# 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))
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()
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')