Esempio n. 1
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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)
Esempio n. 2
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def func9():
    yesterday = (datetime.datetime.now() - datetime.timedelta(days = 1)).strftime("%Y-%m-%d") 
    df_discount_ratio_M = pd.read_csv('discount_ratio_M.csv')
    last_date_discount_ratio_M = time.strftime("%Y-%m-%d",time.strptime(str(int(df_discount_ratio_M.iloc[-1,0])),"%Y%m%d"))
    day_gap_discount_ratio_M = w.tdayscount(last_date_discount_ratio_M, yesterday).Data[0][0]-1
    w_wsd_data={}
    print 9
    stock_ls=[]
    for stock in universe:
        if stock[0]=='1':
            stock = stock + '.SZ'
        else:
            stock = stock + '.SH'

        mA_code = w.wsd(stock, 'fund_smfcode', yesterday).Data[0][0]
        stock_ls.append(mA_code)
        w_wsd_data[mA_code] = w.wsd(mA_code, 'anal_tdiscountratio', (w.tdaysoffset(1,last_date_discount_ratio_M).Data[0][0]).strftime("%Y-%m-%d"),yesterday)  
    for i in xrange(day_gap_discount_ratio_M):
        this_day = w.tdaysoffset(i+1,last_date_discount_ratio_M)
        ls = []
        this_day_csv = (this_day.Data[0][0]).strftime("%Y%m%d")
        
        for stock in stock_ls:
            ls.append(w_wsd_data[stock].Data[0][i])  
        f=open('discount_ratio_M.csv','a') 
        f.write(this_day_csv)
        for stock_data in ls:
            f.write(",")
            f.write(str(stock_data))
        f.write("\n")
        f.close()
    print 'finish'
Esempio n. 3
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def wsd(tickers, fields, startdate, enddate):
    if isinstance(tickers,str):
        tickers = tickers.replace(',','').split()

    if isinstance(fields,str):
        fields = fields.replace(',','').split()

    if len(tickers) == 1:
        tmp = w.wsd(tickers, fields, startdate, enddate)
        return pd.DataFrame(dict(zip(fields, tmp.Data)),index = tmp.Times)
    elif len(fields) == 1:
        tmp = w.wsd(tickers, fields, startdate, enddate)
        return pd.DataFrame(dict(zip(tickers, tmp.Data)),index = tmp.Times)
    else:
        print 'cannot surrport multiple code with multiple fields'
Esempio n. 4
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def func11():
    yesterday = (datetime.datetime.now() - datetime.timedelta(days = 1)).strftime("%Y-%m-%d") 
    df_industry=pd.read_csv('industry.csv')
    last_date_industry = time.strftime("%Y-%m-%d",time.strptime(str(int(df_industry.iloc[-1,0])),"%Y%m%d"))
    day_gap_industry = w.tdayscount(last_date_industry, yesterday).Data[0][0]-1
    print 11
    w_wsd_data={}
    stock_ls=[]
    for stock in list(df_industry.columns.values)[1:]  :
        stock_ls.append(stock)
        w_wsd_data[stock] = w.wsd(stock, 'close',  (w.tdaysoffset(1,last_date_industry).Data[0][0]).strftime("%Y-%m-%d"),yesterday)
        print w_wsd_data
    f=open('industry.csv','a')   
    for i in xrange(day_gap_industry):
  
        this_day = w.tdaysoffset(i+1,last_date_industry)
        ls = []
        this_day_csv = (this_day.Data[0][0]).strftime("%Y%m%d")
        for stock in stock_ls:
            ls.append(w_wsd_data[stock].Data[0][i])  
            
        
        f.write(this_day_csv)
        for stock_data in ls:
            f.write(",")
            f.write(str(stock_data))
        f.write("\n")
    f.close()
        
    print 'finish'
Esempio n. 5
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def vpup():
    '提取持仓与价格变动超过一定幅度的期货合作信息'
    codes = pw.getCodes()
    dic = {}
    for code in codes:
        data = w.wsd(code, "close,pct_chg,oi,oi_chg", "ED-1TD").Data
        if isinstance((data[1][-1]), float):
            price = data[0][-1]
            price_chg = data[1][-1]
            oi = data[2][-1]
            oi_chg = data[3][-1]
        else:
            price = data[0][-2]
            price_chg = data[1][-2]
            oi = data[2][-2]
            oi_chg = data[3][-2]
        if oi != 0:
            oichg = oi_chg / oi
        else:
            oichg = 0
        if abs(oichg) > 0.05 and abs(price_chg) > 1.5:
            dic[code] = ' '.join(['Oichg:%.2f%%' % (100 * oichg), 'Oi:%.0f' % oi,
                                  'Pricechg:%.2f%%' % price_chg, 'Price:%.0f' % price])
        else:
            pass
    return dic
Esempio n. 6
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def GetSWexponent(ExponentID=None,BeginTime=None,EndTime=None):
    if(ExponentID==None):
        print("沒有传入申万之一指数代码,数据将返回所有申万一级指数的数据")
        ExponentID = '801010.SI,801020.SI,801030.SI,\
                     801040.SI,801050.SI,801080.SI,801110.SI,\
                     801120.SI,801130.SI,801140.SI,801150.SI,\
                     801160.SI,801170.SI,801180.SI,801200.SI,\
                     801210.SI,801230.SI,801710.SI,801720.SI,\
                     801730.SI,801740.SI,801750.SI,801760.SI,\
                     801770.SI,801780.SI,801790.SI,801880.SI,\
                     801890.SI'
    if(BeginTime==None):
        BeginTime = '2010-01-01'
        
    if(EndTime==None):
        EndTime=date.today();
    
    tmp = (ExponentID,'close',BeginTime,EndTime,'Fill=Previous')
    data = w.wsd(*tmp)
    
    Value = []
    for i in range(0,len(data.Codes)):
        for j in range(0,len(data.Data[0])):
            value = data.Data[i][j];
            if isnan(value):
                value=0.0
            Value.append((str(data.Codes[i]),data.Times[j].strftime("%Y-%m-%d"),str(value)))
    for n in Value:
        print(n)
    return Value
Esempio n. 7
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def IndustryConfiguration(fundid=None, industry=None, begintime='2010-01-01', endtime=date.today()):
    if(fundid == None):
        print("没选择基金,默认为全部基金")
        fundid = FundID()
        
    if(industry == None):
        print("未选择行业,默认为全部19个新证监会行业指标")
        industry = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15,16, 17, 18, 19]

    Value = []
    for k in range(0, len(industry)):
        param = (fundid, 'prt_stockvalue_industrytoasset2', begintime, endtime,
                 'industry=' + str(industry[k]), 'Period=Q', 'Fill=Previous')
        data = w.wsd(*param)
        
        if(data.ErrorCode != 0):
            print("Wind  Error")
            
        for i in range(0, len(data.Codes)):
            for j in range(0, len(data.Data[0])):
                value = data.Data[i][j]
                if(value is None or isnan(value)):
                    value = 0.0
                record = (data.Codes[i], data.Times[j].strftime("%Y-%m-%d"), industry[k], value)
                Value.append(record)
    return Value
Esempio n. 8
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 def getUpdateCode(self,stockCodeList):
     '''得到直接插入和复权的代码'''
     reRightCode = [] # 需要復權的股票
     # 得到数据库中最新一天的收盘价
     closeList = []
     for code in stockCodeList:
         sql = "select close from stockData_day where stockCode = %s order by date desc limit 1" 
         self.cur.execute(sql,code)
         result = self.cur.fetchone()
         if not result:
             closeList.append(0.0)
         else:
             closeList.append(result[0])
     # 查找今天的數據
     fields = ['pre_close']
     today = datetime.date.today().strftime('%Y%m%d') #今天的日期
     option = 'showblank=0.0;PriceAdj=F' # 要進行前復權處理
     wsd_data = w.wsd(stockCodeList,fields,today,today,option)
     if wsd_data.ErrorCode==0: # 返回成功
         stockData = wsd_data.Data[0]
         
     ifEqualList = (np.array(stockData) == np.array(closeList))
     for i in range(len(ifEqualList)):
         if not ifEqualList[i]:
             rate = (stockData[i] - closeList[i]) / closeList[i]
             reRightCode.append([stockCodeList[i],rate])
             
     print '需要复权的数量为'
     print len(reRightCode)
     print '需要复权的股票代码为:'
     print reRightCode
     logging.info('需要复权的股票代码为:%s' % reRightCode)
     return reRightCode
Esempio n. 9
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def import_data_from_wind(d_start, d_end, file_folder='C:\\dev\\src\\ktlib\\data3\\', freq='d', contracts=[]):
    df = load_live_cont(d_start, d_end)
    contList = [str(inst) for inst in df.index]
    startDates = df.start_date
    endDates = df.expiry
    fields = 'open,close,high,low,volume,oi'
    if len(contracts) !=0:
        contList = contracts
    #w.start()
    for cont in contList:
        if cont[1].isalpha(): key = cont[:2]
        else: key = cont[:1]
        ex = 'SH'
        for exch in product_code.keys():
            if key in product_code[exch]:
                ex = exch
        ticker = cont + '.' + ex
        mth = int(cont[-2:])
        if (key not in contMonth):
            continue
        if mth not in contMonth[key]:
            continue
        start_d = max(d_start, startDates.ix[cont])
        end_d = min(d_end, endDates.ix[cont])
        try:
            if freq == 'm':
                print "loading min data for ticker = %s" % ticker
                raw_data = w.wsi(ticker,fields,start_d,end_d)
                if len(raw_data.Data)>1:
                    outfile = file_folder + cont+'_min.csv'
                    output={'datetime':raw_data.Times, 
                        'open':raw_data.Data[2],
                        'close':raw_data.Data[3],
                        'high':raw_data.Data[4],
                        'low':raw_data.Data[5],
                        'volume':raw_data.Data[6],
                        'openInterest':raw_data.Data[7]}
                    dump2csvfile(output,outfile)
                else:
                    print "no min data obtained for ticker=%s" % ticker
            else:
                print "loading daily data for ticker = %s" % ticker
                raw_data = w.wsd(ticker,fields,start_d,end_d)
                if len(raw_data.Data)>1:
                    outfile = file_folder + cont+'_daily.csv'
                    output={'datetime':raw_data.Times, 
                            'open':raw_data.Data[0],
                            'close':raw_data.Data[1],
                            'high':raw_data.Data[2],
                            'low':raw_data.Data[3],
                            'volume':raw_data.Data[4],
                            'openInterest':raw_data.Data[5]}
                    dump2csvfile(output,outfile)
                else:
                    print "no daily data obtained for ticker=%s" % ticker
        except ValueError:
            pass

    w.stop()
    return True
Esempio n. 10
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def func2():

    yesterday = (datetime.datetime.now() - datetime.timedelta(days = 1)).strftime("%Y-%m-%d") 
    df_openA=pd.read_csv('openA.csv')
    last_date_openA = time.strftime("%Y-%m-%d",time.strptime(str(int(df_openA.iloc[-1,0])),"%Y%m%d"))
    day_gap_openA = w.tdayscount(last_date_openA, yesterday).Data[0][0]-1
    print 2
    w_wsd_data={}
    stock_ls=[]
    for stock in universe:
        if stock[0]=='1':
            stock = stock + '.SZ'
        else:
            stock += '.SH'
        stock_ls.append(stock)
        w_wsd_data[stock] = w.wsd(stock, 'open',  (w.tdaysoffset(1,last_date_openA).Data[0][0]).strftime("%Y-%m-%d"),yesterday)
      
    for i in xrange(day_gap_openA):
        
        this_day = w.tdaysoffset(i+1,last_date_openA)
        ls = []
        this_day_csv = (this_day.Data[0][0]).strftime("%Y%m%d")

        for stock in stock_ls:
            ls.append(w_wsd_data[stock].Data[0][i])  
            
        f=open('openA.csv','a')
        f.write(this_day_csv)
        for stock_data in ls:
            f.write(",")
            f.write(str(stock_data))
        f.write("\n")
        f.close()
    print 'finish'
Esempio n. 11
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 def wsd(self, *args, **kwargs):
     try:
         raw_data = w.wsd(*args, **kwargs)
         if raw_data.ErrorCode < 0:
             return json.dumps(raw_data.Codes)
         df = pd.DataFrame(raw_data.Data)
         df = df.transpose()
         df.columns = [f.lower() for f in raw_data.Fields]
         df['date'] = raw_data.Times
         df.set_index(['date'])
         return df.to_json()
     except:
         return json.dumps({'error': 'something wrong in calculation'})
Esempio n. 12
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def run(fund_code):
    result = w.wsd(fund_code, "NAV_adj,sec_name,fund_fundmanageroftradedate",
                   "2010-01-01", today_date, "Fill=Previous;PriceAdj=F")
    df = pd.DataFrame(result.Data, columns=result.Times).transpose()
    fund_name = df.iloc[0, 1]
    manager = df.iloc[-1, 2]
    df = df.iloc[:, 0:1]
    df.dropna(inplace=True)
    df = del_eql(df)
    df.columns = [fund_name + '(' + manager + ')']

    df.to_csv('D:\\TF\\公募数据\\' + fund_code + ' ' + fund_name + '.csv',
              encoding='gbk')
 def __init__(self, backtest_start_date, backtest_end_date, index_code, threshold=1.5, hold_L=30, mean_L=5):
     self.backtest_start_date = backtest_start_date
     # 策略参数设定
     self.threshold = threshold
     self.hold_L = hold_L
     self.mean_L = mean_L
     # 提取数据
     w.start()
     start_date = get_trading_date_from_now(backtest_start_date, -mean_L-3, ql.Days)
     data = w.wsd(index_code, "amt, close", start_date, backtest_end_date, "")
     self.times = [t.strftime('%Y-%m-%d') for t in data.Times]  # 日期序列
     self.amt = data.Data[0]  # 成交额数据
     self.close = data.Data[1]  # 价格序列
     super().__init__(backtest_start_date, backtest_end_date, index_code)
 def get_weights(self):
     code_list = list_jq2wind(self.code_list)
     w.start()
     return_value = np.array(
         w.wsd(code_list, "pct_chg", "ED-" + str(self.N - 1) + "TD",
               self.date, "").Data)
     return_value = return_value.transpose()
     return_value = pd.DataFrame(return_value, columns=code_list)
     optimizer = HRPOpt(return_value)
     hrp_portfolio = optimizer.hrp_portfolio()
     hrp_portfolio = dict(
         zip(list_wind2jq(list(hrp_portfolio.keys())),
             hrp_portfolio.values()))
     return hrp_portfolio
Esempio n. 15
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def update_recorder(endDate=None, recordFile=None, holdListPath=None):
    endDate = dt.datetime.today().strftime(
        '%Y%m%d') if endDate is None else endDate
    recordFile = r'.\backtest_recorder.csv' if recordFile is None else recordFile
    holdListPath = r'.\holdLists' if holdListPath is None else holdListPath
    recordDF = pd.read_csv(recordFile)
    lastUpdt = recordDF['tradeDate'].values[-1]
    w.start()
    startDate = w.tdaysoffset(1, str(lastUpdt)).Data[0][0].strftime('%Y%m%d')
    if startDate > endDate:
        print('no new data to update')
        return
    betweenDays = w.tdays(startDate, endDate).Data[0]
    betweenDays = [int(tdt.strftime('%Y%m%d')) for tdt in betweenDays]
    print(betweenDays)
    outPut = {
        'tradeDate': [],
        'buyReturn': [],
        'holdReturn': [],
        'netReturn': []
    }
    cols = ['stkcd', 'buyDate', 'ret', 'flag']
    for tdt in betweenDays:
        retList = pd.read_csv(os.path.join(
            holdListPath, 'returns_tradeDate_{}.csv'.format(tdt)),
                              encoding='gbk')
        retList = retList.loc[:, cols]
        validBuy = np.all(
            [retList['buyDate'] == tdt,
             np.isin(retList['flag'], (0, 3, 4))],
            axis=0)
        validHold = np.all(
            [retList['buyDate'] < tdt,
             np.isin(retList['flag'], (0, 2, 3, 4))],
            axis=0)
        outPut['tradeDate'].append(tdt)
        outPut['buyReturn'].append(retList.loc[validBuy, 'ret'].mean())
        outPut['holdReturn'].append(retList.loc[validHold, 'ret'].mean())
        outPut['netReturn'].append(retList.loc[validBuy | validHold,
                                               'ret'].mean())
    outPut = pd.DataFrame(
        outPut).loc[:, ['tradeDate', 'buyReturn', 'holdReturn', 'netReturn']]
    indices = ['000001.SH', '000300.SH', '000905.SH', '000016.SH']
    idxRets = pd.DataFrame(w.wsd(','.join(indices), 'pct_chg', startDate,
                                 endDate).Data,
                           columns=indices)
    idxRets = idxRets.loc[:] / 100
    outPut = pd.concat([outPut, idxRets], axis=1)
    outPut.to_csv(recordFile, mode='a+', index=False, header=False)
    print('recorder updated till {}'.format(endDate))
 def Expire(self):
     today = date.today()
     tommorrow = today + timedelta(days=1)
     expire = w.wsd(self.atCode, "exe_enddate", "ED0TD", today,
                    "TradingCalender=DCE").Data[0][0]
     if self.position == 'flat':
         return False
     elif expire == tommorrow:
         string = "Option will expire tommorrow!"
         print(string + "Please COVER " + str(self.atCode) + 'and' +
               str(self.outCode))
         return True
     else:
         return False
Esempio n. 17
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def QA_fetch_get_stock_xueqiu(name, startDate, endDate):
    with 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)
Esempio n. 18
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def stocks_infomation():
    print('合并股票基础数据')
    codes = pd.read_excel(const.STOCKS_CODE_FILE, skiprows=4, index_col=0)
    codes.columns = ['股票代码', '股票名称']
    hkcodes = pd.read_excel(const.HKSTOCKS_CODE_FILE, skiprows=4, index_col=0)
    hkcodes.columns = ['股票代码', '股票名称']
    codes = codes.merge(hkcodes, on=['股票名称'], how='outer')
    codes['股票代码'] = [
        codes.loc[i, '股票代码_y']
        if pd.isna(codes.loc[i, '股票代码_x']) else codes.loc[i, '股票代码_x']
        for i in codes.index
    ]
    codes = codes[['股票名称', '股票代码']]

    stocks = pd.read_excel(const.STOCKS_LIST_FILE,
                           converters={
                               '增速(2020)': utils.percent2float,
                               '增速(2021)': utils.percent2float,
                               '增速(2022)': utils.percent2float
                           })
    results = stocks.merge(codes, how='left', on=['股票名称'])

    # 计算股票动量
    data = w.wsd(results['股票代码'].tolist(), 'close', start_date, end_date)
    # print(data.Data)
    prices = pd.DataFrame(data.Data, index=data.Codes, columns=data.Times)
    prices = prices.T
    returns = prices.pct_change().mean()
    returns.index = np.arange(results.shape[0])
    results['动量'] = returns

    data = w.wss(','.join(results['股票代码'].dropna().tolist()),
                 "industry_sw,close",
                 "industryType=3;tradeDate=%s;priceAdj=U;cycle=D" % (today))
    info = pd.DataFrame(data.Data, index=['细分行业', '收盘价'], columns=data.Codes)
    info = info.T
    info['细分行业'] = info['细分行业'].astype(str).map(lambda x: x.rstrip('Ⅲ'))
    info['股票代码'] = info.index
    results = results.merge(info, how='left', on=['股票代码'])
    results['pe(2020)'] = results['收盘价'] / results['eps(2020)']
    results['pe(2021)'] = results['收盘价'] / results['eps(2021)']
    results['pe(2022)'] = results['收盘价'] / results['eps(2022)']

    results_columns = [
        '主题行业', '股票名称', '细分行业', '收盘价', '上次预测时间', '短评', '长评', '动量', '增速(2020)',
        '增速(2021)', '增速(2022)', 'pe(2020)', 'pe(2021)', 'pe(2022)', '内部(40%)',
        '外部(20%)', '基金重仓(30%)', '外资重仓(10%)', '总分'
    ]
    results = results[results_columns]
    results.to_excel(const.STOCKS_LIST_FILE, index=False)
Esempio n. 19
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def get_history(date_end, T):
    w.start()
    print('获取指数历史数据......')
    if type(T) == int:
        date_start = w.tdaysoffset(-T, date_end,
                                   '').Data[0][0].strftime('%Y-%m-%d')
    else:
        date_start = T
    Data = w.wsd(list(SW1.keys()), "pct_chg", date_start, date_end, "")
    return_d = np.array(Data.Data)
    date_list = Data.Times
    date_list = [d.strftime('%Y-%m-%d') for d in date_list]
    print('指数历史数据获取完毕')
    return return_d, date_list
Esempio n. 20
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 def getMultiTimeSeriesDataFrame(codeList,
                                 beginDate,
                                 endDate,
                                 para,
                                 period="",
                                 tradingCalendar="",
                                 priceAdj="",
                                 credibility=0):
     """
     para只能是一个参数
     get time series from windPy, each code represents one capture
      月度合约: trade_hiscode
        :param credibility: (int)
        :param codeList: (list)
        :param beginDate: (date or datetime)
        :param endDate: (date or datetime)
        :param para: (string)只能是一个字符参数
        :param period: (int) 频率
        :param tradingCalendar: (string)   交易日历,选择可以选择银行间:NIB,不选择,则默认交易所日历
        :param priceAdj: (string) 价格是否调整,F:前复权,B:后复权
        :return: (DataFrame)
     """
     try:
         w.start()
         codeListStr = ",".join(codeList)
         period = ("Period=" + period) if period == "W" else ""
         tradingCalendar = (
             "TradingCalendar=" +
             tradingCalendar) if tradingCalendar != "" else ""
         priceAdj = ("priceAdj=" + priceAdj) if priceAdj != "" else ""
         credibility = ("credibility=" +
                        str(credibility)) if credibility != 0 else ""
         windData = w.wsd(codeListStr, para, beginDate.strftime("%Y-%m-%d"),
                          endDate.strftime("%Y-%m-%d"), period,
                          tradingCalendar, priceAdj, credibility)
         if len(windData.Data) == 0:
             raise BaseException
         if len(windData.Data[0]) == 0:
             raise BaseException
         dataDict = {}
         for i in range(len(windData.Data)):
             dataDict[windData.Codes[i].lower() + "_" +
                      para] = windData.Data[i]
         df = pd.DataFrame(dataDict, index=windData.Times)
         df.index = pd.to_datetime(df.index)
         df.index.name = "trade_date"
         return df
     except BaseException as e:
         print(format(e))
         raise
Esempio n. 21
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def get_sw_industry(code_list, date):
    """
    :param code_list:
    :param date:
    :return: sw level one industry
    date should be trade date
    code_list can not duplicated
    """
    if len(set(code_list)) != len(code_list):
        raise ValueError("duplicated value in code_list input")
    else:
        sw_industry_code = w.wsd(code_list, "indexcode_sw", date, date,
                                 "industryType=1").Data[0]
    return sw_industry_code
Esempio n. 22
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def QA_fetch_get_stock_day_simple(name, startDate, endDate):
    from WindPy import w
    #w.start
    w.start()
    w.isconnected()
    if(is_valid_date(endDate)==False):
        print ("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")
        print(data.ErrorCode)
        if (data.ErrorCode==0):
            print ("Connent to Wind successfully")
            return data.Data
Esempio n. 23
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def data_download(cmt, start_date, end_date):
    tmp_cnt_list = w.wset(
        "futurecc", "startdate=" + start_date + ";enddate=" + end_date +
        ";wind_code=" + cmt + ";field=wind_code")
    tmp_cnt_list = tmp_cnt_list.Data[0]
    tmp_download_cl_list = []
    tmp_download_open_list = []
    tmp_download_vol_list = []
    tmp_download_oi_list = []
    tmp_download_high_list = []
    tmp_download_low_list = []
    for cnt in tmp_cnt_list:
        tmp_download_data = w.wsd(cnt, "close,open,volume,oi,high,low",
                                  start_date, end_date, "")
        time_index = [
            datetime.strptime(str(x), "%Y-%m-%d")
            for x in tmp_download_data.Times
        ]
        tmp_cl = pd.Series(tmp_download_data.Data[0],
                           index=time_index,
                           name=cnt)
        tmp_open = pd.Series(tmp_download_data.Data[1],
                             index=time_index,
                             name=cnt)
        tmp_vol = pd.Series(tmp_download_data.Data[2],
                            index=time_index,
                            name=cnt)
        tmp_oi = pd.Series(tmp_download_data.Data[3],
                           index=time_index,
                           name=cnt)
        tmp_high = pd.Series(tmp_download_data.Data[4],
                             index=time_index,
                             name=cnt)
        tmp_low = pd.Series(tmp_download_data.Data[5],
                            index=time_index,
                            name=cnt)
        tmp_download_cl_list.append(tmp_cl)
        tmp_download_open_list.append(tmp_open)
        tmp_download_vol_list.append(tmp_vol)
        tmp_download_oi_list.append(tmp_oi)
        tmp_download_high_list.append(tmp_high)
        tmp_download_low_list.append(tmp_low)
    tmp_update_cl = pd.concat(tmp_download_cl_list, axis=1)
    tmp_update_open = pd.concat(tmp_download_open_list, axis=1)
    tmp_update_vol = pd.concat(tmp_download_vol_list, axis=1)
    tmp_update_oi = pd.concat(tmp_download_oi_list, axis=1)
    tmp_update_high = pd.concat(tmp_download_high_list, axis=1)
    tmp_update_low = pd.concat(tmp_download_low_list, axis=1)
    return tmp_update_cl, tmp_update_open, tmp_update_vol, tmp_update_oi, tmp_update_high, tmp_update_low
Esempio n. 24
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def bench_df(trade_days_list):
    #筛选出沪深300每个交易日的成分股的代码、名字和权重,已保存csv防止wind quota exceed
    #存在一个bug,wind数据中指数调整没有及时体现,函数取得值跟实际不符
    bench_df = pd.DataFrame(
        columns=['date', 'stock_code', 'stock_name', 'stock_weight'])
    for day in trade_days_list:
        strday = day[0]
        bench_data = w.wset("indexconstituent", "date=" + strday,
                            "windcode=000300.SH")
        temps_df = pd.DataFrame(bench_data.Data).T
        temps_df.columns = ['date', 'stock_code', 'stock_name', 'stock_weight']
        #保存了当天的样本和权重
        bench_df = pd.concat([bench_df, temps_df])

    # 将日期格式转化为文本
    # 应该可以apply
    bench_df['date'] = bench_df['date'].map(
        lambda x: datetime.datetime.strftime(x, '%Y-%m-%d'))

    #取每个股票的行业信息
    bench_stocks = bench_df['stock_code'].drop_duplicates()
    stocks_list = list(bench_stocks)
    #print(len(stocks_list))
    stocks_code = ','.join(stocks_list)
    data = w.wss(stocks_code, "industry_sw",
                 "tradeDate=20170901;cycle=D;industryType=1")
    stock_industry = pd.DataFrame([data.Codes, data.Data[0]]).T
    stock_industry.columns = ['stock_code', 'stock_industry']
    bench_df = pd.merge(bench_df, stock_industry, on='stock_code')

    # 取出沪深300成分股每天的涨跌幅
    bench_yield_df = pd.DataFrame(
        columns=['date', 'stock_code', 'stock_yield'])

    for stock in stocks_list:
        bench_data = w.wsd(stock, "pct_chg,windcode", "2017-09-01",
                           "2017-12-31", "")
        temps_df = pd.DataFrame(
            [bench_data.Times, bench_data.Data[0], bench_data.Data[1]]).T
        temps_df.columns = ['date', 'stock_yield', 'stock_code']
        bench_yield_df = pd.concat([bench_yield_df, temps_df])

    bench_yield_df['date'] = bench_yield_df['date'].map(
        lambda x: datetime.datetime.strftime(x, '%Y-%m-%d'))
    bench_yield_df.to_csv('data_produce/yield.csv')
    bench_df = pd.merge(bench_df, bench_yield_df, on=['date', 'stock_code'])
    bench_df = bench_df.sort_values(by=['date', 'stock_code'])

    return bench_df
Esempio n. 25
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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)
Esempio n. 26
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def crawl_benchmark(id):
    wind.start()  # 启动wind
    all_id = str(id) + '.OF'
    tmp = wind.wsd(all_id, "NAV_date,nav,NAV_acc", "ED0D", now, "")  # 万得API用法
    df = pd.DataFrame(tmp.Data)
    df = df.T
    if df[0][0] == None:
        pass
    else:
        df.columns = ["statistic_date_wind", "nav_wind", "added_nav_wind"]
        df["statistic_date_wind"] = df["statistic_date_wind"].apply(lambda x: x.strftime('%Y-%m-%d'))
        df["fund_id"] = id
        df["version"] = now
        to_sql("fund_nv", engine5, df, type="update")
        print(df)
Esempio n. 27
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def get_portfolio_PL(portfolio_dict, start_date, end_date, base_date):
    # 从start_date日收盘到end_date日收盘间计算投资组合价格变动
    # base_date为复权定点日
    w.start()
    stock_names = list(portfolio_dict.keys())
    data = w.wsd(stock_names, 'close2', start_date, end_date, 'adjDate=' +
                 ''.join(base_date.split('-')) + ';PriceAdj=T').Data
    data = np.array(data)
    price_start = data[:, 0]
    price_end = data[:, -1]
    stock_numbers = np.array(list(portfolio_dict.values()))
    market_value_start = np.sum(price_start * stock_numbers)
    market_value_end = np.sum(price_end * stock_numbers)
    print('投资组合的初期市值为:%.2f\n投资组合的到期市值为:%.2f' %
          (market_value_start, market_value_end))
Esempio n. 28
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def QA_fetch_get_stock_risk(name, startDate, endDate):
    with 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)
Esempio n. 29
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def append_data(codes, fileds):
    for code in codes:
        print(code)
        fname = '%s/%s.xlsx' % (INDEX_DIR, code)
        if datetime.datetime.now().hour < 15:
            end_date = datetime.datetime.today() - datetime.timedelta(1)
        else:
            end_date = datetime.datetime.today()
        if os.path.exists(fname):
            old_df = pd.read_excel(fname, index_col=0)
            if 'outmessage' in old_df.columns:
                del old_df['outmessage']
            start_date = old_df.index[-1] + datetime.timedelta(1)
            if start_date > end_date:
                continue
            else:
                data = w.wsd(code, fields, start_date, end_date)
                df = wind_data.wind2df(data)
                df = old_df.append(df)
                df.to_excel(fname)
        else:
            data = w.wsd(code, fields, '2001-01-01', end_date)
            df = wind_data.wind2df(data)
            df.to_excel(fname)
Esempio n. 30
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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)
Esempio n. 31
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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("Connect to Wind successfully")
    return pd.DataFrame(np.asarray(data.Data).T, columns=data.Fields, index=data.Times)
 def __init__(self, backtest_start_date, backtest_end_date, index_code, N, M, S1=0.7, S2=-0.7):
     w.start()
     RSRS_start_date = get_trading_date_from_now(backtest_start_date, -N-M, ql.Days)
     data = w.wsd(index_code, "high,low,close", RSRS_start_date, backtest_end_date, "")
     self.RSRS_times = [t.strftime('%Y-%m-%d') for t in data.Times]
     self.RSRS_data_high = data.Data[0]
     self.RSRS_data_low = data.Data[1]
     self.RSRS_data = data.Data[2]
     self.backtest_start_date = backtest_start_date
     self.RSRS_cal_start_date = get_trading_date_from_now(backtest_start_date, -M, ql.Days)
     self.N = N  # 计算RSRS指标采样的历史周期长短
     self.M = M  # 标准化序列的历史周期长短
     self.S1 = S1
     self.S2 = S2
     self.date_list, _, self.signal_list = self._get_data()
Esempio n. 33
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def select_hy_contracts(x,y):
    n=len(x)
    hy_list=[]
    date = pd.to_datetime(y)
    start_date = date - dt.timedelta(20)
    start_date = start_date.strftime('%Y-%m-%d')
    
    d=w.wsd(x,'volume',start_date,y)
    for i in range(n):                   #y means test date
        mean_vol = np.nansum(d.Data[i][-5:])/5.0
        #print(mean_vol)
        if mean_vol > 2e4:
            stock=d.Codes[i]
            hy_list.append(stock)
    return hy_list
Esempio n. 34
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def QA_fetch_get_stock_xueqiu(name, startDate, endDate):
    from WindPy import w
    # w.start()
    w.start()
    w.isconnected()
    if (is_valid_date(endDate) == False):
        print("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):
            print("Connent to Wind successfully")
    return data.Data
Esempio n. 35
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def QA_fetch_get_stock_risk(name, startDate, endDate):
    from WindPy import w
    w.start()
    w.isconnected()
    if (is_valid_date(endDate) == False):
        print("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):
            print("Connent to Wind successfully")
    return data.Data
    def start_market_simulation(self):
        error, data = w.wsd(self.ticker,
                            "open, close, volume",
                            self.start,
                            self.end,
                            "Fill=Previous;PriceAdj=F",
                            usedf=True)

        for time, row in data.iterrows():
            self.md.add_last_price(time, self.ticker, row["close"],
                                   row["volume"])
            self.md_add_open_price(time, self.ticker, row["open"])

        if not self.event_tick is None:
            self.event_tick(self.md)
Esempio n. 37
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def QA_fetch_get_stock_day(name, startDate, endDate):
    with w.start():

        if (QA_util_date_valid(endDate) == False):
            QA_util_log_info("wrong date")
        else:
            if w.isconnected() is False:
                w.start()
            data = w.wsd(name, "sec_name,pre_close,open,high,low,close,volume",
                         startDate, endDate, "PriceAdj=F")
            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 __init__(self, backtest_start_date, backtest_end_date, index_code, L=50, N=3, Long=100, Threshold=1.02):
     self.backtest_start_date = backtest_start_date
     # 策略参数设定
     self.L = L
     self.N = N
     self.Long = Long
     self.Threshold = Threshold
     # 基本数据获取
     w.start()
     start_date = get_trading_date_from_now(backtest_start_date, -(max(L+N, Long)+100), ql.Days)
     data = w.wsd(index_code, "close,amt", start_date, backtest_end_date, "")
     self.times = [t.strftime('%Y-%m-%d') for t in data.Times]  # 日期序列
     self.close = data.Data[0]  # 价格序列
     self.amt = data.Data[1]  # 成交额序列
     self._get_signal('2015-01-27')
     super().__init__(backtest_start_date, backtest_end_date, index_code)
 def __init__(self, backtest_start_date, backtest_end_date, index_code, threshold=0.0, RPS_history=250, max_vol_diff_history=100):
     self.backtest_start_date = backtest_start_date
     # 策略参数设定
     self.threshold = threshold
     self.RPS_history = RPS_history
     self.max_vol_diff_history = max_vol_diff_history
     # 提取数据
     w.start()
     start_date = get_trading_date_from_now(backtest_start_date, -RPS_history-max_vol_diff_history-30, ql.Days)
     data = w.wsd(index_code, "open, close, high, low", start_date, backtest_end_date, "")
     self.times = [t.strftime('%Y-%m-%d') for t in data.Times]  # 日期序列
     self.open = data.Data[0]
     self.close = data.Data[1]
     self.high = data.Data[2]
     self.low = data.Data[3]
     super().__init__(backtest_start_date, backtest_end_date, index_code)
 def __init__(self, backtest_start_date, backtest_end_date, index_code, llt_d, llt_cal_history=100, llt_threshold=0.0):
     '''
     :param llt_d: 上证综指适合参数39
     :param llt_cal_history:
     :param llt_threshold:
     '''
     w.start()
     llt_start_date = get_trading_date_from_now(backtest_start_date, -llt_cal_history, ql.Days)
     data = w.wsd(index_code, "close", llt_start_date, backtest_end_date, "")
     self.llt_times = [t.strftime('%Y-%m-%d') for t in data.Times]
     self.llt_data = data.Data[0]
     self.llt_d = llt_d
     self.llt_threshold = llt_threshold
     self.llt_cal_history = llt_cal_history  # LLT信号计算的历史长度
     self.backtest_start_date = backtest_start_date
     super().__init__(backtest_start_date, backtest_end_date, index_code)
Esempio n. 41
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def column_append(ticker,
                  fields,
                  start_date='2015-01-01',
                  end_date='2017-03-28'):
    fname = '%s/%s.xlsx' % (const.STOCK_DIR, ticker)
    old_df = pd.read_excel(fname, index_col=0)
    for col in fields.split(','):
        if col in old_df.columns:
            return

    print ticker
    data = w.wsd(ticker, fields, start_date, end_date, "traderType=1")
    df = wind2df(data)
    assert (df.shape[0] == old_df.shape[0])
    df = old_df.join(df)
    df.to_excel(fname)
Esempio n. 42
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def alert(chg=3.1):
    '定义警示函数,扫描所有品种的涨幅幅,若超过某个限定的阈值,则发送邮件提醒自己注意!'
    codes = pw.getActivecodes()
    dic = {}
    for code in codes:
        data = w.wsd(code, "close,pct_chg,oi,oi_chg", "ED-0TD").Data
        price = data[0][0]
        price_chg = data[1][0]
        if price:
            if price_chg > chg:
                dic[code] = [price, price_chg]
            else:
                pass
        else:
            print('没有有效的返回数据!')
    return dic
Esempio n. 43
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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
Esempio n. 44
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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
Esempio n. 45
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def get_wind_nev(windid, fre="日"):
    """
    获取wind基金数据
    """
    date = w.wss(windid, "fund_setupdate,NAV_date").Data
    start_date = date[0][0].strftime('%Y-%m-%d')
    end_date = date[1][0].strftime('%Y-%m-%d')
    fund_name = w.wss(windid, "name_official").Data[0]
    if fre == "周":
        para = "Period=W;Fill=Previous"
    else:
        para = "Period=D;Fill=Previous"
    data = w.wsd(windid, "NAV_adj", start_date, end_date, para)
    df = pd.DataFrame(data.Data[0], index=data.Times, columns=fund_name)
    df.index = pd.to_datetime(df.index)
    return df
Esempio n. 46
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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)
Esempio n. 47
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def ExponentScale(fundid=None, begintime='2010-01-01', endtime=date.today()):
    if(fundid == None):
        fundid = FundID()
        
    param = (fundid, 'prt_totalasset', begintime, endtime, 'Period=Q', 'Fill=Previous')
    data = w.wsd(*param)
    Value = []
    for i in range(0, len(data.Codes)):
        for j in range(0, len(data.Data[0])):
            stockvalue = data.Data[i][j]
            print(stockvalue)
            if(stockvalue is None or isnan(stockvalue)):
                stockvalue = 0.0
            record = (data.Codes[i], data.Times[j].strftime("%Y-%m-%d"), stockvalue)
            print(record)
            Value.append(record)
            
    return Value
Esempio n. 48
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 def getDayData(self,stockCodeList,fields,startDate,endDate,option):
     '''日数据的具体的获取步骤'''
     for stockCode in stockCodeList:
         print '添加日数据,代碼%s' % stockCode
         wsd_data = w.wsd(stockCode,fields,startDate,endDate,option)
         if wsd_data.ErrorCode==0: # 返回成功
             stockDate = wsd_data.Times
             dateList = [date.strftime("%Y-%m-%d") for date in stockDate]
             stockDataDict = {'stockCode':stockCode,'date':dateList}
             for i in range(len(fields)):
                 stockDataDict[fields[i]] = wsd_data.Data[i]
             stockData = pd.DataFrame(stockDataDict,columns=['stockCode','date']+fields,index=dateList).dropna() # 只要有缺失的数据就删掉这一行,保证数据最为干净
             stockData['pct_chg'] = stockData['pct_chg'] / 100 # 让涨幅变为实际涨幅(查询出来的是百分比数据)
             # 插入到數據庫中
             sql = "insert ignore into stockdata_day values(" + "%s,"*(len(fields)+1)+"%s)" 
             self.cur.executemany(sql,tuple(stockData.values.tolist()))
             self.con.commit()
         else:
             logging.info('ERROR-%s-day數據下載失敗,錯誤代碼為:%s' % (stockCode,wsd_data.ErrorCode))
Esempio n. 49
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def func6():

    yesterday = (datetime.datetime.now() - datetime.timedelta(days = 1)).strftime("%Y-%m-%d") 
    df_closeB=pd.read_csv('closeB.csv')
    last_date_closeB = time.strftime("%Y-%m-%d",time.strptime(str(int(df_closeB.iloc[-1,0])),"%Y%m%d"))
    day_gap_closeB = w.tdayscount(last_date_closeB, yesterday).Data[0][0]-1
    print 6
    w_wsd_data={}
    stock_ls=[]
    for stock in universe:
        if stock =='150073':
            stock='150075.SZ'
        else:
            if stock[0]=='1':
                stock = str(int(stock[:6])+1) + '.SZ'
            else:
                stock = str(int(stock[:6])+1) + '.SH'
        stock_ls.append(stock)

        w_wsd_data[stock] = w.wsd(stock, 'close',  (w.tdaysoffset(1,last_date_closeB).Data[0][0]).strftime("%Y-%m-%d"),yesterday)
       # print 1
        print stock
        #if w_wsd_data[stock].Data[0][0]==None:
    for i in xrange(day_gap_closeB):
  
        this_day = w.tdaysoffset(i+1,last_date_closeB)
        ls = []
        this_day_csv = (this_day.Data[0][0]).strftime("%Y%m%d")
        for stock in stock_ls:
            ls.append(w_wsd_data[stock].Data[0][i])  
            
        f=open('closeB.csv','a')   
        f.write(this_day_csv)
        for stock_data in ls:
            f.write(",")
            f.write(str(stock_data))
        f.write("\n")
        f.close()
        
    print 'finish'
Esempio n. 50
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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
Esempio n. 51
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def totalAmount(codes, baseDate='20160706', backwardDays=0):
    tradeTotal = 0
    baseDate = baseDate
    backDays = backwardDays
    backwardDays = 'ED-' + str(backwardDays) + "TD"
    for code in codes:
        data = w.wsd(code, "open,high,low,close,volume,oi,pct_chg", backwardDays,
                     baseDate)
        dt = data.Data
        for i in range(backDays + 1):
            date = data.Times[i]
            o = dt[0][i]
            h = dt[1][i]
            l = dt[2][i]
            c = dt[3][i]
            v = dt[4][i]
    #   pchg=data[5][0]
            avg = (o + h + l + c) / 4
            tradeAmount = v * avg * float(TRADEUNIT[code])
            print(str(date.strftime("%Y-%m-%d")) + ":" +
                  code + "的名义成交金额为:", tradeAmount / 10000)
            tradeTotal += tradeAmount
    return '名义成交量:' + str(tradeTotal / 10000) + "(万元)"
Esempio n. 52
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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)
Esempio n. 53
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from WindPy import w
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
import cPickle

w.start();

wsd_data=w.wsd("000300.SH", "amt,turn,pct_chg,pe_ttm,pb,roe,yoy_tr,yoy_or,yoyprofit,close", "2005-10-01", "2016-07-31", "Period=M;Days=Alldays;PriceAdj=F")
time=wsd_data.Times
amt=wsd_data.Data[0]
turn=wsd_data.Data[1]
pct_chg=wsd_data.Data[2]
pe_ttm=wsd_data.Data[3]
# pb=wsd_data.Data[4]
# roe=wsd_data.Data[5]
# yoy_tr=wsd_data.Data[6]
# yoy_or=wsd_data.Data[7]
# yoyprofit=wsd_data.Data[8]
close=wsd_data.Data[9]
nrows=len(time)
lable=0


# generate train data
train_data=[]
train_lable=[]
for i in range(3,nrows-1):
	start=close[i-2]
	end=close[i+1]
	gain=(float(end)/start)**(float(1)/3)
Esempio n. 54
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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
print('\n\n' + '-----通过wsd来提取各个报告期财务数据-----' + '\n')
wsddata2 = w.wsd("600000.SH", "tot_oper_rev,tot_oper_cost,opprofit,net_profit_is", "2008-01-01",
                 "2015-12-22", "rptType=1;Period=Q;Days=Alldays;Fill=Previous")
printpy(wsddata2)

# 通过wss来取截面数据
print('\n\n' + '-----通过wss来取截面数据-----' + '\n')
wssdata = w.wss("600000.SH,600007.SH,600016.SH", "ev,total_shares",
                "tradeDate=20151222;industryType=1")
printpy(wssdata)

# 通过wst来取日内成交数据
Esempio n. 55
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def get_min_data(filename):
    file_folder = 'C:\\dev\\src\\ktlib\\data3\\'
    reader = csv.reader(file(filename, 'rb'))
    contList = []
    fields = 'open,close,high,low,volume,oi'
    contStart = []
    contEnd = []
    for line in reader:
        contList.append(line[0])
        contStart.append(line[1])
        contEnd.append(line[2])
    
    #w.start()
    for cont, startD, endD in zip(contList, contStart, contEnd):
        if cont[1].isalpha(): key = cont[:2]
        else: key = cont[:1]
        ex = 'SH'
        for exch in product_code.keys():
            if key in product_code[exch]:
                ex = exch
                
        ticker = cont + '.' + ex
        startmin = ""
        endMin = ""
        
        if ex == 'CFE':
            startMin = " 09:14:00"
            endMin = " 15:15:00"
        else:
            startMin = " 08:59:00"
            endMin = " 15:00:00"
            
        if ex == 'CFE':
            startMin = " 09:14:00"
            endMin = " 15:15:00"
        else:
            startMin = " 08:59:00"
            endMin = " 15:00:00"
            
        try:
            sdate = datetime.datetime.strptime(startD, "%Y/%m/%d")
            edate = datetime.datetime.strptime(endD, "%Y/%m/%d")
            sMin = datetime.datetime.strptime(startD + startMin, "%Y/%m/%d %H:%M:%S")
            eMin = datetime.datetime.strptime(endD + endMin, "%Y/%m/%d %H:%M:%S")
            print ticker, sMin, eMin
            if edate > datetime.date(2009,1,1):
                raw_data = w.wsi(ticker,fields,sMin,eMin)
                if len(raw_data.Data)>1:
                    outfile = file_folder + cont+'_min.csv'
                    output={'datetime':raw_data.Times, 
                        'open':raw_data.Data[0],
                        'close':raw_data.Data[1],
                        'high':raw_data.Data[2],
                        'low':raw_data.Data[3],
                        'volume':raw_data.Data[4],
                        'openInterest':raw_data.Data[5]}
                    dump2csvfile(output,outfile)
                else:
                    print "no min data obtained for ticker=%s" % ticker
            
            raw_data = w.wsd(ticker,fields,sdate,edate)
            if len(raw_data.Data)>1:
                outfile = file_folder + cont+'_daily.csv'
                output={'datetime':raw_data.Times, 
                        'open':raw_data.Data[0],
                        'close':raw_data.Data[1],
                        'high':raw_data.Data[2],
                        'low':raw_data.Data[3],
                        'volume':raw_data.Data[4],
                        'openInterest':raw_data.Data[5]}
                dump2csvfile(output,outfile)
            else:
                print "no daily data obtained for ticker=%s" % ticker
        except ValueError:
            pass

    w.stop()
    return True
Esempio n. 56
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    PRIMARY KEY (secid,tradedate)
    )
""")

sql = "INSERT OR REPLACE INTO stockprice VALUES (?, ?, ?, ?, ?, ?, ?, ?)"

# 通过wset来取数据集数据
print('\n\n'+'-----通过wset来取数据集数据,获取全部A股代码列表-----'+'\n')
wsetdata=w.wset('SectorConstituent','date=20160625;sectorId=a001010100000000;field=wind_code')
print(wsetdata)

for j in range(0,len(wsetdata.Data[0])):
    # 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( u"\n\n-----第 %i 次通过wsd来提取 %s 开高低收成交量数据-----\n" %(j,str(wsetdata.Data[0][j])) )
    wssdata=w.wss(str(wsetdata.Data[0][j]),'ipo_date')
    wsddata1=w.wsd(str(wsetdata.Data[0][j]), "open,high,low,close,volume,amt", wssdata.Data[0][0], dt, "Fill=Previous")
    if wsddata1.ErrorCode!=0:
        continue
    print (wsddata1)
    for i in range(0,len(wsddata1.Data[0])):
        sqllist=[]
        sqltuple=()
        sqllist.append(str(wsetdata.Data[0][j]))
        if len(wsddata1.Times)>1:
            sqllist.append(wsddata1.Times[i].strftime('%Y%m%d'))
        for k in range(0, len(wsddata1.Fields)):
            sqllist.append(wsddata1.Data[k][i])
        sqltuple=tuple(sqllist)
        cursor.execute(sql,sqltuple)
    conn.commit()
conn.close()
Esempio n. 57
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	# personal version allows 600000-600120
	stockStartIndex=600000
	for j in range(0,100):
		stockIndex=stockStartIndex+j
		print ("j="+str(j))
		stock=str(stockIndex)+".sh"
		oneday=datetime.timedelta(days=1)
		totaldays=datetime.timedelta(days=numdays)
		dateEnd=datetime.date.today()
		dateStr=dateEnd-totaldays



		strdateStr=str(dateStr).replace("-","").split(" ")[0]
		strdateEnd=str(dateEnd).replace("-","").split(" ")[0]
		d=w.wsd(stock,"sec_englishname,open,high,low,close,volume",strdateStr,strdateEnd,"Priceadj=f;tradingcalendar=nib")
		print (d.Data)


		stockname=stock

		#data of each requested entry between the requested dates 
		#will be returned into its own array
		# in this case i asked for 6 kinds of entries
		arrSec_Name=d.Data[0]
		arrOpen=d.Data[1]
		arrHigh=d.Data[2]
		arrLow=d.Data[3]
		arrClose=d.Data[4]
		arrVolume=d.Data[5]
		
Esempio n. 58
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        data = []
        if len(outdata.Times) > 1:
            data.append(str(outdata.Times[i]))
        for k in range(0, len(outdata.Fields)):
            data.append(str(outdata.Data[k][i]))
        x.add_row(data)
    print(x)


if __name__ == '__main__':
    stock = '600000.SH'  # 浦发银行
    now = datetime.now()
    w.start()

    print('\n' + '获取过去三个季度总股本')
    wsddata = w.wsd(stock, "total_shares", "ED-3Q", now, "Period=Q;Fill=Previous")
    printinfo(wsddata)

    print('\n' + '获取过去三个季度自由流通股本')
    wsddata = w.wsd(stock, "free_float_shares", "ED-3Q", now, "Period=Q;Fill=Previous")
    printinfo(wsddata)

    print('\n' + '获取过去三个季度股东户数')
    wsddata = w.wsd(stock, "holder_num", "ED-3Q", now, "Period=Q;Fill=Previous")
    printinfo(wsddata)

    print('\n' + '获取过去三个季度户均持股数量(流通股本)')
    wsddata = w.wsd(stock, "holder_avgnum", "ED-3Q", now, "shareType=1;Period=Q;Fill=Previous")
    printinfo(wsddata)

    print('\n' + '获取过去三个季度户均持股数量(总股本)')
Esempio n. 59
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'''
Created on 2016年4月9日

@author: caiwx
'''
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
from WindPy import w
import datetime
import pandas as pd 

w.start();

# 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.wsd("000001.SZ", "open,high,low,close", "2015-12-10", "2015-12-22", "Fill=Previous")

#演示如何将api返回的数据装入Pandas的Series
open=pd.Series(wsd_data.Data[0])
high=pd.Series(wsd_data.Data[1])
low=pd.Series(wsd_data.Data[2])
close=pd.Series(wsd_data.Data[3])

print('open:/n',open)
print('high:/n',high)
print('low:/n',low)
print('close:/n',close)

#演示如何将api返回的数据装入Pandas的DataFrame
fm=pd.DataFrame(wsd_data.Data,index=wsd_data.Fields,columns=wsd_data.Times)
fm=fm.T #将矩阵转置
print('fm:/n',fm)
Esempio n. 60
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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
print "\n.Codes="+str1D(res.Codes)
print "\n.Fields="+str1D(res.Fields)
print "\n.Times="+str1D([ format(x,'%Y%m%d %H:%M:%S') for x in res.Times])
print "\n.Data="+str2D(res.Data)