class PriceJump(Env):
metadata = {'render.modes': ['human']}
id = 'long-short-v0'
# Turn to true if Bitifinex is in the dataset (e.g., include_bitfinex=True)
features = Sim.get_feature_labels(include_system_time=False,
include_bitfinex=False)
best_bid_index = features.index('coinbase-bid-distance-0')
best_ask_index = features.index('coinbase-ask-distance-0')
notional_bid_index = features.index('coinbase-bid-notional-0')
notional_ask_index = features.index('coinbase-ask-notional-0')
buy_trade_index = features.index('coinbase-buys')
sell_trade_index = features.index('coinbase-sells')
target_pnl = BROKER_FEE * 10 * 5 # e.g., 5 for max_positions
fee = BROKER_FEE
def __init__(self,
*,
fitting_file='ETH-USD_2018-12-31.xz',
testing_file='ETH-USD_2019-01-01.xz',
step_size=1,
max_position=5,
window_size=10,
seed=1,
action_repeats=10,
training=True,
format_3d=False,
z_score=True):
# properties required for instantiation
self.action_repeats = action_repeats
self._seed = seed
self._random_state = np.random.RandomState(seed=self._seed)
self.training = training
self.step_size = step_size
self.max_position = max_position
self.window_size = window_size
self.format_3d = format_3d # e.g., [window, features, *NEW_AXIS*]
self.action = 0
# derive gym.env properties
self.actions = np.eye(3)
self.sym = testing_file[:7] # slice the CCY from the filename
# properties that get reset()
self.reward = 0.0
self.done = False
self.local_step_number = 0
self.midpoint = 0.0
self.observation = None
# get Broker class to keep track of PnL and orders
self.broker = Broker(max_position=max_position)
# get historical data for simulations
self.sim = Sim(use_arctic=False)
self.data = self._load_environment_data(fitting_file, testing_file)
self.prices_ = self.data[
'coinbase_midpoint'].values # used to calculate PnL
self.normalized_data = self.data.copy()
self.data = self.data.values
self.max_steps = self.data.shape[0] - self.step_size * \
self.action_repeats - 1
# normalize midpoint data
self.normalized_data['coinbase_midpoint'] = \
np.log(self.normalized_data['coinbase_midpoint'].values)
self.normalized_data['coinbase_midpoint'] = (
self.normalized_data['coinbase_midpoint'] -
self.normalized_data['coinbase_midpoint'].shift(1)).fillna(0.)
# load indicators into the indicator manager
self.tns = IndicatorManager()
self.rsi = IndicatorManager()
for window in INDICATOR_WINDOW:
self.tns.add(('tns_{}'.format(window), TnS(window=window)))
self.rsi.add(('rsi_{}'.format(window), RSI(window=window)))
if z_score:
logger.info("Pre-scaling {}-{} data...".format(
self.sym, self._seed))
self.normalized_data = self.normalized_data.apply(self.sim.z_score,
axis=1).values
logger.info("...{}-{} pre-scaling complete.".format(
self.sym, self._seed))
else:
self.normalized_data = self.normalized_data.values
# rendering class
self._render = TradingGraph(sym=self.sym)
# graph midpoint prices
self._render.reset_render_data(
y_vec=self.prices_[:np.shape(self._render.x_vec)[0]])
# buffer for appending lags
self.data_buffer = list()
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 step(self, action: int):
for current_step in range(self.action_repeats):
if self.done:
self.reset()
return self.observation, self.reward, self.done
# reset the reward if there ARE action repeats
if current_step == 0:
self.reward = 0.
step_action = action
else:
step_action = 0
# Get current step's midpoint
self.midpoint = self.prices_[self.local_step_number]
# Pass current time step midpoint to broker to calculate PnL,
# or if any open orders are to be filled
buy_volume = self._get_book_data(PriceJump.buy_trade_index)
sell_volume = self._get_book_data(PriceJump.sell_trade_index)
self.tns.step(buys=buy_volume, sells=sell_volume)
self.rsi.step(price=self.midpoint)
self.broker.step(midpoint=self.midpoint)
self.reward += self._send_to_broker_and_get_reward(
action=step_action)
step_observation = self._get_step_observation(action=action)
self.data_buffer.append(step_observation)
if len(self.data_buffer) > self.window_size:
del self.data_buffer[0]
self.local_step_number += self.step_size
self.observation = self._get_observation()
if self.local_step_number > self.max_steps:
self.done = True
order = Order(ccy=self.sym,
side=None,
price=self.midpoint,
step=self.local_step_number)
self.reward = self.broker.flatten_inventory(order=order)
return self.observation, self.reward, self.done, {}
def reset(self):
if self.training:
self.local_step_number = self._random_state.randint(
low=1, high=self.data.shape[0] // 4)
else:
self.local_step_number = 0
msg = ' {}-{} reset. Episode pnl: {:.4f} with {} trades | First step: {}'.format(
self.sym, self._seed,
self.broker.get_total_pnl(midpoint=self.midpoint),
self.broker.get_total_trade_count(), self.local_step_number)
logger.info(msg)
self.reward = 0.0
self.done = False
self.broker.reset()
self.data_buffer.clear()
self.rsi.reset()
self.tns.reset()
for step in range(self.window_size + INDICATOR_WINDOW_MAX):
self.midpoint = self.prices_[self.local_step_number]
step_buy_volume = self._get_book_data(PriceJump.buy_trade_index)
step_sell_volume = self._get_book_data(PriceJump.sell_trade_index)
self.tns.step(buys=step_buy_volume, sells=step_sell_volume)
self.rsi.step(price=self.midpoint)
step_observation = self._get_step_observation(action=0)
self.data_buffer.append(step_observation)
self.local_step_number += self.step_size
if len(self.data_buffer) > self.window_size:
del self.data_buffer[0]
self.observation = self._get_observation()
return self.observation
def render(self, mode='human'):
self._render.render(midpoint=self.midpoint, mode=mode)
def close(self):
logger.info('{}-{} is being closed.'.format(self.id, self.sym))
self.data = None
self.normalized_data = None
self.prices_ = None
self.broker = None
self.sim = None
self.data_buffer = None
self.tns = None
self.rsi = None
return
def seed(self, seed=1):
self._random_state = np.random.RandomState(seed=seed)
self._seed = seed
logger.info('Setting seed in PriceJump.seed({})'.format(seed))
return [seed]
@staticmethod
def _process_data(_next_state):
return np.clip(_next_state.reshape((1, -1)), -10., 10.)
# def _process_data(self, _next_state):
# # return self.sim.scale_state(_next_state).values.reshape((1, -1))
# return np.reshape(_next_state, (1, -1))
def _send_to_broker_and_get_reward(self, action):
reward = 0.0
discouragement = 0.000000000001
if action == 0: # do nothing
reward += discouragement
elif action == 1: # buy
price_fee_adjusted = self.midpoint + (PriceJump.fee *
self.midpoint)
if self.broker.short_inventory_count > 0:
order = Order(ccy=self.sym,
side='short',
price=price_fee_adjusted,
step=self.local_step_number)
self.broker.remove(order=order)
reward += self.broker.get_reward(side=order.side)
elif self.broker.long_inventory_count >= 0:
order = Order(ccy=self.sym,
side='long',
price=price_fee_adjusted,
step=self.local_step_number)
if self.broker.add(order=order) is False:
reward -= discouragement
else:
logger.info(
('gym_trading.get_reward() ' + 'Error for action #{} - ' +
'unable to place an order with broker').format(action))
elif action == 2: # sell
price_fee_adjusted = self.midpoint - (PriceJump.fee *
self.midpoint)
if self.broker.long_inventory_count > 0:
order = Order(ccy=self.sym,
side='long',
price=price_fee_adjusted,
step=self.local_step_number)
self.broker.remove(order=order)
reward += self.broker.get_reward(side=order.side)
elif self.broker.short_inventory_count >= 0:
order = Order(ccy=self.sym,
side='short',
price=price_fee_adjusted,
step=self.local_step_number)
if self.broker.add(order=order) is False:
reward -= discouragement
else:
logger.info(
'gym_trading.get_reward() ' + 'Error for action #{} - ' +
'unable to place an order with broker'.format(action))
else:
logger.info(
('Unknown action to take in get_reward(): ' +
'action={} | midpoint={}').format(action, self.midpoint))
return reward
def _create_position_features(self):
return np.array(
(self.broker.long_inventory.position_count / self.max_position,
self.broker.short_inventory.position_count / self.max_position,
self.broker.get_total_pnl(midpoint=self.midpoint) /
PriceJump.target_pnl,
self.broker.long_inventory.get_unrealized_pnl(self.midpoint) /
self.broker.reward_scale,
self.broker.short_inventory.get_unrealized_pnl(self.midpoint) /
self.broker.reward_scale),
dtype=np.float32)
def _create_action_features(self, action):
return self.actions[action]
def _create_indicator_features(self):
return np.array((*self.tns.get_value(), *self.rsi.get_value()),
dtype=np.float32)
def _get_nbbo(self):
best_bid = round(
self.midpoint - self._get_book_data(PriceJump.best_bid_index), 2)
best_ask = round(
self.midpoint + self._get_book_data(PriceJump.best_ask_index), 2)
return best_bid, best_ask
def _get_book_data(self, index=0):
return self.data[self.local_step_number][index]
def _get_step_observation(self, action=0):
step_position_features = self._create_position_features()
step_action_features = self._create_action_features(action=action)
step_indicator_features = self._create_indicator_features()
return np.concatenate(
(self._process_data(self.normalized_data[self.local_step_number]),
step_indicator_features, step_position_features,
step_action_features, np.array([self.reward])),
axis=None)
def _get_observation(self):
observation = np.array(self.data_buffer, dtype=np.float32)
# Expand the observation space from 2 to 3 dimensions.
# This is necessary for conv nets in Baselines.
if self.format_3d:
observation = np.expand_dims(observation, axis=-1)
return observation
def _load_environment_data(self, fitting_file, testing_file):
fitting_data_filepath = '{}/data_exports/{}'.format(
self.sim.cwd, fitting_file)
data_used_in_environment = '{}/data_exports/{}'.format(
self.sim.cwd, testing_file)
fitting_data = self.sim.import_csv(filename=fitting_data_filepath)
fitting_data['coinbase_midpoint'] = np.log(
fitting_data['coinbase_midpoint'].values)
fitting_data['coinbase_midpoint'] = (
fitting_data['coinbase_midpoint'] -
fitting_data['coinbase_midpoint'].shift(1)).fillna(method='bfill')
self.sim.fit_scaler(fitting_data)
del fitting_data
return self.sim.import_csv(filename=data_used_in_environment)
class PriceJump(Env):
metadata = {'render.modes': ['human']}
id = 'long-short-v0'
# Turn to true if Bitifinex is in the dataset (e.g., include_bitfinex=True)
features = Sim.get_feature_labels(include_system_time=False,
include_bitfinex=False)
best_bid_index = features.index('coinbase_bid_distance_0')
best_ask_index = features.index('coinbase_ask_distance_0')
notional_bid_index = features.index('coinbase_bid_notional_0')
notional_ask_index = features.index('coinbase_ask_notional_0')
buy_trade_index = features.index('coinbase_buys')
sell_trade_index = features.index('coinbase_sells')
target_pnl = 0.03 # 3.0% gain per episode (i.e., day)
fee = MARKET_ORDER_FEE
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):
# properties required for instantiation
self.action_repeats = action_repeats
self._seed = seed
self._random_state = np.random.RandomState(seed=self._seed)
self.training = training
self.step_size = step_size
self.max_position = max_position
self.window_size = window_size
self.format_3d = format_3d # e.g., [window, features, *NEW_AXIS*]
self.action = 0
# derive gym.env properties
self.actions = np.eye(3, dtype=np.float32)
self.sym = testing_file[:7] # slice the CCY from the filename
# properties that get reset()
self.reward = 0.0
self.done = False
self.local_step_number = 0
self.midpoint = 0.0
self.observation = None
# get Broker class to keep track of PnL and orders
self.broker = Broker(max_position=max_position)
# get historical data for simulations
self.sim = Sim(use_arctic=False, z_score=z_score)
self.prices_, self.data, self.normalized_data = self.sim.load_environment_data(
fitting_file, testing_file)
self.max_steps = self.data.shape[0] - self.step_size * \
self.action_repeats - 1
# load indicators into the indicator manager
self.tns = IndicatorManager()
self.rsi = IndicatorManager()
for window in INDICATOR_WINDOW:
self.tns.add(('tns_{}'.format(window), TnS(window=window)))
self.rsi.add(('rsi_{}'.format(window), RSI(window=window)))
# rendering class
self._render = TradingGraph(sym=self.sym)
# graph midpoint prices
self._render.reset_render_data(
y_vec=self.prices_[:np.shape(self._render.x_vec)[0]])
# buffer for appending lags
self.data_buffer = list()
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 step(self, action: int):
for current_step in range(self.action_repeats):
if self.done:
self.reset()
return self.observation, self.reward, self.done
# reset the reward if there ARE action repeats
if current_step == 0:
self.reward = 0.
step_action = action
else:
step_action = 0
# Get current step's midpoint
self.midpoint = self.prices_[self.local_step_number]
# Pass current time step midpoint to broker to calculate PnL,
# or if any open orders are to be filled
buy_volume = self._get_book_data(PriceJump.buy_trade_index)
sell_volume = self._get_book_data(PriceJump.sell_trade_index)
self.tns.step(buys=buy_volume, sells=sell_volume)
self.rsi.step(price=self.midpoint)
self.broker.step(midpoint=self.midpoint)
self.reward += self._send_to_broker_and_get_reward(
action=step_action)
step_observation = self._get_step_observation(action=action)
self.data_buffer.append(step_observation)
if len(self.data_buffer) > self.window_size:
del self.data_buffer[0]
self.local_step_number += self.step_size
self.observation = self._get_observation()
if self.local_step_number > self.max_steps:
self.done = True
order = Order(ccy=self.sym,
side=None,
price=self.midpoint,
step=self.local_step_number)
self.reward = self.broker.flatten_inventory(order=order)
return self.observation, self.reward, self.done, {}
def reset(self):
if self.training:
self.local_step_number = self._random_state.randint(
low=1, high=self.data.shape[0] // 4)
else:
self.local_step_number = 0
msg = ' {}-{} reset. Episode pnl: {:.4f} with {} trades. First step: {}'.format(
self.sym, self._seed,
self.broker.get_total_pnl(midpoint=self.midpoint),
self.broker.get_total_trade_count(), self.local_step_number)
logger.info(msg)
self.reward = 0.0
self.done = False
self.broker.reset()
self.data_buffer.clear()
self.rsi.reset()
self.tns.reset()
for step in range(self.window_size + INDICATOR_WINDOW_MAX):
self.midpoint = self.prices_[self.local_step_number]
step_buy_volume = self._get_book_data(PriceJump.buy_trade_index)
step_sell_volume = self._get_book_data(PriceJump.sell_trade_index)
self.tns.step(buys=step_buy_volume, sells=step_sell_volume)
self.rsi.step(price=self.midpoint)
step_observation = self._get_step_observation(action=0)
self.data_buffer.append(step_observation)
self.local_step_number += self.step_size
if len(self.data_buffer) > self.window_size:
del self.data_buffer[0]
self.observation = self._get_observation()
return self.observation
def render(self, mode='human'):
self._render.render(midpoint=self.midpoint, mode=mode)
def close(self):
logger.info('{}-{} is being closed.'.format(self.id, self.sym))
self.data = None
self.normalized_data = None
self.prices_ = None
self.broker = None
self.sim = None
self.data_buffer = None
self.tns = None
self.rsi = None
return
def seed(self, seed=1):
self._random_state = np.random.RandomState(seed=seed)
self._seed = seed
logger.info('Setting seed in PriceJump.seed({})'.format(seed))
return [seed]
@staticmethod
def _process_data(_next_state):
"""
Reshape observation and clip outliers (values +/- 10)
:param _next_state: observation space
:return: (np.array) clipped observation space
"""
return np.clip(_next_state.reshape((1, -1)), -10., 10.)
def _send_to_broker_and_get_reward(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 = 0.0
discouragement = 0.000000000001
if action == 0: # do nothing
reward += discouragement
elif action == 1: # buy
price_fee_adjusted = self.midpoint + (PriceJump.fee *
self.midpoint)
if self.broker.short_inventory_count > 0:
order = Order(ccy=self.sym,
side='short',
price=price_fee_adjusted,
step=self.local_step_number)
self.broker.remove(order=order)
reward += self.broker.get_reward(side=order.side) / \
self.broker.reward_scale # scale realized PnL
elif self.broker.long_inventory_count >= 0:
order = Order(ccy=self.sym,
side='long',
price=price_fee_adjusted,
step=self.local_step_number)
if self.broker.add(order=order) is False:
reward -= discouragement
else:
logger.info(
('gym_trading.get_reward() ' + 'Error for action #{} - ' +
'unable to place an order with broker').format(action))
elif action == 2: # sell
price_fee_adjusted = self.midpoint - (PriceJump.fee *
self.midpoint)
if self.broker.long_inventory_count > 0:
order = Order(ccy=self.sym,
side='long',
price=price_fee_adjusted,
step=self.local_step_number)
self.broker.remove(order=order)
reward += self.broker.get_reward(side=order.side) / \
self.broker.reward_scale # scale realized PnL
elif self.broker.short_inventory_count >= 0:
order = Order(ccy=self.sym,
side='short',
price=price_fee_adjusted,
step=self.local_step_number)
if self.broker.add(order=order) is False:
reward -= discouragement
else:
logger.info(
('gym_trading.get_reward() ' + 'Error for action #{} - ' +
'unable to place an order with broker').format(action))
else:
logger.info(
('Unknown action to take in get_reward(): ' +
'action={} | midpoint={}').format(action, self.midpoint))
return reward
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.get_total_pnl(midpoint=self.midpoint) /
PriceJump.target_pnl,
self.broker.long_inventory.get_unrealized_pnl(self.midpoint) /
self.broker.reward_scale,
self.broker.short_inventory.get_unrealized_pnl(self.midpoint) /
self.broker.reward_scale),
dtype=np.float32)
def _create_action_features(self, action):
"""
Create a features array for the current time step's action.
:param action: (int) action number
:return: (np.array) One-hot of current action
"""
return self.actions[action]
def _create_indicator_features(self):
"""
Create features vector with environment indicators.
:return: (np.array) Indicator values for current time step
"""
return np.array((*self.tns.get_value(), *self.rsi.get_value()),
dtype=np.float32)
def _get_nbbo(self):
"""
Get best bid and offer
:return: (tuple) best bid and offer
"""
best_bid = round(
self.midpoint - self._get_book_data(PriceJump.best_bid_index), 2)
best_ask = round(
self.midpoint + self._get_book_data(PriceJump.best_ask_index), 2)
return best_bid, best_ask
def _get_book_data(self, index=0):
"""
Return step 'n' of order book snapshot data
:param index: (int) step 'n' to look up in order book snapshot history
:return: (np.array) order book snapshot vector
"""
return self.data[self.local_step_number][index]
def _get_step_observation(self, action=0):
"""
Current step observation, NOT including historical data.
:param action: (int) current step action
:return: (np.array) Current step observation
"""
step_position_features = self._create_position_features()
step_action_features = self._create_action_features(action=action)
step_indicator_features = self._create_indicator_features()
return np.concatenate(
(self._process_data(self.normalized_data[self.local_step_number]),
step_indicator_features, step_position_features,
step_action_features, np.array([self.reward])),
axis=None)
def _get_observation(self):
"""
Current step observation, including historical data.
If format_3d is TRUE: Expand the observation space from 2 to 3 dimensions.
(note: This is necessary for conv nets in Baselines.)
:return: (np.array) Observation state for current time step
"""
observation = np.array(self.data_buffer, dtype=np.float32)
if self.format_3d:
observation = np.expand_dims(observation, axis=-1)
return observation