dtype='str',
delimiter=',',
comments='#',
skiprows=0))
symbols.append("IBM")
#symbols.append("BLAH") # uncomment this line to see what happens
# Set the directories from which we will read data
QSDATA = os.environ.get('QSDATA')
listOfPaths = list()
listOfPaths.append(QSDATA + "/Processed/Norgate/Equities/US_NASDAQ/")
listOfPaths.append(QSDATA + "/Processed/Norgate/Equities/US_NYSE/")
listOfPaths.append(QSDATA + "/Processed/Norgate/Equities/US_NYSE Arca/")
# Set start and end boundary times. They must be specified in Unix Epoch
start_bound = tu.ymd2epoch(2008, 1, 1)
end_bound = tu.ymd2epoch(2010, 1, 1)
# Create the data object. Once the dates are set, this object can not give you
# data from outside this range even though it might be present in the hdf file
data = da.DataAccess(True, listOfPaths, "/StrategyData", "StrategyData", False,
symbols, start_bound, end_bound)
# Find the actual first and last timestamps
timestamps = data.getTimestampArray()
start_time = timestamps[0]
end_time = timestamps[-1]
print("first timestamp:" + str(tu.epoch2date(start_bound)) + " mapped to " +
str(tu.epoch2date(start_time)))
print("last timestamp:" + str(tu.epoch2date(end_bound)) + " mapped to " +
from QSTK.qstkutil import timeutil as tu
from QSTK.qstkutil import timeseries as ts
# Set the list of stocks for us to look at
symbols = list()
symbols = list(
np.loadtxt('example-syms.csv',
dtype='str',
delimiter=',',
comments='#',
skiprows=0))
symbols.append("IBM")
#symbols.append("BLAH") # uncomment this line to see what happens
# Set start and end boundary times. They must be specified in Unix Epoch
tsstart = tu.ymd2epoch(2008, 1, 1)
tsend = tu.ymd2epoch(2010, 1, 1)
# Get the data from the data store
storename = "Norgate" # get data from our daily prices source
fieldname = "adj_close" # adj_open, adj_close, adj_high, adj_low, close, volume
adjcloses = ts.getTSFromData(storename, fieldname, symbols, tsstart, tsend)
# Print out a bit of the data
print("The prices are: ")
print(symbols)
print(adjcloses.values)
# Convert the timestamps to dates for the plot
dates = []
for ts in adjcloses.timestamps:
# Set the list of stocks for us to look at
symbols= list()
symbols = list(np.loadtxt('example-syms.csv',dtype='str',delimiter=',',
comments='#',skiprows=0))
symbols.append("IBM")
#symbols.append("BLAH") # uncomment this line to see what happens
# Set the directories from which we will read data
QSDATA = os.environ.get('QSDATA')
listOfPaths=list()
listOfPaths.append(QSDATA + "/Processed/Norgate/Equities/US_NASDAQ/")
listOfPaths.append(QSDATA + "/Processed/Norgate/Equities/US_NYSE/")
listOfPaths.append(QSDATA + "/Processed/Norgate/Equities/US_NYSE Arca/")
# Set start and end boundary times. They must be specified in Unix Epoch
start_bound = tu.ymd2epoch(2008,1,1)
end_bound = tu.ymd2epoch(2010,1,1)
# Create the data object. Once the dates are set, this object can not give you
# data from outside this range even though it might be present in the hdf file
data= da.DataAccess(True, listOfPaths, "/StrategyData", "StrategyData",
False, symbols, start_bound, end_bound)
# Find the actual first and last timestamps
timestamps = data.getTimestampArray()
start_time = timestamps[0]
end_time = timestamps[-1]
print "first timestamp:" + str(tu.epoch2date(start_bound)) + " mapped to " + str(tu.epoch2date(start_time))
print "last timestamp:" + str(tu.epoch2date(end_bound)) + " mapped to " + str(tu.epoch2date(end_time))
# imports
import matplotlib.pyplot as plt
from pylab import *
from QSTK.qstkutil import DataAccess as da
from QSTK.qstkutil import timeutil as tu
from QSTK.qstkutil import timeseries as ts
# Set the list of stocks for us to look at
symbols= list()
symbols = list(np.loadtxt('example-syms.csv',dtype='str',delimiter=',',
comments='#',skiprows=0))
symbols.append("IBM")
#symbols.append("BLAH") # uncomment this line to see what happens
# Set start and end boundary times. They must be specified in Unix Epoch
tsstart = tu.ymd2epoch(2008,1,1)
tsend = tu.ymd2epoch(2010,1,1)
# Get the data from the data store
storename = "Norgate" # get data from our daily prices source
fieldname = "adj_close" # adj_open, adj_close, adj_high, adj_low, close, volume
adjcloses = ts.getTSFromData(storename,fieldname,symbols,tsstart,tsend)
# Print out a bit of the data
print("The prices are: ")
print(symbols)
print(adjcloses.values)
# Convert the timestamps to dates for the plot
dates = []
for ts in adjcloses.timestamps:
