def stats_to_algo_folder(stats, algo_namespace, recorded_cols=None):
"""
Saves the performance stats to the algo local folder.
Parameters
----------
stats: list[Object]
algo_namespace: str
recorded_cols: list[str]
Returns
-------
str
"""
bytes_to_write = get_csv_stats(stats, recorded_cols=recorded_cols)
timestr = time.strftime('%Y%m%d')
folder = get_algo_folder(algo_namespace)
stats_folder = os.path.join(folder, 'stats')
ensure_directory(stats_folder)
filename = os.path.join(stats_folder, '{}.csv'.format(timestr))
with open(filename, 'wb') as handle:
handle.write(bytes_to_write)
return bytes_to_write
def get_frame_stats(self):
"""
preparing the stats before analyze
:return: stats: pd.Dataframe
"""
# add the last day stats which is not saved in the directory
current_stats = pd.DataFrame(self.frame_stats)
current_stats.set_index('period_close', drop=False, inplace=True)
# get the location of the directory
algo_folder = get_algo_folder(self.algo_namespace)
folder = join(algo_folder, 'frame_stats')
if exists(folder):
files = [f for f in listdir(folder) if isfile(join(folder, f))]
period_stats_list = []
for item in files:
filename = join(folder, item)
with open(filename, 'rb') as handle:
perf_period = pickle.load(handle)
period_stats_list.extend(perf_period)
stats = pd.DataFrame(period_stats_list)
stats.set_index('period_close', drop=False, inplace=True)
return pd.concat([stats, current_stats])
else:
return current_stats
def stats_to_algo_folder(stats, algo_namespace, recorded_cols=None):
"""
Saves the performance stats to the algo local folder.
Parameters
----------
stats: list[Object]
algo_namespace: str
recorded_cols: list[str]
Returns
-------
str
"""
bytes_to_write = get_csv_stats(stats, recorded_cols=recorded_cols)
timestr = time.strftime('%Y%m%d')
folder = get_algo_folder(algo_namespace)
stats_folder = os.path.join(folder, 'stats')
ensure_directory(stats_folder)
filename = os.path.join(stats_folder, '{}.csv'.format(timestr))
with open(filename, 'wb') as handle:
handle.write(bytes_to_write)
return bytes_to_write
def interrupt_algorithm(self):
self.is_running = False
if self._analyze is None:
log.info('Exiting the algorithm.')
else:
log.info('Exiting the algorithm. Calling `analyze()` '
'before exiting the algorithm.')
algo_folder = get_algo_folder(self.algo_namespace)
folder = join(algo_folder, 'daily_performance')
files = [f for f in listdir(folder) if isfile(join(folder, f))]
daily_perf_list = []
for item in files:
filename = join(folder, item)
with open(filename, 'rb') as handle:
perf_period = pickle.load(handle)
perf_period_dict = perf_period.to_dict()
daily_perf_list.append(perf_period_dict)
stats = pd.DataFrame(daily_perf_list)
stats.set_index('period_close', drop=False, inplace=True)
self.analyze(stats)
sys.exit(0)
def interrupt_algorithm(self):
self.is_running = False
if self._analyze is None:
log.info('Exiting the algorithm.')
else:
log.info('Exiting the algorithm. Calling `analyze()` '
'before exiting the algorithm.')
algo_folder = get_algo_folder(self.algo_namespace)
folder = join(algo_folder, 'daily_perf')
files = [f for f in listdir(folder) if isfile(join(folder, f))]
daily_perf_list = []
for item in files:
filename = join(folder, item)
with open(filename, 'rb') as handle:
daily_perf_list.append(pickle.load(handle))
stats = pd.DataFrame(daily_perf_list)
self.analyze(stats)
sys.exit(0)
def interrupt_algorithm(self):
"""
when algorithm comes to an end this function is called.
extracts the stats and calls analyze.
after finishing, it exits the run.
Parameters
----------
Returns
-------
"""
self.is_running = False
if self._analyze is None:
log.info('Exiting the algorithm.')
else:
log.info('Exiting the algorithm. Calling `analyze()` '
'before exiting the algorithm.')
# add the last day stats which is not saved in the directory
current_stats = pd.DataFrame(self.frame_stats)
current_stats.set_index('period_close', drop=False, inplace=True)
# get the location of the directory
algo_folder = get_algo_folder(self.algo_namespace)
folder = join(algo_folder, 'frame_stats')
if exists(folder):
files = [f for f in listdir(folder) if isfile(join(folder, f))]
period_stats_list = []
for item in files:
filename = join(folder, item)
with open(filename, 'rb') as handle:
perf_period = pickle.load(handle)
period_stats_list.extend(perf_period)
stats = pd.DataFrame(period_stats_list)
stats.set_index('period_close', drop=False, inplace=True)
stats = pd.concat([stats, current_stats])
else:
stats = current_stats
self.analyze(stats)
sys.exit(0)