def FillVolumnMatrix(stockId, startDay, endDay, timeSpan):
global AmountMatrix
global VolumeMatrix
global FiveMinOpenPriceMatrix
TradeDayList = []
calendar = md.get_calendar(Market, startDay, endDay)
index = len(calendar) - 1
for i in range(0, timeSpan):
#print('index', index)
timeOri = calendar[index].strtime
strIndex = timeOri.index('T')
timeAft = timeOri[0:strIndex]
TradeDayList.insert(0, timeAft)
index -= 1
print("TradeDayList", TradeDayList)
for day in TradeDayList:
print("day:", day)
dayIndex = TradeDayList.index(day)
startTime = day + ' ' + '9:30:00'
endTime = day + ' ' + '15:00:00'
print("stockId", stockId)
print("IntervalWid", IntervalWid)
print("startTime", startTime)
print("endTime", endTime)
bars = md.get_bars(stockId, IntervalWid, startTime, endTime)
print("bar:", bars)
for interval in range(len(bars)):
VolumeMatrix[dayIndex][interval] = bars[interval].volume
AmountMatrix[dayIndex][interval] = bars[interval].amount
FiveMinOpenPriceMatrix[dayIndex][interval] = bars[interval].open
def FillVolumnMatrix(stockId, startDay, endDay, timeSpan):
global AmountMatrix
global VolumeMatrix
TradeDayList = []
calendar = md.get_calendar(Market, startDay, endDay)
# print('len(calender)', len(calendar))
index = len(calendar) - 1
for i in range(0, timeSpan):
# print('index', index)
timeOri = calendar[index].strtime
strIndex = timeOri.index('T')
timeAft = timeOri[0:strIndex]
TradeDayList.insert(0, timeAft)
index -= 1
for day in TradeDayList:
dayIndex = TradeDayList.index(day)
startTime = day + ' ' + '9:30:00'
endTime = day + ' ' + '15:00:00'
bars = md.get_bars(stockId, IntervalWid, startTime, endTime)
for interval in range(len(bars)):
VolumeMatrix[dayIndex][interval] = bars[interval].volume
AmountMatrix[dayIndex][interval] = bars[interval].amount
FiveMinOpenPriceMatrix[dayIndex][interval] = bars[interval].open
print("AmountMatrix", AmountMatrix)
print("VolumeMatrix", VolumeMatrix)
def get_bars(symbol, bar_type, begin_time, end_time):
var = md.get_bars(symbol, bar_type, begin_time, end_time)
if len(var) == 0:
print("no bar data for stock %s between %s and %s" %(symbol, begin_time, end_time))
return pd.DataFrame()
else:
ret = bar_topd(var,'date')
return ret
def GetTradeDayPriceList(stockId, tradeDay):
tradedayPriceList = []
startTime = tradeDay + ' ' + '9:30:00'
endTime = tradeDay + ' ' + '15:00:00'
bars = md.get_bars(stockId, IntervalWid, startTime, endTime)
for interval in range(len(bars)):
tradedayPriceList.insert(bars[interval].open)
return tradedayPriceList
def get_minutes_bars(stock_list, minutes, begin_time, end_time):
# 连接本地终端时，td_addr为localhost:8001,
if (td.init('*****@*****.**', 'zyj2590@1109', 'strategy_1') == 0):
try:
bars = md.get_bars(stock_list, int(minutes * 60), begin_time,
end_time)
return bars
except:
pass
def get_k_data(self,
code=None,
start=None,
end=ct._TODAY_,
ktype='D',
isOHLC=True):
if code[:2] == '60' or\
code[:2] == '00':
code = 'SHSE.' + code
else:
code = 'SZSE.' + code
if len(start) == 10:
start += ' 09:00:00'
if len(end) == 10:
end += ' 15:00:00'
tmp_end = start
last_end = -1
dat = pd.DataFrame()
while (tmp_end < end and tmp_end != last_end):
if ktype == 'D':
bars = md.get_dailybars(code, tmp_end, end)
else:
bars = md.get_bars(code, self.k_dic[ktype], tmp_end, end)
if len(bars) == 0:
break
rt = []
for i in bars:
rt.append(to_dict(i))
dt = pd.DataFrame(rt)
if start == tmp_end:
dat = dt
else:
dat = dat.append(dt)
print '-----', code, dt.iloc[-1, 12][:19]
last_end = tmp_end
tmp_end = dt.iloc[-1, 12][:19]
next_5min = datetime.strptime(
tmp_end, '%Y-%m-%dT%H:%M:%S') + timedelta(minutes=int(ktype))
tmp_end = datetime.strftime(next_5min, '%Y-%m-%d %H:%M:%S')
if dat.empty:
return dat
dat.drop_duplicates(['strendtime'], inplace=True)
dat['date'] = dat['strendtime'].apply(
lambda x: x[:10] + ' ' + x[11:19])
dat.insert(len(dat.columns), "code", code[5:])
#dat.index = dat['strtime'].apply(lambda x: datetime.strptime(x, '%Y-%m-%dT%H:%M:%S+08:00'))
if isOHLC:
return dat[[
'date', 'high', 'low', 'open', 'close', 'volume', 'code'
]]
return dat
def GetLastOpenPrice(stockId, startDay, endDay, timeSpan):
calender = md.get_calendar(Market, startDay, endDay)
index = len(calender) - timeSpan + 1
timeOri = calender[index].strtime
strIndex = timeOri.index('T')
lastDay = timeOri[0:strIndex]
startTime = lastDay + ' ' + '14:55:00'
endTime = lastDay + ' ' + '15:00:00'
bars = md.get_bars(stockId, 300, startTime, endTime)
result = bars[0].open
return result
def TestIFS():
barList = md.get_bars(Market+'.'+StockId, 300, CurrentDay + ' 9:40:00', CurrentDay + ' 10:40:00')
averageDataList = []
for i in range(0, len(barList)):
vol = barList[i].volume
amt = barList[i].amount
if vol != 0:
averagePrice = amt / vol
averageDataList.append(averagePrice)
else:
tempCount = len(averageDataList)
averageDataList.append(averageDataList[tempCount - 1])
ifsTestList = GetIFS(averageDataList, 0.6)
return ifsTestList
def GetHistoryIntervalOpenPrice(stockId, startDay, endDay, timeSpan):
fiveMinOpenPriceMatrix = [[0 for col in range(48)] for row in range(timeSpan)]
global FiveMinOpenPriceMatrix
TradeDayList = []
calendar = md.get_calendar(Market, startDay, endDay)
index = len(calendar) - 1
for i in range(0, timeSpan):
timeOri = calendar[index].strtime
strIndex = timeOri.index('T')
timeAft = timeOri[0:strIndex]
TradeDayList.insert(0, timeAft)
index -= 1
for day in TradeDayList:
dayIndex = TradeDayList.index(day)
startTime = day + ' ' + '9:30:00'
endTime = day + ' ' + '15:00:00'
bars = md.get_bars(stockId, 300, startTime, endTime)
for interval in range(len(bars)):
fiveMinOpenPriceMatrix[dayIndex][interval] = bars[interval].open
return fiveMinOpenPriceMatrix
def get_hist_data(self, code=None, start=None, end=ct._TODAY_, ktype='D'):
if code[:2] == '60':
code = 'SHSE.' + code
else:
code = 'SZSE.' + code
tmp_end = start
last_end = -1
dat = pd.DataFrame()
while (tmp_end < end and tmp_end != last_end):
if ktype == 'D':
bars = md.get_dailybars(code, tmp_end, end)
else:
bars = md.get_bars(code, self.k_dic[ktype],
tmp_end + ' 09:00:00', end + ' 15:00:00')
if len(bars) == 0:
return pd.DataFrame(
[], columns=['high', 'low', 'open', 'close', 'volume'])
rt = []
for i in bars:
rt.append(to_dict(i))
dt = pd.DataFrame(rt)
if start == tmp_end:
dat = dt
else:
dat = dat.append(dt)
print '-----', code, dt.iloc[-1, 12][:10]
last_end = tmp_end
tmp_end = dt.iloc[-1, 12][:10]
tomorrow = datetime.strptime(tmp_end,
'%Y-%m-%d') + timedelta(days=1)
tmp_end = datetime.strftime(tomorrow, '%Y-%m-%d')
dat.drop_duplicates(['strendtime'], inplace=True)
dat.index = dat['strendtime'].apply(
lambda x: datetime.strptime(x, '%Y-%m-%dT%H:%M:%S+08:00'))
return dat[['high', 'low', 'open', 'close', 'volume']]
)
print('get_ticks: ', len(r))
#提取tick快照
r = md.get_last_ticks('SHSE.600000')
print('get_last_ticks: ', len(r))
#提取最新N笔tick数据
r = md.get_last_n_ticks(
'SHSE.600000', 10)
print('get_last_n_ticks(10): ', len(r))
#提起一分钟分时数据(freq=60 seconds)
r = md.get_bars(
'SHSE.600000',
60,
'2015-12-01 09:30:00',
'2015-12-10 09:31:00',
)
print('get_bars: ', len(r))
#提取bar快照
r = md.get_last_bars('SHSE.600000,', 60)
print('get_last_bars: ', len(r))
#提取最新N笔bar数据
r = md.get_last_n_bars('SHSE.600000',60,10)
print('get_last_n_bars(10): ', len(r))
#提取日频数据
r = md.get_dailybars(
'SHSE.600000',
for row in range(len(FiveMinOpenPriceMatrix)):
print("row:%d", rowIndex)
for col in range(colLen):
print("col:%d", FiveMinOpenPriceMatrix[row][col])
rowIndex += 1
DrawTheData(FiveMinOpenPriceMatrix)'''
#Hurst = GetWaveletHurst(HistoryPriceMatrix)
#OpenPriceHurst = GetWaveletHurst(FiveMinOpenPriceMatrix)
#print('Hurst', Hurst)
#print('OpenPriceHurst', OpenPriceHurst)
#利用加权移动平均法估算第k+2个区间的值
currentDataList = md.get_bars(Market+'.'+StockId, 300, CurrentDay + ' 9:30:00', CurrentDay + ' 15:00:00')
print("currentDataList", currentDataList)
CurrentOpenPriceList = ConvertBarsToOpenPrices(currentDataList)
print('CurrentOpenPriceList', CurrentOpenPriceList)
SampleDataList = ConvertMatrixToList(FiveMinOpenPriceMatrix)
weighList = [0.4, 0.25, 0.15, 0.15, 0.05]
for index in range(0,48):
sumOfLastFive = 0
openPriceCount = len(SampleDataList)
for subIndex in range(1,6):
sumOfLastFive = sumOfLastFive + weighList[subIndex - 1] * SampleDataList[openPriceCount - subIndex]
estimate = sumOfLastFive
temSampleDataList = SampleDataList
temSampleDataList.append(estimate)
#对temSampleDataList求IFS
global ForcastOpenPirceList
def min_data(daima, start_date, end_date, n):
def count_split_days(start_date, end_date):
if datetime.datetime(int(start_date[0:4]), int(start_date[4:6]),
int(start_date[6:])) < datetime.datetime(
2015, 1, 5):
num = (datetime.datetime(int(end_date[0:4]), int(end_date[4:6]),
int(end_date[6:])) -
datetime.datetime(2015, 1, 5)).days
else:
num = (datetime.datetime(int(end_date[0:4]), int(end_date[4:6]),
int(end_date[6:])) -
datetime.datetime(int(start_date[0:4]), int(
start_date[4:6]), int(start_date[6:]))).days
return num
from gmsdk import md
ret = md.init("yourname", "password")
if daima[0:2] == '60':
daima_new = str('SHSE.') + str(daima)
else:
daima_new = str('SZSE.') + str(daima)
mins = n * 60
strtime = []
openp = []
high = []
low = []
close = []
volume = []
amount = []
days_internal = 120
LoopNum = divmod(count_split_days(start_date, end_date),
days_internal)[0] + 1
left = divmod(count_split_days(start_date, end_date), days_internal)[1]
bars_set = []
n = 0
for ln in range(0, LoopNum):
if n == 0:
bars = md.get_bars(
daima_new, mins,
str(
datetime.datetime(int(start_date[0:4]), int(
start_date[4:6]), int(start_date[6:])) +
datetime.timedelta(days=days_internal * (ln)))[0:10] +
' 09:00:00',
str(
datetime.datetime(int(start_date[0:4]), int(
start_date[4:6]), int(start_date[6:])) +
datetime.timedelta(days=days_internal *
(ln + 1)))[0:10] + ' 15:30:00')
n = n + 1
else:
if ln == LoopNum - 1:
bars = md.get_bars(
daima_new, mins,
str(
datetime.datetime(int(start_date[
0:4]), int(start_date[4:6]), int(start_date[6:])) +
datetime.timedelta(days=days_internal *
(ln) + 1))[0:10] + ' 09:00:00',
end_date[0:4] + '-' + end_date[4:6] + '-' + end_date[6:] +
' 15:30:00')
n = n + 1
else:
bars = md.get_bars(
daima_new, mins,
str(
datetime.datetime(int(start_date[
0:4]), int(start_date[4:6]), int(start_date[6:])) +
datetime.timedelta(days=days_internal *
(ln) + 1))[0:10] + ' 09:00:00',
str(
datetime.datetime(int(start_date[
0:4]), int(start_date[4:6]), int(start_date[6:])) +
datetime.timedelta(days=days_internal *
(ln + 1)))[0:10] + ' 15:30:00')
n = n + 1
bars_set.append(bars)
for SignalBars in bars_set:
for b in SignalBars:
strtime.append(b.strtime)
openp.append(b.open)
high.append(b.high)
low.append(b.low)
close.append(b.close)
volume.append(b.volume)
amount.append(b.amount)
mins = pd.DataFrame({
'strtime': strtime,
'open': openp,
'close': close,
'high': high,
'low': low,
'volume': volume,
'amount': amount
})
return mins
transaction_ratio=1,
commission_ratio=0.0001,
slippage_ratio=0.001,
price_type=0)
TodayVRList = GetRatioListOfToday(Market + '.' + StockId, StartHistoryDay,
EndHistoryDay, HistoryDays, WeightList)
IntervalBorderList = GetIntervalList(TradeDate)
print("VolumeMatrix", VolumeMatrix)
print("AmountMatrix", AmountMatrix)
#Hurst = GetWaveletHurst(FiveMinHisAveragePriceMatrix)
#print('Hurst', Hurst)
currentDataList = md.get_bars(Market + '.' + StockId, 300,
TradeDate + ' 9:30:00',
TradeDate + ' 15:00:00')
CurrentOpenPricesList = ConvertBarsToOpenPriceList(currentDataList)
HistoryFiveMinOpenPriceList = ConvertMatrixToList(FiveMinOpenPriceMatrix)
print("HistoryFiveMinOpenPriceList", HistoryFiveMinOpenPriceList)
print("CurrentOpenPricesList", CurrentOpenPricesList)
ForcastAveragePricesList = GetForcastAveragePricesListByMove(
HistoryFiveMinOpenPriceList, CurrentOpenPricesList)
#lastDayPrice = GetLastOpenPrice(Market+'.'+StockId, StartHistoryDay, EndHistoryDay, HistoryDays)
#HistoryFiveMinOpenPriceDifferList = ConvertPriceToDifferenceOne(lastDayPrice, HistoryFiveMinOpenPriceList)
#print("HistoryFiveMinOpenPriceDifferList", HistoryFiveMinOpenPriceDifferList)
#CurrentOpenPricesDifferList = ConvertPriceToDifference(HistoryFiveMinOpenPriceList, CurrentOpenPricesList)
#ForcastAveragePricesDifferList = ConvertPriceToDifference(HistoryFiveMinOpenPriceList, ForcastAveragePricesList)
predictIndex = 5
basicPointNum = 48 * HistoryDays
def min_data(daima,start_date,end_date,n):
def count_split_days(start_date,end_date):
if datetime.datetime(int(start_date[0:4]),int(start_date[4:6]),int(start_date[6:]))<datetime.datetime(2015,1,5):
num = (datetime.datetime(int(end_date[0:4]),int(end_date[4:6]),int(end_date[6:]))-datetime.datetime(2015,1,5)).days
else:
num = (datetime.datetime(int(end_date[0:4]),int(end_date[4:6]),int(end_date[6:]))-datetime.datetime(int(start_date[0:4]),int(start_date[4:6]),int(start_date[6:]))).days
return num
from gmsdk import md
ret = md.init("yourname", "password")
if daima[0:2]=='60':
daima_new = str('SHSE.')+str(daima)
else:
daima_new = str('SZSE.')+str(daima)
mins = n*60
strtime = []
openp = []
high = []
low = []
close = []
volume = []
amount = []
days_internal = 120
LoopNum = divmod(count_split_days(start_date,end_date),days_internal)[0]+1
left = divmod(count_split_days(start_date,end_date),days_internal)[1]
bars_set = []
n = 0
for ln in range(0,LoopNum):
if n == 0:
bars = md.get_bars(daima_new,mins,str(datetime.datetime(int(start_date[0:4]),int(start_date[4:6]),int(start_date[6:]))+datetime.timedelta(days=days_internal*(ln)))[0:10]+' 09:00:00',str(datetime.datetime(int(start_date[0:4]),int(start_date[4:6]),int(start_date[6:]))+datetime.timedelta(days=days_internal*(ln+1)))[0:10]+' 15:30:00')
n = n+1
else:
if ln == LoopNum-1:
bars = md.get_bars(daima_new,mins,str(datetime.datetime(int(start_date[0:4]),int(start_date[4:6]),int(start_date[6:]))+datetime.timedelta(days=days_internal*(ln)+1))[0:10]+' 09:00:00',end_date[0:4]+'-'+end_date[4:6]+'-'+end_date[6:]+' 15:30:00')
n = n+1
else:
bars = md.get_bars(daima_new,mins,str(datetime.datetime(int(start_date[0:4]),int(start_date[4:6]),int(start_date[6:]))+datetime.timedelta(days=days_internal*(ln)+1))[0:10]+' 09:00:00',str(datetime.datetime(int(start_date[0:4]),int(start_date[4:6]),int(start_date[6:]))+datetime.timedelta(days=days_internal*(ln+1)))[0:10]+' 15:30:00')
n = n+1
bars_set.append(bars)
for SignalBars in bars_set:
for b in SignalBars:
strtime.append(b.strtime)
openp.append(b.open)
high.append(b.high)
low.append(b.low)
close.append(b.close)
volume.append(b.volume)
amount.append(b.amount)
mins = pd.DataFrame({'strtime':strtime,
'open':openp,
'close':close,
'high':high,
'low':low,
'volume':volume,
'amount':amount})
return mins
def OneDayMinData_MD(date, stklst):
md.init('18201141877', 'Wqxl7309')
datestr = dt.datetime.strptime(str(date), '%Y%m%d')
datestr = dt.datetime.strftime(datestr, '%Y-%m-%d')
starttime = ' '.join([datestr, '09:30:00'])
endtime = ' '.join([datestr, '15:00:00'])
# day base obj
objday = StocksMinDB(configpath=r'E:\stocks_data_min\StocksMinDB\configs')
objday.connectDB('stocks_data_min_by_day')
daycols = [c.lower() for c in ByDay.colinfo if c != 'STKID']
dayconn = objday._getConn_()
# stk base obj
objstk = StocksMinDB(configpath=r'E:\stocks_data_min\StocksMinDB\configs')
objstk.connectDB('stocks_data_min_by_stock')
stkcols = [c.lower() for c in ByStock.colinfo]
stkconn = objstk._getConn_()
# data to extract
cols = [
'date', 'time', 'stkcd', 'open', 'high', 'low', 'close', 'volume',
'amount'
]
alldata = pd.DataFrame()
for stk in stklst:
szstk = False
if stk >= 600000:
stkcd = '.'.join(['SHSE', str(stk)])
else:
szstk = True
stkstr = str(stk)
stkcd = '.'.join(['SZSE', '0' * (6 - len(stkstr)) + stkstr])
stkbars = md.get_bars(stkcd, 60, starttime, endtime)
stkdata = [[
date,
utc2local(bar.utc_endtime, '%H%M'), stk, bar.open, bar.high,
bar.low, bar.close, bar.volume, bar.amount
] for bar in stkbars]
if szstk and stkdata[-1][1] == 1500 and stkdata[-2][1] >= 1457:
tms = stkdata[-2][1] + 1
cls = stkdata[-2][6]
for tm in range(tms, 1460):
stkdata.insert(-1, [date, tm, stk, cls, cls, cls, cls, 0, 0])
stkdata = pd.DataFrame(stkdata, columns=cols)
stkdata['volamtflag'] = 0
stkdata.loc[stkdata['amount'] >
(stkdata['volume'] + 100) * stkdata['high'],
'volamtflag'] = 1
stkdata.loc[stkdata['amount'] <
(stkdata['volume'] - 100) * stkdata['low'],
'volamtflag'] = 2
stkdata.loc[(stkdata['amount'] == 0) & (stkdata['volume'] > 0),
'volamtflag'] = 3
stkdata.loc[(stkdata['amount'] == 0) & (stkdata['volume'] == 0),
'volamtflag'] = 4
alldata = alldata.append(stkdata, ignore_index=True)
stktable = '_'.join(
['stkmin', stkcd[:2].lower() + stkcd.split('.')[1]])
objstk.update_db(conn=stkconn,
dbname='stocks_data_min_by_stock',
data=stkdata.loc[:, stkcols].values,
tablename=stktable,
colinfo=ByStock.colinfo,
prmkey=ByStock.prmkey,
if_exist='append',
chunksize=1000)
# update day base
daytable = 'stkmin_{0}'.format(date)
daycolinfo = dict(
[itm for itm in ByDay.colinfo.items() if itm[0] != 'STKID'])
objday.update_db(conn=dayconn,
dbname='stocks_data_min_by_day',
data=alldata.loc[:, daycols].values,
tablename=daytable,
colinfo=daycolinfo,
prmkey=ByDay.prmkey,
if_exist='append',
chunksize=1000)
'CFFEX.IF1512,CFFEX.IF1512',
'2015-03-19 11:29:00',
'2015-03-19 12:00:00',
)
print('get_ticks: ', len(r))
r = md.get_last_ticks('CFFEX.IF1512,')
print('get_last_ticks: ', len(r))
r = md.get_last_n_ticks(
'CFFEX.IF1512', 10)
print('get_last_n_ticks(10): ', len(r))
r = md.get_bars(
'CFFEX.IF1512',
60,
'2015-05-01 09:30:00',
'2015-05-10 09:31:00',
)
print('get_bars: ', len(r))
r = md.get_last_bars('CFFEX.IF1512,', 60)
print('get_last_bars: ', len(r))
r = md.get_last_n_bars(
'CFFEX.IF1512',
60,
10)
print('get_last_n_bars(10): ', len(r))
r = md.get_dailybars(
'CFFEX.IF1512',
def get_bars(symbol, bar_type, begin_time, end_time):
var = md.get_bars(symbol, bar_type, begin_time, end_time)
ret = bar_topd(var,'date')
return ret
def get_bars(symbol, bar_type, begin_time, end_time):
var = md.get_bars(symbol, bar_type, begin_time, end_time)
ret = bar_topd(var, 'date')
return ret
conn = pymongo.MongoClient('139.196.6.151', 26666)
db = conn['test_center']
qh_data_one_min = db['qh_data_one_min']
qh_data_five_min = db['qh_data_five_min']
qh_data_five_daily = db['qh_data_five_daily']
###############################
end = datetime.datetime.now()
start_m = end.month - 1
start = datetime.datetime(end.year, start_m, 1, 9, 0, 0)
start_e = datetime.datetime(end.year, start_m, 1, 15, 0, 0)
e_s_timedetlta = (end - start).days
for day in range(e_s_timedetlta):
this_start = start + datetime.timedelta(days=day)
this_end = start_e + datetime.timedelta(days=day)
for f in f_list:
bars = md.get_bars(f, 60, this_start.strftime('%Y-%m-%d %H:%M:%S'),
this_end.strftime('%Y-%m-%d %H:%M:%S'))
for bar in bars:
print to_dict(bar)
qh_data_one_min.insert_one(to_dict(bar))
time.sleep(2)
for day in range(e_s_timedetlta):
this_start = start + datetime.timedelta(days=day)
this_end = start_e + datetime.timedelta(days=day)
for f in f_list:
bars = md.get_bars(f, 300, this_start.strftime('%Y-%m-%d %H:%M:%S'),
this_end.strftime('%Y-%m-%d %H:%M:%S'))
for bar in bars:
print to_dict(bar)
qh_data_five_min.insert(to_dict(bar))
time.sleep(2)
def read_kline_ts(symbol_list,
weeks_in_seconds,
begin_time,
end_time,
max_record=50000):
if (True):
# 类结构体转成dataframe
kdata = []
columns = [
'endtime', 'open', 'high', 'low', 'close', 'volume', 'amount'
]
bars = 0
is_daily = (weeks_in_seconds == 240 * 60)
while (True):
# 返回结果是bar类数组
if is_daily:
bars = md.get_dailybars(symbol_list, begin_time, end_time)
else:
bars = md.get_bars(symbol_list, weeks_in_seconds, begin_time,
end_time)
for bar in bars:
if is_daily:
kdata.append([
int(bar.utc_time), bar.open, bar.high, bar.close,
bar.low, bar.volume, bar.amount
])
else:
kdata.append([
int(bar.utc_endtime), bar.open, bar.high, bar.close,
bar.low, bar.volume, bar.amount
])
count = len(bars)
# TODO 一次最多处理10000项以内数据，超出应有所提示
if (count == 0 or len(kdata) > max_record) \
or (not is_daily and bars[count - 1].strendtime >= end_time) \
or (is_daily and bars[count - 1].strtime >= end_time):
break
# print("read [%s] k line:%s count=%d" % (symbol_list,
# bars[0].strtime[:10] + ' ' + bars[0].strtime[11:19], count))
if is_daily:
if count <= 10:
break
else:
begin_time = bars[count - 1].strtime[:10] \
+ ' ' + bars[count - 1].strtime[11:19]
else:
begin_time = bars[count - 1].strendtime[:10] \
+ ' ' + bars[count - 1].strendtime[11:19]
'''
count=len(kdata)
if count>0:
print("total count:%d,end date:%s " % (count,timestamp_datetime(kdata[count-1][0])))
else:
print("No data" )
'''
return pd.DataFrame(kdata, columns=columns)
def OneDayMinData_MD(date,stklst):
md.init('18201141877','Wqxl7309')
datestr= dt.datetime.strptime(str(date),'%Y%m%d')
datestr = dt.datetime.strftime(datestr,'%Y-%m-%d')
starttime = ' '.join([datestr,'09:30:00'])
endtime = ' '.join([datestr,'15:00:00'])
# day base obj
objday = StocksMinDB(configpath=r'E:\stocks_data_min\StocksMinDB\configs')
objday.connectDB('stocks_data_min_by_day')
daycols = [c.lower() for c in ByDay.colinfo if c!='STKID']
dayconn = objday._getConn_()
# stk base obj
objstk = StocksMinDB(configpath=r'E:\stocks_data_min\StocksMinDB\configs')
objstk.connectDB('stocks_data_min_by_stock')
stkcols = [c.lower() for c in ByStock.colinfo]
stkconn = objstk._getConn_()
# data to extract
cols = ['date','time','stkcd','open','high','low','close','volume','amount']
alldata = pd.DataFrame()
for stk in stklst:
szstk = False
if stk>=600000:
stkcd = '.'.join(['SHSE',str(stk)])
else:
szstk = True
stkstr = str(stk)
stkcd = '.'.join(['SZSE','0'*(6-len(stkstr))+stkstr])
stkbars = md.get_bars(stkcd,60,starttime,endtime)
stkdata = [[date,utc2local(bar.utc_endtime,'%H%M'),stk,bar.open,bar.high,bar.low,bar.close,bar.volume,bar.amount] for bar in stkbars]
if szstk and stkdata[-1][1]==1500 and stkdata[-2][1]>=1457:
tms = stkdata[-2][1]+1
cls = stkdata[-2][6]
for tm in range(tms,1460):
stkdata.insert(-1,[date,tm,stk,cls,cls,cls,cls,0,0])
stkdata = pd.DataFrame(stkdata,columns=cols)
stkdata['volamtflag'] = 0
stkdata.loc[stkdata['amount']>(stkdata['volume']+100)*stkdata['high'],'volamtflag'] = 1
stkdata.loc[stkdata['amount']<(stkdata['volume']-100)*stkdata['low'],'volamtflag'] = 2
stkdata.loc[(stkdata['amount']==0) & (stkdata['volume']>0),'volamtflag'] = 3
stkdata.loc[(stkdata['amount']==0) & (stkdata['volume']==0),'volamtflag'] = 4
alldata = alldata.append(stkdata,ignore_index=True)
stktable = '_'.join(['stkmin',stkcd[:2].lower()+stkcd.split('.')[1]])
objstk.update_db(conn=stkconn,
dbname='stocks_data_min_by_stock',
data=stkdata.loc[:,stkcols].values,
tablename=stktable,
colinfo=ByStock.colinfo,
prmkey=ByStock.prmkey,
if_exist='append',
chunksize=1000
)
# update day base
daytable = 'stkmin_{0}'.format(date)
daycolinfo = dict([itm for itm in ByDay.colinfo.items() if itm[0]!='STKID'])
objday.update_db(conn=dayconn,
dbname='stocks_data_min_by_day',
data=alldata.loc[:,daycols].values,
tablename=daytable,
colinfo=daycolinfo,
prmkey=ByDay.prmkey,
if_exist='append',
chunksize=1000
)
# coding:utf-8
__author__ = 'song.lu'
from dateutil.parser import parse
from gmsdk import md
import pandas as pd
import numpy as np
import time
md.init('cloud.myquant.cn:8000', 'demo', 'demo')
r = md.get_bars(
'CFFEX.IF1506',
60,
'2015-05-27 09:15:00',
'2015-05-27 15:30:00',
)
st = time.time()
times = []
closep = []
openp = []
highp = []
lowp = []
volume = []
for i in r:
times.append(parse(str(i.bar_time)))
closep.append(i.close)
openp.append(i.open)
