def getDataMatrixFromData(dataname, partname, symbols, tsstart, tsend):
pathpre = os.environ.get('QSDATA') + "/Processed"
if dataname == "Norgate":
pathsub = "/Norgate/Equities"
paths = list()
paths.append(pathpre + pathsub + "/US_NASDAQ/")
paths.append(pathpre + pathsub + "/US_NYSE/")
paths.append(pathpre + pathsub + "/US_NYSE Arca/")
paths.append(pathpre + pathsub + "/OTC/")
paths.append(pathpre + pathsub + "/US_AMEX/")
paths.append(pathpre + pathsub + "/Delisted_US_Recent/")
paths.append(pathpre + pathsub + "/US_Delisted/")
datastr1 = "/StrategyData"
datastr2 = "StrategyData"
else:
raise Exception("unknown dataname " + str(dataname))
data = da.DataAccess(True, paths, datastr1, datastr2, False, symbols,
tsstart, tsend)
tss = list(data.getTimestampArray())
start_time = tss[0]
end_time = tss[-1]
dates = []
for ts in tss:
dates.append(tu.epoch2date(ts))
vals = data.getMatrixBetweenTS(symbols, partname, start_time, end_time)
syms = list(data.getListOfSymbols())
del data
return (pandas.DataMatrix(vals, dates, syms))
def getDataMatrixFromData(dataname,partname,symbols,tsstart,tsend):
pathpre = os.environ.get('QSDATA') + "/Processed"
if dataname == "Norgate":
pathsub = "/Norgate/Equities"
paths=list()
paths.append(pathpre + pathsub + "/US_NASDAQ/")
paths.append(pathpre + pathsub + "/US_NYSE/")
paths.append(pathpre + pathsub + "/US_NYSE Arca/")
paths.append(pathpre + pathsub + "/OTC/")
paths.append(pathpre + pathsub + "/US_AMEX/")
paths.append(pathpre + pathsub + "/Delisted_US_Recent/")
paths.append(pathpre + pathsub + "/US_Delisted/")
datastr1 = "/StrategyData"
datastr2 = "StrategyData"
else:
raise Exception("unknown dataname " + str(dataname))
data = da.DataAccess(True, paths, datastr1, datastr2,
False, symbols, tsstart, tsend)
tss = list(data.getTimestampArray())
start_time = tss[0]
end_time = tss[-1]
dates = []
for ts in tss:
dates.append(tu.epoch2date(ts))
vals = data.getMatrixBetweenTS(symbols,partname,
start_time,end_time)
syms = list(data.getListOfSymbols())
del data
return(pandas.DataMatrix(vals,dates,syms))
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:
dates.append(tu.epoch2date(ts))
# Normalize the prices
normdat = adjcloses.values/adjcloses.values[0,:]
# Plot the prices
plt.clf()
for i in range(0,size(normdat[0,:])):
plt.plot(dates,normdat[:,i])
plt.legend(symbols)
plt.ylabel('Adjusted Close')
plt.xlabel('Date')
plt.draw()
savefig("fig1.pdf", format='pdf')
# 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))
# Now get the matrix of data
adj_close = data.getMatrixBetweenTS(symbols, "adj_close", start_time, end_time)
print "The adjusted closing prices are: "
print adj_close
# 1D numpy array with the timestamps. A typecast to list will convert this to a list.
timestamps = data.getTimestampArray()
dates = []
for ts in timestamps:
dates.append(tu.epoch2date(ts))
symbols= data.getListOfSymbols()