class BTgymEnv(gym.Env):
"""OpenAI Gym environment wrapper for Backtrader backtesting/trading library.
"""
# Datafeed Server management:
data_master = True
data_network_address = 'tcp://127.0.0.1:' # using localhost.
data_port = 4999
data_server = None
data_server_pid = None
data_context = None
data_socket = None
data_server_response = None
# Dataset:
dataset = None # BTgymDataset instance.
dataset_stat = None
# Backtrader engine:
engine = None # bt.Cerbro subclass for server to execute.
# Strategy:
strategy = None # strategy to use if no <engine> class been passed.
# Server and network:
server = None # Server process.
context = None # ZMQ context.
socket = None # ZMQ socket, client side.
port = 5500 # network port to use.
network_address = 'tcp://127.0.0.1:' # using localhost.
ctrl_actions = ('_done', '_reset', '_stop', '_getstat', '_render'
) # server control messages.
server_response = None
# Connection timeout:
connect_timeout = 60 # server connection timeout in seconds.
#connect_timeout_step = 0.01 # time between retries in seconds.
# Rendering:
render_enabled = True
render_modes = [
'human',
'episode',
]
# `episode` - plotted episode results.
# `human` - raw_state observation in conventional human-readable format.
# <obs_space_key> - rendering of arbitrary state presented in observation_space with same key.
renderer = None # Rendering support.
rendered_rgb = dict() # Keep last rendered images for each mode.
# Logging:
log = None
verbose = 0 # verbosity mode: 0 - silent, 1 - info, 2 - debugging level (lot of traffic!).
closed = True
def __init__(self, *args, **kwargs):
"""
Environment kwargs applying logic::
if <engine> kwarg is given:
do not use default engine and strategy parameters;
ignore <strategy> kwarg and all strategy and engine-related kwargs.
else (no <engine>):
use default engine parameters;
if any engine-related kwarg is given:
override corresponding default parameter;
if <strategy> is given:
do not use default strategy parameters;
if any strategy related kwarg is given:
override corresponding strategy parameter;
else (no <strategy>):
use default strategy parameters;
if any strategy related kwarg is given:
override corresponding strategy parameter;
if <dataset> kwarg is given:
do not use default dataset parameters;
ignore dataset related kwargs;
else (no <dataset>):
use default dataset parameters;
if any dataset related kwarg is given:
override corresponding dataset parameter;
If any <other> kwarg is given:
override corresponding default parameter.
"""
#print("start backtrader")
# Parameters and default values:
self.params = dict(
# Backtrader engine mandatory parameters:
engine=dict(
start_cash=10.0, # initial trading capital.
broker_commission=
0.001, # trade execution commission, default is 0.1% of operation value.
fixed_stake=10, # single trade stake is fixed type by def.
),
# Dataset mandatory parameters:
dataset=dict(filename=None, ),
strategy=dict(state_shape=dict(), ),
render=dict(),
)
p2 = dict(
# Strategy related parameters:
# Observation state shape is dictionary of Gym spaces,
# at least should contain `raw_state` field.
# By convention first dimension of every Gym Box space is time embedding one;
# one can define any shape; should match env.observation_space.shape.
# observation space state min/max values,
# For `raw_state' - absolute min/max values from BTgymDataset will be used.
state_shape=dict(raw_state=spaces.Box(
shape=(10, 4),
low=-100,
high=100,
)),
drawdown_call=
None, # episode maximum drawdown threshold, default is 90% of initial value.
portfolio_actions=None,
# agent actions,
# should consist with BTgymStrategy order execution logic;
# defaults are: 0 - 'do nothing', 1 - 'buy', 2 - 'sell', 3 - 'close position'.
skip_frame=None,
# Number of environment steps to skip before returning next response,
# e.g. if set to 10 -- agent will interact with environment every 10th episode step;
# Every other step agent's action is assumed to be 'hold'.
# Note: INFO part of environment response is a list of all skipped frame's info's,
# i.e. [info[-9], info[-8], ..., info[0].
)
# Update self attributes, remove used kwargs:
for key in dir(self):
if key in kwargs.keys():
setattr(self, key, kwargs.pop(key))
self.metadata = {'render.modes': self.render_modes}
# Verbosity control:
if True: #self.log is None:
self.log = logging.getLogger('Env')
log_levels = [
(0, 'WARNING'),
(1, 'INFO'),
(2, 'DEBUG'),
]
for key, level in log_levels:
if key == self.verbose:
self.log.setLevel(level)
# Network parameters:
self.network_address += str(self.port)
self.data_network_address += str(self.data_port)
# Set server rendering:
if self.render_enabled:
self.renderer = BTgymRendering(self.metadata['render.modes'],
**kwargs)
else:
self.renderer = BTgymNullRendering()
self.log.info(
'Rendering disabled. Call to render() will return null-plug image.'
)
# Append logging:
self.renderer.log = self.log
# Update params -1: pull from renderer, remove used kwargs:
self.params['render'].update(self.renderer.params)
for key in self.params['render'].keys():
if key in kwargs.keys():
_ = kwargs.pop(key)
if self.data_master:
# DATASET preparation, only data_master executes this:
#
if self.dataset is not None:
# If BTgymDataset instance has been passed:
# do nothing.
msg = 'Custom Dataset class used.'
else:
# If no BTgymDataset has been passed,
# Make default dataset with given CSV file:
try:
os.path.isfile(str(self.params['dataset']['filename']))
except:
raise FileNotFoundError(
'Dataset source data file not specified/not found')
# Use kwargs to instantiate dataset:
self.dataset = BTgymDataset(**kwargs)
msg = 'Base Dataset class used.'
# Append logging:
self.dataset.log = self.log
# Update params -2: pull from dataset, remove used kwargs:
self.params['dataset'].update(self.dataset.params)
for key in self.params['dataset'].keys():
if key in kwargs.keys():
_ = kwargs.pop(key)
self.log.info(msg)
# Connect/Start data server (and get dataset statistic):
self.log.info('Connecting data_server...')
self._start_data_server()
self.log.info('...done.')
# ENGINE preparation:
# Update params -3: pull engine-related kwargs, remove used:
for key in self.params['engine'].keys():
if key in kwargs.keys():
self.params['engine'][key] = kwargs.pop(key)
if self.engine is not None:
# If full-blown bt.Cerebro() subclass has been passed:
# Update info:
msg = 'Custom Cerebro class used.'
self.strategy = msg
for key in self.params['engine'].keys():
self.params['engine'][key] = msg
# Note: either way, bt.observers.DrawDown observer [and logger] will be added to any BTgymStrategy instance
# by BTgymServer process at runtime.
else:
# Default configuration for Backtrader computational engine (Cerebro),
# if no bt.Cerebro() custom subclass has been passed,
# get base class Cerebro(), using kwargs on top of defaults:
self.engine = bt.Cerebro()
msg = 'Base Cerebro class used.'
# First, set STRATEGY configuration:
if self.strategy is not None:
# If custom strategy has been passed:
msg2 = 'Custom Strategy class used.'
else:
# Base class strategy :
self.strategy = BTgymBaseStrategy
msg2 = 'Base Strategy class used.'
# Add, using kwargs on top of defaults:
strat_idx = self.engine.addstrategy(self.strategy, **kwargs)
msg += ' ' + msg2
# Second, set Cerebro-level configuration:
self.engine.broker.setcash(self.params['engine']['start_cash'])
self.engine.broker.setcommission(
self.params['engine']['broker_commission'])
self.engine.addsizer(bt.sizers.SizerFix,
stake=self.params['engine']['fixed_stake'])
self.log.info(msg)
# Define observation space shape, minimum / maximum values and agent action space.
# Retrieve values from configured engine or...
# ...Update params -4:
# Pull strategy defaults to environment params dict :
for t_key, t_value in self.engine.strats[0][0][0].params._gettuple():
self.params['strategy'][t_key] = t_value
# Update it with values from strategy 'passed-to params':
for key, value in self.engine.strats[0][0][2].items():
self.params['strategy'][key] = value
# ... Push it all back (don't ask):
for key, value in self.params['strategy'].items():
self.engine.strats[0][0][2][key] = value
# For 'raw_state' min/max values,
# the only way is to infer from raw Dataset price values (we already got those from data_server):
if 'raw_state' in self.params['strategy']['state_shape'].keys():
# Exclude 'volume' from columns we count:
self.dataset_columns.remove('volume')
#print(self.params['strategy'])
#print('self.engine.strats[0][0][2]:', self.engine.strats[0][0][2])
#print('self.engine.strats[0][0][0].params:', self.engine.strats[0][0][0].params._gettuple())
# Override with absolute price min and max values:
self.params['strategy']['state_shape']['raw_state'].low =\
self.engine.strats[0][0][2]['state_shape']['raw_state'].low =\
np.zeros(self.params['strategy']['state_shape']['raw_state'].shape) +\
self.dataset_stat.loc['min', self.dataset_columns].min()
self.params['strategy']['state_shape']['raw_state'].high = \
self.engine.strats[0][0][2]['state_shape']['raw_state'].high = \
np.zeros(self.params['strategy']['state_shape']['raw_state'].shape) + \
self.dataset_stat.loc['max', self.dataset_columns].max()
self.log.info(
'Inferring `state_raw` high/low values form dataset: {:.6f} / {:.6f}.'
.format(
self.dataset_stat.loc['min', self.dataset_columns].min(),
self.dataset_stat.loc['max', self.dataset_columns].max()))
# Set observation space shape from engine/strategy parameters:
self.observation_space = DictSpace(
self.params['strategy']['state_shape'])
self.log.debug('Obs. shape: {}'.format(self.observation_space.spaces))
#self.log.debug('Obs. min:\n{}\nmax:\n{}'.format(self.observation_space.low, self.observation_space.high))
# Set action space and corresponding server messages:
self.action_space = spaces.Discrete(
len(self.params['strategy']['portfolio_actions']))
self.server_actions = self.params['strategy']['portfolio_actions']
# Finally:
self.server_response = None
self.env_response = None
# If instance is datamaster - it may or may not want to launch self BTgymServer (can do it later via reset);
# else it always need to launch it:
#if not self.data_master:
self._start_server()
self.closed = False
self.log.info('Environment is ready.')
def _seed(self, seed=None):
"""
Sets env. random seed.
Args:
seed: int or None
"""
np.random.seed(seed)
def _comm_with_timeout(
self,
socket,
message,
):
"""
Exchanges messages via socket, timeout sensitive.
Args:
socket: zmq connected socket to communicate via;
message: message to send;
Note:
socket zmq.RCVTIMEO and zmq.SNDTIMEO should be set to some finite number of milliseconds.
Returns:
dictionary:
status: communication result;
message: received message if status == `ok` or None;
time: remote side response time.
"""
response = dict(
status='ok',
message=None,
)
try:
socket.send_pyobj(message)
except zmq.ZMQError as e:
if e.errno == zmq.EAGAIN:
response['status'] = 'send_failed_due_to_connect_timeout'
else:
response['status'] = 'send_failed_for_unknown_reason'
return response
start = time.time()
try:
response['message'] = socket.recv_pyobj()
response['time'] = time.time() - start
except zmq.ZMQError as e:
if e.errno == zmq.EAGAIN:
response['status'] = 'receive_failed_due_to_connect_timeout'
else:
response['status'] = 'receive_failed_for_unknown_reason'
return response
return response
def _start_server(self):
"""
Configures backtrader REQ/REP server instance and starts server process.
"""
# Ensure network resources:
# 1. Release client-side, if any:
if self.context:
self.context.destroy()
self.socket = None
# 2. Kill any process using server port:
cmd = "kill $( lsof -i:{} -t ) > /dev/null 2>&1".format(self.port)
os.system(cmd)
# Set up client channel:
self.context = zmq.Context()
self.socket = self.context.socket(zmq.REQ)
self.socket.setsockopt(zmq.RCVTIMEO, self.connect_timeout * 1000)
self.socket.setsockopt(zmq.SNDTIMEO, self.connect_timeout * 1000)
self.socket.connect(self.network_address)
# Configure and start server:
self.server = BTgymServer(
cerebro=self.engine,
render=self.renderer,
network_address=self.network_address,
data_network_address=self.data_network_address,
connect_timeout=self.connect_timeout,
log=self.log)
self.server.daemon = False
self.server.start()
# Wait for server to startup:
time.sleep(1)
# Check connection:
self.log.info('Server started, pinging {} ...'.format(
self.network_address))
self.server_response = self._comm_with_timeout(
socket=self.socket, message={'ctrl': 'ping!'})
if self.server_response['status'] in 'ok':
self.log.debug('Server seems ready with response: <{}>'.format(
self.server_response['message']))
else:
msg = 'Server unreachable with status: <{}>.'.format(
self.server_response['status'])
self.log.error(msg)
raise ConnectionError(msg)
self._closed = False
def _stop_server(self):
"""
Stops BT server process, releases network resources.
"""
if self.server:
if self._force_control_mode():
# In case server is running and client side is ok:
self.socket.send_pyobj({'ctrl': '_stop'})
self.server_response = self.socket.recv_pyobj()
else:
self.server.terminate()
self.server.join()
self.server_response = 'Server process terminated.'
self.log.info('{} Exit code: {}'.format(self.server_response,
self.server.exitcode))
# Release client-side, if any:
if self.context:
self.context.destroy()
def _force_control_mode(self):
"""Puts BT server to control mode.
"""
# Check is there any faults with server process and connection?
network_error = [
(not self.server or not self.server.is_alive(),
'No running server found. Hint: forgot to call reset()?'),
(not self.context
or self.context.closed, 'No network connection found.'),
]
for (err, msg) in network_error:
if err:
self.log.info(msg)
self.server_response = msg
return False
# If everything works, insist to go 'control':
self.server_response = {}
attempt = 0
while 'ctrl' not in self.server_response:
self.socket.send_pyobj({'ctrl': '_done'})
self.server_response = self.socket.recv_pyobj()
attempt += 1
self.log.debug(
'FORCE CONTROL MODE attempt: {}.\nResponse: {}'.format(
attempt, self.server_response))
return True
def _assert_response(self, response):
"""
Simple watcher:
roughly checks if we really talking to environment (== episode is running).
Rises exception if response given is not as expected.
"""
if type(response) == tuple and len(response) == 4:
pass
else:
msg = 'Unexpected environment response: {}\nHint: Forgot to call reset() or reset_data()?'.format(
response)
raise AssertionError(msg)
self.log.debug(
'Env response checker received:\n{}\nas type: {}'.format(
response, type(response)))
def _print_space(self, space, _tab=''):
"""
TODO: MAKe IT WORK
Parses observation space shape or response.
Args:
space: gym observation space or response.
Returns:
description as string.
"""
response = ''
if type(space) in [dict, OrderedDict]:
for key, value in space.items():
response += '\n{}{}:{}\n'.format(
_tab, key, self._print_space(value, ' '))
elif type(space) in [spaces.Dict, DictSpace]:
for s in space.spaces:
response += self._print_space(s, ' ')
elif type(space) in [tuple, list]:
for i in space:
response += self._print_space(i, ' ')
elif type(space) == np.ndarray:
response += '\n{}array of shape: {}, low: {}, high: {}'.format(
_tab, space.shape, space.min(), space.max())
else:
response += '\n{}{}, '.format(_tab, space)
try:
response += 'low: {}, high: {}'.format(space.low.min(),
space.high.max())
except (KeyError, AttributeError, ArithmeticError,
ValueError) as e:
pass
#response += '\n{}'.format(e)
return response
def _reset(
self,
state_only=True
): # By default, returns only initial state observation (Gym convention).
"""
Implementation of OpenAI Gym env.reset method. Starts new episode.
"""
# Data Server check:
if self.data_master:
if not self.data_server or not self.data_server.is_alive():
self.log.info('No running data_server found, starting...')
self._start_data_server()
# Dataset status check:
self.data_server_response = self._comm_with_timeout(
socket=self.data_socket, message={'ctrl': '_get_info'})
if not self.data_server_response['message']['dataset_is_ready']:
self.log.warning(
'Data_master `reset()` called prior to `reset_data()` with [possibly inconsistent] defaults.'
)
self.reset_data()
# Server process check:
if not self.server or not self.server.is_alive():
self.log.info('No running server found, starting...')
self._start_server()
if self._force_control_mode():
self.socket.send_pyobj({'ctrl': '_reset'})
self.server_response = self.socket.recv_pyobj()
# Get initial environment response:
self.env_response = self._step(0)
# Check (once) if it is really (o,r,d,i) tuple:
self._assert_response(self.env_response)
# Check (once) if state_space is as expected:
try:
assert self.observation_space.contains(self.env_response[0])
except (AssertionError, AttributeError) as e:
msg1 = self._print_space(self.observation_space.spaces)
msg2 = self._print_space(self.env_response[0])
msg3 = ''
for step_info in self.env_response[-1]:
msg3 += '{}\n'.format(step_info)
msg = ('\nState observation shape/range mismatch!\n' +
'Space set by env: \n{}\n' +
'Space returned by server: \n{}\n' +
'Full response:\n{}\n' + 'Reward: {}\n' + 'Done: {}\n' +
'Info:\n{}\n' +
'Hint: Wrong Strategy.get_state() parameters?').format(
msg1,
msg2,
self.env_response[0],
self.env_response[1],
self.env_response[2],
msg3,
)
self.log.error(msg)
self._stop_server()
raise AssertionError(msg)
if state_only:
return self.env_response[0]
else:
return self.env_response
else:
msg = 'Something went wrong. env.reset() can not get response from server.'
self.log.error(msg)
raise ChildProcessError(msg)
def _step(self, action):
"""
Implementation of OpenAI Gym env.step method.
Relies on remote backtrader server for actual environment dynamics computing.
"""
# Are you in the list, ready to go and all that?
if self.action_space.contains(action)\
and not self._closed\
and (self.socket is not None)\
and not self.socket.closed:
pass
else:
msg = ('\nAt least one of these is true:\n' +
'Action error: (space is {}, action sent is {}): {}\n' +
'Environment closed: {}\n' +
'Network error [socket doesnt exists or closed]: {}\n' +
'Hint: forgot to call reset()?').format(
self.action_space,
action,
not self.action_space.contains(action),
self._closed,
not self.socket or self.socket.closed,
)
self.log.info(msg)
raise AssertionError(msg)
# Send action to backtrader engine, receive environment response
env_response = self._comm_with_timeout(
socket=self.socket,
message={'action': self.server_actions[action]})
if not env_response['status'] in 'ok':
msg = 'Env.step: server unreachable with status: <{}>.'.format(
env_response['status'])
self.log.error(msg)
raise ConnectionError(msg)
self.env_response = env_response['message']
#print("STEP PERFORMED!!!!")
return self.env_response
def _close(self):
"""
Implementation of OpenAI Gym env.close method.
Puts BTgym server in Control Mode.
"""
self._stop_server()
self._stop_data_server()
self.log.info('Environment closed.')
def get_stat(self):
"""
Returns last run episode statistics.
Note:
when invoked, forces running episode to terminate.
"""
if self._force_control_mode():
self.socket.send_pyobj({'ctrl': '_getstat'})
return self.socket.recv_pyobj()
else:
return self.server_response
def _render(self, mode='other_mode', close=False):
"""
Implementation of OpenAI Gym env.render method.
Visualises current environment state.
Args:
`mode`: str, any of these::
`human` - current state observation as price lines;
`episode` - plotted results of last completed episode.
[other_key] - corresponding to any custom observation space key
"""
if close:
return None
if not self._closed\
and self.socket\
and not self.socket.closed:
pass
else:
msg = ('\nCan'
't get renderings.'
'\nAt least one of these is true:\n' +
'Environment closed: {}\n' +
'Network error [socket doesnt exists or closed]: {}\n' +
'Hint: forgot to call reset()?').format(
self._closed,
not self.socket or self.socket.closed,
)
self.log.warning(msg)
return None
if mode not in self.render_modes:
raise ValueError('Unexpected render mode {}'.format(mode))
self.socket.send_pyobj({'ctrl': '_render', 'mode': mode})
rgb_array_dict = self.socket.recv_pyobj()
self.rendered_rgb.update(rgb_array_dict)
return self.rendered_rgb[mode]
def stop(self):
"""
Finishes current episode if any, does nothing otherwise. Leaves server running.
"""
if self._force_control_mode():
self.log.info('Episode stop forced.')
def _restart_server(self):
"""Restarts server.
"""
self._stop_server()
self._start_server()
self.log.info('Server restarted.')
def _start_data_server(self):
"""
For data_master environment:
- configures backtrader REQ/REP server instance and starts server process.
For others:
- establishes network connection to existing data_server.
"""
self.data_server = None
# Ensure network resources:
# 1. Release client-side, if any:
if self.data_context:
self.data_context.destroy()
self.data_socket = None
# Only data_master launches/stops data_server process:
if self.data_master:
# 2. Kill any process using server port:
cmd = "kill $( lsof -i:{} -t ) > /dev/null 2>&1".format(
self.data_port)
os.system(cmd)
# Configure and start server:
self.data_server = BTgymDataFeedServer(
dataset=self.dataset,
network_address=self.data_network_address,
log=self.log,
)
self.data_server.daemon = False
self.data_server.start()
# Wait for server to startup
time.sleep(1)
# Set up client channel:
self.data_context = zmq.Context()
self.data_socket = self.data_context.socket(zmq.REQ)
self.data_socket.setsockopt(zmq.RCVTIMEO, self.connect_timeout * 1000)
self.data_socket.setsockopt(zmq.SNDTIMEO, self.connect_timeout * 1000)
self.data_socket.connect(self.data_network_address)
# Check connection:
self.log.info('Pinging data_server at: {} ...'.format(
self.data_network_address))
self.data_server_response = self._comm_with_timeout(
socket=self.data_socket, message={'ctrl': 'ping!'})
if self.data_server_response['status'] in 'ok':
self.log.debug(
'Data_server seems ready with response: <{}>'.format(
self.data_server_response['message']))
else:
msg = 'Data_server unreachable with status: <{}>.'.\
format(self.data_server_response['status'])
self.log.error(msg)
raise SystemExit(msg)
# Get info and statistic:
self.dataset_stat, self.dataset_columns, self.data_server_pid = self._get_dataset_info(
)
def _stop_data_server(self):
"""
For data_master:
- stops BT server process, releases network resources.
"""
if self.data_master:
if self.data_server is not None and self.data_server.is_alive():
# In case server is running and is ok:
self.data_socket.send_pyobj({'ctrl': '_stop'})
self.data_server_response = self.data_socket.recv_pyobj()
else:
self.data_server.terminate()
self.data_server.join()
self.data_server_response = 'Data_server process terminated.'
self.log.info('{} Exit code: {}'.format(self.data_server_response,
self.data_server.exitcode))
#if self.data_context:
# self.data_context.destroy()
# self.data_socket = None
def _restart_data_server(self):
"""
Restarts data_server.
"""
self._stop_data_server()
self._start_data_server()
def _get_dataset_info(self):
"""
Retrieves dataset descriptive statistic.
"""
self.data_socket.send_pyobj({'ctrl': '_get_info'})
self.data_server_response = self.data_socket.recv_pyobj()
return self.data_server_response['dataset_stat'],\
self.data_server_response['dataset_columns'],\
self.data_server_response['pid']
def reset_data(self, **kwargs):
"""
Resets data provider class used, whatever it means for that class. Gets data_server ready to provide data.
Supposed to be called before first env.reset().
Note:
when invoked, forces running episode to terminate.
Args:
**kwargs: data provider class .reset() method specific.
"""
if self.closed:
self._start_server()
if self.data_master:
self._start_data_server()
self.closed = False
else:
_ = self._force_control_mode()
if self.data_master:
if self.data_server is None or not self.data_server.is_alive():
self._restart_data_server()
self.data_server_response = self._comm_with_timeout(
socket=self.data_socket,
message={
'ctrl': '_reset_data',
'kwargs': kwargs
})
if self.data_server_response['status'] in 'ok':
self.log.debug(
'Dataset seems ready with response: <{}>'.format(
self.data_server_response['message']))
else:
msg = 'Data_server unreachable with status: <{}>.'. \
format(self.data_server_response['status'])
self.log.error(msg)
raise SystemExit(msg)
else:
pass
class BTgymEnv(gym.Env):
"""
Base OpenAI Gym API shell for Backtrader backtesting/trading library.
"""
# Datafeed Server management:
data_master = True
data_network_address = 'tcp://127.0.0.1:' # using localhost.
data_port = 4999
data_server = None
data_server_pid = None
data_context = None
data_socket = None
data_server_response = None
# Dataset:
dataset = None # BTgymDataset instance.
dataset_stat = None
# Backtrader engine:
engine = None # bt.Cerbro subclass for server to execute.
# Strategy:
strategy = None # strategy to use if no <engine> class been passed.
# Server and network:
server = None # Server process.
context = None # ZMQ context.
socket = None # ZMQ socket, client side.
port = 5500 # network port to use.
network_address = 'tcp://127.0.0.1:' # using localhost.
ctrl_actions = ('_done', '_reset', '_stop', '_getstat', '_render'
) # server control messages.
server_response = None
# Connection timeout:
connect_timeout = 220 # server connection timeout in seconds.
#connect_timeout_step = 0.01 # time between retries in seconds.
# Rendering:
render_enabled = True
render_modes = [
'human',
'episode',
]
# `episode` - plotted episode results.
# `human` - raw_state observation in conventional human-readable format.
# <obs_space_key> - rendering of arbitrary state presented in observation_space with same key.
renderer = None # Rendering support.
rendered_rgb = dict() # Keep last rendered images for each mode.
# Logging and id:
log = None
log_level = None # logbook level: NOTICE, WARNING, INFO, DEBUG etc. or its integer equivalent;
verbose = 0 # verbosity mode, valid only if no `log_level` arg has been provided:
# 0 - WARNING, 1 - INFO, 2 - DEBUG.
task = 0
asset_names = ('default_asset', )
data_lines_names = ('default_asset', )
cash_name = 'default_cash'
closed = True
def __init__(self, **kwargs):
"""
Keyword Args:
filename=None (str, list): csv data file.
**datafeed_args (any): any datafeed-related args, passed through to
default btgym.datafeed class.
dataset=None (btgym.datafeed): BTgymDataDomain instance,
overrides `filename` or any other datafeed-related args.
strategy=None (btgym.startegy): strategy to be used by `engine`, any subclass of
btgym.strategy.base.BTgymBaseStrateg
engine=None (bt.Cerebro): environment simulation engine, any bt.Cerebro subclass,
overrides `strategy` arg.
network_address=`tcp://127.0.0.1:` (str): BTGym_server address.
port=5500 (int): network port to use for server - API_shell communication.
data_master=True (bool): let this environment control over data_server;
data_network_address=`tcp://127.0.0.1:` (str): data_server address.
data_port=4999 (int): network port to use for server -- data_server communication.
connect_timeout=20 (int): server connection timeout in seconds.
render_enabled=True (bool): enable rendering for this environment;
render_modes=['human', 'episode'] (list): `episode` - plotted episode results;
`human` - raw_state observation.
**render_args (any): any render-related args, passed through to renderer class.
verbose=0 (int): verbosity mode, {0 - WARNING, 1 - INFO, 2 - DEBUG}
log_level=None (int): logbook level {DEBUG=10, INFO=11, NOTICE=12, WARNING=13},
overrides `verbose` arg;
log=None (logbook.Logger): external logbook logger,
overrides `log_level` and `verbose` args.
task=0 (int): environment id
Environment kwargs applying logic::
if <engine> kwarg is given:
do not use default engine and strategy parameters;
ignore <strategy> kwarg and all strategy and engine-related kwargs.
else (no <engine>):
use default engine parameters;
if any engine-related kwarg is given:
override corresponding default parameter;
if <strategy> is given:
do not use default strategy parameters;
if any strategy related kwarg is given:
override corresponding strategy parameter;
else (no <strategy>):
use default strategy parameters;
if any strategy related kwarg is given:
override corresponding strategy parameter;
if <dataset> kwarg is given:
do not use default dataset parameters;
ignore dataset related kwargs;
else (no <dataset>):
use default dataset parameters;
if any dataset related kwarg is given:
override corresponding dataset parameter;
If any <other> kwarg is given:
override corresponding default parameter.
"""
# Parameters and default values:
self.params = dict(
# Backtrader engine mandatory parameters:
engine=dict(
start_cash=100.0, # initial trading capital.
broker_commission=
0.001, # trade execution commission, default is 0.1% of operation value.
fixed_stake=10, # single trade stake is fixed type by def.
),
# Dataset mandatory parameters:
dataset=dict(filename=None, ),
strategy=dict(state_shape=dict(), ),
render=dict(),
)
p2 = dict( # IS HERE FOR REFERENCE ONLY
# Strategy related parameters:
# Observation state shape is dictionary of Gym spaces,
# at least should contain `raw_state` field.
# By convention first dimension of every Gym Box space is time embedding one;
# one can define any shape; should match env.observation_space.shape.
# observation space state min/max values,
# For `raw_state' - absolute min/max values from BTgymDataset will be used.
state_shape=dict(raw=spaces.Box(
shape=(10, 4), low=-100, high=100, dtype=np.float32)),
drawdown_call=
None, # episode maximum drawdown threshold, default is 90% of initial value.
portfolio_actions=None,
# agent actions,
# should consist with BTgymStrategy order execution logic;
# defaults are: 0 - 'do nothing', 1 - 'buy', 2 - 'sell', 3 - 'close position'.
skip_frame=None,
# Number of environment steps to skip before returning next response,
# e.g. if set to 10 -- agent will interact with environment every 10th episode step;
# Every other step agent's action is assumed to be 'hold'.
# Note: INFO part of environment response is a list of all skipped frame's info's,
# i.e. [info[-9], info[-8], ..., info[0].
)
# Update self attributes, remove used kwargs:
for key in dir(self):
if key in kwargs.keys():
setattr(self, key, kwargs.pop(key))
self.metadata = {'render.modes': self.render_modes}
# Logging and verbosity control:
if self.log is None:
StreamHandler(sys.stdout).push_application()
if self.log_level is None:
log_levels = [(0, NOTICE), (1, INFO), (2, DEBUG)]
self.log_level = WARNING
for key, value in log_levels:
if key == self.verbose:
self.log_level = value
self.log = Logger('BTgymAPIshell_{}'.format(self.task),
level=self.log_level)
# Network parameters:
self.network_address += str(self.port)
self.data_network_address += str(self.data_port)
# Set server rendering:
if self.render_enabled:
self.renderer = BTgymRendering(self.metadata['render.modes'],
log_level=self.log_level,
**kwargs)
else:
self.renderer = BTgymNullRendering()
self.log.info(
'Rendering disabled. Call to render() will return null-plug image.'
)
# Append logging:
self.renderer.log = self.log
# Update params -1: pull from renderer, remove used kwargs:
self.params['render'].update(self.renderer.params)
for key in self.params['render'].keys():
if key in kwargs.keys():
_ = kwargs.pop(key)
# Disable multiply data streams (multi-assets) [for data-master]:
try:
assert not isinstance(self.dataset, BTgymMultiData)
except AssertionError:
self.log.error(
'Using multiply data streams with base BTgymEnv class not supported. Use designated class.'
)
raise ValueError
if self.data_master:
# DATASET preparation, only data_master executes this:
#
if self.dataset is not None:
# If BTgymDataset instance has been passed:
# do nothing.
msg = 'Custom Dataset class used.'
else:
# If no BTgymDataset has been passed,
# Make default dataset with given CSV file:
try:
os.path.isfile(str(self.params['dataset']['filename']))
except:
raise FileNotFoundError(
'Dataset source data file not specified/not found')
# Use kwargs to instantiate dataset:
self.dataset = BTgymDataset(**kwargs)
msg = 'Base Dataset class used.'
# Append logging:
self.dataset.set_logger(self.log_level, self.task)
# Update params -2: pull from dataset, remove used kwargs:
self.params['dataset'].update(self.dataset.params)
for key in self.params['dataset'].keys():
if key in kwargs.keys():
_ = kwargs.pop(key)
self.log.info(msg)
# Connect/Start data server (and get dataset configuration and statistic):
self.log.info('Connecting data_server...')
self._start_data_server()
self.log.info('...done.')
# After starting data-server we have self.data_names attribute filled.
# ENGINE preparation:
# Update params -3: pull engine-related kwargs, remove used:
for key in self.params['engine'].keys():
if key in kwargs.keys():
self.params['engine'][key] = kwargs.pop(key)
if self.engine is not None:
# If full-blown bt.Cerebro() subclass has been passed:
# Update info:
msg = 'Custom Cerebro class used.'
self.strategy = msg
for key in self.params['engine'].keys():
self.params['engine'][key] = msg
# Note: either way, bt.observers.DrawDown observer [and logger] will be added to any BTgymStrategy instance
# by BTgymServer process at runtime.
else:
# Default configuration for Backtrader computational engine (Cerebro),
# if no bt.Cerebro() custom subclass has been passed,
# get base class Cerebro(), using kwargs on top of defaults:
self.engine = bt.Cerebro()
msg = 'Base Cerebro class used.'
# First, set STRATEGY configuration:
if self.strategy is not None:
# If custom strategy has been passed:
msg2 = 'Custom Strategy class used.'
else:
# Base class strategy :
self.strategy = BTgymBaseStrategy
msg2 = 'Base Strategy class used.'
# Add, using kwargs on top of defaults:
#self.log.debug('kwargs for strategy: {}'.format(kwargs))
strat_idx = self.engine.addstrategy(self.strategy, **kwargs)
msg += ' ' + msg2
# Second, set Cerebro-level configuration:
self.engine.broker.setcash(self.params['engine']['start_cash'])
self.engine.broker.setcommission(
self.params['engine']['broker_commission'])
self.engine.addsizer(bt.sizers.SizerFix,
stake=self.params['engine']['fixed_stake'])
self.log.info(msg)
# Define observation space shape, minimum / maximum values and agent action space.
# Retrieve values from configured engine or...
# ...Update params -4:
# Pull strategy defaults to environment params dict :
for t_key, t_value in self.engine.strats[0][0][0].params._gettuple():
self.params['strategy'][t_key] = t_value
# Update it with values from strategy 'passed-to params':
for key, value in self.engine.strats[0][0][2].items():
self.params['strategy'][key] = value
self.asset_names = self.params['strategy']['asset_names']
self.server_actions = {
name: self.params['strategy']['portfolio_actions']
for name in self.asset_names
}
self.cash_name = self.params['strategy']['cash_name']
self.params['strategy']['initial_action'] = self.get_initial_action()
self.params['strategy'][
'initial_portfolio_action'] = self.get_initial_portfolio_action()
# Only single asset is supported by base class:
try:
assert len(list(self.asset_names)) == 1
except AssertionError:
self.log.error(
'Using multiply assets with base BTgymEnv class not supported. Use designated class.'
)
raise ValueError
try:
assert set(self.asset_names).issubset(set(self.data_lines_names))
except AssertionError:
msg = 'Assets names should be subset of data_lines names, but got: assets: {}, data_lines: {}'.format(
set(self.asset_names), set(self.data_lines_names))
self.log.error(msg)
raise ValueError(msg)
# ... Push it all back (don't ask):
for key, value in self.params['strategy'].items():
self.engine.strats[0][0][2][key] = value
# For 'raw_state' min/max values,
# the only way is to infer from raw Dataset price values (we already got those from data_server):
if 'raw' in self.params['strategy']['state_shape'].keys():
# Exclude 'volume' from columns we count:
self.dataset_columns.remove('volume')
#print(self.params['strategy'])
#print('self.engine.strats[0][0][2]:', self.engine.strats[0][0][2])
#print('self.engine.strats[0][0][0].params:', self.engine.strats[0][0][0].params._gettuple())
# Override with absolute price min and max values:
self.params['strategy']['state_shape']['raw'].low =\
self.engine.strats[0][0][2]['state_shape']['raw'].low =\
np.zeros(self.params['strategy']['state_shape']['raw'].shape) +\
self.dataset_stat.loc['min', self.dataset_columns].min()
self.params['strategy']['state_shape']['raw'].high = \
self.engine.strats[0][0][2]['state_shape']['raw'].high = \
np.zeros(self.params['strategy']['state_shape']['raw'].shape) + \
self.dataset_stat.loc['max', self.dataset_columns].max()
self.log.info(
'Inferring `state[raw]` high/low values form dataset: {:.6f} / {:.6f}.'
.format(
self.dataset_stat.loc['min', self.dataset_columns].min(),
self.dataset_stat.loc['max', self.dataset_columns].max()))
# Set observation space shape from engine/strategy parameters:
self.observation_space = DictSpace(
self.params['strategy']['state_shape'])
self.log.debug('Obs. shape: {}'.format(self.observation_space.spaces))
#self.log.debug('Obs. min:\n{}\nmax:\n{}'.format(self.observation_space.low, self.observation_space.high))
# Set action space (one-key dict for this class) and corresponding server messages:
self.action_space = ActionDictSpace(
base_actions=self.params['strategy']['portfolio_actions'],
assets=self.asset_names)
# Finally:
self.server_response = None
self.env_response = None
self._start_server()
self.closed = False
self.log.info('Environment is ready.')
def _seed(self, seed=None):
"""
Sets env. random seed.
Args:
seed: int or None
"""
np.random.seed(seed)
@staticmethod
def _comm_with_timeout(
socket,
message,
):
"""
Exchanges messages via socket, timeout sensitive.
Args:
socket: zmq connected socket to communicate via;
message: message to send;
Note:
socket zmq.RCVTIMEO and zmq.SNDTIMEO should be set to some finite number of milliseconds.
Returns:
dictionary:
`status`: communication result;
`message`: received message if status == `ok` or None;
`time`: remote side response time.
"""
response = dict(
status='ok',
message=None,
)
try:
socket.send_pyobj(message)
except zmq.ZMQError as e:
if e.errno == zmq.EAGAIN:
response['status'] = 'send_failed_due_to_connect_timeout'
else:
response['status'] = 'send_failed_for_unknown_reason'
return response
start = time.time()
try:
response['message'] = socket.recv_pyobj()
response['time'] = time.time() - start
except zmq.ZMQError as e:
if e.errno == zmq.EAGAIN:
response['status'] = 'receive_failed_due_to_connect_timeout'
else:
response['status'] = 'receive_failed_for_unknown_reason'
return response
return response
def _start_server(self):
"""
Configures backtrader REQ/REP server instance and starts server process.
"""
# Ensure network resources:
# 1. Release client-side, if any:
if self.context:
self.context.destroy()
self.socket = None
# 2. Kill any process using server port:
cmd = "kill $( lsof -i:{} -t ) > /dev/null 2>&1".format(self.port)
os.system(cmd)
# Set up client channel:
self.context = zmq.Context()
self.socket = self.context.socket(zmq.REQ)
self.socket.setsockopt(zmq.RCVTIMEO, self.connect_timeout * 1000)
self.socket.setsockopt(zmq.SNDTIMEO, self.connect_timeout * 1000)
self.socket.connect(self.network_address)
# Configure and start server:
self.server = BTgymServer(
cerebro=self.engine,
render=self.renderer,
network_address=self.network_address,
data_network_address=self.data_network_address,
connect_timeout=self.connect_timeout,
log_level=self.log_level,
task=self.task,
)
self.server.daemon = False
self.server.start()
# Wait for server to startup:
time.sleep(1)
# Check connection:
self.log.info('Server started, pinging {} ...'.format(
self.network_address))
self.server_response = self._comm_with_timeout(
socket=self.socket, message={'ctrl': 'ping!'})
if self.server_response['status'] in 'ok':
self.log.info('Server seems ready with response: <{}>'.format(
self.server_response['message']))
else:
msg = 'Server unreachable with status: <{}>.'.format(
self.server_response['status'])
self.log.error(msg)
raise ConnectionError(msg)
self._closed = False
def _stop_server(self):
"""
Stops BT server process, releases network resources.
"""
if self.server:
if self._force_control_mode():
# In case server is running and client side is ok:
self.socket.send_pyobj({'ctrl': '_stop'})
self.server_response = self.socket.recv_pyobj()
else:
self.server.terminate()
self.server.join()
self.server_response = 'Server process terminated.'
self.log.info('{} Exit code: {}'.format(self.server_response,
self.server.exitcode))
# Release client-side, if any:
if self.context:
self.context.destroy()
self.socket = None
def _force_control_mode(self):
"""Puts BT server to control mode.
"""
# Check is there any faults with server process and connection?
network_error = [
(not self.server or not self.server.is_alive(),
'No running server found. Hint: forgot to call reset()?'),
(not self.context
or self.context.closed, 'No network connection found.'),
]
for (err, msg) in network_error:
if err:
self.log.info(msg)
self.server_response = msg
return False
# If everything works, insist to go 'control':
self.server_response = {}
attempt = 0
while 'ctrl' not in self.server_response:
self.socket.send_pyobj({'ctrl': '_done'})
self.server_response = self.socket.recv_pyobj()
attempt += 1
self.log.debug(
'FORCE CONTROL MODE attempt: {}.\nResponse: {}'.format(
attempt, self.server_response))
return True
def _assert_response(self, response):
"""
Simple watcher:
roughly checks if we really talking to environment (== episode is running).
Rises exception if response given is not as expected.
"""
try:
assert type(response) == tuple and len(response) == 4
except AssertionError:
msg = 'Unexpected environment response: {}\nHint: Forgot to call reset() or reset_data()?'.format(
response)
self.log.exception(msg)
raise AssertionError(msg)
self.log.debug('Response checker received:\n{}\nas type: {}'.format(
response, type(response)))
def _print_space(self, space, _tab=''):
"""
Parses observation space shape or response.
Args:
space: gym observation space or state.
Returns:
description as string.
"""
response = ''
if type(space) in [dict, OrderedDict]:
for key, value in space.items():
response += '\n{}{}:{}\n'.format(
_tab, key, self._print_space(value, ' '))
elif type(space) in [spaces.Dict, DictSpace]:
for s in space.spaces:
response += self._print_space(s, ' ')
elif type(space) in [tuple, list]:
for i in space:
response += self._print_space(i, ' ')
elif type(space) == np.ndarray:
response += '\n{}array of shape: {}, low: {}, high: {}'.format(
_tab, space.shape, space.min(), space.max())
else:
response += '\n{}{}, '.format(_tab, space)
try:
response += 'low: {}, high: {}'.format(space.low.min(),
space.high.max())
except (KeyError, AttributeError, ArithmeticError,
ValueError) as e:
pass
#response += '\n{}'.format(e)
return response
def get_initial_action(self):
return {asset: 0 for asset in self.asset_names}
def get_initial_portfolio_action(self):
return {
asset: actions[0]
for asset, actions in self.server_actions.items()
}
def reset(self, **kwargs):
"""
Implementation of OpenAI Gym env.reset method. Starts new episode. Episode data are sampled
according to data provider class logic, controlled via kwargs. Refer `BTgym_Server` and data provider
classes for details.
Args:
kwargs: any kwargs; this dictionary is passed through to BTgym_server side without any checks and
modifications; currently used for data sampling control;
Returns:
observation space state
Notes:
Current kwargs accepted is::
episode_config=dict(
get_new=True,
sample_type=0,
b_alpha=1,
b_beta=1
),
trial_config=dict(
get_new=True,
sample_type=0,
b_alpha=1,
b_beta=1
)
"""
# Data Server check:
if self.data_master:
if not self.data_server or not self.data_server.is_alive():
self.log.info('No running data_server found, starting...')
self._start_data_server()
# Domain dataset status check:
self.data_server_response = self._comm_with_timeout(
socket=self.data_socket, message={'ctrl': '_get_info'})
if not self.data_server_response['message']['dataset_is_ready']:
self.log.info(
'Data domain `reset()` called prior to `reset_data()` with [possibly inconsistent] defaults.'
)
self.reset_data()
# Server process check:
if not self.server or not self.server.is_alive():
self.log.info('No running server found, starting...')
self._start_server()
if self._force_control_mode():
self.server_response = self._comm_with_timeout(socket=self.socket,
message={
'ctrl':
'_reset',
'kwargs': kwargs
})
# Get initial environment response:
self.env_response = self.step(self.get_initial_action())
# Check (once) if it is really (o,r,d,i) tuple:
self._assert_response(self.env_response)
# Check (once) if state_space is as expected:
try:
assert self.observation_space.contains(self.env_response[0])
except (AssertionError, AttributeError) as e:
msg1 = self._print_space(self.observation_space.spaces)
msg2 = self._print_space(self.env_response[0])
msg3 = ''
for step_info in self.env_response[-1]:
msg3 += '{}\n'.format(step_info)
msg = ('\nState observation shape/range mismatch!\n' +
'Space set by env: \n{}\n' +
'Space returned by server: \n{}\n' +
'Full response:\n{}\n' + 'Reward: {}\n' + 'Done: {}\n' +
'Info:\n{}\n' +
'Hint: Wrong Strategy.get_state() parameters?').format(
msg1,
msg2,
self.env_response[0],
self.env_response[1],
self.env_response[2],
msg3,
)
self.log.exception(msg)
self._stop_server()
raise AssertionError(msg)
return self.env_response[0]
else:
msg = 'Something went wrong. env.reset() can not get response from server.'
self.log.exception(msg)
raise ChildProcessError(msg)
def step(self, action):
"""
Implementation of OpenAI Gym env.step() method.
Makes a step in the environment.
Args:
action: int or dict, action compatible to env.action_space
Returns:
tuple (Observation, Reward, Info, Done)
"""
# If we got int as action - try to treat it as an action for single-valued action space dict:
if isinstance(action, int) and len(
list(self.action_space.spaces.keys())) == 1:
a = copy.deepcopy(action)
action = {key: a for key in self.action_space.spaces.keys()}
# Are you in the list, ready to go and all that?
if self.action_space.contains(action)\
and not self._closed\
and (self.socket is not None)\
and not self.socket.closed:
pass
else:
msg = ('\nAt least one of these is true:\n' +
'Action error: (space is {}, action sent is {}): {}\n' +
'Environment closed: {}\n' +
'Network error [socket doesnt exists or closed]: {}\n' +
'Hint: forgot to call reset()?').format(
self.action_space,
action,
not self.action_space.contains(action),
self._closed,
not self.socket or self.socket.closed,
)
self.log.exception(msg)
raise AssertionError(msg)
# Send action (as dict of strings) to backtrader engine, receive environment response:
action_as_dict = {
key: self.server_actions[key][value]
for key, value in action.items()
}
#print('step: ', action, action_as_dict)
env_response = self._comm_with_timeout(
socket=self.socket, message={'action': action_as_dict})
if not env_response['status'] in 'ok':
msg = '.step(): server unreachable with status: <{}>.'.format(
env_response['status'])
self.log.error(msg)
raise ConnectionError(msg)
self.env_response = env_response['message']
return self.env_response
def close(self):
"""
Implementation of OpenAI Gym env.close method.
Puts BTgym server in Control Mode.
"""
self.log.debug('close.call()')
self._stop_server()
self._stop_data_server()
self.log.info('Environment closed.')
def get_stat(self):
"""
Returns last run episode statistics.
Note:
when invoked, forces running episode to terminate.
"""
if self._force_control_mode():
self.socket.send_pyobj({'ctrl': '_getstat'})
return self.socket.recv_pyobj()
else:
return self.server_response
def render(self, mode='other_mode', close=False):
"""
Implementation of OpenAI Gym env.render method.
Visualises current environment state.
Args:
`mode`: str, any of these::
`human` - current state observation as price lines;
`episode` - plotted results of last completed episode.
[other_key] - corresponding to any custom observation space key
"""
if close:
return None
if not self._closed\
and self.socket\
and not self.socket.closed:
pass
else:
msg = ('\nCan'
't get renderings.'
'\nAt least one of these is true:\n' +
'Environment closed: {}\n' +
'Network error [socket doesnt exists or closed]: {}\n' +
'Hint: forgot to call reset()?').format(
self._closed,
not self.socket or self.socket.closed,
)
self.log.warning(msg)
return None
if mode not in self.render_modes:
raise ValueError('Unexpected render mode {}'.format(mode))
self.socket.send_pyobj({'ctrl': '_render', 'mode': mode})
rgb_array_dict = self.socket.recv_pyobj()
self.rendered_rgb.update(rgb_array_dict)
return self.rendered_rgb[mode]
def _stop(self):
"""
Finishes current episode if any, does nothing otherwise. Leaves server running.
"""
if self._force_control_mode():
self.log.info('Episode stop forced.')
def _restart_server(self):
"""Restarts server.
"""
self._stop_server()
self._start_server()
self.log.info('Server restarted.')
def _start_data_server(self):
"""
For data_master environment:
- configures backtrader REQ/REP server instance and starts server process.
For others:
- establishes network connection to existing data_server.
"""
self.data_server = None
# Ensure network resources:
# 1. Release client-side, if any:
if self.data_context:
self.data_context.destroy()
self.data_socket = None
# Only data_master launches/stops data_server process:
if self.data_master:
# 2. Kill any process using server port:
cmd = "kill $( lsof -i:{} -t ) > /dev/null 2>&1".format(
self.data_port)
os.system(cmd)
# Configure and start server:
self.data_server = BTgymDataFeedServer(
dataset=self.dataset,
network_address=self.data_network_address,
log_level=self.log_level,
task=self.task)
self.data_server.daemon = False
self.data_server.start()
# Wait for server to startup
time.sleep(1)
# Set up client channel:
self.data_context = zmq.Context()
self.data_socket = self.data_context.socket(zmq.REQ)
self.data_socket.setsockopt(zmq.RCVTIMEO, self.connect_timeout * 1000)
self.data_socket.setsockopt(zmq.SNDTIMEO, self.connect_timeout * 1000)
self.data_socket.connect(self.data_network_address)
# Check connection:
self.log.debug('Pinging data_server at: {} ...'.format(
self.data_network_address))
self.data_server_response = self._comm_with_timeout(
socket=self.data_socket, message={'ctrl': 'ping!'})
if self.data_server_response['status'] in 'ok':
self.log.debug(
'Data_server seems ready with response: <{}>'.format(
self.data_server_response['message']))
else:
msg = 'Data_server unreachable with status: <{}>.'.\
format(self.data_server_response['status'])
self.log.error(msg)
raise ConnectionError(msg)
# Get info and statistic:
self.dataset_stat, self.dataset_columns, self.data_server_pid, self.data_lines_names = self._get_dataset_info(
)
def _stop_data_server(self):
"""
For data_master:
- stops BT server process, releases network resources.
"""
if self.data_master:
if self.data_server is not None and self.data_server.is_alive():
# In case server is running and is ok:
self.data_socket.send_pyobj({'ctrl': '_stop'})
self.data_server_response = self.data_socket.recv_pyobj()
else:
self.data_server.terminate()
self.data_server.join()
self.data_server_response = 'Data_server process terminated.'
self.log.info('{} Exit code: {}'.format(self.data_server_response,
self.data_server.exitcode))
if self.data_context:
self.data_context.destroy()
self.data_socket = None
def _restart_data_server(self):
"""
Restarts data_server.
"""
if self.data_master:
self._stop_data_server()
self._start_data_server()
def _get_dataset_info(self):
"""
Retrieves dataset configuration and descriptive statistic.
"""
self.data_socket.send_pyobj({'ctrl': '_get_info'})
self.data_server_response = self.data_socket.recv_pyobj()
return self.data_server_response['dataset_stat'],\
self.data_server_response['dataset_columns'],\
self.data_server_response['pid'], \
self.data_server_response['data_names']
def reset_data(self, **kwargs):
"""
Resets data provider class used, whatever it means for that class. Gets data_server ready to provide data.
Supposed to be called before first env.reset().
Note:
when invoked, forces running episode to terminate.
Args:
**kwargs: data provider class .reset() method specific.
"""
if self.closed:
self._start_server()
if self.data_master:
self._start_data_server()
self.closed = False
else:
_ = self._force_control_mode()
if self.data_master:
if self.data_server is None or not self.data_server.is_alive():
self._restart_data_server()
self.data_server_response = self._comm_with_timeout(
socket=self.data_socket,
message={
'ctrl': '_reset_data',
'kwargs': kwargs
})
if self.data_server_response['status'] in 'ok':
self.log.debug(
'Dataset seems ready with response: <{}>'.format(
self.data_server_response['message']))
else:
msg = 'Data_server unreachable with status: <{}>.'. \
format(self.data_server_response['status'])
self.log.error(msg)
raise SystemExit(msg)
else:
pass
