def __init__(self, training_candles: ndarray, testing_candles: ndarray, optimal_total: int, cpu_cores: int, csv: bool,
json: bool, start_date: str, finish_date: str) -> None:
if len(router.routes) != 1:
raise NotImplementedError('optimize_mode mode only supports one route at the moment')
self.strategy_name = router.routes[0].strategy_name
self.optimal_total = optimal_total
self.exchange = router.routes[0].exchange
self.symbol = router.routes[0].symbol
self.timeframe = router.routes[0].timeframe
StrategyClass = jh.get_strategy_class(self.strategy_name)
self.strategy_hp = StrategyClass.hyperparameters(None)
solution_len = len(self.strategy_hp)
if solution_len == 0:
raise exceptions.InvalidStrategy('Targeted strategy does not implement a valid hyperparameters() method.')
super().__init__(
iterations=2000 * solution_len,
population_size=solution_len * 100,
solution_len=solution_len,
options={
'strategy_name': self.strategy_name,
'exchange': self.exchange,
'symbol': self.symbol,
'timeframe': self.timeframe,
'strategy_hp': self.strategy_hp,
'csv': csv,
'json': json,
'start_date': start_date,
'finish_date': finish_date,
}
)
if cpu_cores > cpu_count():
raise ValueError(f'Entered cpu cores number is more than available on this machine which is {cpu_count()}')
elif cpu_cores == 0:
self.cpu_cores = cpu_count()
else:
self.cpu_cores = cpu_cores
self.training_candles = training_candles
self.testing_candles = testing_candles
key = jh.key(self.exchange, self.symbol)
training_candles_start_date = jh.timestamp_to_time(self.training_candles[key]['candles'][0][0]).split('T')[0]
training_candles_finish_date = jh.timestamp_to_time(self.training_candles[key]['candles'][-1][0]).split('T')[0]
testing_candles_start_date = jh.timestamp_to_time(self.testing_candles[key]['candles'][0][0]).split('T')[0]
testing_candles_finish_date = jh.timestamp_to_time(self.testing_candles[key]['candles'][-1][0]).split('T')[0]
self.training_initial_candles = []
self.testing_initial_candles = []
for c in config['app']['considering_candles']:
self.training_initial_candles.append(
required_candles.load_required_candles(c[0], c[1], training_candles_start_date,
training_candles_finish_date))
self.testing_initial_candles.append(
required_candles.load_required_candles(c[0], c[1], testing_candles_start_date,
testing_candles_finish_date))
def __init__(self, training_candles, testing_candles, optimal_total):
if len(router.routes) != 1:
raise NotImplementedError(
'optimize_mode mode only supports one route at the moment')
self.strategy_name = router.routes[0].strategy_name
self.optimal_total = optimal_total
self.exchange = router.routes[0].exchange
self.symbol = router.routes[0].symbol
self.timeframe = router.routes[0].timeframe
StrategyClass = jh.get_strategy_class(self.strategy_name)
self.strategy_hp = StrategyClass.hyperparameters(None)
solution_len = len(self.strategy_hp)
if solution_len == 0:
raise exceptions.InvalidStrategy(
'Targeted strategy does not implement a valid hyperparameters() method.'
)
super().__init__(iterations=2000 * solution_len,
population_size=solution_len * 100,
solution_len=solution_len,
options={
'strategy_name': self.strategy_name,
'exchange': self.exchange,
'symbol': self.symbol,
'timeframe': self.timeframe
})
self.training_candles = training_candles
self.testing_candles = testing_candles
key = jh.key(self.exchange, self.symbol)
training_candles_start_date = jh.timestamp_to_time(
self.training_candles[key]['candles'][0][0]).split('T')[0]
training_candles_finish_date = jh.timestamp_to_time(
self.training_candles[key]['candles'][-1][0]).split('T')[0]
testing_candles_start_date = jh.timestamp_to_time(
self.testing_candles[key]['candles'][0][0]).split('T')[0]
testing_candles_finish_date = jh.timestamp_to_time(
self.testing_candles[key]['candles'][-1][0]).split('T')[0]
self.training_initial_candles = []
self.testing_initial_candles = []
for c in config['app']['considering_candles']:
self.training_initial_candles.append(
required_candles.load_required_candles(
c[0], c[1], training_candles_start_date,
training_candles_finish_date))
self.testing_initial_candles.append(
required_candles.load_required_candles(
c[0], c[1], testing_candles_start_date,
testing_candles_finish_date))
def __init__(self, training_candles, testing_candles):
if len(router.routes) != 1:
raise NotImplementedError(
'optimize_mode mode only supports one route at the moment')
self.strategy_name = router.routes[0].strategy_name
self.exchange = router.routes[0].exchange
self.symbol = router.routes[0].symbol
self.timeframe = router.routes[0].timeframe
StrategyClass = jh.get_strategy_class(self.strategy_name)
self.strategy_hp = StrategyClass.hyper_parameters()
solution_len = len(self.strategy_hp)
if solution_len == 0:
raise InvalidStrategy(
'Targeted strategy does not implement a valid hyper_parameters() method.'
)
super().__init__(iterations=2000 * solution_len,
population_size=solution_len * 100,
solution_len=solution_len,
options={
'strategy_name': self.strategy_name,
'exchange': self.exchange,
'symbol': self.symbol,
'timeframe': self.timeframe
})
self.training_candles = training_candles
self.testing_candles = testing_candles
key = jh.key(self.exchange, self.symbol)
# training
self.required_initial_training_candles = required_candles.load_required_candles(
self.exchange, self.symbol,
jh.timestamp_to_time(self.training_candles[key]['candles'][0][0]),
jh.timestamp_to_time(self.training_candles[key]['candles'][-1][0]))
# testing
self.required_initial_testing_candles = required_candles.load_required_candles(
self.exchange, self.symbol,
jh.timestamp_to_time(self.testing_candles[key]['candles'][0][0]),
jh.timestamp_to_time(self.testing_candles[key]['candles'][-1][0]))
def load_candles(
start_date_str: str,
finish_date_str: str) -> Dict[str, Dict[str, Union[str, np.ndarray]]]:
start_date = jh.date_to_timestamp(start_date_str)
finish_date = jh.date_to_timestamp(finish_date_str) - 60000
# validate
if start_date == finish_date:
raise ValueError('start_date and finish_date cannot be the same.')
if start_date > finish_date:
raise ValueError('start_date cannot be bigger than finish_date.')
if finish_date > arrow.utcnow().int_timestamp * 1000:
raise ValueError("Can't load candle data from the future!")
# load and add required warm-up candles for backtest
if jh.is_backtesting():
for c in config['app']['considering_candles']:
required_candles.inject_required_candles_to_store(
required_candles.load_required_candles(c[0], c[1],
start_date_str,
finish_date_str), c[0],
c[1])
# download candles for the duration of the backtest
candles = {}
for c in config['app']['considering_candles']:
exchange, symbol = c[0], c[1]
key = jh.key(exchange, symbol)
cache_key = '{}-{}-'.format(start_date_str, finish_date_str) + key
cached_value = cache.get_value(cache_key)
# if cache exists
if cached_value:
candles_tuple = cached_value
# not cached, get and cache for later calls in the next 5 minutes
else:
# fetch from database
candles_tuple = Candle.select(
Candle.timestamp, Candle.open, Candle.close, Candle.high,
Candle.low, Candle.volume).where(
Candle.timestamp.between(start_date, finish_date),
Candle.exchange == exchange,
Candle.symbol == symbol).order_by(
Candle.timestamp.asc()).tuples()
# validate that there are enough candles for selected period
required_candles_count = (finish_date - start_date) / 60_000
if len(candles_tuple) == 0 or candles_tuple[-1][
0] != finish_date or candles_tuple[0][0] != start_date:
raise exceptions.CandleNotFoundInDatabase(
'Not enough candles for {}. Try running "jesse import-candles"'
.format(symbol))
elif len(candles_tuple) != required_candles_count + 1:
raise exceptions.CandleNotFoundInDatabase(
'There are missing candles between {} => {}'.format(
start_date_str, finish_date_str))
# cache it for near future calls
cache.set_value(cache_key,
tuple(candles_tuple),
expire_seconds=60 * 60 * 24 * 7)
candles[key] = {
'exchange': exchange,
'symbol': symbol,
'candles': np.array(candles_tuple)
}
return candles
