=['shock1','shock2'],index=data.index)
error = pd.DataFrame(data=np.zeros(shape=(len(data.index), 2)),
columns=['shock1', 'shock2'],
index=data.index)
kf = KalmanFilter(Z=data.iloc[6:] - data_sm.mean(),
U=error,
A=dfm_em.A,
B=dfm_em.B,
def calRatio(index1, index2, startDate, endDate):
w.start()
#wsddata1 = w.wsd("000905.SH,000016.SH", "close", "2007-01-01", "2016-06-21", "")
wsddata1 = wind_data = w.wsd("204001.SH", "close", "ED-3M", endDate, "")
ret = getReturn(wsddata1.Times, wsddata1.Data[0])
def updatePal(palPath=None):
start = time.time()
md.init('18201141877', 'Wqxl7309')
if not w.isconnected():
w.start()
palPath = r'E:\bqfcts\bqfcts\data\Paltest' if palPath is None else palPath
tempFilePath = os.path.join(palPath,'temp_files')
if not os.path.exists(tempFilePath):
os.mkdir(tempFilePath)
matName = 'data_20150701_now.mat'
savedPal = h5py.File(os.path.join(palPath,matName))
# print(read_cell(savedPal,'sec_names'))
nextTrd = dt.datetime.strptime(str(int(savedPal['nexttrd'][0][0])),'%Y%m%d')
nextTrdStr = nextTrd.strftime('%Y-%m-%d')
updateTime = dt.datetime(nextTrd.year,nextTrd.month,nextTrd.day,15,30,0)
if updateTime > dt.datetime.now():
print('not update time yet')
return
else:
availableDateStr = md.get_last_dailybars('SHSE.000001')[0].strtime[:10]
if int(availableDateStr.replace('-','')) <= int(nextTrdStr.replace('-','')):
print('new data not avaliable yet')
return
else:
print('will update from {0} to {1}'.format(nextTrdStr,availableDateStr))
betweenDays = [tdt.strtime[:10] for tdt in md.get_calendar('SHSE',nextTrdStr,availableDateStr)]
if nextTrdStr!=availableDateStr: # 避免同一日期重复
betweenDays.append(availableDateStr)
betweenDaysNumber = [int(tdt.replace('-','')) for tdt in betweenDays]
newDateNum = len(betweenDaysNumber)
# 更新前 先备份数据
backupPath = os.path.join(palPath,'backup')
cpResult = os.system(r'COPY {0} {1} /Y'.format(os.path.join(palPath,matName),os.path.join(backupPath,matName)))
assert cpResult==0,'backup failed'
gmDateFmt = 'yyyy-mm-dd'
# update indice
indiceNames = ['sh','hs300','zz500','sz50']
indiceCodes = ['000001','000300','000905','000016']
symbols = ','.join(['SHSE.{}'.format(sbl) for sbl in indiceCodes])
indiceBars = md.get_dailybars(symbols,nextTrdStr,availableDateStr)
for dumi,idx in enumerate(indiceNames):
bars = indiceBars[dumi::4]
idxret = np.array([bar.close for bar in bars])/np.array([bar.pre_close for bar in bars]) - 1
idxArray = np.array([betweenDaysNumber,
[bar.open for bar in bars],
[bar.high for bar in bars],
[bar.low for bar in bars],
[bar.close for bar in bars],
[bar.volume for bar in bars],
[bar.amount for bar in bars],
idxret
])
# newIndex = np.column_stack([savedPal['index_{}'.format(idx)][:], idxArray])
pd.DataFrame(np.transpose(idxArray)).to_csv(os.path.join(tempFilePath,'index_{}.csv'.format(idx)),index=False,header=False)
# update stock info
nCut = savedPal['N_cut'][0][0] # 6000
nEnd = savedPal['N_end'][0][0] # last end date id ex.6732
stockNames = read_cell(savedPal, 'stockname')
savedStkcdsGM = ['.'.join([stk[-2:]+'SE',stk[:6]]) for stk in stockNames]
savedStkNum = len(stockNames)
listedStkcdsWind = w.wset('sectorconstituent','date={};sectorid=a001010100000000'.format(availableDateStr)).Data[1]
newStkcdsWind = sorted(list(set(listedStkcdsWind) - set(stockNames)))
if newStkcdsWind:
stockNames.extend( newStkcdsWind )
newStkIpos = [int(tdt.strftime('%Y%m%d')) for tdt in w.wss(newStkcdsWind, 'ipo_date').Data[0]]
newIpoIds = [(w.tdayscount(nextTrd,str(ipo)).Data[0][0]+nEnd) for ipo in newStkIpos]
newStockip = pd.DataFrame([[int(newStkcdsWind[dumi][:6]), newStkIpos[dumi], newIpoIds[dumi],0,0,0,0,0] for dumi in range(len(newStkcdsWind))])
newStockip.to_csv( os.path.join(tempFilePath,'stockip.csv'),index=False,header=False )
else:
pd.DataFrame([]).to_csv(os.path.join(tempFilePath, 'stockip.csv'), index=False, header=False)
newStkcdsGm = ['.'.join([stk[-2:]+'SE',stk[:6]]) for stk in newStkcdsWind]
allStkcdsGM = savedStkcdsGM + newStkcdsGm # 全体股票包含已退市 与pal行数相同
# allSecNames = pd.DataFrame(w.wss(stockNames,'sec_name').Data[0])
allInstruments = md.get_instruments('SZSE', 1, 0) + md.get_instruments('SHSE', 1, 0)
allInstrumentsDF = pd.DataFrame([[inds.symbol, inds.sec_name] for inds in allInstruments],columns=['symbol','sec_name']).set_index('symbol')
allSecNames = allInstrumentsDF.loc[allStkcdsGM,'sec_name']
allSecNames.to_csv( os.path.join(tempFilePath, 'sec_names.csv'), index=False, header=False )
pd.DataFrame(newStkcdsWind).to_csv( os.path.join(tempFilePath, 'stockname.csv'), index=False, header=False )
# update trade info
pages = ['date','open','high','low','close','volume','amount','pctchg','flow_a_share','total_share','adjfct','adjprc','isst']
newPal = {}
for page in pages:
newPal[page] = pd.DataFrame(np.zeros([len(allStkcdsGM), newDateNum]),index=allStkcdsGM,columns=betweenDays)
lastPal = pd.DataFrame(savedPal['Pal'][:,-1,:],columns=savedStkcdsGM)
barsDaily = md.get_dailybars(','.join(allStkcdsGM), nextTrdStr, availableDateStr)
for bar in barsDaily:
tdt = bar.strtime[:10]
stk = '.'.join([bar.exchange,bar.sec_id])
newPal['date'].loc[stk, tdt] = int(tdt.replace('-',''))
newPal['open'].loc[stk, tdt] = bar.open
newPal['high'].loc[stk, tdt] = bar.high
newPal['low'].loc[stk, tdt] = bar.low
newPal['close'].loc[stk, tdt] = bar.close
newPal['volume'].loc[stk, tdt] = bar.volume
newPal['amount'].loc[stk, tdt] = bar.amount
newPal['pctchg'].loc[stk, tdt] = bar.close/bar.pre_close - 1
# 计算自算复权因子 ： 前一日收盘价*(1+当日收益率)/当日收盘价 s.t. （当日收盘价*当日复权因子）/前一日收盘价 = 1+ret
# 若当日没有交易 ： 沿用前一日 复权因子 循环外处理
# 若前一日没有交易 前一日收盘价 特殊处理：
# 当日有交易 ： 取停牌前最后一个交易日的 收盘价
# 当日没交易 没有退市 ： 沿用前一日复权因子 循环外处理
# 当日没交易 已经退市 ： 沿用前一日复权因子 循环外处理
# 若新股上市第一天 ： 复权因子为1
if stk in newStkcdsGm:
newPal['adjfct'].loc[stk, tdt] = 1
else:
noTrdLast = (lastPal.loc[0, stk] == 0) if tdt == nextTrdStr else (newPal['date'].loc[stk, betweenDays[betweenDays.index(tdt) - 1]] == 0)
if noTrdLast: # 前一日没交易 今日有交易（否则不应出现在bars里面）
lastBar = md.get_last_n_dailybars(stk, 2, end_time=tdt)[-1]
newPal['adjfct'].loc[stk, tdt] = lastPal.loc[15, stk] * lastBar.close * (1 + newPal['pctchg'].loc[stk, tdt]) / bar.close
else:
preClose = lastPal.loc[4,stk] if tdt==nextTrdStr else newPal['close'].loc[stk,betweenDays[betweenDays.index(tdt)-1]]
newPal['adjfct'].loc[stk, tdt] = lastPal.loc[15, stk] * preClose * (1 + newPal['pctchg'].loc[stk, tdt]) / bar.close
for dumi,tdt in enumerate(betweenDays):
idx = newPal['adjfct'].loc[:,tdt]==0
idx = idx.values
if tdt==nextTrdStr:
newPal['adjfct'].loc[idx[:savedStkNum], tdt] = lastPal.loc[15,:].values[idx[:savedStkNum]]
else:
newPal['adjfct'].loc[idx, tdt] = newPal['adjfct'].loc[idx, betweenDays[dumi-1]]
newPal['adjprc'] = newPal['adjfct']*newPal['close']
shareBar = md.get_share_index(','.join(allStkcdsGM), nextTrdStr, availableDateStr)
for bar in shareBar:
tdt = bar.pub_date
stk = bar.symbol
newPal['flow_a_share'].loc[stk, tdt] = bar.flow_a_share
newPal['total_share'].loc[stk, tdt] = bar.total_share
isST = np.array([int('ST' in sn) for sn in allSecNames.values])
newPal['isst'] = pd.DataFrame(np.repeat(np.reshape(isST,(isST.shape[0],1)),len(betweenDays),axis=1), index=allStkcdsGM, columns=betweenDays)
for page in newPal:
newPal[page].to_csv(os.path.join(tempFilePath,'{}.csv'.format(page)),index=False,header=False )
print('Pal temp files update finished with {0} stocks and {1} days in {2} seconds '.format(len(newStkcdsWind),len(betweenDays),time.time() - start))
def _windPyStart():
w.start()
print("w.isconnected: {}".format(w.isconnected()))
def QA_fetch_get_stock_financial(name, startDate, endDate):
with w.start():
pass
def AllStockHistoryData(self, symbols, start_date, end_date, step=0):
print(self.getCurrentTime(), ": start to download stock data:")
for symbol in symbols:
w.start()
try:
stock = w.wsd(
symbol, "trade_code,open_price,high_price,\
low_price,close_price,volumn,\
pre_close", start_date, end_date)
index_data = pd.DataFrame()
index_data['trade_code'] = stock.Times
stock.Data[0] = symbol
index_data['stock_code'] = stock.Data[0]
index_data['open_price'] = stock.Data[1]
index_data['high_price'] = stock.Data[2]
index_data['low_price'] = stock.Data[3]
index_data['close_price'] = stock.Data[4]
index_data['volumn'] = stock.Data[5]
index_data['pre_close'] = stock.Data[6]
index_data['data_source'] = 'Wind'
index_data['created_date'] = time.strftime('%Y-%m-%d %H:%M:%S'\
,time.localtime(time.time()))
index_data['updated_date'] = time.strftime('%Y-%m-%d %H:%M:%S'\
,time.localtime(time.time()))
index_data = index_data[index_data['open_price' > 0]]
try:
index_data.to_sql('stock_daily_data',\
engine,if_exists='append')
except Exception as e:
error_log = pd.DataFrame()
error_log['trade_date'] = stock.Times
error_log['stock_code'] = stock.Data[0]
error_log['strat_date'] = start_date
error_log['end_date'] = end_date
error_log['status'] = None
error_log['table'] = 'stock_daily_data'
error_log['args'] = 'Symbol' + symbol + 'From'\
+ start_date + 'To' + end_date
error_log['error_info'] = e
error_log['created_date'] = time.strftime(\
'%Y-%m-%d %H:%M:%S',time.localtime(time.time()))
error_log.to_sql('stock_error_log',\
engine,if_exists='append')
print(self.getCurrentTime(), ":SQL Exception: %s" % (e))
continue
w.start()
except Exception as e:
error_log = pd.DataFrame()
error_log['trade_date'] = stock.Times
error_log['stock_code'] = stock.Data[0]
error_log['strat_date'] = start_date
error_log['end_date'] = end_date
error_log['status'] = None
error_log['table'] = 'stock_daily_data'
error_log['args'] = 'Symbol' + symbol + 'From'\
+ start_date + 'To' + end_date
error_log['error_info'] = e
error_log['created_date'] = time.strftime(\
'%Y-%m-%d %H:%M:%S',time.localtime(time.time()))
error_log.to_sql('stock_error_log',\
engine,if_exists='append')
print (self.getCurrentTime(),":index_data %s : Exception :%s" \
% (symbol,e) )
time.sleep(sleep_time)
w.start()
continue
print(self.getCurrentTime(),": Downloading [",symbol,"] From "\
+start_date+" to "+end_date)
print(self.getCurrentTime(), ": Symbols have been downloaded.")
def get_info_wind(code, fields, date):
w.start()
data = w.wss(code, fields, "tradeDate=%s"%(date))
df = pd.DataFrame(np.array(data.Data).T, index=[date], columns=data.Fields)
return df
def dmpStkMin1FromWind(self,
folder: str,
sdate: int = 19900101,
edate: int = 0,
codes: list = None,
isIndex: bool = False):
from WindPy import w
if edate == 0:
edate = int(datetime.datetime.now().strftime('%Y%m%d'))
if codes is None:
if isIndex:
codes = self.__indice__.keys()
else:
codes = self.__stocks__.keys()
total = len(codes)
endtime = str(edate)
endtime = endtime[0:4] + "-" + endtime[4:6] + "-" + endtime[
6:] + " 15:30:00"
begintime = str(sdate)
begintime = begintime[0:4] + "-" + begintime[4:6] + "-" + begintime[
6:] + " 09:00:00"
count = 0
w.start()
for code in codes:
count += 1
rawcode = code
code = code[2:] + "." + code[:2]
exchg = ""
if rawcode[:2] == "SH":
exchg = "SSE"
else:
exchg = "SZSE"
print("正在拉取%s[%d/%d]的1分钟线数据[%d-%d]……" %
(rawcode, count, total, sdate, edate))
if not os.path.exists(folder + 'min/' + exchg + "/"):
create_dirs(folder + 'min/' + exchg + "/")
csvpath = (folder + 'min/%s/%s.csv') % (exchg, rawcode[2:])
err, df_bars = w.wsi(code,
fields="open,high,low,close,volume",
beginTime=begintime,
endTime=endtime,
options="BarSize=1;Fill=Previous",
usedf=True)
if err != 0:
print("拉取出错")
print(df_bars)
continue
if len(df_bars) == 0:
print("历史数据为空,跳过处理")
continue
print("正在写入数据文件")
isEmpty = is_file_empty(csvpath)
f = open(csvpath, 'a')
if isEmpty:
f.write("date, time, open, high, low, close, volumn\n")
df_bars.fillna(0, inplace=True)
content = ""
for idx in range(0, len(df_bars)):
curBar = df_bars.iloc[idx]
bartime = df_bars.index[idx]
curMin = int(bartime.strftime("%H%M"))
if curMin == 1130 or curMin == 1500:
continue
if curMin == 1129 or curMin == 1459:
nextBar = df_bars.iloc[idx + 1]
if curBar["volume"] != 0:
if nextBar["volume"] != 0:
curBar["volume"] += nextBar["volume"]
curBar["close"] = nextBar["close"]
curBar["high"] = max(curBar["high"],
nextBar["high"])
curBar["low"] = min(curBar["low"], nextBar["low"])
else:
curBar = nextBar
bartime = bartime + datetime.timedelta(minutes=1)
line = ""
line += (bartime.strftime("%Y/%m/%d") + ", ")
line += (bartime.strftime("%H:%M:%S") + ", ")
line += (str(curBar["open"]) + ",")
line += (str(curBar["high"]) + ",")
line += (str(curBar["low"]) + ",")
line += (str(curBar["close"]) + ",")
line += (str(curBar["volume"]) + "\n")
content += line
f.write(content)
f.close()
w.stop()
from WindPy import w
import numpy as np
from matplotlib import pyplot as plt
w.start() # 默认命令超时时间为120秒，如需设置超时时间可以加入waitTime参数，例如waitTime=60,即设置命令超时时间为60秒
w.isconnected() # 判断WindPy是否已经登录成功
a = w.wsd("600030.SH", "CLOSE", "20190101", "20200101")
#
# closePriceList = a.Data[0]
# print(closePriceList)
# plt.plot(closePriceList)
# plt.show()
# wssdata=w.wss("600000.SH,600007.SH,600016.SH", "ev,total_shares","tradeDate=20151222;industryType=1")
# print(wssdata)
#
# a=w.edb("M5567876", "2019-04-01", "2020-05-23","Fill=Previous")
# print(a)
#
# w.edb("M5567876", "2019-04-01", "2020-05-23","Fill=Previous")
#
# b = w.edb("M0009805", "2019-06-21", "2020-06-20","Fill=Previous")
b = w.edb("M5567876,M5567877,M5567878,M5567879,M0000612", "2019-07-20",
"2020-07-19", "Fill=Previous")
print(b)
# dict1={}
# # wind api
# b = w.wss("000001.OF,000005.OF,000009.OF",
PlatformCode = '0000' #交易/模拟平台（经纪商）代码
Account = 'W109839501202' #交易/模拟账户名
Passwd = '000000' #交易/模拟账户名密码
Kind = 'RB1901.SHF' #交易的商品品种
monthlist = [4, 9, 12] #每年哪几个月的第二个星期五需要强制平仓
#####################
### 初始化 ###
AccountInfo = [PlatformCode, Account, Passwd] #模拟账户平台/账号/*
LogFileName = GetDetailFileName(
) #根据月份获取log文件的文件名，格式为yyyymm_Everyday_Detail.txt
DayFileName = "Day_Close.txt" #记录每天摘要结果的文件名
LastAct = 0 #上次操作的标识，1为开仓，-1为平仓
ActFlag = 0 #当天操作标识，1为开仓，-1为平仓，-2为强制全部平仓，-3为强制平仓后的休整期,2为休整后的开仓
timelist2 = TransTimeList(timelist) #转换时间节点列表为datetime格式的列表
w.start() #打开Wind-Python接口
Money, Pos, LastPX, LastAct, OriginMAFlag = ReadDayData(
DayFileName, OriginAsset, Kind) #获取昨日收盘后信息：资金，持仓，收盘价，上次操作标识,均值交叉标识
LogFile = CheckLogFile(LogFileName) #获取Log文件
TodayMoney = Money #记录当天初始资金
TodayPos = Pos #记录当天初始仓位
NumAct = 0 #每天交易次数
PreMA5, PreMA10, PreMA20 = GetClosePX(DayFileName, Kind) #前4/9/19天收盘价预先求和
### 开始当天操作,检查日期 ###
TodayDate = str(datetime.date(datetime.now())) #获取当天时间，格式为yyyy-mm-dd
#TodayDate = "2018-09-15"
ActFlag = CheckFriday(TodayDate, monthlist) #检查当天是否强制平仓(-2)，或在休整期(-3),或继续(0)
print("%s Records of %s %s" % ('-' * 10, TodayDate, '-' * 10)) #分割线
##for i in range(5): #测试运行n次
def crawl(cls):
wind.start()
result = cls._fetch_swstype()
cls.store(result)
def initialize(self):
w.start()
self.initLogging()
self.con = Connection().getConnection()
self.cur = self.con.cursor()
def main():
product_gain = 0
date = input("输入日期\n")
chart = "估值表_" + date
file_dir = ".\\lists" # file directory
all_csv_list = os.listdir(file_dir) # get csv list
for single_csv in all_csv_list:
if chart in single_csv:
filename = single_csv
# 将所有债券名称存到bonds中
# print(single_csv)
dataset = pd.DataFrame(pd.read_excel(file_dir + "\\" + filename))
shishouziben = 0
bonds = []
wenxin = ''
for index, row in dataset.iterrows():
# 找出债券名称
if str(row[0])[-2:] == 'SH' or str(row[0])[-2:] == 'SZ' or str(
row[0])[-2:] == 'IB' and str(
row[0])[-9] != '6' and not (row[14] != row[14]):
bonds.append(row[1])
# 找出稳鑫表
elif "OTC" in str(row[0]):
wenxin = row[1]
elif str(row[0]) == "实收资本":
shishouziben = float(row[6])
# print(bonds)
# 得到稳鑫表名
temp = list(wenxin)
temp.pop(-1)
temp.pop(-3)
temp.pop(-3)
temp.pop(-11)
temp.pop(-11)
temp.pop(-11)
temp.pop(-11)
wenxin = ''.join(temp)
wenxinname = wenxin + '_' + '[' + date[0:4] + '-' + date[
4:6] + '-' + date[6:8] + ']'
#print(wenxinname)
for single_csv1 in all_csv_list:
if wenxin in single_csv1:
filename = single_csv1
break
# 读取温馨表
dataset = pd.DataFrame(pd.read_excel(file_dir + "\\" + filename))
for index, row in dataset.iterrows():
# 找出债券名称
if len(str(row[0])) == 14:
# print(row)
if (row[1][0] >= '0' and row[1][0] <= '9'):
bonds.append(row[1])
# 去除重复债券,all_bonds为债券名字集合
all_bonds = sorted(set(bonds), key=bonds.index)
print(bonds)
print(all_bonds)
# 读取所有对账单
duizhangdan = merge_duizhang()
# print(duizhangdan)
# 遍历债券
w.start()
#从wind提取所有债券名称和代码
#everything = (w.wset("sectorconstituent","date=2021-06-07;sectorid=a101010801000000;field=wind_code,sec_name")).Data
everything = pd.read_excel("bonds.xlsx", encoding='gbk')
for bond in all_bonds:
for index, row in everything.iterrows():
if row[1] == bond:
print(row)
code = row[0]
product_gain += bond_gain(bond, duizhangdan, code)
break
#计算产品绝对收益
print(product_gain)
product_gainper = product_gain / shishouziben
print(product_gainper)
######## coding=gbk
__author__ = 'henry'
import site
import sys
reload(sys)
sys.setdefaultencoding( "utf-8" )
from WindPy import w
from Utility import str1D
from Utility import str2D
if (w.isconnected() != True):
w.start(); #pwd607387
stock_code = sys.argv[1]
start_date = sys.argv[2]
end_date = sys.argv[3]
bar_size = sys.argv[4]
fields = "open,close,high,low,volume,amt"
print "WindStockReuslt:"
#3.getKData
#·ÖÖÓÐòÁÐKÏß
# print w.wsi("000001.SZ", "open,close,high,low,volume,amt","2014-11-10 09:00:00", "2014-11-10 02:50:46", "")
# print w.wsi("000001.SZ", "open,close,high,low,volume,amt","2014-11-10 09:00:00", "2014-11-10 11:50:46", "BarSize=30")
# print w.wsd("000001.SZ", fields, "2014-11-17", "2014-11-20", "Fill=Previous")
res = w.wsi(stock_code,fields,start_date,end_date,bar_size)
# -*- coding:utf-8 -*-
from __builtin__ import str # Python 3.0 以上版本的用户请注释掉这一句
from WindPy import w
# from datetime import *
w.start();
# 命令如何写可以用命令生成器来辅助完成
# 定义打印输出函数，用来展示数据使用
def printpy(outdata):
if outdata.ErrorCode != 0:
print('error code:' + str(outdata.ErrorCode) + '\n');
return ();
for i in range(0, len(outdata.Data[0])):
strTemp = ''
if len(outdata.Times) > 1:
strTemp = str(outdata.Times[i]) + ' '
for k in range(0, len(outdata.Fields)):
strTemp = strTemp + str(outdata.Data[k][i]) + ' '
print(strTemp)
# 通过wsd来提取时间序列数据，比如取开高低收成交量，成交额数据
print('\n\n' + '-----通过wsd来提取时间序列数据，比如取开高低收成交量，成交额数据-----' + '\n')
wsddata1 = w.wsd("000001.SZ", "open,high,low,close,volume,amt", "2015-11-22", "2015-12-22",
"Fill=Previous")
printpy(wsddata1)
# 通过wsd来提取各个报告期财务数据
def __init__(self, rep, pub):
super(WindServer, self).__init__(rep, pub)
self.register(self.wsd)
self.register(self.wsi)
w.start()
def post(self):
"""
json str:{"tablename": "sectorconstituent", "options": "date=2017-03-21;sectorid=1000023121000000"}
:return: 返回万得返回数据dict
"""
args = receive_wset_parser.parse_args()
logger.info('/wset/ args:%s' % args)
# print('args:%s' % args)
# table_name = args['table_name']
# options = args['options']
if args['options'] == "":
args['options'] = None
if not w.isconnected():
w.start()
ret_data = None
for nth in range(2):
ret_data = w.wset(**args)
error_code = ret_data.ErrorCode
if error_code != 0:
if nth == 0 and error_code == -40521010:
w.stop()
w.start()
logger.warning('尝试重新登陆成功，再次调用函数')
continue
msg = ERROR_CODE_MSG_DIC.setdefault(error_code, "")
logger.error('wset(%s) ErrorCode=%d %s' %
(args, error_code, msg))
raise RequestError(msg, None, error_code)
else:
break
else:
if ret_data is None:
msg = 'wst(%s) ret_data is None' % args
logger.error(msg)
raise RequestError(msg, None, 0)
data_count = len(ret_data.Data)
# if data_count > 0:
# print('ret_data.Fields\n', ret_data.Fields)
# ret_data.Data[0] = [format_2_date_str(dt) for dt in ret_data.Data[0]]
# print('ret_data.Data\n', ret_data.Data)
for n_data in range(data_count):
data = ret_data.Data[n_data]
data_len2 = len(data)
if data_len2 > 0:
# 取出第一个部位None的数据
item_check = None
for item_check in data:
if item_check is not None:
break
# 进行类型检查，如果发现是 datetime, date 类型之一，则进行类型转换
if item_check is not None and type(item_check) in (datetime,
date):
ret_data.Data[n_data] = [
format_2_date_str(dt) for dt in data
]
logger.info('%d column["%s"] date to str', n_data,
ret_data.Fields[n_data])
ret_df = pd.DataFrame(ret_data.Data,
index=ret_data.Fields,
columns=ret_data.Codes)
# print('ret_df\n', ret_df)
ret_dic = ret_df.to_dict()
# print('ret_dic:\n', ret_dic)
return ret_dic
def __init__(self):
self.logger = mylog.set_log()
mysql_con_demo = MysqlCon()
self.engine = mysql_con_demo.getMysqlCon(flag='engine')
w.start()
class Future(Security):
'''
期货类，继承了Security类
子类包括：
'''
class IndexFuture(Future):
'''
股指期货，继承了Future类
'''
class Index(WindProduct):
'''
指数类，继承了WindProduct
子类包括：Sector（板块类），Industry（行业类）
'''
class Sector(Index):
'''
板块类，继承了Index类。板块由行业组成
'''
class Industry(Index):
'Class for Industry. Child class of Index class.'
# def query_weight(self, date):
# 'return weight of constituent '
w.start(waitTime=120, showmenu=False)
def post(self):
"""
json str:{"codes": "M0017126,M0017127,M0017128",
"begin_time": "2016-11-10", "end_time": "2017-11-10", "options": "Fill=Previous"}
:return: 返回万得返回数据dict
"""
args = receive_edb_parser.parse_args()
logger.info('/edb/ args:%s', args)
codes = args['codes']
# begin_time = args['begin_time']
# end_time = args['end_time']
# options = args['options']
if args['options'] == "":
args['options'] = None
if not w.isconnected():
w.start()
ret_data = None
for nth in range(2):
ret_data = w.edb(**args)
error_code = ret_data.ErrorCode
if error_code != 0:
if nth == 0 and error_code == -40521010:
w.stop()
w.start()
logger.warning('尝试重新登陆成功，再次调用函数')
continue
msg = ERROR_CODE_MSG_DIC.setdefault(error_code, "")
logger.error('wst(%s) ErrorCode=%d %s' %
(args, error_code, msg))
raise RequestError(msg, None, error_code)
else:
break
else:
if ret_data is None:
msg = 'wst(%s) ret_data is None' % args
logger.error(msg)
raise RequestError(msg, None, 0)
# 将 Data数据中所有 datetime date 类型的数据转换为 string
data_len = len(ret_data.Data)
for n_data in range(data_len):
data = ret_data.Data[n_data]
data_len2 = len(data)
if data_len2 > 0:
# 取出第一个部位None的数据
item_check = None
for item_check in data:
if item_check is not None:
break
# 进行类型检查，如果发现是 datetime, date 类型之一，则进行类型转换
if item_check is not None and type(item_check) in (datetime,
date):
ret_data.Data[n_data] = [
format_2_date_str(dt) for dt in data
]
logger.info('%d column["%s"] date to str', n_data,
ret_data.Fields[n_data])
# 组成 DataFrame
ret_df = pd.DataFrame(
ret_data.Data,
index=[xx.strip() for xx in codes.split(',')],
columns=[format_2_date_str(dt) for dt in ret_data.Times])
# print(ret_df)
ret_dic = ret_df.to_dict()
# print('ret_dic:\n', ret_dic)
return ret_dic
def import_smfund_daily():
w.start()
today = date.today().strftime('%Y-%m-%d')
engine = get_db_engine()
sql_str = """select wind_code, ifnull(trade_date_max, fund_setupdate) date_start, class_a_code, class_b_code
from wind_smfund_info fi left outer join
(select code_p, adddate(max(trade_date), 1) trade_date_max from wind_smfund_daily group by code_p) smd
on fi.wind_code = smd.code_p
where fund_setupdate is not null
and class_a_code is not null
and class_b_code is not null"""
df = pd.read_sql(sql_str, engine)
df.set_index('wind_code', inplace=True)
df_count = df.shape[0]
print('df_count', df_count)
index_start = 0
for i, code in enumerate(df.index): # 可调整 # [100:min([df_count, 200])]
if i < index_start:
continue
print('%d/%d) %s start to import' % (i, df_count, code))
if type(df.loc[code, 'date_start']) not in (date, datetime, Timestamp):
print('%d %s has no fund_setupdate will be ignored' % (i, code))
# print(df.iloc[i, :])
continue
beginTime = df.loc[code, 'date_start'].strftime('%Y-%m-%d')
field = "open,high,low,close,volume,amt,pct_chg"
df_p = wsd_cache(w, code, field, beginTime, today, "")
df_p.rename(columns=lambda x: x.swapcase(), inplace=True)
df_p['code_p'] = code
code_a = df.loc[code, 'class_a_code']
if code_a is None:
print('%d %s has no code_a will be ignored' % (i, code))
# print(df.iloc[i, :])
continue
df_a = wsd_cache(w, code_a, field, beginTime, today, "")
df_a.rename(columns=lambda x: x.swapcase() + '_a', inplace=True)
code_b = df.loc[code, 'class_b_code']
df_b = wsd_cache(w, code_b, field, beginTime, today, "")
df_b.columns = df_b.columns.map(lambda x: x.swapcase() + '_b')
new_df = pd.DataFrame()
for d in df_p.index:
time = d.date().strftime('%Y-%m-%d')
field = "date=%s;windcode=%s;field=a_nav,b_nav,a_fs_inc,b_fs_inc,cur_interest,next_interest,tm_type,ptm_year,anal_pricelever,anal_navlevel,t1_premium,t2_premium,next_pcvdate,dq_status" % (
time, code)
temp = wset_cache(w, "leveragedfundinfo", field)
temp['date'] = d
new_df = new_df.append(temp)
new_df['next_pcvdate'] = new_df['next_pcvdate'].map(lambda x: x.date() if x is not None else x)
new_df.set_index('date', inplace=True)
one_df = pd.concat([df_p, df_a, df_b, new_df], axis=1)
one_df.reset_index(inplace=True)
# one_df['date'] = one_df['date'].map(lambda x: x.date())
one_df.rename(columns={'date': 'trade_date'}, inplace=True)
one_df.set_index(['code_p', 'trade_date'], inplace=True)
one_df.to_sql('wind_smfund_daily', engine, if_exists='append', index_label=['code_p', 'trade_date'],
dtype={
'code_p': String(20),
'trade_date': Date,
'next_pcvdate': Date,
'a_nav': Float,
'b_nav': Float,
'a_fs_inc': Float,
'b_fs_inc': Float,
'cur_interest': Float,
'next_interest': Float,
'ptm_year': Float,
'anal_pricelever': Float,
'anal_navlevel': Float,
't1_premium': Float,
't2_premium': Float,
'dq_status': String(50),
'open': Float, 'high': Float, 'low': Float, 'close': Float,
'volume': Float, 'amt': Float, 'pct_chg': Float,
'open_a': Float, 'high_a': Float, 'low_a': Float, 'close_a': Float,
'volume_a': Float, 'amt_a': Float, 'pct_chg_a': Float,
'open_b': Float, 'high_b': Float, 'low_b': Float, 'close_b': Float,
'volume_b': Float, 'amt_b': Float, 'pct_chg_b': Float,
})
print('%d/%d) %s import success' % (i, df_count, code))
# %% mysql连接和配置
connect = pymysql.Connect(
host='localhost',
port=3306,
user='******',
passwd='123456',
db='stock_data',
charset='utf8'
)
cursor = connect.cursor() # 获取游标
CreateTableInMysql()
# %% mongodb连接和配置
conn = MongoClient('localhost', 27017)
db = conn.stock_data # 连接mydb数据库，没有则自动创建
# %% wind连接和配置
field_list = ['open', 'high', "low", "close", "volume", "amt", "vwap"]
w.start() # 启动连接
last_date = GetLastTradeDate()
start_time = time.clock()
UpdateAllStockDataFromWind()
# 关闭连接
cursor.close()
connect.close()
end_time = time.clock()
print('Running time: %s Seconds' % (end_time - start_time))
class WindDataLive(DataBase):
from WindPy import w
w.start()
params = (
('nocase', True),
('dataname', 'None'),
('dataname_live', 'None'),
('datetime', None),
('open', -1),
('high', -1),
('low', -1),
('close', -1),
('volume', -1),
('frequency', 'day'),
('security', 'None'),
('sleep', 1),
)
def islive(self):
'''Returns ``True`` to notify ``Cerebro`` that preloading and runonce
should be deactivated'''
return True
def live(self):
return True
def __init__(self):
super().__init__()
self.backtest = True
def start(self):
super().start()
self._idx = -1
s = self.p.dataname
start_date = str(self.p.fromdate.date())
frequency = self.p.frequency
user = config['user']
sql_password = config['password']
host = config['host']
port = config['port']
c = 'd_' + frequency + ',open,high,low,close,volume'
if self.p.security == 'stock':
if self.p.frequency == 'day':
sql = "select "+c+" from md_day where stock = '"+ s[2:] +"' and d_day > '"+start_date+"'"
self.security = 'stock'
else:
sql = "select "+c+" from md_" + frequency + "_" + s +" where d_"+ frequency+">'"+start_date+"'"
self.security = 'stock'
elif self.p.security == 'future':
if self.p.frequency == 'day':
sql = "select "+c+" from md_day where contract = '"+ s +"' and d_day > '"+start_date+"'"
self.security = 'future'
else:
sql = "select "+c+" from md_" + frequency + "_" + s +" where d_"+ frequency+">'"+start_date+"'"
self.security = 'future'
elif self.p.security == 'index':
if self.p.frequency == 'day':
sql = "select "+c+" from md_day where index = '"+ s[2:] +"' and d_day > '"+start_date+"'"
self.security = 'index'
else:
sql = "select "+c+" from md_" + frequency + "_" + s +" where d_"+ frequency+">'"+start_date+"'"
self.security = 'index'
self.conn = pymysql.connect(host = host, user = user, password = sql_password, database = 'md_' + self.security, port = port)
self.cursor = self.conn.cursor()
self.cursor.execute(sql)
self.description = self.cursor.description
def _load(self):
self._idx += 1
if self.backtest:
try:
bar = self.cursor.fetchone()
while None in bar:
bar = self.cursor.fetchone()
self.lines.datetime[0] = date2num(bar[0])
self.lines.open[0] = bar[1]
self.lines.high[0] = bar[2]
self.lines.low[0] = bar[3]
self.lines.close[0] = bar[4]
self.lines.volume[0] = bar[5]
self.temp = bar[0]
return True
except TypeError:
self.backtest = False
self.conn.close()
if self.p.sleep == 1:
if datetime.datetime.now().time() > datetime.time(15,1,0):
market_opentime = datetime.datetime.combine(datetime.date.today()+datetime.timedelta(days=1),datetime.time(9,30,0))
print('wait for market opening')
time.sleep((market_opentime-datetime.datetime.now()).seconds)
elif datetime.datetime.now() < datetime.datetime.combine(datetime.date.today(),datetime.time(9,30,0)):
market_opentime = datetime.datetime.combine(datetime.date.today(),datetime.time(9,30,0))
print('wait for market opening')
time.sleep((market_opentime-datetime.datetime.now()).seconds)
else:
if self.p.frequency == 'day':
time.sleep(19800)
else:
print('等待数据')
time.sleep(60*int(self.p.frequency[0:-3]))
elif self.p.sleep == 0:
pass
s = self.p.dataname
frequency = self.p.frequency
now_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
if self.p.security == 'stock':
if frequency != 'day':
data_wind = w.wsi(s, "open,high,low,close,volume", str(self.temp), now_time, "BarSize="+frequency+";periodstart=09:00:00;periodend="+datetime.datetime.now().strftime('%H:%M:%S'))
else:
data_wind = w.wsd(s, "open,high,low,close,volume", str(self.temp), now_time, "PriceAdj=B")
elif self.p.security == 'future':
if frequency != 'day':
data_wind = w.wsi(s, "open,high,low,close,volume", str(self.temp), now_time, "BarSize="+frequency+";periodstart=09:00:00;periodend="+datetime.datetime.now().strftime('%H:%M:%S'))
else:
data_wind = w.wsd(s, "open,high,low,close,volume", str(self.temp), now_time, "PriceAdj=B")
elif self.p.security == 'index':
if frequency != 'day':
data_wind = w.wsi(s, "open,high,low,close,volume", str(self.temp), now_time, "BarSize="+frequency+";periodstart=09:00:00;periodend="+datetime.datetime.now().strftime('%H:%M:%S'))
else:
data_wind = w.wsd(s, "open,high,low,close,volume", str(self.temp), now_time, "PriceAdj=B")
self.times = data_wind.Times
self.data = data_wind.Data
if len(self.times) < 2:
#print('中午休市，等待数据')
return None
bar = [self.times[1]]
for i in self.data:
if np.isnan(i[1]) == True:
#print('停牌了')
return None
bar.append(i[1])
self.lines.datetime[0] = date2num(bar[0])
self.lines.open[0] = bar[1]
self.lines.high[0] = bar[2]
self.lines.low[0] = bar[3]
self.lines.close[0] = bar[4]
self.lines.volume[0] = bar[5]
self.temp = bar[0]
return True
def AStockHisData(self,symbols,start_date,end_date,step=0):
'''
逐个股票代码查询行情数据
wsd代码可以借助 WindNavigator自动生成copy即可使用;时间参数不设，默认取当前日期，可能是非交易日没数据;
只有一个时间参数时，默认作为为起始时间，结束时间默认为当前日期；如设置两个时间参数则依次为起止时间
'''
print(self.getCurrentTime(),": Download A Stock Starting:")
for symbol in symbols:
w.start()
try:
#stock=w.wsd(symbol,'trade_code,open,high,low,close,volume,amt',start_date,end_date)
'''
wsd代码可以借助 WindNavigator自动生成copy即可使用;
时间参数不设，默认取当前日期，可能是非交易日没数据;
只有一个时间参数，默认为起始时间到最新；如设置两个时间参数则依次为起止时间
'''
stock=w.wsd(symbol, "trade_code,open,high,low,close,pre_close,volume,amt,dealnum,chg,pct_chg,vwap, adjfactor,close2,turn,free_turn,oi,oi_chg,pre_settle,settle,chg_settlement,pct_chg_settlement, lastradeday_s,last_trade_day,rel_ipo_chg,rel_ipo_pct_chg,susp_reason,close3, pe_ttm,val_pe_deducted_ttm,pe_lyr,pb_lf,ps_ttm,ps_lyr,dividendyield2,ev,mkt_cap_ard,pb_mrq,pcf_ocf_ttm,pcf_ncf_ttm,pcf_ocflyr,pcf_nflyr,trade_status", start_date,end_date)
index_data = pd.DataFrame()
index_data['trade_date']=stock.Times
stock.Data[0]=symbol
index_data['stock_code']=stock.Data[0]
#index_data['stock_code'] =symbol
index_data['open'] =stock.Data[1]
index_data['high'] =stock.Data[2]
index_data['low'] =stock.Data[3]
index_data['close']=stock.Data[4]
index_data['pre_close']=stock.Data[5]
index_data['volume']=stock.Data[6]
index_data['amt']=stock.Data[7]
index_data['dealnum']=stock.Data[8]
index_data['chg']=stock.Data[9]
index_data['pct_chg']=stock.Data[10]
#index_data['pct_chg']=index_data['pct_chg']/100
index_data['vwap']=stock.Data[11]
index_data['adj_factor']=stock.Data[12]
index_data['close2']=stock.Data[13]
index_data['turn']=stock.Data[14]
index_data['free_turn']=stock.Data[15]
index_data['oi']=stock.Data[16]
index_data['oi_chg']=stock.Data[17]
index_data['pre_settle']=stock.Data[18]
index_data['settle']=stock.Data[19]
index_data['chg_settlement']=stock.Data[20]
index_data['pct_chg_settlement']=stock.Data[21]
index_data['lastradeday_s']=stock.Data[22]
index_data['last_trade_day']=stock.Data[23]
index_data['rel_ipo_chg']=stock.Data[24]
index_data['rel_ipo_pct_chg']=stock.Data[25]
index_data['susp_reason']=stock.Data[26]
index_data['close3']=stock.Data[27]
index_data['pe_ttm']=stock.Data[28]
index_data['val_pe_deducted_ttm']=stock.Data[29]
index_data['pe_lyr']=stock.Data[30]
index_data['pb_lf']=stock.Data[31]
index_data['ps_ttm']=stock.Data[32]
index_data['ps_lyr']=stock.Data[33]
index_data['dividendyield2']=stock.Data[34]
index_data['ev']=stock.Data[35]
index_data['mkt_cap_ard']=stock.Data[36]
index_data['pb_mrq']=stock.Data[37]
index_data['pcf_ocf_ttm']=stock.Data[38]
index_data['pcf_ncf_ttm']=stock.Data[39]
index_data['pcf_ocflyr']=stock.Data[40]
index_data['pcf_ncflyr']=stock.Data[41]
index_data['trade_status']=stock.Data[42]
index_data['data_source']='Wind'
index_data['created_date']=time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
index_data['updated_date']=time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
index_data = index_data[index_data['open'] > 0]
#index_data.fillna(0)
try:
index_data.to_sql('stock_daily_data',engine,if_exists='append');
except Exception as e:
#如果写入数据库失败，写入日志表，便于后续分析处理
error_log=pd.DataFrame()
error_log['trade_date']=stock.Times
error_log['stock_code']=stock.Data[0]
error_log['start_date']=start_date
error_log['end_date']=end_date
error_log['status']=None
error_log['table']='stock_daily_data'
error_log['args']='Symbol: '+symbol+' From '+start_date+' To '+end_date
error_log['error_info']=e
error_log['created_date']=time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
error_log.to_sql('stock_error_log',engine,if_exists='append')
print ( self.getCurrentTime(),": SQL Exception :%s" % (e) )
continue
w.start()
except Exception as e:
#如果读取处理失败，可能是网络中断、频繁访问被限、历史数据缺失等原因。写入相关信息到日志表，便于后续补充处理
error_log=pd.DataFrame()
error_log['trade_date']=stock.Times
error_log['stock_code']=stock.Data[0]
error_log['start_date']=start_date
error_log['end_date']=end_date
error_log['status']=None
error_log['table']='stock_daily_data'
error_log['args']='Symbol: '+symbol+' From '+start_date+' To '+end_date
error_log['error_info']=e
error_log['created_date']=time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
error_log.to_sql('stock_error_log',engine,if_exists='append')
print ( self.getCurrentTime(),":index_data %s : Exception :%s" % (symbol,e) )
time.sleep(sleep_time)
w.start()
continue
print(self.getCurrentTime(),": Downloading [",symbol,"] From "+start_date+" to "+end_date)
print(self.getCurrentTime(),": Download A Stock Has Finished .")
def QA_fetch_get_stock_financial(name, startDate, endDate):
from WindPy import w
# w.start()
w.start()
w.isconnected()
pass
def __enter__(self):
w.start()
return self
from WindPy import w as wind
import re
import time
from engine import *
wind.start() # 启动wind
now = time.strftime("%Y-%m-%d")
def jrbbb(jr):
yy = "'J172220.OF','JR132245','XT1703499.XT','JR135238', 'XT1703378.XT','JR136323', 'XT1703377.XT','JR058300', 'XT1703375.XT','JR122820', 'XT1703264.XT','JR049705', 'XT1703262.XT','JR040919', 'XT1703182.XT','JR126682', 'XT1703070.XT','JR002194', 'XT1702907.XT','JR131724', 'XT1702906.XT','JR126710', 'XT1702905.XT','JR020944', 'XT1702776.XT','JR001403', 'XT1702709.XT','JR126360', 'XT1702606.XT','JR132113', 'XT1702605.XT','JR126669', 'XT1702604.XT','JR126636', 'XT1702603.XT','JR126638', 'XT1702601.XT','JR126998', 'XT1702600.XT','JR127004', 'XT1702599.XT','JR127005', 'XT1702598.XT','JR131917', 'XT1702461.XT','JR131815', 'XT1702455.XT','JR128119', 'XT1702452.XT','JR136259', 'XT1702349.XT','JR101947', 'XT1702344.XT','JR101954', 'XT1702274.XT','JR132029', 'XT1702273.XT','JR029247', 'XT1702258.XT','JR131780', 'XT1702249.XT','JR131754', 'XT1702248.XT','JR126478', 'XT1702228.XT','JR132189', 'XT1702227.XT','JR132190', 'XT1702226.XT','JR132191', 'XT1702225.XT','JR132192', 'XT1702224.XT','JR132193', 'XT1702223.XT','JR132194', 'XT1702222.XT','JR132195', 'XT1702174.XT','JR131838', 'XT1702146.XT','JR132177', 'XT1702113.XT','JR134563', 'XT1702012.XT','JR132185', 'XT1701960.XT','JR132221', 'XT1701959.XT','JR132222', 'XT1701899.XT','JR043956', 'XT1701847.XT','JR132186', 'XT1701815.XT','JR126562', 'XT1701814.XT','JR126442', 'XT1701786.XT','JR132213', 'XT1701731.XT','JR132215', 'XT1701698.XT','JR101951', 'XT1701691.XT','JR132214', 'XT1701498.XT','JR101950', 'XT1701154.XT','JR073347', 'XT1700828.XT','JR128135', 'XT1700697.XT','JR118653', 'XT1700618.XT','JR029899', 'XT1700294.XT','JR015847', 'XT1700249.XT','JR041459', 'XT1700120.XT','JR068530', 'XT1623847.XT','JR132196', 'XT1623554.XT','JR125796', 'XT1623386.XT','JR116547', 'XT1623286.XT','JR116589', 'XT1623158.XT','JR125840', 'XT1622941.XT','JR113824', 'XT1622776.XT','JR073511', 'XT1622764.XT','JR084324', 'XT1622122.XT','JR115408', 'XT1622062.XT','JR115578', 'XT1621773.XT','JR000797', 'XT1621631.XT','JR065550', 'XT1620582.XT','JR103587', 'XT1620312.XT','JR103204', 'XT1619594.XT','JR108594', 'XT1619418.XT','JR103298', 'XT1618352.XT','JR074575', 'XT1617384.XT','JR116102', 'XT1617247.XT','JR104643', 'XT1616417.XT','JR105052', 'XT1615601.XT','JR087048', 'XT1615364.XT','JR001260', 'XT1615214.XT','JR119390', 'XT1615213.XT','JR035874', 'XT1615212.XT','JR036435', 'XT1614258.XT','JR092151', 'XT1613828.XT','JR051081', 'XT1611709.XT','JR087127', 'XT1610612.XT','JR109365', 'XT1609628.XT','JR062545', 'XT1608951.XT','JR002320', 'XT1608950.XT','JR041339', 'XT1607512.XT','JR026903', 'XT1607427.XT','JR116380', 'XT1606781.XT','JR000526', 'XT1606160.XT','JR011122', 'XT1605966.XT','JR026200', 'XT1604822.XT','JR010904', 'XT1604180.XT','JR010903', 'XT1603670.XT','JR010655', 'XT1600738.XT','JR027830', 'XT1600692.XT','JR009760', 'XT1600476.XT','JR107450', 'XT1600414.XT','JR009421', 'XT1600413.XT','JR009420', 'XT1600412.XT','JR009419', 'XT1600411.XT','JR009418', 'XT1600410.XT','JR009417', 'XT1600409.XT','JR009416', 'XT1600408.XT','JR009415', 'XT1600407.XT','JR009414', 'XT1600406.XT','JR009413', 'XT1600405.XT','JR009412', 'XT1525337.XT','JR011083', 'XT1524968.XT','JR126271', 'XT1522783.XT','JR022444', 'XT1522350.XT','JR022385', 'XT1521929.XT','JR020288', 'XT1519863.XT','JR022269', 'XT1519278.XT','JR018950', 'XT1519072.XT','JR020384', 'XT1518369.XT','JR020643', 'XT1517794.XT','JR019083', 'XT1517586.XT','JR007242', 'XT1517561.XT','JR019124', 'XT1517263.XT','JR019646', 'XT1517221.XT','JR019256', 'XT1515811.XT','JR015323', 'XT1515581.XT','JR019349', 'XT1515440.XT','JR016357', 'XT1515324.XT','JR002175', 'XT1513445.XT','JR016251', 'XT1513444.XT','JR017082', 'XT1513353.XT','JR014481', 'XT1513304.XT','JR013684', 'XT1513280.XT','JR014275', 'XT1513261.XT','JR014199', 'XT1513249.XT','JR014200', 'XT1512441.XT','JR013298', 'XT1512014.XT','JR014458', 'XT1511967.XT','JR016358', 'XT1511852.XT','JR015442', 'XT1511548.XT','JR016088', 'XT1511072.XT','JR015471', 'XT1510999.XT','JR030565', 'XT1510648.XT','JR014228', 'XT1510413.XT','JR014934', 'XT1510001.XT','JR007052', 'XT1509966.XT','JR013850', 'XT1509529.XT','JR045951', 'XT1508962.XT','JR016502', 'XT1507419.XT','JR013774', 'XT1507236.XT','JR034917', 'XT1506866.XT','JR027330', 'XT1505930.XT','JR005469', 'XT1505805.XT','JR012667', 'XT1505483.XT','JR009930', 'XT1502896.XT','JR012985', 'XT1502703.XT','JR027296', 'XT1502335.XT','JR012762', 'XT1501436.XT','JR001174', 'XT1501413.XT','JR078633', 'XT1501316.XT','JR012836', 'XT1501269.XT','JR012778', 'XT1501232.XT','JR027284', 'XT1501144.XT','JR109419', 'XT1501143.XT','JR134483', 'XT1500941.XT','JR101000', 'XT149321.XT','JR022936', 'XT145167.XT','JR004463', 'XT145163.XT','JR131631', 'XT144700.XT','JR112536', 'XT143128.XT','JR048971', 'XT142961.XT','JR002609', 'XT1414339.XT','JR005236', 'XT1412185.XT','JR027394', 'XT1411346.XT','JR080054', 'XT141040.XT','JR027187', 'XT1410225.XT','JR018705', 'XT134338.XT','JR105182', 'XT134245.XT','JR005661', 'XT130923.XT','JR000048', 'XT125375.XT','JR003680', 'XT125374.XT','JR058618', 'XT125311.XT','JR082302', 'XT125269.XT','JR003746', 'XT123370.XT','JR004154', 'XT122359.XT','JR006874', 'XT1205897.XT','JR116035', 'XT115084.XT','JR018948', 'XT113162.XT','JR100579', 'XT112030.XT','JR100188', 'XT102636.XT','JR003655', 'XT102211.XT','JR121387', 'XT090745.XT','JR003110', 'XT090712.XT','JR003140', 'XT090384.XT','JR003097', 'XT080123.XT','JR099734', 'XT071130.XT','JR054799', 'XT070415.XT','JR001759', 'XT0300891.XT','JR078639', 'XT1702235.XT','JR123230', 'XT1702116.XT','JR132804', 'XT1701704.XT','JR043391', 'XT1701495.XT','JR126811', 'XT1700771.XT','JR126938', 'XT1700557.XT','JR132238', 'XT1623578.XT','JR117844', 'XT1621451.XT','JR132333', 'XT1621450.XT','JR132330', 'XT1620974.XT','JR103141', 'XT1620558.XT','JR116971', 'XT1618884.XT','JR116052', 'XT1616801.XT','JR105303', 'XT1616655.XT','JR119273', 'XT1616112.XT','JR132408', 'XT1611791.XT','JR075406', 'XT1611366.XT','JR098521', 'XT1606638.XT','JR132580', 'XT1606493.XT','JR099775', 'XT1606054.XT','JR060675', 'XT1606053.XT','JR054983', 'XT1606052.XT','JR049455', 'XT1606051.XT','JR077940', 'XT1600552.XT','JR100444', 'XT1525557.XT','JR079803', 'XT1520281.XT','JR099591', 'XT1520280.XT','JR078088', 'XT1520279.XT','JR099590', 'XT1517768.XT','JR027598', 'XT1517493.XT','JR101642', 'XT1516243.XT','JR128744', 'XT1515180.XT','JR100026', 'XT1512611.XT','JR073308', 'XT1511633.XT','JR027554', 'XT1511527.XT','JR118121', 'XT1509045.XT','JR027497', 'XT1508043.XT','JR101641', 'XT1506310.XT','JR100923', 'XT1501578.XT','JR005819', 'XT146866.XT','JR051689', 'XT146373.XT','JR012217', 'XT1411550.XT','JR099763', 'XT1410032.XT','JR099762', 'XT140108.XT','JR118746', 'XT135521.XT','JR044807', 'XT134420.XT','JR003961', 'XT131714.XT','JR099756', 'XT131241.XT','JR099755', 'XT131121.XT','JR101148', 'XT1306936.XT','JR034104', 'XT115236.XT','JR041724', 'XT101617.XT','JR079854', 'XT090908.XT','JR078054', 'XT070393.XT','JR003135', 'XT070222.XT','JR003134',"
y = re.findall(r'{}\',\'(\w+\d+)\''.format(jr), yy)
return y
def crawl_benchmark(id): # 定义方法
tmp = wind.wsd(id, "NAV_date,nav,NAV_acc", "ED", now, "") # 万得API用法
df = pd.DataFrame(tmp.Data) # 用结果(list)去构造一个DataFrame二维表
df = df.T # 把表转置
print(df)
# ID1=jrbbb(id)
# ID=str(ID1[0])
ID = id
A = str(df.iloc[0, 0])
if A == "None":
R = "None"
B = str(df.iloc[0, 1])
C = str(df.iloc[0, 2])
return (ID, R, B, C)
import sys
import pandas as pd
from WindPy import w as wind
wind.start()
_PREVIOUS_DAY = 0
_CODE_MAP = {
"000300.SH": "hs300",
"000905.SH": "csi500",
"000016.SH": "sse50",
"000002.SH": "ssia",
"H11011.CSI": "cbi",
"NH0100.NHF": "nfi",
# "M1001940.NHF": "y1_treasury_rate",
"M1001940": "y1_treasury_rate"
}
_CODE_MAP = {
"000300.SH": "hs300",
"000905.SH": "csi500",
"000016.SH": "sse50",
"000002.SH": "ssia",
"H11011.CSI": "cbi",
"NH0100.NHF": "nfi",
# "M1001940.NHF": "y1_treasury_rate",
"M1001940": "y1_treasury_rate"
}
def sql_cols(df, usage="sql"):
cols = tuple(df.columns)
import os,time
from WindPy import w
import PerformanceAnalytics
###设置工作路径
path = os.getcwd()
os.chdir(path)
os.chdir("E:\\Github\\AdvancedPython\\PythonInAction\\Task1")
###读取数据
#托管产品数据
AllNetValueData = pd.read_excel('副本产品净值.xlsx','SQL Results',index_col = None, na_values=['NA'])
#沪深300指数数据
HS300Data = pd.read_csv('沪深300指数.csv')
"""
从Wind中提取沪深300指数数据
w.start()
HS300 = w.wsd("000300.SH","close",'20100101','20161223')
HS300Data = HS300.Data
HS300Data = HS300Data[0]
HS300Times = HS300.Times
data = pd.DataFrame({'date_line':HS300Times,'index_capital_line':HS300Data})
data.to_csv('沪深300指数.csv',index=None)
"""
###计算产品评价指标
AllProduct = np.unique(AllNetValueData['VC_CPMC'])
result = pd.DataFrame(AllProduct,columns = ['产品名称'],index = AllProduct)
#数据清洗(处理日期数据)
for i in range(len(HS300Data.index)):
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 27 15:31:50 2016
@author: gsyuan
"""
import PyWind as pw
from WindPy import w
import smtplib
from email.mime.text import MIMEText
from email.header import Header
w.start()
TRADEUNIT = {'A.DCE': '10',
'MA.CZC': '10',
'RI.CZC': '20',
'RM.CZC': '10',
'FG.CZC': '20',
'J.DCE': '100',
'PM.CZC': '50',
'CU.SHF': '5',
'LR.CZC': '20',
'P.DCE': '10',
'BB.DCE': '500',
'AL.SHF': '5',
'HC.SHF': '10',
'OI.CZC': '10',
'JM.DCE': '60',
'AG.SHF': '15',
import datetime
from pandas import Series
from pandas import DataFrame
import pandas as pd
from WindPy import w
index_code = "399006.SZ"
benchmark_code = index_code
w.start(showmenu=False)
wsd_data = w.wsd(index_code, "close, pct_chg", "2011-01-01", "2016-08-23", "")
dates = wsd_data.Times
index_df = DataFrame(wsd_data.Data,
index=wsd_data.Fields,
columns=wsd_data.Times).T
index_df['nav'] = (index_df['PCT_CHG'] / 100 + 1).cumprod()
pe_df = DataFrame()
for date in dates:
date = datetime.datetime.strftime(date, '%Y-%m-%d')
cons_code = w.wset("sectorconstituent",
"date=%s;windcode=%s" % (date, index_code)).Data[1]
code_str = ','.join(cons_code)
wsd_data = w.wsd(code_str, 'pe_ttm', date, date, "")
print(wsd_data)
pe_df[date] = (DataFrame(wsd_data.Data,
index=[date],
columns=wsd_data.Codes).T).loc[:, date]
print(pe_df)