def run(dna: bool = False) -> None:
# trading routes
arr = []
if not dna:
print(
jh.color('{}{}{}'.format('#' * 25, ' Trading Routes ', '#' * 25), 'blue')
)
arr.append(('exchange', 'symbol', 'timeframe', 'strategy name', 'DNA'))
else:
print(
jh.color('Translated DNAs into hyper-parameters:', 'blue')
)
translated_DNAs_count = 0
for i, r in enumerate(router.routes):
if dna and r.dna:
translated_DNAs_count += 1
StrategyClass = jh.get_strategy_class(r.strategy_name)
hyperparameters = jh.dna_to_hp(StrategyClass.hyperparameters(None), r.dna)
table.key_value(hyperparameters.items(), r.strategy_name, uppercase_title=False)
print('\n')
else:
arr.append([r.exchange, r.symbol, r.timeframe, r.strategy_name, r.dna])
if not dna:
table.multi_value(arr)
print('\n')
else:
if not translated_DNAs_count:
print('No DNA string found.')
# extra_candles
if not dna:
print(
jh.color('{}{}{}'.format('#' * 25, ' Extra Candles ', '#' * 25), 'blue')
)
arr = [('exchange', 'symbol', 'timeframe')]
for i, r in enumerate(router.extra_candles):
arr.append([r[0], r[1], r[2]])
table.multi_value(arr)
print('\n')
def run(start_date: str,
finish_date: str,
candles: Dict[str, Dict[str, Union[str, np.ndarray]]] = None,
chart: bool = False,
tradingview: bool = False,
full_reports: bool = False,
csv: bool = False,
json: bool = False) -> None:
# clear the screen
if not jh.should_execute_silently():
click.clear()
# validate routes
validate_routes(router)
# initiate candle store
store.candles.init_storage(5000)
# load historical candles
if candles is None:
print('loading candles...')
candles = load_candles(start_date, finish_date)
click.clear()
if not jh.should_execute_silently():
# print candles table
key = '{}-{}'.format(config['app']['considering_candles'][0][0],
config['app']['considering_candles'][0][1])
table.key_value(stats.candles(candles[key]['candles']),
'candles',
alignments=('left', 'right'))
print('\n')
# print routes table
table.multi_value(stats.routes(router.routes))
print('\n')
# print guidance for debugging candles
if jh.is_debuggable('trading_candles') or jh.is_debuggable(
'shorter_period_candles'):
print(
' Symbol | timestamp | open | close | high | low | volume'
)
# run backtest simulation
simulator(candles)
if not jh.should_execute_silently():
# print trades metrics
if store.completed_trades.count > 0:
change = []
# calcualte market change
for e in router.routes:
if e.strategy is None:
return
first = Candle.select(Candle.close).where(
Candle.timestamp == jh.date_to_timestamp(start_date),
Candle.exchange == e.exchange,
Candle.symbol == e.symbol).first()
last = Candle.select(Candle.close).where(
Candle.timestamp == jh.date_to_timestamp(finish_date) -
60000, Candle.exchange == e.exchange,
Candle.symbol == e.symbol).first()
change.append(
((last.close - first.close) / first.close) * 100.0)
data = report.portfolio_metrics()
data.append(
['Market Change',
str(round(np.average(change), 2)) + "%"])
print('\n')
table.key_value(data, 'Metrics', alignments=('left', 'right'))
print('\n')
# save logs
store_logs(json, tradingview, csv)
if chart:
charts.portfolio_vs_asset_returns()
# QuantStats' report
if full_reports:
quantstats.quantstats_tearsheet()
else:
print(jh.color('No trades were made.', 'yellow'))
def generate_initial_population(self) -> None:
"""
generates the initial population
"""
loop_length = int(self.population_size / self.cpu_cores)
with click.progressbar(length=loop_length, label='Generating initial population...') as progressbar:
for i in range(loop_length):
people = []
with Manager() as manager:
dna_bucket = manager.list([])
workers = []
def get_fitness(dna: str, dna_bucket: list) -> None:
try:
fitness_score, fitness_log_training, fitness_log_testing = self.fitness(dna)
dna_bucket.append((dna, fitness_score, fitness_log_training, fitness_log_testing))
except Exception as e:
proc = os.getpid()
logger.error(f'process failed - ID: {str(proc)}')
logger.error("".join(traceback.TracebackException.from_exception(e).format()))
raise e
try:
for _ in range(self.cpu_cores):
dna = ''.join(choices(self.charset, k=self.solution_len))
w = Process(target=get_fitness, args=(dna, dna_bucket))
w.start()
workers.append(w)
# join workers
for w in workers:
w.join()
if w.exitcode > 0:
logger.error(f'a process exited with exitcode: {str(w.exitcode)}')
except KeyboardInterrupt:
print(
jh.color('Terminating session...', 'red')
)
# terminate all workers
for w in workers:
w.terminate()
# shutdown the manager process manually since garbage collection cannot won't get to do it for us
manager.shutdown()
# now we can terminate the main session safely
jh.terminate_app()
except:
raise
for d in dna_bucket:
people.append({
'dna': d[0],
'fitness': d[1],
'training_log': d[2],
'testing_log': d[3]
})
# update dashboard
click.clear()
progressbar.update(1)
print('\n')
table_items = [
['Started at', jh.timestamp_to_arrow(self.start_time).humanize()],
['Index', f'{len(self.population)}/{self.population_size}'],
['errors/info', f'{len(store.logs.errors)}/{len(store.logs.info)}'],
['Trading Route', f'{router.routes[0].exchange}, {router.routes[0].symbol}, {router.routes[0].timeframe}, {router.routes[0].strategy_name}'],
# TODO: add generated DNAs?
# ['-'*10, '-'*10],
# ['DNA', people[0]['dna']],
# ['fitness', round(people[0]['fitness'], 6)],
# ['training|testing logs', people[0]['log']],
]
if jh.is_debugging():
table_items.insert(3, ['Population Size', self.population_size])
table_items.insert(3, ['Iterations', self.iterations])
table_items.insert(3, ['Solution Length', self.solution_len])
table_items.insert(3, ['-' * 10, '-' * 10])
table.key_value(table_items, 'Optimize Mode', alignments=('left', 'right'))
# errors
if jh.is_debugging() and len(report.errors()):
print('\n')
table.key_value(report.errors(), 'Error Logs')
for p in people:
self.population.append(p)
# sort the population
self.population = list(sorted(self.population, key=lambda x: x['fitness'], reverse=True))
def evolve(self) -> List[Any]:
"""
the main method, that runs the evolutionary algorithm
"""
# generate the population if starting
if self.started_index == 0:
self.generate_initial_population()
if len(self.population) < 0.5 * self.population_size:
raise ValueError('Too many errors: less then half of the planned population size could be generated.')
# if even our best individual is too weak, then we better not continue
if self.population[0]['fitness'] == 0.0001:
print(jh.color('Cannot continue because no individual with the minimum fitness-score was found. '
'Your strategy seems to be flawed or maybe it requires modifications. ', 'yellow'))
jh.terminate_app()
loop_length = int(self.iterations / self.cpu_cores)
i = self.started_index
with click.progressbar(length=loop_length, label='Evolving...') as progressbar:
while i < loop_length:
with Manager() as manager:
people = manager.list([])
workers = []
def get_baby(people: List) -> None:
try:
# let's make a baby together LOL
baby = self.make_love()
# let's mutate baby's genes, who knows, maybe we create a x-man or something
baby = self.mutate(baby)
people.append(baby)
except Exception as e:
proc = os.getpid()
logger.error(f'process failed - ID: {str(proc)}')
logger.error("".join(traceback.TracebackException.from_exception(e).format()))
raise e
try:
for _ in range(self.cpu_cores):
w = Process(target=get_baby, args=[people])
w.start()
workers.append(w)
for w in workers:
w.join()
if w.exitcode > 0:
logger.error(f'a process exited with exitcode: {str(w.exitcode)}')
except KeyboardInterrupt:
print(
jh.color('Terminating session...', 'red')
)
# terminate all workers
for w in workers:
w.terminate()
# shutdown the manager process manually since garbage collection cannot won't get to do it for us
manager.shutdown()
# now we can terminate the main session safely
jh.terminate_app()
except:
raise
# update dashboard
click.clear()
progressbar.update(1)
print('\n')
table_items = [
['Started At', jh.timestamp_to_arrow(self.start_time).humanize()],
['Index/Total', f'{(i + 1) * self.cpu_cores}/{self.iterations}'],
['errors/info', f'{len(store.logs.errors)}/{len(store.logs.info)}'],
['Route', f'{router.routes[0].exchange}, {router.routes[0].symbol}, {router.routes[0].timeframe}, {router.routes[0].strategy_name}']
]
if jh.is_debugging():
table_items.insert(
3,
['Population Size, Solution Length',
f'{self.population_size}, {self.solution_len}']
)
table.key_value(table_items, 'info', alignments=('left', 'right'))
# errors
if jh.is_debugging() and len(report.errors()):
print('\n')
table.key_value(report.errors(), 'Error Logs')
print('\n')
print('Best DNA candidates:')
print('\n')
# print fittest individuals
if jh.is_debugging():
fittest_list = [['Rank', 'DNA', 'Fitness', 'Training log || Testing log'], ]
else:
fittest_list = [['Rank', 'DNA', 'Training log || Testing log'], ]
if self.population_size > 50:
number_of_ind_to_show = 15
elif self.population_size > 20:
number_of_ind_to_show = 10
elif self.population_size > 9:
number_of_ind_to_show = 9
else:
raise ValueError('self.population_size cannot be less than 10')
for j in range(number_of_ind_to_show):
log = f"win-rate: {self.population[j]['training_log']['win-rate']}%, total: {self.population[j]['training_log']['total']}, PNL: {self.population[j]['training_log']['PNL']}% || win-rate: {self.population[j]['testing_log']['win-rate']}%, total: {self.population[j]['testing_log']['total']}, PNL: {self.population[j]['testing_log']['PNL']}%"
if self.population[j]['testing_log']['PNL'] is not None and self.population[j]['training_log'][
'PNL'] > 0 and self.population[j]['testing_log'][
'PNL'] > 0:
log = jh.style(log, 'bold')
if jh.is_debugging():
fittest_list.append(
[
j + 1,
self.population[j]['dna'],
self.population[j]['fitness'],
log
],
)
else:
fittest_list.append(
[
j + 1,
self.population[j]['dna'],
log
],
)
if jh.is_debugging():
table.multi_value(fittest_list, with_headers=True, alignments=('left', 'left', 'right', 'left'))
else:
table.multi_value(fittest_list, with_headers=True, alignments=('left', 'left', 'left'))
# one person has to die and be replaced with the newborn baby
for baby in people:
random_index = randint(1, len(self.population) - 1) # never kill our best perforemr
try:
self.population[random_index] = baby
except IndexError:
print('=============')
print(f'self.population_size: {self.population_size}')
print(f'self.population length: {len(self.population)}')
jh.terminate_app()
self.population = list(sorted(self.population, key=lambda x: x['fitness'], reverse=True))
# reaching the fitness goal could also end the process
if baby['fitness'] >= self.fitness_goal:
progressbar.update(self.iterations - i)
print('\n')
print(f'fitness goal reached after iteration {i}')
return baby
# save progress after every n iterations
if i != 0 and int(i * self.cpu_cores) % 50 == 0:
self.save_progress(i)
# store a take_snapshot of the fittest individuals of the population
if i != 0 and i % int(100 / self.cpu_cores) == 0:
self.take_snapshot(i * self.cpu_cores)
i += 1
print('\n\n')
print(f'Finished {self.iterations} iterations.')
return self.population
def generate_initial_population(self):
"""
generates the initial population
"""
cores_num = cpu_count()
loop_length = int(self.population_size / cores_num)
with click.progressbar(
length=loop_length,
label='Generating initial population...') as progressbar:
for i in range(loop_length):
people = []
with Manager() as manager:
dna_bucket = manager.list([])
workers = []
def get_fitness(dna, dna_bucket):
fitness_score, fitness_log = self.fitness(dna)
dna_bucket.append((dna, fitness_score, fitness_log))
for _ in range(cores_num):
dna = ''.join(
choices(self.charset, k=self.solution_len))
w = Process(target=get_fitness, args=(dna, dna_bucket))
w.start()
workers.append(w)
# join workers
for w in workers:
w.join()
for d in dna_bucket:
people.append({
'dna': d[0],
'fitness': d[1],
'log': d[2]
})
# update dashboard
click.clear()
progressbar.update(1)
print('\n')
table.key_value([
['started at',
jh.get_arrow(self.start_time).humanize()],
[
'index/total', '{}/{}'.format(len(self.population),
self.population_size)
],
['-', '-'],
['population_size', self.population_size],
['iterations', self.iterations],
['solution_len', self.solution_len],
[
'route', '{}, {}, {}, {}'.format(
router.routes[0].exchange, router.routes[0].symbol,
router.routes[0].timeframe,
router.routes[0].strategy_name)
],
['-', '-'],
['DNA', people[0]['dna']],
['fitness', round(people[0]['fitness'], 6)],
['training|testing logs', people[0]['log']],
],
'baby',
alignments=('left', 'right'))
for p in people:
self.population.append(p)
# sort the population
self.population = list(
sorted(self.population, key=lambda x: x['fitness'], reverse=True))
def evolve(self):
"""
the main method, that runs the evolutionary algorithm
"""
# generate the population if starting
if self.started_index == 0:
self.generate_initial_population()
cores_num = cpu_count()
loop_length = int(self.iterations / cores_num)
i = self.started_index
with click.progressbar(length=loop_length,
label='Evolving...') as progressbar:
while i < loop_length:
with Manager() as manager:
people = manager.list([])
workers = []
def get_baby(people):
# let's make a baby together LOL
baby = self.make_love()
# let's mutate baby's genes, who knows, maybe we create a x-man or something
baby = self.mutate(baby)
people.append(baby)
for _ in range(cores_num):
w = Process(target=get_baby, args=[people])
w.start()
workers.append(w)
for w in workers:
w.join()
# update dashboard
click.clear()
progressbar.update(1)
print('\n')
table.key_value(
[[
'started at',
jh.get_arrow(self.start_time).humanize()
],
[
'index/total', '{}/{}'.format(
(i + 1) * cores_num, self.iterations)
],
[
'population_size, solution_len', '{}, {}'.format(
self.population_size, self.solution_len)
],
[
'route', '{}, {}, {}, {}'.format(
router.routes[0].exchange,
router.routes[0].symbol,
router.routes[0].timeframe,
router.routes[0].strategy_name)
]],
'info',
alignments=('left', 'right'))
print('\n')
# print fittest individuals
fittest_list = [
['rank', 'DNA', 'fitness', 'training|testing logs'],
]
if self.population_size > 50:
number_of_ind_to_show = 25
elif self.population_size > 20:
number_of_ind_to_show = 20
elif self.population_size > 9:
number_of_ind_to_show = 9
else:
raise ValueError(
'self.population_size cannot be less than 10')
for j in range(number_of_ind_to_show):
fittest_list.append([
j + 1, self.population[j]['dna'],
self.population[j]['fitness'],
self.population[j]['log']
], )
table.multi_value(fittest_list,
with_headers=True,
alignments=('left', 'left', 'right',
'left'))
# one person has to die and be replaced with the newborn baby
for baby in people:
random_index = randint(0, len(self.population) - 1)
try:
self.population[random_index] = baby
except IndexError:
print('=============')
print('self.population_size: {}'.format(
self.population_size))
print('self.population length: {}'.format(
len(self.population)))
jh.terminate_app()
self.population = list(
sorted(self.population,
key=lambda x: x['fitness'],
reverse=True))
# reaching the fitness goal could also end the process
if baby['fitness'] >= self.fitness_goal:
progressbar.update(self.iterations - i)
print('\n')
print('fitness goal reached after iteration {}'.
format(i))
return baby
# save progress after every n iterations
if i != 0 and int(i * cores_num) % 50 == 0:
self.save_progress(i)
# store a take_snapshot of the fittest individuals of the population
if i != 0 and i % int(100 / cores_num) == 0:
self.take_snapshot(i * cores_num)
i += 1
print('\n\n')
print('Finished {} iterations.'.format(self.iterations))
return self.population
def run(start_date: str,
finish_date: str,
candles=None,
chart=False,
tradingview=False,
csv=False,
json=False):
# clear the screen
if not jh.should_execute_silently():
click.clear()
# validate routes
validate_routes(router)
# initiate candle store
store.candles.init_storage(5000)
# load historical candles
if candles is None:
print('loading candles...')
candles = load_candles(start_date, finish_date)
click.clear()
if not jh.should_execute_silently():
# print candles table
key = '{}-{}'.format(config['app']['trading_exchanges'][0],
config['app']['trading_symbols'][0])
table.key_value(stats.candles(candles[key]['candles']),
'candles',
alignments=('left', 'right'))
print('\n')
# print routes table
table.multi_value(stats.routes(router.routes))
print('\n')
# print guidance for debugging candles
if jh.is_debuggable('trading_candles') or jh.is_debuggable(
'shorter_period_candles'):
print(
' Symbol | timestamp | open | close | high | low | volume'
)
# run backtest simulation
simulator(candles)
if not jh.should_execute_silently():
# print trades statistics
if store.completed_trades.count > 0:
print('\n')
table.key_value(report.portfolio_metrics(),
'Metrics',
alignments=('left', 'right'))
print('\n')
# save logs
store_logs(json, tradingview, csv)
if chart:
charts.portfolio_vs_asset_returns()
else:
print(jh.color('No trades were made.', 'yellow'))
def run(start_date: str,
finish_date: str,
candles: Dict[str, Dict[str, Union[str, np.ndarray]]] = None,
chart: bool = False,
tradingview: bool = False,
full_reports: bool = False,
csv: bool = False,
json: bool = False) -> None:
# clear the screen
if not jh.should_execute_silently():
click.clear()
# validate routes
validate_routes(router)
# initiate candle store
store.candles.init_storage(5000)
# load historical candles
if candles is None:
print('loading candles...')
candles = load_candles(start_date, finish_date)
click.clear()
if not jh.should_execute_silently():
# print candles table
key = f"{config['app']['considering_candles'][0][0]}-{config['app']['considering_candles'][0][1]}"
table.key_value(stats.candles(candles[key]['candles']),
'candles',
alignments=('left', 'right'))
print('\n')
# print routes table
table.multi_value(stats.routes(router.routes))
print('\n')
# print guidance for debugging candles
if jh.is_debuggable('trading_candles') or jh.is_debuggable(
'shorter_period_candles'):
print(
' Symbol | timestamp | open | close | high | low | volume'
)
# run backtest simulation
simulator(candles)
if not jh.should_execute_silently():
# print trades metrics
if store.completed_trades.count > 0:
change = []
# calcualte market change
for e in router.routes:
if e.strategy is None:
return
first = Candle.select(Candle.close).where(
Candle.timestamp == jh.date_to_timestamp(start_date),
Candle.exchange == e.exchange,
Candle.symbol == e.symbol).first()
last = Candle.select(Candle.close).where(
Candle.timestamp == jh.date_to_timestamp(finish_date) -
60000, Candle.exchange == e.exchange,
Candle.symbol == e.symbol).first()
change.append(
((last.close - first.close) / first.close) * 100.0)
data = report.portfolio_metrics()
data.append(
['Market Change', f"{str(round(np.average(change), 2))}%"])
print('\n')
table.key_value(data, 'Metrics', alignments=('left', 'right'))
print('\n')
# save logs
more = ""
routes_count = len(router.routes)
if routes_count > 1:
more = f"-and-{routes_count-1}-more"
study_name = f"{router.routes[0].strategy_name}-{router.routes[0].exchange}-{router.routes[0].symbol}-{router.routes[0].timeframe}{more}-{start_date}-{finish_date}"
store_logs(study_name, json, tradingview, csv)
if chart:
charts.portfolio_vs_asset_returns(study_name)
# QuantStats' report
if full_reports:
price_data = []
# load close candles for Buy and hold and calculate pct_change
for index, c in enumerate(
config['app']['considering_candles']):
exchange, symbol = c[0], c[1]
if exchange in config['app'][
'trading_exchanges'] and symbol in config['app'][
'trading_symbols']:
# fetch from database
candles_tuple = Candle.select(
Candle.timestamp, Candle.close).where(
Candle.timestamp.between(
jh.date_to_timestamp(start_date),
jh.date_to_timestamp(finish_date) - 60000),
Candle.exchange == exchange,
Candle.symbol == symbol).order_by(
Candle.timestamp.asc()).tuples()
candles = np.array(candles_tuple)
timestamps = candles[:, 0]
price_data.append(candles[:, 1])
price_data = np.transpose(price_data)
price_df = pd.DataFrame(price_data,
index=pd.to_datetime(timestamps,
unit="ms"),
dtype=float).resample('D').mean()
price_pct_change = price_df.pct_change(1).fillna(0)
bh_daily_returns_all_routes = price_pct_change.mean(1)
quantstats.quantstats_tearsheet(bh_daily_returns_all_routes,
study_name)
else:
print(jh.color('No trades were made.', 'yellow'))
def generate_signal(start_date: str,
finish_date: str,
candles=None,
chart=False,
tradingview=False,
csv=False,
json=False):
# clear the screen
if not jh.should_execute_silently():
click.clear()
# validate routes
validate_routes(router)
# initiate candle store
store.candles.init_storage(5000)
# load historical candles
if candles is None:
print('loading candles...')
candles = load_candles(start_date, finish_date)
click.clear()
if not jh.should_execute_silently():
# print candles table
key = '{}-{}'.format(config['app']['considering_candles'][0][0],
config['app']['considering_candles'][0][1])
table.key_value(stats.candles(candles[key]['candles']),
'candles',
alignments=('left', 'right'))
print('\n')
# print routes table
table.multi_value(stats.routes(router.routes))
print('\n')
# print guidance for debugging candles
if jh.is_debuggable('trading_candles') or jh.is_debuggable(
'shorter_period_candles'):
print(
' Symbol | timestamp | open | close | high | low | volume'
)
# run backtest simulation
signal_simulator(candles)
if not jh.should_execute_silently():
# print trades statistics
if store.completed_trades.count > 0:
print('\n')
table.key_value(report.portfolio_metrics(),
'Metrics',
alignments=('left', 'right'))
print('\n')
# save logs
store_logs(json, tradingview, csv)
if chart:
charts.portfolio_vs_asset_returns()
# # clone the trades so that the original is not mutated
# completed_trades = copy.deepcopy(store.completed_trades.trades)
#
# for trade in completed_trades:
# print(trade.to_dict())
#
# # filter trades which were generated for current day
# completed_trades = filter(lambda x: x.opened_at == jh.get_current_time_in_epoch(), completed_trades)
#
# # TODO: add a slack notification here instead of print to notify the user
# print("Trades to execute today: ")
# for trade in completed_trades:
# print(trade.to_dict())
else:
print(jh.color('No trades were completed/closed.', 'yellow'))
open_positions = store.positions.get_open_positions()
open_orders = store.orders.get_all_active_orders()
current_report = jh.generate_signals_report(open_positions,
open_orders)
return current_report
def evolve(self):
"""
the main method, that runs the evolutionary algorithm
"""
# generate the population if starting
if self.started_index == 0:
self.generate_initial_population()
if len(self.population) < 0.5 * self.population_size:
raise ValueError(
'Too many errors: less then half of the planned population size could be generated.'
)
cores_num = cpu_count()
loop_length = int(self.iterations / cores_num)
i = self.started_index
with click.progressbar(length=loop_length,
label='Evolving...') as progressbar:
while i < loop_length:
with Manager() as manager:
people = manager.list([])
workers = []
def get_baby(people):
try:
# let's make a baby together LOL
baby = self.make_love()
# let's mutate baby's genes, who knows, maybe we create a x-man or something
baby = self.mutate(baby)
people.append(baby)
except Exception as e:
proc = os.getpid()
logger.error('process failed - ID: {}'.format(
str(proc)))
logger.error("".join(
traceback.TracebackException.from_exception(
e).format()))
raise e
try:
for _ in range(cores_num):
w = Process(target=get_baby, args=[people])
w.start()
workers.append(w)
for w in workers:
w.join()
if w.exitcode > 0:
logger.error(
'a process exited with exitcode: {}'.
format(str(w.exitcode)))
except KeyboardInterrupt:
print(jh.color('Terminating session...', 'red'))
# terminate all workers
for w in workers:
w.terminate()
# shutdown the manager process manually since garbage collection cannot won't get to do it for us
manager.shutdown()
# now we can terminate the main session safely
jh.terminate_app()
except:
raise
# update dashboard
click.clear()
progressbar.update(1)
print('\n')
table_items = [[
'Started At',
jh.get_arrow(self.start_time).humanize()
],
[
'Index/Total',
'{}/{}'.format((i + 1) * cores_num,
self.iterations)
],
[
'errors/info',
'{}/{}'.format(len(store.logs.errors),
len(store.logs.info))
],
[
'Route', '{}, {}, {}, {}'.format(
router.routes[0].exchange,
router.routes[0].symbol,
router.routes[0].timeframe,
router.routes[0].strategy_name)
]]
if jh.is_debugging():
table_items.insert(3, [
'Population Size, Solution Length',
'{}, {}'.format(self.population_size,
self.solution_len)
])
table.key_value(table_items,
'info',
alignments=('left', 'right'))
# errors
if jh.is_debugging() and len(report.errors()):
print('\n')
table.key_value(report.errors(), 'Error Logs')
print('\n')
print('Best DNA candidates:')
print('\n')
# print fittest individuals
fittest_list = [
[
'Rank', 'DNA', 'Fitness',
'Training log || Testing log'
],
]
if self.population_size > 50:
number_of_ind_to_show = 15
elif self.population_size > 20:
number_of_ind_to_show = 10
elif self.population_size > 9:
number_of_ind_to_show = 9
else:
raise ValueError(
'self.population_size cannot be less than 10')
for j in range(number_of_ind_to_show):
fittest_list.append([
j + 1, self.population[j]['dna'],
self.population[j]['fitness'],
self.population[j]['log']
], )
table.multi_value(fittest_list,
with_headers=True,
alignments=('left', 'left', 'right',
'left'))
# one person has to die and be replaced with the newborn baby
for baby in people:
random_index = randint(0, len(self.population) - 1)
try:
self.population[random_index] = baby
except IndexError:
print('=============')
print('self.population_size: {}'.format(
self.population_size))
print('self.population length: {}'.format(
len(self.population)))
jh.terminate_app()
self.population = list(
sorted(self.population,
key=lambda x: x['fitness'],
reverse=True))
# reaching the fitness goal could also end the process
if baby['fitness'] >= self.fitness_goal:
progressbar.update(self.iterations - i)
print('\n')
print('fitness goal reached after iteration {}'.
format(i))
return baby
# save progress after every n iterations
if i != 0 and int(i * cores_num) % 50 == 0:
self.save_progress(i)
# store a take_snapshot of the fittest individuals of the population
if i != 0 and i % int(100 / cores_num) == 0:
self.take_snapshot(i * cores_num)
i += 1
print('\n\n')
print('Finished {} iterations.'.format(self.iterations))
return self.population
def generate_initial_population(self):
"""
generates the initial population
"""
cores_num = cpu_count()
loop_length = int(self.population_size / cores_num)
with click.progressbar(length=loop_length, label='Generating initial population...') as progressbar:
for i in range(loop_length):
people = []
with Manager() as manager:
dna_bucket = manager.list([])
workers = []
def get_fitness(dna, dna_bucket):
fitness_score, fitness_log = self.fitness(dna)
dna_bucket.append((dna, fitness_score, fitness_log))
try:
for _ in range(cores_num):
dna = ''.join(choices(self.charset, k=self.solution_len))
w = Process(target=get_fitness, args=(dna, dna_bucket))
w.start()
workers.append(w)
# join workers
for w in workers:
w.join()
except KeyboardInterrupt:
print(
jh.color('Terminating session...', 'red')
)
# terminate all workers
for w in workers:
w.terminate()
# shutdown the manager process manually since garbage collection cannot won't get to do it for us
manager.shutdown()
# now we can terminate the main session safely
jh.terminate_app()
except:
raise
for d in dna_bucket:
people.append({
'dna': d[0],
'fitness': d[1],
'log': d[2]
})
# update dashboard
click.clear()
progressbar.update(1)
print('\n')
table_items = [
['Started at', jh.get_arrow(self.start_time).humanize()],
['Index', '{}/{}'.format(len(self.population), self.population_size)],
['Trading Route', '{}, {}, {}, {}'.format(
router.routes[0].exchange, router.routes[0].symbol, router.routes[0].timeframe,
router.routes[0].strategy_name
)],
# TODO: add generated DNAs?
# ['-'*10, '-'*10],
# ['DNA', people[0]['dna']],
# ['fitness', round(people[0]['fitness'], 6)],
# ['training|testing logs', people[0]['log']],
]
if jh.is_debugging():
table_items.insert(3, ['Population Size', self.population_size])
table_items.insert(3, ['Iterations', self.iterations])
table_items.insert(3, ['Solution Length', self.solution_len])
table_items.insert(3, ['-'*10, '-'*10])
table.key_value(table_items, 'Optimize Mode', alignments=('left', 'right'))
for p in people:
self.population.append(p)
# sort the population
self.population = list(sorted(self.population, key=lambda x: x['fitness'], reverse=True))
