def get_data(tickers, tBeg, tEnd, base_dir=quant_quote_home, crop=True):
iBeg = date_utils.datetime2iDate(tBeg)
iEnd = date_utils.datetime2iDate(tEnd)
trading_dates = date_utils.nyseDates(iBeg, iEnd)
output = []
for tkr in tickers:
tkr_lower = tkr.lower()
tkr_upper = tkr.upper()
for date in trading_dates:
fname = posixpath.join(base_dir, 'allstocks_%i' % date, 'table_%s.csv' % tkr_lower)
bars = np.genfromtxt(fname, delimiter=',', skip_header=False, dtype=DTYPE)
bars = bars[(bars['time'] >= 931) & (bars['time'] <= 1600)]
# if bars.shape[0] != 390:
# print 'Number of bars != 390: %s on %i has %i bars. [%s, %s]' % \
# (tkr_upper, date, bars.shape[0], bars['time'][0], bars['time'][-1])
for bar in bars:
dt = make_datetime(bar[0], bar[1])
output.append([dt, tkr, bar[5], bar[6]])
# sort by datetime then ticker for use with Zipline
return sorted(output, key=operator.itemgetter(0,1))
def get_data(tickers, tBeg, tEnd, base_dir=quant_quote_home, crop=True):
iBeg = date_utils.datetime2iDate(tBeg)
iEnd = date_utils.datetime2iDate(tEnd)
trading_dates = date_utils.nyseDates(iBeg, iEnd)
output = []
for tkr in tickers:
tkr_lower = tkr.lower()
tkr_upper = tkr.upper()
for date in trading_dates:
fname = posixpath.join(base_dir, 'allstocks_%i' % date,
'table_%s.csv' % tkr_lower)
bars = np.genfromtxt(fname,
delimiter=',',
skip_header=False,
dtype=DTYPE)
bars = bars[(bars['time'] >= 931) & (bars['time'] <= 1600)]
# if bars.shape[0] != 390:
# print 'Number of bars != 390: %s on %i has %i bars. [%s, %s]' % \
# (tkr_upper, date, bars.shape[0], bars['time'][0], bars['time'][-1])
for bar in bars:
dt = make_datetime(bar[0], bar[1])
output.append([dt, tkr, bar[5], bar[6]])
# sort by datetime then ticker for use with Zipline
return sorted(output, key=operator.itemgetter(0, 1))
def get_data(tickers, tBeg, tEnd, include_open=True, portfolio='ETFs'):
iBeg = date_utils.datetime2iDate(tBeg)
iEnd = date_utils.datetime2iDate(tEnd)
trading_dates = date_utils.nyseDates(iBeg, iEnd)
sPortDir = posixpath.join(csi_home, portfolio)
vsFiles = glob.glob(posixpath.join(sPortDir, '*.CSV'))
output = []
for tkr in tickers:
idxFile = []
for i, sFile in enumerate(vsFiles):
if '%s_' % tkr in sFile:
idxFile.append(i)
if len(idxFile) == 0:
raise Exception('Cannot find file for ticker: %s (looking in %s)' %
(tkr, sPortDir))
if len(idxFile) > 1:
raise Exception('Multiple files found for ticker: %s' % tkr)
csvPath = vsFiles[idxFile[0]]
bars = np.genfromtxt(csvPath,
delimiter=',',
skip_header=False,
dtype=DTYPE)
for bar in bars:
tkr = bar[0]
sDate = bar[2]
nOpen = bar[3]
nClose = bar[6]
nVolume = bar[7]
dt = datetime.datetime.strptime(sDate, '%Y-%m-%d')
if dt.date() >= tBeg.date() and dt.date() <= tEnd.date():
if include_open:
output.append(
[dt.replace(hour=9, minute=30), tkr, nOpen, nVolume])
output.append(
[dt.replace(hour=16, minute=0), tkr, nClose, nVolume])
return sorted(output, key=operator.itemgetter(0, 1))
def get_data(tickers, tBeg, tEnd, include_open=True, portfolio='ETFs'):
iBeg = date_utils.datetime2iDate(tBeg)
iEnd = date_utils.datetime2iDate(tEnd)
trading_dates = date_utils.nyseDates(iBeg, iEnd)
sPortDir = posixpath.join(csi_home, portfolio)
vsFiles = glob.glob(posixpath.join(sPortDir, '*.CSV'))
output = []
for tkr in tickers:
idxFile = []
for i, sFile in enumerate(vsFiles):
if '%s_' % tkr in sFile:
idxFile.append(i)
if len(idxFile) == 0:
raise Exception('Cannot find file for ticker: %s (looking in %s)' % (tkr, sPortDir))
if len(idxFile) > 1:
raise Exception('Multiple files found for ticker: %s' % tkr)
csvPath = vsFiles[idxFile[0]]
bars = np.genfromtxt(csvPath, delimiter=',', skip_header=False, dtype=DTYPE)
for bar in bars:
tkr = bar[0]
sDate = bar[2]
nOpen = bar[3]
nClose = bar[6]
nVolume = bar[7]
dt = datetime.datetime.strptime(sDate, '%Y-%m-%d')
if dt.date() >= tBeg.date() and dt.date() <= tEnd.date():
if include_open:
output.append([dt.replace(hour=9, minute=30), tkr, nOpen, nVolume])
output.append([dt.replace(hour=16, minute=0), tkr, nClose, nVolume])
return sorted(output, key=operator.itemgetter(0,1))
def check_up_to_date(self):
iS = len(self.tickers)
data_recent = np.array(self.rows_prices[-iS:])
# check that all tickers have a common most recent update date
dt_unique = np.unique(data_recent[:,0])
if len(dt_unique) != 1:
raise Exception('All tickers do not have a common recent update date.')
dt_last_quote = dt_unique[0]
# check that this common update date matches the most recent close
dt_now = datetime.datetime.now()
iNow = date_utils.datetime2iDate(dt_now)
iBeg = date_utils.datetime2iDate(dt_now - datetime.timedelta(days=5))
iEnd = date_utils.datetime2iDate(dt_now)
viBusdays = date_utils.nyseDates(iBeg, iEnd)
if iNow != viBusdays[-1]:
print viBusdays
raise Exception('iNow = %i but most recent business day is %i' % (iNow, viBusdays[-1]))
'''
def check_up_to_date(self):
iS = len(self.tickers)
data_recent = np.array(self.rows_prices[-iS:])
# check that all tickers have a common most recent update date
dt_unique = np.unique(data_recent[:, 0])
if len(dt_unique) != 1:
raise Exception(
'All tickers do not have a common recent update date.')
dt_last_quote = dt_unique[0]
# check that this common update date matches the most recent close
dt_now = datetime.datetime.now()
iNow = date_utils.datetime2iDate(dt_now)
iBeg = date_utils.datetime2iDate(dt_now - datetime.timedelta(days=5))
iEnd = date_utils.datetime2iDate(dt_now)
viBusdays = date_utils.nyseDates(iBeg, iEnd)
if iNow != viBusdays[-1]:
print viBusdays
raise Exception('iNow = %i but most recent business day is %i' %
(iNow, viBusdays[-1]))
'''
