PygameButton(game_display, left_arm_g, (0, 0, 255), font, " C",
(255, 255, 255))
PygameButton(game_display, right_arm_g, (0, 0, 255), font, " O",
(255, 255, 255))
finish_button = PygameButton(game_display, finish_button_position, (255, 0, 0),
font, "Finish", (255, 255, 255))
pygame.display.update()
crashed = False
# directory = max(glob.glob('*'), key=os.path.getctime) # path to last modified folder
directory = 'Your model directory name' # hard coded path to trained model
with open(directory + '/Data.pickle', 'rb') as handle:
rows_per_epoch = pickle.load(handle)['X'].shape[0]
with open(directory + '/Model.pickle', 'rb') as handle:
clf = pickle.load(handle)
# clf.zero_thresh = 0.5 # this classifier parameter can be changed during control stage
bci = BCI()
prp = BatyaGGPreprocessor(rows_per_epoch)
recent_data = prp.fit_test(bci.get_recent_data(rows_per_epoch))
current_class = clf.predict(recent_data)
control_updater = ControlUpdater(['x', 'y', 'z', 'g'])
current_control = control_updater.get_next()
ur = UR()
ur.go_home()
mixer.music.load('x_axis.mp3')
mixer.music.play()
while not crashed:
if current_class == 3:
current_control = control_updater.get_next()
if current_control == 'x':
mixer.music.load('x_axis.mp3')
mixer.music.play()
f"secondary_volume_filter: {secondary_volume_filter}, "
f"max_allocation: {max_allocation}, "
f"running_avg_volume_period: {running_avg_volume_period}, "
f"candidates: {candidates}"
f"primary_candidate: {primary_candidate}, "
f"secondary_candidate: {secondary_candidate}, "
f"offset: {offset}")
try:
bci = BCI(index_size=index,
rebalancing_period=rebalancing,
primary_usd_filtering=primary_volume_filter,
secondary_usd_filtering=secondary_volume_filter,
max_asset_allocation=max_allocation,
fee=0.02,
running_avg_volume_period=running_avg_volume_period,
index_candidate_size=candidates,
primary_candidate_size=primary_candidate,
secondary_candidate_size=secondary_candidate,
initial_funds=1000,
offset=offset,
start_dt=start_dt,
end_dt=end_dt)
# use previously calculated data to save initialization time
if data_by_coin is None:
bci.set_input_data(input_data)
else:
bci.data = data
bci.dates = dates
bci.data_by_coin = data_by_coin
return vars(parser.parse_args())
if __name__ == "__main__":
LOG.info("Bitpanda Crypto Index Simulator")
args = parse_args()
bci = BCI(
index_size = args['index'],
rebalancing_period = args['rebalancing'],
primary_usd_filtering = args['primary_volume_filter'],
secondary_usd_filtering = args['secondary_volume_filter'],
max_asset_allocation = args['max_allocation'],
fee = args['fee'],
running_avg_volume_period = args['volume_period'],
index_candidate_size = args['candidates'],
primary_candidate_size = args['primary_candidates'],
secondary_candidate_size = args['secondary_candidates'],
initial_funds = args['funds'],
offset = args['offset'],
bypass_validation = args['bypass_validation'],
input_file_name = args['input_file'],
start_dt = args['start_date'],
end_dt = args['end_date'],
show_graph = args['show_graph'],
save_graph = args['save_graph']
)
bci.run()
