Ejemplo n.º 1
0
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)
Ejemplo n.º 2
0
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
Ejemplo n.º 3
0
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]
Ejemplo n.º 4
0
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)
Ejemplo n.º 5
0
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)
Ejemplo n.º 6
0
    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()
Ejemplo n.º 7
0
 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()
Ejemplo n.º 8
0
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)
Ejemplo n.º 9
0
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)
Ejemplo n.º 10
0
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
Ejemplo n.º 11
0
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)
Ejemplo n.º 12
0
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  
    '''
Ejemplo n.º 13
0
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
Ejemplo n.º 14
0
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
Ejemplo n.º 15
0
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)
Ejemplo n.º 16
0
"""
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")  
Ejemplo n.º 17
0
    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
Ejemplo n.º 18
0
######## 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
Ejemplo n.º 19
0
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()
Ejemplo n.º 20
0
 def __init__(self):
     w.start()
Ejemplo n.º 21
0
 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
Ejemplo n.º 22
0
 def __init__(self):
     w.start()
Ejemplo n.º 23
0
# -*- 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,
Ejemplo n.º 24
0
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])
Ejemplo n.º 25
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))
Ejemplo n.º 26
0
def _windPyStart():
    w.start()
    print("w.isconnected: {}".format(w.isconnected()))
Ejemplo n.º 27
0
def QA_fetch_get_stock_financial(name, startDate, endDate):
    with w.start():
        pass
Ejemplo n.º 28
0
 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.")
Ejemplo n.º 29
0
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
Ejemplo n.º 30
0
    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()
Ejemplo n.º 31
0
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",
Ejemplo n.º 32
0
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
Ejemplo n.º 33
0
 def crawl(cls):
     wind.start()
     result = cls._fetch_swstype()
     cls.store(result)
Ejemplo n.º 34
0
 def initialize(self):
     w.start()
     self.initLogging()
     self.con = Connection().getConnection()
     self.cur = self.con.cursor()
Ejemplo n.º 35
0
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)
Ejemplo n.º 36
0
######## 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)
Ejemplo n.º 37
0
# -*- 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
Ejemplo n.º 38
0
 def __init__(self, rep, pub):
     super(WindServer, self).__init__(rep, pub)
     self.register(self.wsd)
     self.register(self.wsi)
     w.start()
Ejemplo n.º 39
0
    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
Ejemplo n.º 40
0
 def __init__(self):
     self.logger = mylog.set_log()
     mysql_con_demo = MysqlCon()
     self.engine = mysql_con_demo.getMysqlCon(flag='engine')
     w.start()
Ejemplo n.º 41
0
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)
Ejemplo n.º 42
0
    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
Ejemplo n.º 43
0
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))
Ejemplo n.º 44
0
    # %% 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))
Ejemplo n.º 45
0
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      
Ejemplo n.º 46
0
 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 .")
Ejemplo n.º 47
0
def QA_fetch_get_stock_financial(name, startDate, endDate):
    from WindPy import w
    # w.start()
    w.start()
    w.isconnected()
    pass
Ejemplo n.º 48
0
 def __enter__(self):
     w.start()
     return self
Ejemplo n.º 49
0
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)
Ejemplo n.º 50
0
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)
Ejemplo n.º 51
0
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)):
Ejemplo n.º 52
0
# -*- 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',
Ejemplo n.º 53
0
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)