###############################################################################
#
# Copyright (C) 2017 Andrew Muzikin
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
###############################################################################
import backtrader as bt
import backtrader.indicators as btind
from gym import spaces
from btgym import DictSpace
import numpy as np
from collections import deque
from btgym.strategy.utils import norm_value, decayed_result, exp_scale
############################## Base BTgymStrategy Class ###################
[docs]class BTgymBaseStrategy(bt.Strategy):
"""
Controls Environment inner dynamics and backtesting logic. Provides gym'my (State, Action, Reward, Done, Info) data.
Any State, Reward and Info computation logic can be implemented by subclassing BTgymStrategy and overriding
get_[mode]_state(), get_reward(), get_info(), is_done() and set_datalines() methods.
One can always go deeper and override __init__ () and next() methods for desired
server cerebro engine behaviour, including order execution logic etc.
Note:
- base class supports single asset iteration via default data_line named 'base_asset', see derived classes
multi-asset support
- bt.observers.DrawDown observer will be automatically added to BTgymStrategy instance at runtime.
- Since it is bt.Strategy subclass, refer to https://www.backtrader.com/docu/strategy.html for more information.
"""
# Time embedding period:
time_dim = 4 # NOTE: changed this --> change Policy UNREAL for aux. pix control task upsampling params
# Number of environment steps to skip before returning next response,
# e.g. if set to 10 -- agent will interact with environment every 10th step;
# every other step agent action is assumed to be 'hold':
skip_frame = 1
# Number of timesteps reward estimation statistics are averaged over, should be:
# skip_frame_period <= avg_period <= time_embedding_period:
avg_period = time_dim
gamma = 0.99 # fi_gamma, should match MDP gamma decay
reward_scale = 1.0 # reward multiplicator
# Possible agent actions; Note: place 'hold' first! :
portfolio_actions = ('hold', 'buy', 'sell', 'close')
params = dict(
# 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' (default) - absolute min/max values from BTgymDataset will be used.
state_shape={
'raw': spaces.Box(
shape=(time_dim, 4),
low=0, # will get overridden.
high=0,
dtype=np.float32,
),
'metadata': DictSpace(
{
'type': spaces.Box(
shape=(),
low=0,
high=1,
dtype=np.uint32
),
'trial_num': spaces.Box(
shape=(),
low=0,
high=10 ** 10,
dtype=np.uint32
),
'trial_type': spaces.Box(
shape=(),
low=0,
high=1,
dtype=np.uint32
),
'sample_num': spaces.Box(
shape=(),
low=0,
high=10 ** 10,
dtype=np.uint32
),
'first_row': spaces.Box(
shape=(),
low=0,
high=10 ** 10,
dtype=np.uint32
),
'timestamp': spaces.Box(
shape=(),
low=0,
high=np.finfo(np.float64).max,
dtype=np.float64
),
}
)
},
cash_name='default_cash',
asset_names=['default_asset'],
start_cash=None,
commission=None,
leverage=1.0,
gamma=gamma,
reward_scale=reward_scale,
drawdown_call=10, # finish episode when hitting drawdown treshghold , in percent.
target_call=10, # finish episode when reaching profit target, in percent.
dataset_stat=None, # Summary descriptive statistics for entire dataset and
episode_stat=None, # current episode. Got updated by server.
metadata={},
trial_stat=None,
trial_metadata=None,
portfolio_actions=portfolio_actions,
skip_frame=skip_frame,
order_size=None,
initial_action=None,
initial_portfolio_action=None,
)
def __init__(self, **kwargs):
"""
Keyword Args:
params (dict): parameters dictionary, see Note below.
Notes:
Due to backtrader convention, any strategy arguments should be defined inside `params` dictionary
or passed as kwargs to bt.Cerebro() class via .addstrategy() method. Parameter dictionary
should contain at least these keys::
state_shape: Observation state shape is dictionary of Gym spaces, by convention
first dimension of every Gym Box space is time embedding one;
cash_name: str, name for cash asset
asset_names: iterable of str, names for assets
start_cash: float, broker starting cash
commission: float, broker commission value, .01 stands for 1%
leverage: broker leverage, default is 1.0
order_size: dict of fixed order stakes (floats); keys should match assets names.
drawdown_call: finish episode when hitting this drawdown treshghold , in percent.
target_call: finish episode when reaching this profit target, in percent.
portfolio_actions: possible agent actions.
skip_frame: number of environment steps to skip before returning next response,
e.g. if set to 10 -- agent will interact with environment every 10th step;
every other step agent action is assumed to be 'hold'.
Default values are::
state_shape=dict(raw_state=spaces.Box(shape=(4, 4), low=0, high=0,))
cash_name='default_cash'
asset_names=['default_asset']
start_cash=None
commission=None
leverage=1.0
drawdown_call=10
target_call=10
dataset_stat=None
episode_stat=None
portfolio_actions=('hold', 'buy', 'sell', 'close')
skip_frame=1
order_size=None
"""
try:
self.time_dim = self.p.state_shape['raw'].shape[0]
except KeyError:
pass
try:
self.skip_frame = self.p.skip_frame
except KeyError:
pass
self.iteration = 0
self.env_iteration = 0
self.inner_embedding = 1
self.is_done = False
self.is_done_enabled = False
self.steps_till_is_done = 2 # extra steps to make when episode terminal conditions are met
self.action = self.p.initial_portfolio_action
self.action_to_repeat = self.p.initial_portfolio_action
self.action_repeated = 0
self.num_action_repeats = None
self.reward = 0
self.order = None
self.order_failed = 0
self.broker_message = '_'
self.final_message = '_'
self.raw_state = None
self.time_stamp = 0
# Inherit logger from cerebro:
self.log = self.env._log
# Prepare broker:
if self.p.start_cash is not None:
self.env.broker.setcash(self.p.start_cash)
if self.p.commission is not None:
self.env.broker.setcommission(commission=self.p.commission, leverage=self.p.leverage)
# Normalisation constant for statistics derived from account value:
self.broker_value_normalizer = 1 / \
self.env.broker.startingcash / (self.p.drawdown_call + self.p.target_call) * 100
self.target_value = self.env.broker.startingcash * (1 + self.p.target_call / 100)
# Try to define stake, if no self.p.order_size dict has been set:
if self.p.order_size is None:
# If no order size has been set for every data_line,
# try to infer stake size from sizer set by bt.Cerebro.addsizer() method:
try:
assert len(list(self.env.sizers.values())) == 1
env_sizer_params = list(self.env.sizers.values())[0][-1] # pull dict of outer set sizer params
assert 'stake' in env_sizer_params.keys()
except (AssertionError, KeyError) as e:
msg = 'Order stake is not set neither via strategy.param.order_size nor via bt.Cerebro.addsizer method.'
self.log.error(msg)
raise ValueError(msg)
self.p.order_size = {name: env_sizer_params['stake'] for name in self.p.asset_names}
elif isinstance(self.p.order_size, int) or isinstance(self.p.order_size, float):
unimodal_stake = {name: self.p.order_size for name in self.p.asset_names}
self.p.order_size = unimodal_stake
self.trade_just_closed = False
self.trade_result = 0
self.unrealized_pnl = None
self.norm_broker_value = None
self.realized_pnl = None
self.current_pos_duration = 0
self.current_pos_min_value = 0
self.current_pos_max_value = 0
self.realized_broker_value = self.env.broker.startingcash
self.episode_result = 0 # not used
# Service sma to get correct first features values:
self.data.dim_sma = btind.SimpleMovingAverage(
self.datas[0],
period=self.time_dim
)
self.data.dim_sma.plotinfo.plot = False
# self.log.warning('self.p.dir: {}'.format(dir(self.params)))
# Episode-wide metadata:
self.metadata = {
'type': np.asarray(self.p.metadata['type']),
'trial_num': np.asarray(self.p.metadata['parent_sample_num']),
'trial_type': np.asarray(self.p.metadata['parent_sample_type']),
'sample_num': np.asarray(self.p.metadata['sample_num']),
'first_row': np.asarray(self.p.metadata['first_row']),
'timestamp': np.asarray(self.time_stamp, dtype=np.float64)
}
self.state = {
'raw': None,
'metadata': None
}
# This flag shows to the outer world if this episode can broadcast world-state information, e.g. move global
# time forward (see: btgym.server._BTgymAnalyzer.next() method);
# default logic: true iff. it is test episode from target domain:
self.can_broadcast = self.metadata['type'] and self.metadata['trial_type']
self.log.debug('strategy.metadata: {}'.format(self.metadata))
self.log.debug('can_broadcast: {}'.format(self.can_broadcast))
# Broker data lines of interest (used for estimation inner state of agent:
self.broker_datalines = [
'cash',
'value',
'exposure',
'drawdown',
'pos_direction',
'pos_duration',
'realized_pnl',
'unrealized_pnl',
'min_unrealized_pnl',
'max_unrealized_pnl',
]
# Define collection dictionary looking for methods for estimating broker statistics, one method for one mode,
# should be named .get_broker_[mode_name]():
self.collection_get_broker_stat_methods = {}
for line in self.broker_datalines:
try:
self.collection_get_broker_stat_methods[line] = getattr(self, 'get_broker_{}'.format(line))
except AttributeError:
raise NotImplementedError('Callable get_broker_{}.() not found'.format(line))
# Broker and account related sliding statistics accumulators, globally normalized last `avg_perod` values,
# so it's a bit more comp. efficient than use of bt.Observers:
self.broker_stat = {key: deque(maxlen=self.avg_period) for key in self.broker_datalines}
# Add custom data Lines if any (convenience wrapper):
self.set_datalines()
self.log.debug('Kwargs:\n{}\n'.format(str(kwargs)))
# Define collection dictionary looking for methods for estimating observation state, one method for one mode,
# should be named .get_[mode_name]_state():
self.collection_get_state_methods = {}
for key in self.p.state_shape.keys():
try:
self.collection_get_state_methods[key] = getattr(self, 'get_{}_state'.format(key))
except AttributeError:
raise NotImplementedError('Callable get_{}_state.() not found'.format(key))
for data in self.datas:
self.log.debug('data_name: {}'.format(data._name))
self.log.debug('stake size: {}'.format(self.p.order_size))
# Define how this strategy should handle actions: either as discrete or continuous:
if self.p.portfolio_actions is None or set(self.p.portfolio_actions) == {}:
# No discrete actions provided, assume continuous:
try:
assert self.p.skip_frame > 1
except AssertionError:
msg = 'For continuous actions it is essential to set `skip_frame` parameter > 1, got: {}'.format(
self.p.skip_frame
)
self.log.error(msg)
raise ValueError(msg)
# Disable broker checking margin,
# see: https://community.backtrader.com/topic/152/multi-asset-ranking-and-rebalancing/2?page=1
self.env.broker.set_checksubmit(False)
self.next_process_fn = self._next_target_percent
# Repeat action 2 times:
self.num_action_repeats = 2
else:
# Use discrete handling method otherwise:
self.env.broker.set_checksubmit(True)
self.next_process_fn = self._next_discrete
# Do not repeat action for discrete:
self.num_action_repeats = 0
def prenext(self):
self.update_broker_stat()
def nextstart(self):
self.inner_embedding = self.data.close.buflen()
self.log.debug('Inner time embedding: {}'.format(self.inner_embedding))
[docs] def next(self):
"""
Default implementation for built-in backtrader method.
Defines one step environment routine;
Handles order execution logic according to action received.
Note that orders can only be submitted for data_lines in action_space (assets).
`self.action` attr. is updated by btgym.server._BTgymAnalyzer, and `None` actions
are emitted while doing `skip_frame` loop.
"""
self.update_broker_stat()
if '_skip_this' in self.action.keys():
# print('a_skip, b_message: ', self.broker_message)
if self.action_repeated < self.num_action_repeats:
self.next_process_fn(self.action_to_repeat)
self.action_repeated += 1
else:
self.next_process_fn(self.action)
self.action_repeated = 0
self.action_to_repeat = self.action
# print('a_process, b_message: ', self.broker_message)
def notify_trade(self, trade):
if trade.isclosed:
# Set trade flags: True if trade have been closed just now and within last frame-skip period,
# and store trade result:
self.trade_just_closed = True
# Note: `trade_just_closed` flag has to be reset manually after evaluating.
self.trade_result = trade.pnlcomm
# Store realized prtfolio value:
self.realized_broker_value = self.broker.get_value()
[docs] def update_broker_stat(self):
"""
Updates all sliding broker statistics deques with latest-step values such as:
- normalized broker value
- normalized broker cash
- normalized exposure (position size)
- exp. scaled episode duration in steps, normalized wrt. max possible episode steps
- normalized realized profit/loss for last closed trade (is zero if no pos. closures within last env. step)
- normalized profit/loss for current opened trade (unrealized p/l);
"""
current_value = self.env.broker.get_value()
for key, method in self.collection_get_broker_stat_methods.items():
self.broker_stat[key].append(method(current_value=current_value))
# Reset one-time flags:
self.trade_just_closed = False
[docs] def get_broker_value(self, current_value, **kwargs):
"""
Args:
current_value: current portfolio value
Returns:
normalized broker value.
"""
return norm_value(
current_value,
self.env.broker.startingcash,
self.p.drawdown_call,
self.p.target_call,
)
[docs] def get_broker_cash(self, **kwargs):
"""
Returns:
normalized broker cash
"""
return norm_value(
self.env.broker.get_cash(),
self.env.broker.startingcash,
99.0,
self.p.target_call,
)
[docs] def get_broker_exposure(self, **kwargs):
"""
Returns:
normalized exposure (position size)
"""
return self.position.size / (self.env.broker.startingcash * self.env.broker.get_leverage() + 1e-2)
[docs] def get_broker_pos_direction(self, **kwargs):
"""
Returns:
short/long/out position indicator
"""
if self.position.size > 0:
return 1.0
elif self.position.size < 0:
return - 1.0
else:
return 0.0
[docs] def get_broker_realized_pnl(self, current_value, **kwargs):
"""
Args:
current_value: current portfolio value
Returns:
normalized realized profit/loss for last closed trade (is zero if no pos. closures within last env. step)
"""
if self.trade_just_closed:
pnl = decayed_result(
self.trade_result,
current_value,
self.env.broker.startingcash,
self.p.drawdown_call,
self.p.target_call,
gamma=1
)
# Reset flag:
# self.trade_just_closed = False
#print('broker_realized_pnl: step {}, just closed.'.format(self.iteration))
else:
pnl = 0.0
return pnl
[docs] def get_broker_unrealized_pnl(self, current_value, **kwargs):
"""
Args:
current_value: current portfolio value
Returns:
normalized profit/loss for current opened trade
"""
return (current_value - self.realized_broker_value) * self.broker_value_normalizer
[docs] def get_broker_episode_step(self, **kwargs):
"""
Returns:
exp. scaled episode duration in steps, normalized wrt. max possible episode steps
"""
return exp_scale(
self.iteration / (self.data.numrecords - self.inner_embedding),
gamma=3
)
[docs] def get_broker_drawdown(self, **kwargs):
"""
Returns:
current drawdown value
"""
try:
dd = self.stats.drawdown.drawdown[-1] #/ self.p.drawdown_call
except IndexError:
dd = 0.0
return dd
def get_broker_pos_duration(self, current_value):
if self.position.size == 0:
self.current_pos_duration = 0
# self.current_pos_min_value = current_value
# self.current_pos_max_value = current_value
# print('ZERO_POSITION\n')
else:
self.current_pos_duration += 1
# if self.current_pos_max_value < current_value:
# self.current_pos_max_value = current_value
#
# elif self.current_pos_min_value > current_value:
# self.current_pos_min_value = current_value
return self.current_pos_duration #/ (self.data.numrecords - self.inner_embedding)
def get_broker_max_unrealized_pnl(self, current_value, **kwargs):
if self.position.size == 0:
self.current_pos_max_value = current_value
else:
if self.current_pos_max_value < current_value:
self.current_pos_max_value = current_value
return (self.current_pos_max_value - self.realized_broker_value) * self.broker_value_normalizer
def get_broker_min_unrealized_pnl(self, current_value, **kwargs):
if self.position.size == 0:
self.current_pos_min_value = current_value
else:
if self.current_pos_min_value > current_value:
self.current_pos_min_value = current_value
return (self.current_pos_min_value - self.realized_broker_value) * self.broker_value_normalizer
[docs] def set_datalines(self):
"""
Default datalines are: Open, Low, High, Close, Volume.
Any other custom data lines, indicators, etc. should be explicitly defined by overriding this method.
Invoked once by Strategy.__init__().
"""
pass
[docs] def get_raw_state(self):
"""
Default state observation composer.
Returns:
and updates time-embedded environment state observation as [n,4] numpy matrix, where:
4 - number of signal features == state_shape[1],
n - time-embedding length == state_shape[0] == <set by user>.
Note:
`self.raw_state` is used to render environment `human` mode and should not be modified.
"""
self.raw_state = np.row_stack(
(
np.frombuffer(self.data.open.get(size=self.time_dim)),
np.frombuffer(self.data.high.get(size=self.time_dim)),
np.frombuffer(self.data.low.get(size=self.time_dim)),
np.frombuffer(self.data.close.get(size=self.time_dim)),
)
).T
return self.raw_state
[docs] def get_internal_state(self):
"""
Composes internal state tensor by calling all statistics from broker_stat dictionary.
Generally, this method should not be modified, implement corresponding get_broker_[mode]() methods.
"""
x_broker = np.concatenate(
[np.asarray(stat)[..., None] for stat in self.broker_stat.values()],
axis=-1
)
return x_broker[:, None, :]
def get_metadata_state(self):
self.metadata['timestamp'] = np.asarray(self._get_timestamp())
return self.metadata
def _get_time(self):
"""
Retrieves current time point of the episode data.
Returns:
datetime object
"""
return self.data.datetime.datetime()
def _get_timestamp(self):
"""
Sets attr. and returns current data timestamp.
Returns:
POSIX timestamp
"""
self.time_stamp = self._get_time().timestamp()
return self.time_stamp
def _get_broadcast_info(self):
"""
Transmits broadcasting message.
Returns:
dictionary of global settings
"""
return dict()
[docs] def get_state(self):
"""
Collects estimated values for every mode of observation space by calling methods from
`collection_get_state_methods` dictionary.
As a rule, this method should not be modified, override or implement corresponding get_[mode]_state() methods,
defining necessary calculations and return arbitrary shaped tensors for every space mode.
Note:
- 'data' referes to bt.startegy datafeeds and should be treated as such.
Datafeed Lines that are not default to BTgymStrategy should be explicitly defined by
__init__() or define_datalines().
"""
# Update inner state statistic and compose state: <- moved to .next()
# self.update_broker_stat()
self.state = {key: method() for key, method in self.collection_get_state_methods.items()}
# Above line is generalisation of, say:
# self.state = {
# 'external': self.get_external_state(),
# 'internal': self.get_internal_state(),
# 'datetime': self.get_datetime_state(),
# 'metadata': self.get_metadata_state(),
# }
return self.state
[docs] def get_reward(self):
"""
Shapes reward function as normalized single trade realized profit/loss,
augmented with potential-based reward shaping functions in form of:
F(s, a, s`) = gamma * FI(s`) - FI(s);
- potential FI_1 is current normalized unrealized profit/loss;
EXCLUDED/ - potential FI_2 is current normalized broker value.
EXCLUDED/ - FI_3: penalizing exposure toward the end of episode
Paper:
"Policy invariance under reward transformations:
Theory and application to reward shaping" by A. Ng et al., 1999;
http://www.robotics.stanford.edu/~ang/papers/shaping-icml99.pdf
"""
# All sliding statistics for this step are already updated by get_state().
# Potential-based shaping function 1:
# based on potential of averaged profit/loss for current opened trade (unrealized p/l):
unrealised_pnl = np.asarray(self.broker_stat['unrealized_pnl'])
current_pos_duration = self.broker_stat['pos_duration'][-1]
# We want to estimate potential `fi = gamma*fi_prime - fi` of current opened position,
# thus need to consider different cases given skip_fame parameter:
if current_pos_duration == 0:
# Set potential term to zero if there is no opened positions:
f1 = 0
else:
if current_pos_duration < self.p.skip_frame:
fi_1 = 0
fi_1_prime = np.average(unrealised_pnl[-current_pos_duration:])
elif current_pos_duration < 2 * self.p.skip_frame:
fi_1 = np.average(
unrealised_pnl[-(self.p.skip_frame + current_pos_duration):-self.p.skip_frame]
)
fi_1_prime = np.average(unrealised_pnl[-self.p.skip_frame:])
else:
fi_1 = np.average(
unrealised_pnl[-2 * self.p.skip_frame:-self.p.skip_frame]
)
fi_1_prime = np.average(unrealised_pnl[-self.p.skip_frame:])
# Potential term:
f1 = self.p.gamma * fi_1_prime - fi_1
# Main reward function: normalized realized profit/loss:
realized_pnl = np.asarray(self.broker_stat['realized_pnl'])[-self.p.skip_frame:].sum()
# Weights are subject to tune:
self.reward = (10.0 * f1 + 10.0 * realized_pnl) * self.p.reward_scale
self.reward = np.clip(self.reward, -self.p.reward_scale, self.p.reward_scale)
return self.reward
[docs] def get_info(self):
"""
Composes information part of environment response,
can be any object. Override to own taste.
Note:
Due to 'skip_frame' feature, INFO part of environment response transmitted by server can be a list
containing either all skipped frame's info objects, i.e. [info[-9], info[-8], ..., info[0]] or
just latest one, [info[0]]. This behaviour is set inside btgym.server._BTgymAnalyzer().next() method.
"""
return dict(
step=self.iteration,
time=self.data.datetime.datetime(),
action=self.action,
broker_message=self.broker_message,
broker_cash=self.stats.broker.cash[0],
broker_value=self.stats.broker.value[0],
drawdown=self.stats.drawdown.drawdown[0],
max_drawdown=self.stats.drawdown.maxdrawdown[0],
)
[docs] def get_done(self):
"""
Episode termination estimator,
defines any trading logic conditions episode stop is called upon, e.g. <OMG! Stop it, we became too rich!>.
It is just a structural a convention method. Default method is empty.
Expected to return:
tuple (<is_done, type=bool>, <message, type=str>).
"""
return False, '-'
def _get_done(self):
"""
Default episode termination method,
checks base conditions episode stop is called upon:
1. Reached maximum episode duration. Need to check it explicitly, because <self.is_done> flag
is sent as part of environment response.
2. Got '_done' signal from outside. E.g. via env.reset() method invoked by outer RL algorithm.
3. Hit drawdown threshold.
4. Hit target profit threshold.
This method shouldn't be overridden or called explicitly.
Runtime execution logic is:
terminate episode if:
get_done() returned (True, 'something')
OR
ANY _get_done() default condition is met.
"""
if not self.is_done_enabled:
# Episode is on its way,
# apply base episode termination rules:
is_done_rules = [
# Do we approaching the end of the episode?:
(self.iteration >= \
self.data.numrecords - self.inner_embedding - self.p.skip_frame - self.steps_till_is_done,
'END OF DATA'),
# Any money left?:
(self.stats.drawdown.maxdrawdown[0] >= self.p.drawdown_call, 'DRAWDOWN CALL'),
# Party time?
(self.env.broker.get_value() > self.target_value, 'TARGET REACHED'),
]
# Append custom get_done() results, if any:
is_done_rules += [self.get_done()]
# Sweep through rules:
for (condition, message) in is_done_rules:
if condition:
# Start episode termination countdown for clean exit:
# to forcefully execute final `close` order and compute proper reward
# we need to make `steps_till_is_done` number of steps until `is_done` flag can be safely risen:
self.is_done_enabled = True
self.broker_message += message
self.final_message = message
self.order = self.close()
self.log.debug(
'Episode countdown started at: {}, {}, r:{}'.format(self.iteration, message, self.reward)
)
else:
# Now in episode termination phase,
# just keep hitting `Close` button:
self.steps_till_is_done -=1
self.broker_message = 'CLOSE, {}'.format(self.final_message)
self.order = self.close()
self.log.debug(
'Episode countdown contd. at: {}, {}, r:{}'.format(self.iteration, self.broker_message, self.reward)
)
if self.steps_till_is_done <= 0:
# Now we've done, terminate:
self.is_done = True
return self.is_done
[docs] def notify_order(self, order):
"""
Shamelessly taken from backtrader tutorial.
TODO: better multi data support
"""
if order.status in [order.Submitted, order.Accepted]:
# Buy/Sell order submitted/accepted to/by broker - Nothing to do
return
# Check if an order has been completed
# Attention: broker could reject order if not enough cash
if order.status in [order.Completed]:
if order.isbuy():
self.broker_message = 'BUY executed,\nPrice: {:.5f}, Cost: {:.4f}, Comm: {:.4f}'. \
format(order.executed.price,
order.executed.value,
order.executed.comm)
self.buyprice = order.executed.price
self.buycomm = order.executed.comm
else: # Sell
self.broker_message = 'SELL executed,\nPrice: {:.5f}, Cost: {:.4f}, Comm: {:.4f}'. \
format(order.executed.price,
order.executed.value,
order.executed.comm)
self.bar_executed = len(self)
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.broker_message = 'ORDER FAILED with status: ' + str(order.getstatusname())
# Rise order_failed flag until get_reward() will [hopefully] use and reset it:
self.order_failed += 1
self.order = None
def _next_discrete(self, action):
"""
Default implementation for discrete actions.
Note that orders can be submitted only for data_lines in action_space (assets).
Args:
action: dict, string encoding of btgym.spaces.ActionDictSpace
"""
for key, single_action in action.items():
# Simple action-to-order logic:
if single_action == 'hold' or self.is_done_enabled:
pass
elif single_action == 'buy':
self.order = self.buy(data=key, size=self.p.order_size[key])
self.broker_message = 'new {}_BUY created; '.format(key) + self.broker_message
elif single_action == 'sell':
self.order = self.sell(data=key, size=self.p.order_size[key])
self.broker_message = 'new {}_SELL created; '.format(key) + self.broker_message
elif single_action == 'close':
self.order = self.close(data=key)
self.broker_message = 'new {}_CLOSE created; '.format(key) + self.broker_message
# Somewhere after this point, server-side _BTgymAnalyzer() is exchanging information with environment wrapper,
# obtaining <self.action> , composing and sending <state,reward,done,info> etc... never mind.
def _next_target_percent(self, action):
"""
Uses `order_target_percent` method to rebalance assets to given ratios. Expects action for every asset to be
a float scalar in [0,1], with actions sum to 1 over all assets (including base one).
Note that action for base asset (cash) is ignored.
For details refer to: https://www.backtrader.com/docu/order_target/order_target.html
"""
# TODO 1: filter similar actions to prevent excessive orders issue e.g by DKL on two consecutive ones
# TODO 2: actions discretesation on level of execution
for asset in self.p.asset_names:
# Reducing assets positions subj to 5% margin reserve:
single_action = round(float(action[asset]) * 0.9, 2)
self.order = self.order_target_percent(data=asset, target=single_action )
self.broker_message += ' new {}->{:1.0f}% created; '.format(asset, single_action * 100)