def __init__(self, data_frame: pd.DataFrame = None, **kwargs):
self._commission = self.default('commission', 0.003, kwargs)
self._base_instrument = self.default('base_instrument', USD, kwargs)
self._quote_instrument = self.default('quote_instrument', BTC, kwargs)
self._initial_balance = self.default('initial_balance', 10000, kwargs)
self._min_trade_size = self.default('min_trade_size', 1e-6, kwargs)
self._max_trade_size = self.default('max_trade_size', 1e6, kwargs)
self._min_trade_price = self.default('min_trade_price', 1e-8, kwargs)
self._max_trade_price = self.default('max_trade_price', 1e8, kwargs)
self._randomize_time_slices = self.default('randomize_time_slices',
False, kwargs)
self._min_time_slice = self.default('min_time_slice', 128, kwargs)
self._price_column = self.default('price_column', 'close', kwargs)
self.data_frame = self.default('data_frame', data_frame)
slippage_model = self.default('slippage_model', 'uniform', kwargs)
self._slippage_model = slippage.get(slippage_model) if isinstance(
slippage_model, str) else slippage_model()
self._initial_step = 0
self._final_step = len(self._data_frame) - 1
if self._randomize_time_slices:
self._initial_step = np.random.randint(
0,
len(self._data_frame) - self._min_time_slice - 2)
self._final_step = np.random.randint(
self._initial_step + self._min_time_slice,
len(self._data_frame) - 1)
def __init__(self, data_frame: pd.DataFrame = None, **kwargs):
super().__init__(dtype=self.default('dtype', np.float16),
feature_pipeline=self.default('feature_pipeline',
None))
self._commission_percent = self.default('commission_percent', 0.3,
kwargs)
self._base_precision = self.default('base_precision', 2, kwargs)
self._instrument_precision = self.default('instrument_precision', 8,
kwargs)
self._min_trade_price = self.default('min_trade_price', 1e-6, kwargs)
self._max_trade_price = self.default('max_trade_price', 1e6, kwargs)
self._min_trade_amount = self.default('min_trade_amount', 1e-3, kwargs)
self._max_trade_amount = self.default('max_trade_amount', 1e6, kwargs)
self._min_order_amount = self.default('min_order_amount', 1e-3, kwargs)
self._initial_balance = self.default('initial_balance', 1e4, kwargs)
self._observation_columns = self.default(
'observation_columns', ['open', 'high', 'low', 'close', 'volume'],
kwargs)
self._price_column = self.default('price_column', 'close', kwargs)
self._window_size = self.default('window_size', 1, kwargs)
self._pretransform = self.default('pretransform', True, kwargs)
self._price_history = None
self.data_frame = self.default('data_frame', data_frame)
model = self.default('slippage_model', 'uniform', kwargs)
self._slippage_model = slippage.get(model) if isinstance(
model, str) else model()
def __init__(self, data_frame: pd.DataFrame = None, **kwargs):
super().__init__(
dtype=self.default('dtype', np.float32),
feature_pipeline=self.default('feature_pipeline', None),
**kwargs
)
self._commission_percent = self.default('commission_percent', 0.075, kwargs)
self._base_precision = self.default('base_precision', 5, kwargs)
self._instrument_precision = self.default('instrument_precision', 1, kwargs)
self._initial_balance = self.default('initial_balance', 1, kwargs)
self._price_column = self.default('price_column', 'close', kwargs)
self._pretransform = self.default('pretransform', True, kwargs)
self._stop_loss_percent = self.default('stop_loss_percent', 30, kwargs)
self.leverage = self.default('leverage', 1, kwargs)
self.n_env = self.default('n_env', 1, kwargs)
self.data_frame = self.default('data_frame', data_frame)
self._current_step = 0
model = self.default('slippage_model', 'uniform', kwargs)
self._slippage_model = slippage.get(model) if isinstance(model, str) else model()
self.last_direction = 0
self.total_margin = 0
self.total_profit = 0
self.profit = 0
self.hold_profit = 0
self.total_position = 0
self.profit_percent = 0
self.total_profit_percent = 0
self.entry_price = 0
self.last_price = 0
def __init__(self, data_frame: pd.DataFrame = None, **kwargs):
super().__init__(
dtype=self.default('dtype', np.float32),
feature_pipeline=self.default('feature_pipeline', None),
**kwargs
)
self._commission_percent = self.default('commission_percent', 0.3, kwargs)
self._base_precision = self.default('base_precision', 2, kwargs)
self._instrument_precision = self.default('instrument_precision', 8, kwargs)
self._initial_balance = self.default('initial_balance', 1e4, kwargs)
self._price_column = self.default('price_column', 'close', kwargs)
self._pretransform = self.default('pretransform', True, kwargs)
self.data_frame = self.default('data_frame', data_frame)
model = self.default('slippage_model', 'uniform', kwargs)
self._slippage_model = slippage.get(model) if isinstance(model, str) else model()
def test_random_uniform_slippage_model():
assert isinstance(slippage.get('uniform'), RandomUniformSlippageModel)