ax2 = pyplot.twinx()
pyplot.plot(SPY_close, "k")
ax2.set_ylabel("S (black)")
pyplot.title("V and S Closing Px")
pyplot.subplot(3, 2, 3)
pyplot.plot(pnl)
pyplot.plot(cost)
pyplot.title("PnL v cost")
pyplot.subplot(3, 2, 2)
pyplot.plot(running_pnl)
pyplot.plot(running_cost)
pyplot.title("Running PnL v Cost")
pyplot.subplot(3, 2, 4)
pyplot.plot(running_net)
pyplot.title("Running Net")
pyplot.subplot(3, 2, 5)
pyplot.bar(range(len(trade_count)), trade_count)
pyplot.title("Trade Count")
pyplot.subplot(3, 2, 6)
correlation = get_corr(offset=0)
pyplot.plot(correlation)
pyplot.title("Correlation (1 period lag)")
pyplot.show()
def main():
with h5py.File(filename) as root:
trade_dates = list(root)[start_day:end_day]
pnls = np.zeros(len(trade_dates))
costs = np.zeros(len(trade_dates))
VXX_closes = np.zeros(len(trade_dates))
SPY_closes = np.zeros(len(trade_dates))
trade_counts = np.zeros(len(trade_dates))
std = 0.00015
for day, trade_date in enumerate(trade_dates):
# print trade_date
names = root[trade_date]["names"].value
SPY_prices = root[trade_date][("prices")].value[:, 0][:]
VXX_prices = root[trade_date][("prices")].value[:, 1][:]
epoch_times = root[trade_date]["dates"].value
SPY_closes[day] = SPY_prices[-1]
VXX_closes[day] = VXX_prices[-1]
pnls[day], costs[day], std, trade_counts[day] = \
backtest_one_day(SPY_prices, VXX_prices, std)
running_pnls = np.cumsum(pnls)
running_costs = np.cumsum(costs)
running_nets = running_pnls - running_costs
total_pnl = sum(pnls)
total_cost = sum(costs)
print " *" * 40
print "Net pnl: %s" % (total_pnl - total_cost)
print "Trading pnl: %s" % total_pnl
print "Gross cost: %s" % total_cost
print "Shares traded: %s" % "hello"
print "Average: %s" % (np.mean(pnls) - np.mean(costs))
print "Standard Deviation: %s" % np.std(pnls - costs)
print "Average Trades/Day: %s" % np.mean(trade_counts)
print
ax1 = pyplot.subplot(3, 2, 1)
pyplot.plot(VXX_closes, "r")
ax1.set_ylabel("VXX (red)")
ax2 = pyplot.twinx()
pyplot.plot(SPY_closes, "k")
ax2.set_ylabel("SPY (black)")
pyplot.title("VXX and SPY Closing Px")
pyplot.subplot(3, 2, 3)
pyplot.plot(pnls)
pyplot.plot(costs)
pyplot.title("PnL v cost")
pyplot.subplot(3, 2, 2)
pyplot.plot(running_pnls)
pyplot.plot(running_costs)
pyplot.title("Running PnL v Cost")
pyplot.subplot(3, 2, 4)
pyplot.plot(running_nets)
pyplot.title("Running Net")
pyplot.subplot(3, 2, 5)
pyplot.bar(range(len(trade_counts)), trade_counts)
pyplot.title("Trade Count")
pyplot.subplot(3, 2, 6)
correlation = get_corr(offset=1)
pyplot.plot(correlation)
pyplot.title("Correlation (1 period lag)")
pyplot.show()
datasink = pe.Node(interface = DataSink(), name = 'sinker')
datasink.inputs.base_directory = out_path
# Workflow and it's connections
vbm_preprocess = pe.Workflow(name = "vbm_preprocess")
vbm_preprocess.connect([(converter, reorient, [('out_files','in_file')]),\
(reorient, segmentation, [('out_file', 'channel_files')]),\
(segmentation, list_normalized_images, [('normalized_class_images','normalized_class_images')]),\
(list_normalized_images, smoothing, [('list_norm_images','in_files')]),\
(segmentation, datasink, [('modulated_class_images','vbm_spm12'),('native_class_images','vbm_spm12.@1'),\
('normalized_class_images','vbm_spm12.@2'),('transformation_mat','vbm_spm12.@3')]),\
(smoothing, datasink, [('smoothed_files','vbm_spm12.@4')])])
try:
# Run the workflow
sys.stderr.write("running vbm workflow")
res = vbm_preprocess.run()
except:
# If fails raise the excpetion and set status False
status = False
else:
# If succeds, set status True
status = True
finally:
# Finally , write the status to .json object and calculate correlation coefficent
segmented_file = glob.glob(out_path+"/vbm_spm12/swc1*nii")[0]
corr_value = correlation.get_corr(tpm_path,segmented_file)
sys.stdout.write(json.dumps({"vbm_preprocess" : status}, sort_keys=True, indent=4, separators=(',', ': ')))
