class PriceJump(BaseEnvironment):
id = 'long-short-v0'
def __init__(self,
*,
fitting_file='LTC-USD_2019-04-07.csv.xz',
testing_file='LTC-USD_2019-04-08.csv.xz',
step_size=1,
max_position=5,
window_size=10,
seed=1,
action_repeats=10,
training=True,
format_3d=False,
z_score=True,
reward_type='trade_completion',
scale_rewards=True):
super(PriceJump, self).__init__(fitting_file=fitting_file,
testing_file=testing_file,
step_size=step_size,
max_position=max_position,
window_size=window_size,
seed=seed,
action_repeats=action_repeats,
training=training,
format_3d=format_3d,
z_score=z_score,
reward_type=reward_type,
scale_rewards=scale_rewards)
self.actions = np.eye(3, dtype=np.float32)
# get Broker class to keep track of PnL and orders
self.broker = Broker(max_position=max_position,
transaction_fee=MARKET_ORDER_FEE)
self.action_space = spaces.Discrete(len(self.actions))
self.reset() # reset to load observation.shape
self.observation_space = spaces.Box(low=-10,
high=10,
shape=self.observation.shape,
dtype=np.float32)
print(
'{} PriceJump #{} instantiated.\nself.observation_space.shape : {}'
.format(self.sym, self._seed, self.observation_space.shape))
def __str__(self):
return '{} | {}-{}'.format(PriceJump.id, self.sym, self._seed)
def map_action_to_broker(self, action: int):
"""
Create or adjust orders per a specified action and adjust for penalties.
:param action: (int) current step's action
:return: (float) reward
"""
reward = pnl = 0.0
discouragement = 0.000000000001
if action == 0: # do nothing
reward += discouragement
elif action == 1: # buy
if self.broker.short_inventory_count > 0:
order = MarketOrder(ccy=self.sym,
side='short',
price=self.midpoint,
step=self.local_step_number)
pnl += self.broker.remove(order=order)
elif self.broker.long_inventory_count >= 0:
order = MarketOrder(
ccy=self.sym,
side='long',
price=self.midpoint,
# price_fee_adjusted,
step=self.local_step_number)
if self.broker.add(order=order) is False:
reward -= discouragement
else:
print(
('gym_trading.get_reward() ' + 'Error for action #{} - ' +
'unable to place an order with broker').format(action))
elif action == 2: # sell
if self.broker.long_inventory_count > 0:
order = MarketOrder(ccy=self.sym,
side='long',
price=self.midpoint,
step=self.local_step_number)
pnl += self.broker.remove(order=order)
elif self.broker.short_inventory_count >= 0:
order = MarketOrder(ccy=self.sym,
side='short',
price=self.midpoint,
step=self.local_step_number)
if self.broker.add(order=order) is False:
reward -= discouragement
else:
print(
('gym_trading.get_reward() ' + 'Error for action #{} - ' +
'unable to place an order with broker').format(action))
else:
print(('Unknown action to take in get_reward(): ' +
'action={} | midpoint={}').format(action, self.midpoint))
return reward, pnl
def _create_position_features(self):
"""
Create an array with features related to the agent's inventory
:return: (np.array) normalized position features
"""
return np.array(
(self.broker.long_inventory.position_count / self.max_position,
self.broker.short_inventory.position_count / self.max_position,
self.broker.realized_pnl / self.broker.reward_scale,
self.broker.get_unrealized_pnl(self.best_bid, self.best_ask) /
self.broker.reward_scale),
dtype=np.float32)
class PriceJump(BaseEnvironment):
id = 'long-short-v0'
def __init__(self, transaction_fee=MARKET_ORDER_FEE, **kwargs):
"""
Environment designed to trade price jumps using market orders
:param kwargs: refer to BaseEnvironment.py
"""
super(PriceJump, self).__init__(**kwargs)
# environment attributes to override in sub-class
self.actions = np.eye(3, dtype=np.float32)
# get Broker class to keep track of PnL and orders
self.broker = Broker(max_position=self.max_position,
transaction_fee=transaction_fee)
self.action_space = spaces.Discrete(len(self.actions))
self.reset() # reset to load observation.shape
self.observation_space = spaces.Box(low=-10,
high=10,
shape=self.observation.shape,
dtype=np.float32)
print(
'{} PriceJump #{} instantiated.\nobservation_space : {}'.format(
self.sym, self._seed, self.observation_space.shape),
'reward_type = {}'.format(self.reward_type))
def __str__(self):
return '{} | {}-{}'.format(PriceJump.id, self.sym, self._seed)
def map_action_to_broker(self, action: int):
"""
Create or adjust orders per a specified action and adjust for penalties.
:param action: (int) current step's action
:return: (float) reward
"""
reward = pnl = 0.0
discouragement = 0.000000000001
if action == 0: # do nothing
reward += discouragement
elif action == 1: # buy
if self.broker.short_inventory_count > 0:
order = MarketOrder(ccy=self.sym,
side='short',
price=self.midpoint,
step=self.local_step_number)
pnl += self.broker.remove(order=order)
elif self.broker.long_inventory_count >= 0:
order = MarketOrder(
ccy=self.sym,
side='long',
price=self.midpoint,
# price_fee_adjusted,
step=self.local_step_number)
if self.broker.add(order=order) is False:
reward -= discouragement
else:
print((
'gym_trading.get_reward() Error for action #{} - unable to place '
'an order with broker').format(action))
elif action == 2: # sell
if self.broker.long_inventory_count > 0:
order = MarketOrder(ccy=self.sym,
side='long',
price=self.midpoint,
step=self.local_step_number)
pnl += self.broker.remove(order=order)
elif self.broker.short_inventory_count >= 0:
order = MarketOrder(ccy=self.sym,
side='short',
price=self.midpoint,
step=self.local_step_number)
if self.broker.add(order=order) is False:
reward -= discouragement
else:
print((
'gym_trading.get_reward() Error for action #{} - unable to place '
'an order with broker').format(action))
else:
print((
'Unknown action to take in get_reward(): action={} | midpoint={}'
).format(action, self.midpoint))
return reward, pnl
def _create_position_features(self):
"""
Create an array with features related to the agent's inventory
:return: (np.array) normalized position features
"""
return np.array(
(self.broker.long_inventory.position_count / self.max_position,
self.broker.short_inventory.position_count / self.max_position,
self.broker.realized_pnl * self.broker.reward_scale,
self.broker.get_unrealized_pnl(self.best_bid, self.best_ask) *
self.broker.reward_scale),
dtype=np.float32)
