def df_eod_prices(timestr):
w.start()
w.isconnected()
#timestr=input('tradedate')
timey = timestr[0:4]
timem = timestr[4:6]
timed = timestr[6:8]
date = datetime.datetime(int(timey), int(timem), int(timed))
windds1 = w.wset('SectorConstituent', 'date=' + timestr + ';sector=全部A股')
datac = windds1.Data[1]
#datat=['000001.SZ','000002.SZ']
windds2 = w.wss(
datac,
"pre_close,open,high,low,close,chg,pct_chg,volume,amt,adjfactor,vwap,trade_status",
"tradeDate=" + timestr + ";priceAdj=U;cycle=D")
dc1 = {
'Ticker': datac,
'Date': [timestr] * len(datac),
'PreClose': windds2.Data[0],
'Open': windds2.Data[1],
'High': windds2.Data[2],
'Low': windds2.Data[3],
'Close': windds2.Data[4],
'Change': windds2.Data[5],
'PctChg': windds2.Data[6],
'Volume': windds2.Data[7],
'Amount': windds2.Data[8],
'AdjFactor': windds2.Data[9],
'VWAP': windds2.Data[10],
'TradeStatus': windds2.Data[11]
}
df = pd.DataFrame(dc1)
return (df)
def index_info(timestr):
w.start()
w.isconnected()
#timestr=input('tradedate')
timey=timestr[0:4]
timem=timestr[4:6]
timed=timestr[6:8]
date= datetime.datetime(int(timey), int(timem), int(timed))
windds1=w.wset("indexhistory","startdate=2001-01-01;enddate="+timestr+";windcode=000300.SH;field=tradedate,tradecode,tradestatus")
dc1={'tradedate':windds1.Data[0],'tradecode':windds1.Data[1],'tradestatus':windds1.Data[2]}
df1=pd.DataFrame(dc1)
df1=df1.sort_index(axis=0,by=['tradecode','tradedate'],ascending=True)
df1=df1.reset_index(drop=True)
i=0
df=pd.DataFrame({},columns=['Ticker','EntryDate','RemoveDate'])
while(i<len(df1)):
if(df1.loc[i,'tradecode']==df1.loc[i+1,'tradecode']):
dc={'Ticker':df1.loc[i,'tradecode'],'EntryDate':str(df1.loc[i,'tradedate'])[0:4]+str(df1.loc[i,'tradedate'])[5:7]+str(df1.loc[i,'tradedate'])[8:10],'RemoveDate':str(df1.loc[i+1,'tradedate'])[0:4]+str(df1.loc[i+1,'tradedate'])[5:7]+str(df1.loc[i+1,'tradedate'])[8:10]}
df=df.append(dc,ignore_index=True)
i=i+2
else:
dc={'Ticker':df1.loc[i,'tradecode'],'EntryDate':str(df1.loc[i,'tradedate'])[0:4]+str(df1.loc[i,'tradedate'])[5:7]+str(df1.loc[i,'tradedate'])[8:10],'RemoveDate':None}
df=df.append(dc,ignore_index=True)
i=i+1
return(df)
def st(timestr):
w.start()
w.isconnected()
#timestr=input('tradedate')
windds1 = w.wset(
"carryoutspecialtreatment", "startdate=1998-04-28;enddate=" + timestr +
";field=wind_code,implementation_date")
wcode = []
wdate = []
status = []
for i in range(len(windds1.Codes)):
if (windds1.Data[0][i][7:9] != 'OC'):
wcode.append(windds1.Data[0][i])
wdate.append(windds1.Data[1][i])
status.append(1)
dc1 = {'codes': wcode, 'date': wdate, 'status': status}
windds2 = w.wset(
"cancelspecialtreatment", "startdate=1998-04-28;enddate=" + timestr +
";field=wind_code,implementation_date")
wcode = []
wdate = []
status = []
for i in range(len(windds2.Codes)):
if (windds1.Data[0][i][7:9] != 'OC'):
wcode.append(windds2.Data[0][i])
wdate.append(windds2.Data[1][i])
status.append(2)
dc2 = {'codes': wcode, 'date': wdate, 'status': status}
df1 = pd.DataFrame(dc1)
df2 = pd.DataFrame(dc2)
df = pd.concat([df1, df2], ignore_index=True)
df = df.sort_values(by=["codes", 'date'])
code = []
entrydate = []
removedate = []
i = 0
while (i < len(df)):
if (df.iloc[i][2] == 2):
i += 1
elif (i < (len(df) - 1) and df.iloc[i][0] == df.iloc[i + 1][0]):
code.append(df.iloc[i][0])
entrydate.append(
str(df.iloc[i][1])[0:4] + str(df.iloc[i][1])[5:7] +
str(df.iloc[i][1])[8:10])
removedate.append(
str(df.iloc[i + 1][1])[0:4] + str(df.iloc[i + 1][1])[5:7] +
str(df.iloc[i + 1][1])[8:10])
i += 2
else:
code.append(df.iloc[i][0])
entrydate.append(
str(df.iloc[i][1])[0:4] + str(df.iloc[i][1])[5:7] +
str(df.iloc[i][1])[8:10])
removedate.append(None)
i += 1
dc = {'Ticker': code, 'EntryDate': entrydate, 'RemoveDate': removedate}
df = pd.DataFrame(dc)
return (df)
def QA_fetch_get_stock_list(date):
w.start()
w.isconnected()
if (is_valid_date(date) == False):
print("wrong date")
else:
awgs = 'date=' + date + ';sectorid=a001010100000000'
data = w.wset("sectorconstituent", awgs)
return data.Data
def wConnect():
if w.isconnected():
print("w.isconnected: {}".format(w.isconnected()))
#continue
elif os.system('tasklist | find "wmain.exe"') == 0:
_windPyStart()
else:
_startWindExe()
_windPyStart()
def suspend1(timestr):
w.start()
w.isconnected()
#timestr=input('tradedate')
windds1 = w.wset(
"pauselistsecurity", "startdate=1998-04-28;enddate=" + timestr +
";sectorid=a001010100000000;field=wind_code,susp_list_date")
windds2 = w.wset(
"resumelistsecurity", "startdate=1998-04-28;enddate=" + timestr +
";sectorid=a001010100000000;field=wind_code,resume_list_date")
dc1 = {
'codes': windds1.Data[0],
'date': windds1.Data[1],
'status': [1 for i in range(len(windds1.Codes))]
}
dc2 = {
'codes': windds2.Data[0],
'date': windds2.Data[1],
'status': [2 for i in range(len(windds2.Codes))]
}
df1 = pd.DataFrame(dc1)
df2 = pd.DataFrame(dc2)
df = pd.concat([df1, df2], ignore_index=True)
df = df.sort_values(by=["codes", 'date'])
code = []
entrydate = []
removedate = []
i = 0
while (i < len(df)):
if (df.iloc[i][2] == 2):
i += 1
elif (i < (len(df) - 1) and df.iloc[i][0] == df.iloc[i + 1][0]):
code.append(df.iloc[i][0])
entrydate.append(
str(df.iloc[i][1])[0:4] + str(df.iloc[i][1])[5:7] +
str(df.iloc[i][1])[8:10])
removedate.append(
str(df.iloc[i + 1][1])[0:4] + str(df.iloc[i + 1][1])[5:7] +
str(df.iloc[i + 1][1])[8:10])
i += 2
else:
code.append(df.iloc[i][0])
entrydate.append(
str(df.iloc[i][1])[0:4] + str(df.iloc[i][1])[5:7] +
str(df.iloc[i][1])[8:10])
removedate.append(None)
i += 1
dc = {'Ticker': code, 'EntryDate': entrydate, 'RemoveDate': removedate}
df = pd.DataFrame(dc)
code = []
for i in range(len(df)):
if (df.iloc[i][2] == None):
code.append(df.iloc[i][0])
dc = {'Ticker': code, 'Date': [timestr for i in range(len(code))]}
df = pd.DataFrame(dc)
return (df)
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 QA_fetch_get_stock_list(date):
from WindPy import w
w.start()
w.isconnected()
if (is_valid_date(date) == False):
print("wrong date")
else:
awgs = 'date=' + date + ';sectorid=a001010100000000'
data = w.wset("sectorconstituent", awgs)
return pd.DataFrame(np.asarray(data.Data).T,
columns=data.Fields,
index=data.Times)
def QA_fetch_get_stock_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
def QA_fetch_get_stock_shape(name, startDate, endDate):
from WindPy import w
#w.start()
w.start()
w.isconnected()
if(is_valid_date(endDate)==False):
print ("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):
print ("Connent to Wind successfully")
return data.Data
def QA_fetch_get_stock_indicator(name, startDate, endDate):
from WindPy import w
#w.start()
w.start()
w.isconnected()
if(is_valid_date(endDate)==False):
print ("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;SR MI_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):
print ("Connent to Wind successfully")
return data.Data
def suspend1(timestr):
w.start()
w.isconnected()
#timestr=input('tradedate')
windds1 = w.wset(
"tradesuspend", "startdate=" + timestr + ";enddate=" + timestr +
";field=date,wind_code")
dc1 = {
'Ticker': windds1.Data[1],
'Date': [timestr for i in range(len(windds1.Data[1]))]
}
df = pd.DataFrame(dc1)
return (df)
def QA_fetch_get_stock_list_special(date,id):
from WindPy import w
#w.start()
w.start()
w.isconnected()
if(is_valid_date(date)==False):
print ("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 data.Data
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
def download_data_wind(start_date, end_date, var_list):
if w.isconnected() == False:
w.start()
raw = w.wsd(var_list, "open, high, low, close", start_date, end_date, "")
raw_df = pd.DataFrame(raw.Data, index=raw.Fields, columns=raw.Times)
raw_df = raw_df.T
return raw_df
def start_service():
if w.isconnected():
w.start()
try:
app.run(host="0.0.0.0", debug=True)
finally:
w.close()
def get_seq_w(self, start, end):
"""
根据开始时间和结束时间，返回频率为“W"的回测任务确定新仓位时间list.
:param start: str, 开始时间.
:param end: str, 结束时间.
:return: list, 计算下期仓位各时点list。
"""
if not w.isconnected():
w.start()
sleep(3)
# wind的w.tdays函数准确性较差，需要严格检验
# seq = w.tdays(start, end, f"Period=W;TradingCalendar={self.calendar}").Data[0]
# 转换为工作日
seq = w.tdays(start, end, "Period=W;Days=Weekdays").Data[0]
if seq[-1].weekday() != 4:
seq.pop()
seq = [dd.strftime("%Y-%m-%d") for dd in seq]
month = []
seq_ = []
for ss in seq:
if ss[0:7] not in month:
# 只有在新月份出现时才可能出现更新
month.append(ss[0:7])
seq_.append((ss, len(month) - 1))
else:
seq_.append((ss, 0.5))
return seq_
def __init__(self,
spec,
basic_indices,
ics_indices,
ics,
ics_fv,
ics_rank,
mode="run",
check_w=False):
self._date = ""
self._spec = spec
self.basic_indices = basic_indices
self.ics_indices = ics_indices
self.ics = ics
self.ics_fv = ics_fv
self.ics_rank = ics_rank
self._codes = []
self._fields = None
self._spec_id = []
self._names = []
self.df_all = None
self.df_filter = None
self.pool_codes = None
self.print_obj = PrintOnce()
if mode == "test":
self._flag = True
else:
self._flag = False
if check_w:
if not w.isconnected():
w.start()
self.check_spec_id()
def set_universe(code, refDate=None):
if Settings.data_source == DataSource.WIND:
from WindPy import w
if not w.isconnected():
w.start()
if not refDate:
rawData = w.wset('IndexConstituent',
'windcode=' + convert2WindSymbol(code),
'field=wind_code')
else:
rawData = w.wset('IndexConstituent', 'date=' + refDate,
'windcode=' + convert2WindSymbol(code),
'field=wind_code')
if len(rawData.Data) == 0:
return
# convert to .xshg/.xshe suffix
idx = [s.replace('SH', 'xshg') for s in rawData.Data[0]]
idx = [s.replace('SZ', 'xshe') for s in idx]
return idx
elif Settings.data_source != DataSource.DXDataCenter:
import os
import tushare as ts
try:
ts.set_token(os.environ['DATAYES_TOKEN'])
except KeyError:
raise
idx = ts.Idx()
return list(idx.IdxCons(secID=code, field='consID')['consID'])
else:
from DataAPI import api
data = api.GetIndexConstitutionInfo(
code, refDate=refDate).sort_values('conSecurityID')
return list(data.conSecurityID)
def download_data_wind(start_date, end_date, var_list):
if w.isconnected() == False:
w.start()
raw = w.edb(var_list, start_date, end_date)
raw_df = pd.DataFrame(raw.Data, index=raw.Codes, columns=raw.Times)
raw_df = raw_df.T
return raw_df
def get_sec_return_on_date(cls,
start_date,
end_date,
sec_ids,
freq=FreqType.EOD,
field=['close'],
return_type=DfReturnType.DateIndexAndSecIDCol,
is_cumul=False):
"""
:param start_date: str, start date of the query period
:param end_date: str, end date of the query period
:param sec_ids: list of str, sec IDs
:param field: str, filed of data to be queried
:param freq: FreqType
:param return_type
:param is_cumul: return is cumul or not
:return: pd.DataFrame, index = date, col = sec ID
"""
if not w.isconnected():
w.start()
ret = WindMarketDataHandler.get_sec_price_on_date(
start_date, end_date, sec_ids, freq, field, return_type)
ret = ret.pct_change()
if is_cumul:
ret = ret.fillna(0)
ret = cum_returns(ret, starting_value=1.0)
else:
ret = ret.dropna()
return ret
def get_sec_price_on_date(cls,
start_date,
end_date,
sec_ids,
freq=FreqType.EOD,
field=['close'],
return_type=DfReturnType.DateIndexAndSecIDCol):
"""
:param start_date: str, start date of the query period
:param end_date: str, end date of the query period
:param sec_ids: list of str, sec IDs
:param freq: FreqType
:param field: str, filed of data to be queried
:param return_type: dfReturnType
:return: pd.DataFrame, index = date, col = sec ID
"""
if not w.isconnected():
w.start()
pyFinAssert(
freq == FreqType.EOD, ValueError,
"for the moment the function only accepts freq type = EOD")
start_date = str(start_date) if not isinstance(
start_date, basestring) else start_date
end_date = str(end_date) if not isinstance(end_date,
basestring) else end_date
raw_data = w.wsd(sec_ids, field, start_date, end_date, 'PriceAdj=F',
'Fill=Previous')
ret = format_raw_data(raw_data, sec_ids, freq, field, return_type)
return ret
def get_seq_d(self, start, end):
"""
根据开始时间和结束时间，返回频率为“D"的回测任务确定新仓位时间list.
:param start: str, 开始时间.
:param end: str, 结束时间.
:return: list, 计算下期仓位各时点list。
"""
if not w.isconnected():
w.start()
sleep(3)
# 日频级别暂时不变更为工作日日历，对于假期造成的不交易，当天仓位无法变更
seq = w.tdays(start, end, f"TradingCalendar={self.calendar}").Data[0]
seq = [dd.strftime("%Y-%m-%d") for dd in seq]
month = []
seq_ = []
for ss in seq:
if ss[0:7] not in month:
# 只有在新月份出现时才可能出现更新
month.append(ss[0:7])
seq_.append((ss, len(month) - 1))
else:
seq_.append((ss, 0.5))
return seq_
def wss(cls, codes, paras):
"""多代码多维信息序列
:param codes:(list) or (str)
:param paras:(list) or (str)
:return:(DataFrame);
"""
try:
if not w.isconnected():
w.start()
if type(codes) is not list:
codes = codes.split(",") # 全部转换为list格式
codes = [cls.mapper[x] if x in cls.mapper else x for x in codes]
if type(paras) is not list:
paras = paras.split(",")
paras.insert(0, "windcode")
windData = w.wss(codes,
paras, options=None)
if len(windData.Data) == 0:
return None
if len(windData.Data[0]) == 0:
return None
dataDict = {}
for i in range(len(windData.Data)):
dataDict[windData.Fields[i].lower()] = windData.Data[i]
df = pd.DataFrame(dataDict)
df = df[paras]
df.rename(columns={"windcode": "code"}, inplace=True)
return df
except BaseException as e:
print(format(e))
raise
def post(self):
"""
json str:{"offset": "1", "begin_time": "2017-3-31", "options": ""}
:return: 返回万得返回数据dict
"""
data_dic = request.json
logger.info('/tdaysoffset/ data_dic:%s', data_dic)
offset = int(data_dic['offset'])
begin_time = data_dic['begin_time']
options = data_dic['options']
if not w.isconnected():
w.start()
if options == "":
options = None
ret_data = w.tdaysoffset(offset, begin_time, options)
error_code = ret_data.ErrorCode
if error_code != 0:
msg = ERROR_CODE_MSG_DIC.setdefault(error_code, "")
logger.error('tdaysoffset("%s", "%s", "%s") ErrorCode=%d %s' %
(offset, begin_time, options, error_code, msg))
return {'error_code': ret_data.ErrorCode, 'message': msg}, 404
# if ret_data.ErrorCode != 0:
# logger.error(
# 'tdaysoffset("%s", "%s", "%s") ErrorCode=%d' % (offset, begin_time, options, ret_data.ErrorCode))
# return {'error_code': ret_data.ErrorCode}, 404
# 将 Data数据中所有 datetime date 类型的数据转换为 string
if len(ret_data.Data) > 0 and len(ret_data.Data[0]) > 0:
date_str = format_2_date_str(ret_data.Data[0][0])
else:
logger.warning('tdaysoffset(%s, %s, %s) No value return' %
(offset, begin_time, options))
date_str = ''
ret_dic = {'Date': date_str}
# print('offset:\n', ret_dic)
return ret_dic
def QA_fetch_get_trade_date(endDate, exchange):
from WindPy import w
# w.start()
w.start()
w.isconnected()
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)
# print(data.Data)
dates = data.Data
else:
print("exchange name problem")
return dates
def get_cons_maxUpOrDown(self, beginDate, endDate):
'''利用wind获得列表中每个股票在特定日期的涨跌停情况
Args:
self.consTicker: Series, 交易代码序列
self.tradeDate: str, 交易时间
Returns:
self.consMaxUpOrDown: dict, 交易代码对应是否涨跌停
'''
# w.start()
exchange = '.SH'
if (w.isconnected()):
length = len(self.consTicker)
for i in range(length):
Ticker = str(self.consTicker.values[i])
wind = w.wsd(Ticker + exchange,
"maxupordown",
beginDate,
endDate,
usedf=True)
maxUpOrDown = wind[1]
self.consMaxUpOrDown[Ticker] = maxUpOrDown
# print("NO. %d, Ticker = %s"%(i,Ticker))
print(" %.2d " % i, sep=">", end='')
print("get_cons_maxUpOrDown ...... Done!")
print("\n")
else:
print("Wind Starting Error!")
def post(self):
"""
json str:{"codes": "XT1522613.XT",
"fields": "fund_setupdate,fund_maturitydate,fund_mgrcomp,fund_existingyear,fund_fundmanager", "options": ""}
:return: 返回万得返回数据dict
"""
args = receive_wss_parser.parse_args()
logger.info('/wss/ args:%s', args)
# codes = args['codes']
# fields = args['fields']
# 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.wss(**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('wss(%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)
logger.debug('ret_data.Data len:%d', data_len)
logger.debug('ret_data.Codes : %s', ret_data.Codes)
for n_data in range(data_len):
data = ret_data.Data[n_data]
data_len2 = len(data)
if data_len2 > 0:
if type(data[0]) 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])
# print('ret_data.Data:\n', ret_data.Data)
# 组成 DataFrame
ret_df = pd.DataFrame(ret_data.Data,
index=ret_data.Fields,
columns=ret_data.Codes)
ret_dic = ret_df.to_dict()
# print('ret_dic:\n', ret_dic)
return ret_dic
def QA_fetch_get_stock_info(name, startDate, endDate):
from WindPy import w
#w.start()
w.start()
w.isconnected()
#get the all stock list on the endDate
# judge the vaild date
if(is_valid_date(endDate)==False):
print ("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)
#print(data)
if (data.ErrorCode!=0):
print ("Connent to Wind successfully")
return data.Data
def login(is_quiet=False):
if not WDServer.isconnected():
login = WDServer.start()
if not is_quiet:
print(colored("WDServer is connected successfully!", "green"))
else:
if not is_quiet:
print(colored("WDServer is already connected!", "green"))
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()
import datetime
import sqlite3
import numpy
conn=sqlite3.connect("stockr.db3")
cur=conn.cursor()
#get data for one year
numdays=150
#clear the table and keep only data a year from now
cur.execute("delete from dp")
#start connection with Wind
w.start()
# if connected to Wind
if w.isconnected():
#fetch data
# 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]
######## 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
#_*_coding:utf-8 _*_
from WindPy import w
from _datetime import date
import pymysql
from math import isnan
w.start()
print('connection wind success', w.isconnected())
db = pymysql.connect(host="localhost", user="******", passwd="A123123123", db="fund", charset="utf8")
cursor = db.cursor()
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)
# coding: utf-8
# In[1]:
from WindPy import w
from datetime import *
from pandas import *
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import csv
import math
import glob
plt.style.use('ggplot');
w.start();
w.isconnected();
#set some dates
today = datetime.today()
yesterday = today-timedelta(4)
d = yesterday.strftime('%Y%m%d')
#set index for stock
s = '000905.SH'
import mdapi.stock
from math import *
import random
# In[2]:
def tradedate(startDate,endDate):
# 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()
# index_importer = IndexImporter(loop, w, config)
