def backtest_tune(ticks: np.ndarray, backtest_config: dict, current_best: Union[dict, list] = None):
config = create_config(backtest_config)
n_days = round_((ticks[-1][2] - ticks[0][2]) / (1000 * 60 * 60 * 24), 0.1)
session_dirpath = make_get_filepath(os.path.join('reports', backtest_config['exchange'], backtest_config['symbol'],
f"{n_days}_days_{ts_to_date(time())[:19].replace(':', '')}", ''))
iters = 10
if 'iters' in backtest_config:
iters = backtest_config['iters']
else:
print('Parameter iters should be defined in the configuration. Defaulting to 10.')
num_cpus = 2
if 'num_cpus' in backtest_config:
num_cpus = backtest_config['num_cpus']
else:
print('Parameter num_cpus should be defined in the configuration. Defaulting to 2.')
n_particles = 10
if 'n_particles' in backtest_config:
n_particles = backtest_config['n_particles']
phi1 = 1.4962
phi2 = 1.4962
omega = 0.7298
if 'options' in backtest_config:
phi1 = backtest_config['options']['c1']
phi2 = backtest_config['options']['c2']
omega = backtest_config['options']['w']
current_best_params = []
if current_best:
if type(current_best) == list:
for c in current_best:
c = clean_start_config(c, config, backtest_config['ranges'])
current_best_params.append(c)
else:
current_best = clean_start_config(current_best, config, backtest_config['ranges'])
current_best_params.append(current_best)
ray.init(num_cpus=num_cpus, logging_level=logging.FATAL, log_to_driver=False)
pso = ng.optimizers.ConfiguredPSO(transform='identity', popsize=n_particles, omega=omega, phip=phi1, phig=phi2)
algo = NevergradSearch(optimizer=pso, points_to_evaluate=current_best_params)
algo = ConcurrencyLimiter(algo, max_concurrent=num_cpus)
scheduler = AsyncHyperBandScheduler()
analysis = tune.run(tune.with_parameters(backtest, ticks=ticks), metric='objective', mode='max', name='search',
search_alg=algo, scheduler=scheduler, num_samples=iters, config=config, verbose=1,
reuse_actors=True, local_dir=session_dirpath,
progress_reporter=LogReporter(metric_columns=['daily_gain', 'closest_liquidation', 'objective'],
parameter_columns=[k for k in backtest_config['ranges']]))
ray.shutdown()
df = analysis.results_df
df.reset_index(inplace=True)
df.drop(columns=['trial_id', 'time_this_iter_s', 'done', 'timesteps_total', 'episodes_total', 'training_iteration',
'experiment_id', 'date', 'timestamp', 'time_total_s', 'pid', 'hostname', 'node_ip',
'time_since_restore', 'timesteps_since_restore', 'iterations_since_restore', 'experiment_tag'],
inplace=True)
df.to_csv(os.path.join(backtest_config['session_dirpath'], 'results.csv'), index=False)
print('Best candidate found:')
pprint.pprint(analysis.best_config)
plot_wrap(backtest_config, ticks, clean_result_config(analysis.best_config))
return analysis
def backtest_tune(ticks: np.ndarray, backtest_config: dict, current_best: Union[dict, list] = None):
config = create_config(backtest_config)
n_days = round_((ticks[-1][2] - ticks[0][2]) / (1000 * 60 * 60 * 24), 0.1)
session_dirpath = make_get_filepath(os.path.join('reports', backtest_config['exchange'], backtest_config['symbol'],
f"{n_days}_days_{ts_to_date(time())[:19].replace(':', '')}", ''))
iters = 10
if 'iters' in backtest_config:
iters = backtest_config['iters']
else:
print('Parameter iters should be defined in the configuration. Defaulting to 10.')
num_cpus = 2
if 'num_cpus' in backtest_config:
num_cpus = backtest_config['num_cpus']
else:
print('Parameter num_cpus should be defined in the configuration. Defaulting to 2.')
n_particles = 10
if 'n_particles' in backtest_config:
n_particles = backtest_config['n_particles']
phi1 = 1.4962
phi2 = 1.4962
omega = 0.7298
if 'options' in backtest_config:
phi1 = backtest_config['options']['c1']
phi2 = backtest_config['options']['c2']
omega = backtest_config['options']['w']
current_best_params = []
if current_best:
if type(current_best) == list:
for c in current_best:
c = clean_start_config(c, config, backtest_config['ranges'])
current_best_params.append(c)
else:
current_best = clean_start_config(current_best, config, backtest_config['ranges'])
current_best_params.append(current_best)
ray.init(num_cpus=num_cpus, logging_level=logging.FATAL, log_to_driver=False)
pso = ng.optimizers.ConfiguredPSO(transform='identity', popsize=n_particles, omega=omega, phip=phi1, phig=phi2)
algo = NevergradSearch(optimizer=pso, points_to_evaluate=current_best_params)
algo = ConcurrencyLimiter(algo, max_concurrent=num_cpus)
scheduler = AsyncHyperBandScheduler()
analysis = tune.run(tune.with_parameters(wrap_backtest, ticks=ticks), metric='objective', mode='max', name='search',
search_alg=algo, scheduler=scheduler, num_samples=iters, config=config, verbose=1,
reuse_actors=True, local_dir=session_dirpath,
progress_reporter=LogReporter(metric_columns=['daily_gain',
'closest_liquidation',
'max_hours_between_fills',
'objective'],
parameter_columns=[k for k in backtest_config['ranges'] if type(
config[k]) == ray.tune.sample.Float or type(
config[k]) == ray.tune.sample.Integer]))
ray.shutdown()
return analysis
def backtest_tune(ticks: np.ndarray,
backtest_config: dict,
current_best: Union[dict, list] = None):
config = create_config(backtest_config)
n_days = round_((ticks[-1][2] - ticks[0][2]) / (1000 * 60 * 60 * 24), 0.1)
backtest_config['optimize_dirpath'] = os.path.join(
backtest_config['optimize_dirpath'],
ts_to_date(time())[:19].replace(':', ''), '')
if 'iters' in backtest_config:
iters = backtest_config['iters']
else:
print(
'Parameter iters should be defined in the configuration. Defaulting to 10.'
)
iters = 10
if 'num_cpus' in backtest_config:
num_cpus = backtest_config['num_cpus']
else:
print(
'Parameter num_cpus should be defined in the configuration. Defaulting to 2.'
)
num_cpus = 2
n_particles = backtest_config[
'n_particles'] if 'n_particles' in backtest_config else 10
phi1 = 1.4962
phi2 = 1.4962
omega = 0.7298
if 'options' in backtest_config:
phi1 = backtest_config['options']['c1']
phi2 = backtest_config['options']['c2']
omega = backtest_config['options']['w']
current_best_params = []
if current_best:
if type(current_best) == list:
for c in current_best:
c = clean_start_config(c, config, backtest_config['ranges'])
if c not in current_best_params:
current_best_params.append(c)
else:
current_best = clean_start_config(current_best, config,
backtest_config['ranges'])
current_best_params.append(current_best)
ray.init(num_cpus=num_cpus,
logging_level=logging.FATAL,
log_to_driver=False)
pso = ng.optimizers.ConfiguredPSO(transform='identity',
popsize=n_particles,
omega=omega,
phip=phi1,
phig=phi2)
algo = NevergradSearch(optimizer=pso,
points_to_evaluate=current_best_params)
algo = ConcurrencyLimiter(algo, max_concurrent=num_cpus)
scheduler = AsyncHyperBandScheduler()
if 'wfo' in config and config['wfo']:
print('\n\nwalk forward optimization\n\n')
wfo = WFO(ticks, backtest_config, P_train=0.5).set_train_N(4)
backtest_wrap = lambda config: tune_report(wfo.backtest(config))
else:
print('\n\nsimple sliding window optimization\n\n')
backtest_wrap = tune.with_parameters(simple_sliding_window_wrap,
ticks=ticks)
analysis = tune.run(backtest_wrap,
metric='objective',
mode='max',
name='search',
search_alg=algo,
scheduler=scheduler,
num_samples=iters,
config=config,
verbose=1,
reuse_actors=True,
local_dir=backtest_config['optimize_dirpath'],
progress_reporter=LogReporter(
metric_columns=[
'daily_gain', 'closest_liquidation',
'max_hrs_no_fills',
'max_hrs_no_fills_same_side', 'objective'
],
parameter_columns=[
k for k in backtest_config['ranges']
if type(config[k]) == ray.tune.sample.Float
or type(config[k]) == ray.tune.sample.Integer
]),
raise_on_failed_trial=False)
ray.shutdown()
return analysis
def backtest_tune(data: np.ndarray,
config: dict,
current_best: Union[dict, list] = None):
memory = int(sys.getsizeof(data) * 1.2)
virtual_memory = psutil.virtual_memory()
print(f'data size in mb {memory / (1000 * 1000):.4f}')
if (virtual_memory.available - memory) / virtual_memory.total < 0.1:
print(
"Available memory would drop below 10%. Please reduce the time span."
)
return None
config = create_config(config)
print('tuning:')
for k, v in config.items():
if type(v) in [ray.tune.sample.Float, ray.tune.sample.Integer]:
print(k, (v.lower, v.upper))
phi1 = 1.4962
phi2 = 1.4962
omega = 0.7298
if 'options' in config:
phi1 = config['options']['c1']
phi2 = config['options']['c2']
omega = config['options']['w']
current_best_params = []
if current_best is not None:
if type(current_best) == list:
for c in current_best:
c = clean_start_config(c, config)
if c not in current_best_params:
current_best_params.append(c)
else:
current_best = clean_start_config(current_best, config)
current_best_params.append(current_best)
ray.init(num_cpus=config['num_cpus'],
object_store_memory=memory if memory > 4000000000 else
None) # , logging_level=logging.FATAL, log_to_driver=False)
pso = ng.optimizers.ConfiguredPSO(transform='identity',
popsize=config['n_particles'],
omega=omega,
phip=phi1,
phig=phi2)
algo = NevergradSearch(optimizer=pso,
points_to_evaluate=current_best_params)
algo = ConcurrencyLimiter(algo, max_concurrent=config['num_cpus'])
scheduler = AsyncHyperBandScheduler()
print('\n\nsimple sliding window optimization\n\n')
parameter_columns = []
for side in ['long', 'shrt']:
if config[f'{side}£enabled']:
parameter_columns.append(f'{side}£grid_span')
parameter_columns.append(f'{side}£eprice_pprice_diff')
parameter_columns.append(f'{side}£eprice_exp_base')
parameter_columns.append(f'{side}£secondary_pprice_diff')
parameter_columns.append(f'{side}£min_markup')
backtest_wrap = tune.with_parameters(
simple_sliding_window_wrap,
data=data,
do_print=(config['print_slice_progress']
if 'print_slice_progress' in config else False))
analysis = tune.run(
backtest_wrap,
metric='obj',
mode='max',
name='search',
search_alg=algo,
scheduler=scheduler,
num_samples=config['iters'],
config=config,
verbose=1,
reuse_actors=True,
local_dir=config['optimize_dirpath'],
progress_reporter=LogReporter(metric_columns=[
'min_adg', 'avg_adg', 'min_bkr', 'eqbal_ratio_min',
'hrs_stuck_max_l', 'hrs_stuck_max_s', 'pac_mean_l', 'pac_mean_s',
'n_slc', 'obj'
],
parameter_columns=parameter_columns,
max_report_frequency=30),
raise_on_failed_trial=False)
ray.shutdown()
print('\nCleaning up temporary optimizer data...\n')
try:
shutil.rmtree(os.path.join(config['optimize_dirpath'], 'search'))
except Exception as e:
print('Failed cleaning up.')
print(e)
return analysis