def OnTickSpread(self,Tick):
if self.tsData.has_key(str(Tick.Codes[0])):
self.tsData[str(Tick.Codes[0])].append(Tick.Data[0][0])
print tsData
for i in range(len(self.tradingOrder)):
if self.tsData[self.tradingOrder[i][0]]!=[] and self.tsData[self.tradingOrder[i][1]]!=[]:
if self.tsData[self.tradingOrder[i][0]][-1]-self.tsData[self.tradingOrder[i][1]][-1]*self.tradingOrder[i][2]>self.tradingOrder[i][4] or self.tsData[self.tradingOrder[i][0]][-1]-self.tsData[self.tradingOrder[i][1]][-1]*self.tradingOrder[i][2]<self.tradingOrder[i][3]:
#定义买单下单参数,并下单
print'it is trading spread order between %s and %s'%(self.tradingOrder[i][0],self.tradingOrder[i][1])
instrument_id=self.tradingOrder[i][0]
direction='buy'
limitPrice=w.wsq(instrument_id,'rt_last').Data[0]
volume=1
logonid=2
w.torder(instrument_id,direction,limitPrice,volume,logonid=logonid)
#定义卖单下单参数,并下单
instrument_id=self.tradingOrder[i][1]
direction='short'
limitPrice=w.wsq(instrument_id,'rt_last').Data[0]
volume=self.tradingOrder[i][2]
logonid=2
w.torder(instrument_id,direction,limitPrice,volume,logonid=logonid)
def Monitor(self):
print("Sugar SKEW", self.atCode, self.outCode, datetime.now())
try:
atIVX = w.wsq(self.atCode, "rt_imp_volatility").Data[0][0]
# the real time ivx of wind
outIVX = w.wsq(
self.outCode,
"rt_imp_volatility").Data[0][0] # the real time ivx of wind
except IndexError:
pass
if atIVX != 0:
skewnow = outIVX / atIVX
else:
return
if self.Expire():
return
print("skew: ", skewnow)
# open a position
if skewnow > self.per21 and self.position == 'flat':
print('NOTICE! buy one' + str(self.atCode) + '，short two ' +
str(self.outCode))
elif skewnow < self.per24 and self.position == 'flat':
print('NOTICE! short one' + str(self.atCode) + '，buy two ' +
str(self.outCode))
# close a position
if skewnow < self.per22 and self.position == 'buy':
print('COVER OPTIONS! short one' + str(self.atCode) + '，buy two' +
str(self.outCode))
elif skewnow > self.per23 and self.position == 'short':
print('COVER OPTIONS! buy one' + str(self.atCode) + '，short two ' +
str(self.outCode))
def start(self):
w.start()
w.wsq(
self.code,
"rt_date,rt_time,rt_pre_close,rt_open,rt_high,rt_low,rt_last,rt_last_amt,rt_last_vol,rt_ask1,rt_bid1",
func=self.tradeSignalCallback)
while 1:
self.positionDetail()
print('持仓列表更新')
time.sleep(np.random.randint(5, 30))
def mail_index_pct():
"""
发送今日指数临近收盘价数据，用来基金定投
"""
data = w.wsq("000300.SH,000905.SH,CI005165.WI,CI005166.WI",
"rt_pct_chg,rt_date,rt_pe_ttm,rt_pb_lf")
data = pd.DataFrame(data.Data, index=data.Fields, columns=data.Codes).T
data['NAME'] = ["沪深300", "中证500", "中信证券二级行业指数", "中信保险二级行业指数"]
string_total = ""
for i in range(len(data)):
string = str(
data.ix[i, "RT_DATE"].astype(int)) + "日, " + data.ix[i, "NAME"]
string += "现在涨幅为" + str(np.round(data.ix[i, "RT_PCT_CHG"] * 100,
2)) + '%, '
string += "PB_LF为" + str(data.ix[i, "RT_PB_LF"].round(2)) + ', '
string += "PE_TTM为" + str(data.ix[i,
"RT_PE_TTM"].round(1)) + '.' + '<br>'
print(string)
string_total += string
sender_mail_name = '*****@*****.**'
receivers_mail_name = ['*****@*****.**', '*****@*****.**']
acc_mail_name = []
subject_header = "今日指数涨跌幅"
email = EmailSender()
email.attach_html(string_total)
email.send_mail_qq(sender_mail_name, receivers_mail_name, acc_mail_name,
subject_header)
def get_wsq(universe, if_convert=False):
'''
实时行情快照.
Parameters
-----------
universe
list,标的代码
if_convert
是否将代码转换成万德格式
Returns
--------
Series
index 万德代码, name 快照时间.
'''
if if_convert:
universe_wind = code_2_wind_symbol(universe)
universe_wind = ','.join(universe_wind)
else:
universe_wind = ','.join(universe)
data = w.wsq(universe_wind, 'rt_last')
data = pd.DataFrame(data.Data, index=data.Times, columns=data.Codes).T
return data[data.columns[0]]
def get_data(cnt,start,end,end2):
if intrade==True:
w_data=w.wsd(cnt,'close,low,high',start,end)
data=np.array(w_data.Data).T
fields=list(map(lambda x:x.lower(),w_data.Fields))
df=pd.DataFrame(data,index=w_data.Times,columns=fields)
df.dropna(inplace=True)
w_data=w.wsq(cnt,'rt_latest,rt_low,rt_high')
ti=dt.datetime.now()
if ti.hour>20:
Times=pd.to_datetime(w_data.Times[0]).date()+dt.timedelta(days=1)
else:
Times=pd.to_datetime(w_data.Times[0]).date()
Data=w_data.Data
rn=pd.DataFrame(Data,columns=[Times])
rn=rn.T
rn.columns=df.columns
df=df.append(rn)
# u'模拟下交易日数据'
nextday=pd.to_datetime(Times).date()+dt.timedelta(days=1)
nextdata=pd.DataFrame(list(df.iloc[-1]),index=df.columns,columns=[nextday]).T
df=df.append(nextdata)
else:
w_data=w.wsd(cnt,'close,low,high',start,end2)
data=np.array(w_data.Data).T
fields=list(map(lambda x:x.lower(),w_data.Fields))
df=pd.DataFrame(data,index=w_data.Times,columns=fields)
df.dropna(inplace=True)
nextday=pd.to_datetime(end2).date()+dt.timedelta(days=1)
nextdata=pd.DataFrame(list(df.iloc[-1]),index=df.columns,columns=[nextday]).T
df=df.append(nextdata)
return df
def onTick():
# 计算K线
"""读取行情数据"""
wdata = w.wsq("GBPUSD.FX",
"rt_date,rt_time,rt_open,rt_high,rt_low,rt_last,rt_last_amt,rt_last_vol,rt_latest,rt_vol,rt_amt,rt_bid1,rt_ask1")
tick = pd.DataFrame(wdata.Data,index=wdata.Fields,columns=wdata.Times)
tick = tick.T
tickMinute = tick.index[0].minute
print (u'bar minute:', bar['Minute'])
if tickMinute != bar['Minute']:
if bar['Minute']:
print ('start strategy')
# 执行策略
runstrategy(bar)
bar['open'] = tick['RT_LAST'][0]
bar['high'] = tick['RT_LAST'][0]
bar['low'] = tick['RT_LAST'][0]
bar['close'] = tick['RT_LAST'][0]
bar['date'] = tick.index[0].date()
bar['time'] = tick.index[0].time()
bar['datetime'] = tick.index[0] # K线的时间设为第一个Tick的时间
bar['Minute'] = tickMinute # 更新当前的分钟
else: # 否则继续累加新的K线
bar['high'] = max(bar['high'], tick['RT_LAST'][0])
bar['low'] = min(bar['low'], tick['RT_LAST'][0])
bar['close'] = tick['RT_LAST'][0]
bar['ask1'] = tick['RT_ASK1'][0]
bar['bid1'] = tick['RT_BID1'][0]
def __init__(self, options, etf=0, unit=10000, eps_diff=0.01):
self.etf = float(etf) # 是否买入ETF的标识，买入为正数，卖出为负数，单位为ETF的份数（100份为一手）
super().__init__(options, unit, eps_diff)
w.start()
self.ql_options, self.date_end, self.strike_list = self._set_ql_option(
)
self.etf_now = w.wsq('510050.SH', "rt_latest").Data[0][0]
print(self.etf_now)
def _get_cost(self):
cost = 0
for option in self.ql_options:
_, volume = self.ql_options[option]
if volume >= 0: # 表示买入
ask_price = w.wsq(option, "rt_ask1").Data[0][0]
cost += ask_price * volume
else: # 表示卖出
bid_price = w.wsq(option, "rt_bid1").Data[0][0]
cost += bid_price * volume
if self.etf >= 0:
ask_price = w.wsq('510050.SH', "rt_ask1").Data[0][0]
cost += ask_price * self.etf
else:
bid_price = w.wsq('510050.SH', "rt_bid1").Data[0][0]
cost += bid_price * self.etf
cost /= self.unit
return cost
def wsq(tickers, fields):
if isinstance(tickers,str):
tickers = tickers.replace(',','').split()
if isinstance(fields,str):
fields = fields.replace(',','').split()
tmp = w.wsq(tickers, fields)
return pd.DataFrame(dict(zip(tmp.Fields, tmp.Data)),index = tmp.Codes, columns= tmp.Fields)
def post(self):
"""
json str:{"codes": "600008.SH,600010.SH,600017.SH", "fields": "rt_open,rt_low_limit", "options": ""}
:return: 返回万得返回数据dict
"""
args = receive_wsq_parser.parse_args()
logger.info('/wsq/ 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.wsq(**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('wsq(%s) ErrorCode=%d %s' % args)
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 rtLast(stocklists):
data = w.wsq(
stocklists,
"rt_last,rt_pct_chg,rt_activenetin_amt,rt_activeinvol_prop,rt_mf_ratio,rt_mf_ratio_5d,rt_mf_ratio_10d,rt_mf_ratio_20d,rt_mf_ratio_60d,rt_mf_days_5d,rt_mf_days_10d,rt_mf_days_20d,rt_mf_days_60d"
)
df = pd.DataFrame(data=data.Data, index=data.Fields, columns=data.Codes).T
df = df.sort_values(by='RT_MF_RATIO_10D', ascending=False)
print(" real last price stock num : {}".format(len(df)))
return df
def getorderbook(self, sec):
reqid=None
try:
# print('loading %s' %(underlying))
data=w.wsq(sec.code, "rt_ask1,rt_bid1,rt_asize1,rt_bsize1,rt_open,rt_pre_close, rt_last", func=sec.onoderbookcallback)
reqid=data.RequestID
except:
print('查找期权时异常')
return reqid
def wsq(tickers, fields):
if isinstance(tickers, str):
tickers = tickers.replace(',', '').split()
if isinstance(fields, str):
fields = fields.replace(',', '').split()
tmp = w.wsq(tickers, fields)
return pd.DataFrame(dict(zip(tmp.Fields, tmp.Data)),
index=tmp.Codes,
columns=tmp.Fields)
def rtdprice(self):
#实时价格获取
wd = w.wsq([self.cnt1, self.cnt2], "rt_latest")
ti = dt.datetime.now()
if ti.hour > 20:
Times = pd.to_datetime(wd.Times[0]).date() + dt.timedelta(days=1)
else:
Times = pd.to_datetime(wd.Times[0]).date()
return pd.DataFrame(np.array(wd.Data[0]),
columns=[Times],
index=['cnt1', 'cnt2']).T
def OnTickSpread(self, Tick):
if self.tsData.has_key(str(Tick.Codes[0])):
self.tsData[str(Tick.Codes[0])].append(Tick.Data[0][0])
print tsData
for i in range(len(self.tradingOrder)):
if self.tsData[self.tradingOrder[i][0]] != [] and self.tsData[
self.tradingOrder[i][1]] != []:
if self.tsData[self.tradingOrder[i][0]][-1] - self.tsData[
self.tradingOrder[i]
[1]][-1] * self.tradingOrder[i][2] > self.tradingOrder[i][
4] or self.tsData[self.tradingOrder[i][0]][
-1] - self.tsData[self.tradingOrder[i]
[1]][-1] * self.tradingOrder[i][
2] < self.tradingOrder[i][3]:
#定义买单下单参数,并下单
print 'it is trading spread order between %s and %s' % (
self.tradingOrder[i][0], self.tradingOrder[i][1])
instrument_id = self.tradingOrder[i][0]
direction = 'buy'
limitPrice = w.wsq(instrument_id, 'rt_last').Data[0]
volume = 1
logonid = 2
w.torder(instrument_id,
direction,
limitPrice,
volume,
logonid=logonid)
#定义卖单下单参数,并下单
instrument_id = self.tradingOrder[i][1]
direction = 'short'
limitPrice = w.wsq(instrument_id, 'rt_last').Data[0]
volume = self.tradingOrder[i][2]
logonid = 2
w.torder(instrument_id,
direction,
limitPrice,
volume,
logonid=logonid)
def run(self):
future1 = MyTable.future1+".CFE"
future2 = MyTable.future2+".CFE"
future3 = MyTable.future3+".CFE"
future4 = MyTable.future4+".CFE"
future5 = MyTable.future5+".CFE"
future6 = MyTable.future6+".CFE"
while True:
cnt1 = w.wsq(future1, "rt_bid1,rt_bsize1,rt_ask1,rt_asize1,rt_latest,rt_last,rt_vol")
cnt2 = w.wsq(future2, "rt_bid1,rt_bsize1,rt_ask1,rt_asize1,rt_latest,rt_last,rt_vol")
cnt3 = w.wsq(future3, "rt_bid1,rt_bsize1,rt_ask1,rt_asize1,rt_latest,rt_last,rt_vol")
cnt4 = w.wsq(future4, "rt_bid1,rt_bsize1,rt_ask1,rt_asize1,rt_latest,rt_last,rt_vol")
cnt5 = w.wsq(future5, "rt_bid1,rt_bsize1,rt_ask1,rt_asize1,rt_latest,rt_last,rt_vol")
cnt6 = w.wsq(future6, "rt_bid1,rt_bsize1,rt_ask1,rt_asize1,rt_latest,rt_last,rt_vol")
cnt = str(cnt1.Data[0][0])+"\n"+str(int(cnt1.Data[1][0]))+"\n"+str(cnt1.Data[2][0])+"\n"+str(int(cnt1.Data[3][0]))+"\n"+str(cnt1.Data[4][0])+"\n"+str(cnt1.Data[5][0])+"\n"+str(int(cnt1.Data[6][0]))+"\n"+\
str(cnt2.Data[0][0])+"\n"+str(int(cnt2.Data[1][0]))+"\n"+str(cnt2.Data[2][0])+"\n"+str(int(cnt2.Data[3][0]))+"\n"+str(cnt2.Data[4][0])+"\n"+str(cnt2.Data[5][0])+"\n"+str(int(cnt2.Data[6][0]))+"\n"+\
str(cnt3.Data[0][0])+"\n"+str(int(cnt3.Data[1][0]))+"\n"+str(cnt3.Data[2][0])+"\n"+str(int(cnt3.Data[3][0]))+"\n"+str(cnt3.Data[4][0])+"\n"+str(cnt3.Data[5][0])+"\n"+str(int(cnt3.Data[6][0]))+"\n"+\
str(cnt4.Data[0][0])+"\n"+str(int(cnt4.Data[1][0]))+"\n"+str(cnt4.Data[2][0])+"\n"+str(int(cnt4.Data[3][0]))+"\n"+str(cnt4.Data[4][0])+"\n"+str(cnt4.Data[5][0])+"\n"+str(int(cnt4.Data[6][0]))+"\n"+\
str(cnt5.Data[0][0])+"\n"+str(int(cnt5.Data[1][0]))+"\n"+str(cnt5.Data[2][0])+"\n"+str(int(cnt5.Data[3][0]))+"\n"+str(cnt5.Data[4][0])+"\n"+str(cnt5.Data[5][0])+"\n"+str(int(cnt5.Data[6][0]))+"\n"+\
str(cnt6.Data[0][0])+"\n"+str(int(cnt6.Data[1][0]))+"\n"+str(cnt6.Data[2][0])+"\n"+str(int(cnt6.Data[3][0]))+"\n"+str(cnt6.Data[4][0])+"\n"+str(cnt6.Data[5][0])+"\n"+str(int(cnt6.Data[6][0]))#这些是把所有的信息都发送了
self.update_date.emit(cnt)
time.sleep(1)
def get_wind():
data = []
date_str = str(datetime.date.today())
#sw = w.wss(
# "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",
# "sec_name,pct_chg", "tradeDate=" + date_str + ";cycle=D", usedf = True)[1]
sw = w.wsq(
"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",
"rt_pct_chg_1min", usedf=True)[1]
name = ['农林牧渔(申万)', '采掘(申万)', '化工(申万)', '钢铁(申万)', '有色金属(申万)', '电子(申万)', '家用电器(申万)', '食品饮料(申万)', '纺织服装(申万)', '轻工制造(申万)', '医药生物(申万)', '公用事业(申万)', '交通运输(申万)', '房地产(申万)', '商业贸易(申万)', '休闲服务(申万)', '综合(申万)', '建筑材料(申万)', '建筑装饰(申万)', '电气设备(申万)', '国防军工(申万)', '计算机(申万)', '传媒(申万)', '通信(申万)', '银行(申万)', '非银金融(申万)', '汽车(申万)', '机械设备(申万)']
for i, j in zip(name, sw.RT_PCT_CHG_1MIN.tolist()):
data.append({'name': i, 'value': j+1})
return data
def refreshref():
w.start()
exchange,contract=GetUnderling.Getunderling()
numex=exchange.shape[0]
for i in range(numex):
numcon=len(contract[i]['contract'])
for ii in range(numcon):
concode=contract[i]['contract'][ii]
excode=exchange.exchange[i]
combine=concode+'.'+excode[:3]
winddata=w.wsq(combine, "rt_last")
pricedata=str(winddata.Data[0][0])
done=ChangeParamData.Writeparamdata('f_ref',pricedata,excode,concode)
print(done)
def FindData(date,
mkt=False,
industry=False,
df_sh=pd.DataFrame(),
df_sz=pd.DataFrame()):
#
shdata = w.wset("shstockholdings", "date={}".format(date)).Data
if shdata != []:
wind_code_sh = shdata[0]
hold_stocks_sh = shdata[2]
df_sh = pd.DataFrame(hold_stocks_sh,
index=wind_code_sh,
columns=['stock_holding'])
#
szdata = w.wset("szstockholdings", "date={}".format(date)).Data
if szdata != []:
wind_code_sz = szdata[0]
hold_stocks_sz = szdata[2]
df_sz = pd.DataFrame(hold_stocks_sz,
index=wind_code_sz,
columns=['stock_holding'])
#
if shdata != [] or szdata != []:
df = pd.concat([df_sh, df_sz], axis=0)
symbol = df.index.values
c = symbol[0]
for i in range(1, len(symbol)):
if isinstance(symbol[i], str):
c = c + ',' + symbol[i]
#
price = w.wsq(c, "rt_pre_close").Data[0]
#
df['price'] = price
df['market_value'] = df['price'] * df['stock_holding']
if mkt == True:
mkt = w.wss(c, "mkt").Data[0]
df['mkt'] = mkt
return df[['mkt', 'market_value']]
if industry == True:
industry = w.wss(
c, "industry_citic",
"tradeDate={}; industryType=1".format(date)).Data[0]
df['industry'] = industry
return df[['industry', 'market_value']]
else:
return None
def post(self):
"""
json str:{"codes": "600008.SH,600010.SH,600017.SH", "fields": "rt_open,rt_low_limit", "options": ""}
:return: 返回万得返回数据dict
"""
data_dic = request.json
logger.info('/wsq/ data_dic:%s', data_dic)
codes = data_dic['codes']
fields = data_dic['fields']
options = data_dic['options']
if not w.isconnected():
w.start()
if options == "":
options = None
ret_data = w.wsq(codes, fields, options)
error_code = ret_data.ErrorCode
if error_code != 0:
msg = ERROR_CODE_MSG_DIC.setdefault(error_code, "")
logger.error('wsq("%s", "%s", "%s") ErrorCode=%d %s' %
(codes, fields, options, error_code, msg))
return {'error_code': ret_data.ErrorCode, 'message': msg}, 404
# 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 load_trade_status_today(self):
"""
下载股票交易状态
超过9：15 就用今天的数据
不超过9：15 就用昨天的数据
"""
today = datetime.today()
today_str = Date.change_to_str(today)
before_date = Date().get_trade_date_offset(today, -1)
out_path = os.path.join(self.data_path_static, "trade_status")
out_file = os.path.join(out_path, 'trade_status_' + today_str + '.csv')
code_list = self.get_all_stock_code_now()
code_list_str = ','.join(code_list)
if today.time() > time(9, 15):
print("######### Loading Trade Status At %s #########" % today_str)
trade_status = w.wsq(code_list_str, "rt_trade_status")
trade_status_pd = pd.DataFrame(trade_status.Data, index=['Trade_Status'], columns=trade_status.Codes).T
trade_status_pd = trade_status_pd[(trade_status_pd['Trade_Status'] != 1.0)
& (trade_status_pd['Trade_Status'] != 4.0)]
if len(trade_status_pd) > 1000:
trade_status = w.wss(code_list_str, "trade_status", "tradeDate=" + today_str)
trade_status_pd = pd.DataFrame(trade_status.Data, index=['Trade_Status'], columns=trade_status.Codes).T
trade_status_pd = trade_status_pd[trade_status_pd['Trade_Status'] != "交易"]
trade_status_pd.to_csv(out_file)
else:
trade_status_pd.to_csv(out_file)
else:
print("######### Loading Trade Status At %s #########" % before_date)
trade_status = w.wss(code_list_str, "trade_status", "tradeDate=" + before_date)
trade_status_pd = pd.DataFrame(trade_status.Data, index=['Trade_Status'], columns=trade_status.Codes).T
trade_status_pd = trade_status_pd[trade_status_pd['Trade_Status'] != "交易"]
trade_status_pd.to_csv(out_file)
def mail_index_pct():
data = w.wsq("000300.SH,000905.SH,CI005165.WI,CI005166.WI",
"rt_pct_chg,rt_date,rt_pe_ttm,rt_pb_lf")
data = pd.DataFrame(data.Data, index=data.Fields, columns=data.Codes).T
data['NAME'] = ["沪深300", "中证500", "中信证券二级行业指数", "中信保险二级行业指数"]
string_total = ""
for i in range(len(data)):
string = str(
data.ix[i, "RT_DATE"].astype(int)) + "日, " + data.ix[i, "NAME"]
string += "现在涨幅为" + str(data.ix[i, "RT_PCT_CHG"] * 100) + '%, '
string += "PB_LF为" + str(data.ix[i, "RT_PB_LF"].round(2)) + ', '
string += "PE_TTM为" + str(data.ix[i,
"RT_PE_TTM"].round(1)) + '.' + '\n'
print(string)
string_total += string
send_mail_qq('*****@*****.**', 'bxfiljzifsaggdea', '今日指数涨跌幅',
string_total, '*****@*****.**')
send_mail_qq('*****@*****.**', 'bxfiljzifsaggdea', '今日指数涨跌幅',
string_total, '*****@*****.**')
def subscribeBond(self):
fieldsMap = {
'rt_last_cp': 'windCleanPrice',
'rt_last_dp': 'windDirtyPrice',
'rt_last_ytm': 'windYield',
'rt_bid1': 'bidPrice',
'rt_bid_price1ytm': 'bidYield',
'rt_bsize1': 'bidVolumn',
'rt_ask1': 'askPrice',
'rt_ask_price1ytm': 'askYield',
'rt_asize1': 'askVolumn',
'rt_mac_duration': 'maclayDuration',
'rt_pre_close': 'last'
}
def windCallBack(data):
print(data)
for i, wBondCode in enumerate(data.Codes):
binfo = {'priceTime': data.Times}
for j, field in enumerate(data.Fields):
bInfoField = fieldsMap[field.lower()]
binfo[bInfoField] = data.Data[j][i]
codeId = self.bondCode.index(wBondCode)
self.deliveryBondList[codeId].updatePrice(binfo)
for item in self.subBondHandle:
try:
item(data)
except:
print('handle' + item + '错误')
bsub = w.wsq(self.bondCode,
"rt_last_cp,rt_last_dp,rt_last_ytm,\
rt_bid1,rt_bid_price1ytm,rt_bsize1,rt_ask1,rt_ask_price1ytm,rt_asize1,\
rt_mac_duration,rt_pre_close",
func=windCallBack)
self.requestId.append(bsub.RequestID)
return
def subscribeBdf(self):
fieldsMap = {
'rt_latest': 'lastPrice',
'rt_bid1': 'bidPrice',
'rt_bsize1': 'bidVolumn',
'rt_ask1': 'askPrice',
'rt_asize1': 'askVolumn'
}
def windCallBack(data):
print(data)
for i, wBdfCode in enumerate(data.Codes):
cinfo = {'priceTime': data.Times[0]}
for j, field in enumerate(data.Fields):
# 确定更新的信息
cInfoField = fieldsMap[field.lower()]
cinfo[cInfoField] = data.Data[j][i]
# 'midPrice':data.Data[0][i],# 合约最新价
# 'bidPrice':data.Data[1][i],
# 'bidVolumn':data.Data[2][i],
# 'askPrice':data.Data[3][i],
# 'askVolumn':data.Data[4][i]
# 确定获取标债代码
codeId = self.bdfCode.index(wBdfCode)
self.contractList[codeId].updateInfo(cinfo)
for item in self.subBdfHandle:
try:
item(data)
except:
print('handle' + item + '错误')
bsub = w.wsq(self.bdfCode,"rt_latest,rt_bid1,rt_bsize1,rt_ask1,rt_asize1",\
func=windCallBack)
self.requestId.append(bsub.RequestID)
return
def ReadTradeInfo(codelist):
naturedict = {
1: '空开',
2: '空平',
3: '空换',
4: '多开',
5: '多平',
6: '多换',
7: '双开',
8: '双平'
}
tradedata = {}
code = ','.join(codelist)
tradeinfo = w.wsq(
code,
"rt_date,rt_time,rt_last,rt_last_vol,rt_oi_change,rt_nature").Data
print(tradeinfo)
if tradeinfo != []:
datelist = tradeinfo[0]
timelist = tradeinfo[1]
lastlist = tradeinfo[2]
vollist = tradeinfo[3]
oilist = tradeinfo[4]
naturelist = tradeinfo[5]
for i in range(len(codelist)):
#tradecode = codelist[i]
tradedate = str(datelist[i]).split('.')[0]
tradetime = str(timelist[i]).split('.')[0]
tradelast = lastlist[i]
tradevol = vollist[i]
tradeoi = oilist[i]
tradenature = int(naturelist[i])
#print(tradedate,tradetime,tradelast,tradevol,tradeoi,tradenature)
tradedata[i] = [
tradedate, tradetime, tradelast, tradevol, tradeoi, tradenature
]
return tradedata
t = time.localtime()
if (t[3] > 9 and t[4] > 30):
i = 1
while i <= 7200:
print("\n读秒", i)
time.sleep(1)
i += 1
time1 = time.time()
print("时间：", time1)
mo = w.tquery("Capital").Data[1][0]
print("现有资金：", "%.2f" % mo)
pr = w.wsq("600536.SH", "rt_latest,rt_ma_10d").Data
print("现价：", "%.2f" % pr[0][0], "；月均价：", "%.2f" % pr[1][0])
vo = int(m / pr[0][0] // 100)
print("可以购买：", vo, "手。")
print(time.clock())
# if p[0][0] > p[1][0]:
#
# direction = "Buy"
# volume = "500"
# w.torder(stock, direction, "43","500","OrderType=LMT")
# print("下单成功！", time1, "买入" + volume)
# po = w.tquery("Position").Data
# print("现有持仓：","\n",positions[0][0],"\n", po[1][0],"\n",po[2][0],"\n",
# po[10][0],"\n",po[11][0],"\n",po[12][0],"\n",po[13][0])
return rtn.Data[0][0]
return None
def getOptionDirType(self, code):
return self.__getBaseInfo(code, 'exe_mode')
def getStrikePrice(self, code):
return self.__getBaseInfo(code, 'exe_price')
def getLastTradingDate(self, code):
return self.__getBaseInfo(code, 'lasttradingdate')
def getOptionRemainingDays(self, code):
return self.__getBaseInfo(code, 'ptmday')
###########################################################
if __name__ == '__main__':
TODAY_DATE = DT.datetime.today().strftime(r'%Y-%m-%d')
indata = w.wsq("10000183.SH", "rt_latest")
basedata = w.wsd("510050.SH", """us_code,us_name,
us_type,exe_mode,exe_type,exe_price,
exe_ratio,ptmday,totaltm,startdate,
lasttradingdate,exe_startdate,exe_enddate""",
TODAY_DATE, TODAY_DATE, "Fill=Previous")
rtn = w.wsd("10000419.SH", "exe_ratio", TODAY_DATE, TODAY_DATE, "Fill=Previous")
a = 1
def fetch(conf_file):
config = get_fetch_config(conf_file)
# rt_time,rt_low,rt_last,rt_last,rt_last_vol,rt_upward_vol,rt_downward_vol
#data = w.wsq('000001.SZ','rt_last,rt_last_vol,rt_upward_vol,rt_downward_vol',func=democb)
data = w.wsq(config['codes'],config['fields'],func=context(config,'tick'))
return data
def getLastPrice(self, code):
rtn = w.wsq(str(code), "rt_latest")
if rtn.ErrorCode == 0:
return rtn.Data[0][0]
return None
def cal_ipo_sell():
out_path = 'E:\\3_Data\\7_other_data\\4_cal_ipo_sell\\'
new_days = 60
today = datetime.today().strftime("%Y%m%d")
Date().load_trade_date_series("D")
# MfcData().load_ftp_daily(date=today)
# MfcData().change_ftp_file(date=today)
before_trade_data = Date().get_trade_date_offset(today, -1)
data = MfcData().get_fund_security(before_trade_data)
data = data.dropna(subset=['基金名称'])
data = data[['基金名称', '证券代码', '持仓', '证券类别']]
data.columns = ['FundName', 'StockCode', 'Holding', 'Type']
data = data[data.Type == '股票']
data.StockCode = data.StockCode.map(stock_code_add_postfix)
data["Market"] = data.StockCode.map(get_stcok_market)
Stock().load_all_stock_code_now()
Stock().load_ipo_date()
stock = Stock().get_ipo_date()
stock.columns = ['IpoDate', 'DelistDate']
stock['StockCode'] = stock.index
stock['IpoDate'] = stock['IpoDate'].map(lambda x: str(int(x)))
new_stock_date = datetime.today() - timedelta(days=new_days)
new_stock_date = new_stock_date.strftime("%Y%m%d")
all_data = pd.merge(data, stock, on=['StockCode'], how="left")
all_data = all_data[all_data.IpoDate > new_stock_date]
code_list = list(set(all_data.StockCode.values))
code_str = ','.join(code_list)
pct = w.wsq(code_str, "rt_pct_chg,rt_vol")
pct = pd.DataFrame(pct.Data, columns=pct.Codes, index=['Pct', 'Vol']).T
pct['StockCode'] = pct.index
new_data = pd.merge(all_data, pct, on=['StockCode'], how="left")
new_data = new_data[new_data['Vol'] > 0]
new_data = new_data[new_data['Pct'] < 0.09]
fund_list = list(set(data['FundName']))
for i_fund in range(len(fund_list)):
fund_name = fund_list[i_fund]
fund_data = new_data[new_data.FundName == fund_name]
out_sub_path = os.path.join(out_path, today)
if not os.path.exists(out_sub_path):
os.mkdir(out_sub_path)
print(" Make Folder At ", today)
if len(fund_data) > 0:
warnings.filterwarnings("ignore")
fund_data_out = fund_data[['StockCode', 'Holding', 'Market']]
fund_data_out.columns = ['Ticker', 'Shares', 'Market']
fund_data_out['Direction'] = 2
fund_data_out['Price'] = 0
fund_data_out['Market Code'] = fund_data_out['Market'].map(
lambda x: 1 if x == 'SH' else 2)
fund_data_out['Price Model'] = 4
fund_data_out['Ticker'] = fund_data_out['Ticker'].map(
lambda x: x[0:6])
fund_data_out = fund_data_out[[
'Ticker', 'Direction', 'Shares', 'Price', 'Price Model',
'Market Code'
]]
file = fund_name + '.xls'
out_file = os.path.join(out_sub_path, file)
print(out_file)
fund_data_out.to_excel(out_file, index=None)
ftp = MyFtp()
ftp.connect()
ftp_file = os.path.join("\\ipo_stock\\", today, file)
ftp.upload_file(ftp_file, out_file)
ftp.close()
def getDailyLowPrice(self, code):
rtn = w.wsq(str(code), 'rt_low')
if rtn.ErrorCode == 0:
return rtn.Data[0][0]
return None
w.start()
start_date = '2018-12-10 14:29:00'
end_date = '2018-12-12 15:00:01'
i = '000002.SZ'
# w_data = w.wsi(i, "open,high,low,close", start_date, end_date,"unit=1;currencyType=")
pf = open('C:\\codes\\NEW\\pywsqdataif.data', 'w')
def myCallback(indata):
if indata.ErrorCode != 0:
print('error code:' + str(indata.ErrorCode) + '\n')
return ()
global begintime
lastvalue = ""
for k in range(0, len(indata.Fields)):
if (indata.Fields[k] == "RT_TIME"):
begintime = indata.Data[k][0]
if (indata.Fields[k] == "RT_LAST"):
lastvalue = str(indata.Data[k][0])
string = str(begintime) + " " + lastvalue + "\n"
pf.writelines(string)
print(string)
w.wsq("600000.SH", "rt_low,rt_last_vol", func=myCallback)
import numpy as np
import matplotlib.pyplot as plt
from WindPy import w
w.start()
plt.axis([0, 100, 97.2, 97.22])
plt.ion()
rtPrice = w.wsq("USDX.FX", "rt_last")
for i in range(100):
plt.scatter(i, rtPrice.Data[0][0])
plt.pause(1)
from WindPy import w
from datetime import datetime
w.start(showmenu=False)
sum = 0
count = 10
for i in range(count):
time1 = datetime.now()
data = w.wsq("P1701.DCE", "rt_latest,rt_ask1,rt_bid1")
time2 = datetime.now()
print('avg=', (time2 - time1) / count)
datas00 = Tools.GetDatas2(if0, sampleCount+2, sampleCount)
datas11 = Tools.GetDatas2(if1, sampleCount+2, sampleCount)
print datas00
print datas11
bullLs, bearLs = Arbitrage.CalculateD1D2(Ls, 0.4, a, b, sigmaEpsilon, muC, sigmaC, deltaT, datas00, datas11)
figure = pylab.figure()
pi = figure.add_subplot(211)
_dt = {}
line, = pi.plot( [], [], 'k-')
def OnTick(d):
if not d.Codes[0] in _dt:
_dt[d.Codes[0]] = []
datas = _dt[d.Codes[0]]
datas.append(d.Data[0][0])
line.set_data(range(datas),datas)
w.start()
data = w.wsq("IF1406.CFE", "rt_last", func=OnTick)
pylab.show()
time.sleep(10)
Tools.ShowLinearAndPricesDelta(b, a, datas0, datas1)
print'it is trading spread order between %s and %s'%(self.tradingOrder[i][0],self.tradingOrder[i][1])
instrument_id=self.tradingOrder[i][0]
direction='buy'
limitPrice=w.wsq(instrument_id,'rt_last').Data[0]
volume=1
logonid=2
w.torder(instrument_id,direction,limitPrice,volume,logonid=logonid)
#定义卖单下单参数,并下单
instrument_id=self.tradingOrder[i][1]
direction='short'
limitPrice=w.wsq(instrument_id,'rt_last').Data[0]
volume=self.tradingOrder[i][2]
logonid=2
w.torder(instrument_id,direction,limitPrice,volume,logonid=logonid)
#定义交易对象,此对象来自于计算机器结果
tradingOrder=[['RU1401.SHF', 'FG401.CZC', 36.011767364126612, -28373.745064529503, -33208.077687938567], ['ZN1401.SHF', 'TA401.CZC', 1.2126731163504074, 5500.6030064563192, 4893.9256643498748], ['RU1401.SHF', 'J1401.DCE', 17.642695764706176, -6310.4765649161745, -8590.0412593592864], ['RU1401.SHF', 'TA401.CZC', 7.6723619297322729, -38316.284059386315, -42628.309323957808], ['CU1401.SHF', 'RU1401.SHF', 1.0815425178390803, 31978.191774908541, 29644.633609589815], ['AG1312.SHF', 'AU1312.SHF', 30.191027322441442, -3600.9750756256853, -3970.7648976048631]]
TSNames=list(set([str(x[0]) for x in tradingOrder]+[str(x[1]) for x in tradingOrder]))
#登陆下单
TD=w.tlogon('0000',0,['W675324301','W675324302'],'000000',['sh','cfe'])
print TD
#启动OnTickSpread交易策略
#print w.wsq("000001.sz","rt_last")
data=w.wsq("000001.sz","rt_last",func=Trading(TD,tradingOrder).OnTickSpread)
print data
while True:
time.sleep(1)
option_month = 9
if option_month == 7:
option_month = 1
option_year = option_year + 1
if option_month == 11:
option_month = 5
option_year = option_year + 1
return option_year, option_month
w_wset_data = pd.DataFrame(
w.wset('SectorConstituent', u'date=20171009;sector=白糖期权').Data)
w_wset_data = w_wset_data.T
if __name__ == '__main__':
etf = w.wsq("SR801.CZC", "rt_last")
a = np.array(etf.Data[0])[0]
OPC2 = optionchoose2(a, date)
price = OPC2.mround()
option_at_call = "" + str(format(price))
option_at_put = "" + str(format(price))
option_out_call = "" + str(format(price + 100))
option_out_put = "" + str(format(price - 100))
option_out2_call = "" + str(format(price + 200))
option_out2_put = "" + str(format(price - 200))
position = 'flat' # flat long-short buy-sell
'''监控开始'''
timeInterval = 1
def delayrun():
def ipo_sell(self,
today=datetime.today().strftime("%Y%m%d")):
""" 计算所有基金新股是否打开 """
before_trade_data = Date().get_trade_date_offset(today, -1)
# get holding data
data = MfcData().get_group_security(before_trade_data)
data = data.dropna(subset=['基金名称'])
data = data[['基金名称', '基金编号', '组合名称', '组合编号', '证券代码', '持仓', '证券类别']]
data.columns = [['基金名称', '基金编号（序号）', '组合名称', '组合编号', '证券代码', '指令数量', '证券类别']]
data = data[data['证券类别'] == '股票']
data['证券代码'] = data['证券代码'].map(CodeFormat().stock_code_add_postfix)
data["交易市场内部编号"] = data['证券代码'].map(CodeFormat().get_stcok_market)
fund_info = MfcData().get_mfc_fund_info()
fund_info = fund_info.dropna(subset=['FundId'])
fund_info['FundId'] = fund_info['FundId'].map(CodeFormat.stock_code_add_postfix)
fund_info['FundId'] = fund_info['FundId'].map(lambda x: x[0:6])
# get ipo data
Stock().load_all_stock_code_now()
Stock().load_ipo_date()
stock = Stock().get_ipo_date()
stock.columns = ['IpoDate', 'DelistDate']
stock['证券代码'] = stock.index
stock['IpoDate'] = stock['IpoDate'].map(lambda x: str(int(x)))
# get New Stock
new_stock_date = datetime.today() - timedelta(days=self.new_days)
new_stock_date = new_stock_date.strftime("%Y%m%d")
all_data = pd.merge(data, stock, on=['证券代码'], how="left")
all_data = all_data[all_data.IpoDate > new_stock_date]
# get Vol and Pct of New Stock
code_list = list(set(all_data['证券代码'].values))
code_str = ','.join(code_list)
pct = w.wsq(code_str, "rt_pct_chg,rt_vol")
pct = pd.DataFrame(pct.Data, columns=pct.Codes, index=['Pct', 'Vol']).T
pct['证券代码'] = pct.index
# filter multi_factor
new_data = pd.merge(all_data, pct, on=['证券代码'], how="left")
new_data = new_data[new_data['Vol'] > 0]
new_data = new_data[new_data['Pct'] < 0.09]
# New Local Folder
out_sub_path = os.path.join(self.data_path, today)
if not os.path.exists(out_sub_path):
os.mkdir(out_sub_path)
# New FTP Folder
ftp = MyFtp()
ftp.connect()
ftp_folder = os.path.join(self.ftp_path, today)
ftp.upload_folder(ftp_folder)
ftp.close()
# manager data
manager_data = MfcData().get_manager_fund()
for i_manager in range(len(manager_data.columns)):
manager_name = manager_data.columns[i_manager]
manager_fund = manager_data[manager_name]
manager_fund = manager_fund.dropna()
fund_data = new_data[new_data['基金名称'].map(lambda x: x in manager_fund.values)]
if len(fund_data) > 0:
warnings.filterwarnings("ignore")
fund_data_out = fund_data[['证券代码', '指令数量', '交易市场内部编号',
'基金编号（序号）', '基金名称', '组合编号']]
fund_data_out['委托方向'] = 2
fund_data_out['指令价格'] = 0
fund_data_out['交易市场内部编号'] = fund_data_out['交易市场内部编号'].map(lambda x: 1 if x == 'SH' else 2)
fund_data_out['价格模式'] = ""
fund_data_out['当前指令市值/净值（%）'] = ""
fund_data_out['目标市值/净值（%）'] = ""
fund_data_out['基金名称'] = ""
fund_data_out['证券代码'] = fund_data_out['证券代码'].map(lambda x: x[0:6])
fund_data_out = fund_data_out[['证券代码', '委托方向', '指令数量', '指令价格',
'价格模式', '交易市场内部编号', '当前指令市值/净值（%）', '目标市值/净值（%）',
'基金编号（序号）', '基金名称', '组合编号']]
file = manager_name + '.xls'
out_file = os.path.join(out_sub_path, file)
print(out_file)
fund_data_out.to_excel(out_file, index=None)
ftp = MyFtp()
ftp.connect()
ftp_file = os.path.join(self.ftp_path, today, file)
ftp.upload_file(ftp_file, out_file)
ftp.close()
self.dtColumnIndex = {"USDCAD.FX":0,"EURUSD.FX":1}
sizer = wx.BoxSizer(wx.VERTICAL)
sizer.Add(self.grid, 1, wx.EXPAND, 5)
panel.SetSizer(sizer)
def SetAccount(self,USDCAD,EURUSD):
self.grid.SetCellValue(0, 0, str(USDCAD))
self.grid.SetCellValue(0,1,str(EURUSD))
def SetPrice(self,code,price):
ci = self.dtColumnIndex[code]
self.grid.SetCellValue(0, ci, str(price))
def DemoWSQCallback(Tick):
USDCAD=Tick.Data[0][0]
EURUSD=Tick.Codes[0]
#form.SetAccount(USDCAD,EURUSD)
form.SetPrice(Tick.Codes[0], Tick.Data[0][0])
if not g_dt.has_key(Tick.Codes[0]): g_dt[Tick.Codes[0]] = []
g_dt[Tick.Codes[0]].append(Tick.Data[0][0])
g_dt={}
# Run the program
if __name__ == "__main__":
w.start()
app = wx.PySimpleApp()
form = MyForm()
data=w.wsq(["USDCAD.FX","EURUSD.FX"],"rt_last",func=DemoWSQCallback)
frame = form.Show()
app.MainLoop()
print g_dt
