def set_feed(self, episode: 'int') -> None:
"""Sets the current feed.
Parameters
----------
episode : `int`
The episodes of the feed to set.
"""
for exchange in self.portfolio.exchanges:
exchange.set_episode(episode)
internal_group = Stream.group(_create_internal_streams(self.portfolio)).rename("internal")
external_group = Stream.group(self.feeds[episode].inputs).rename("external")
if self.renderer_feeds:
renderer_group = Stream.group(self.renderer_feeds[episode].inputs).rename("renderer")
self.feed = DataFeed([
internal_group,
external_group,
renderer_group
])
else:
self.feed = DataFeed([
internal_group,
external_group
])
self.feed = self.feed.attach(self.portfolio)
self.feed.compile()
def __init__(self,
portfolio: 'Portfolio',
feed: 'DataFeed' = None,
renderer_feed: 'DataFeed' = None,
stop_time: 'datetime.time' = dt.time(16, 0, 0),
window_size: int = 1,
min_periods: int = None,
randomize: bool = False,
**kwargs) -> None:
internal_group = Stream.group(
_create_internal_streams(portfolio)).rename("internal")
external_group = Stream.group(feed.inputs).rename("external")
if renderer_feed:
renderer_group = Stream.group(
renderer_feed.inputs).rename("renderer")
self.feed = DataFeed(
[internal_group, external_group, renderer_group])
else:
self.feed = DataFeed([internal_group, external_group])
self.stop_time = stop_time
self.window_size = window_size
self.min_periods = min_periods
self.randomize = randomize
self._observation_dtype = kwargs.get('dtype', np.float32)
self._observation_lows = kwargs.get('observation_lows', -np.inf)
self._observation_highs = kwargs.get('observation_highs', np.inf)
self.history = ObservationHistory(window_size=window_size)
initial_obs = self.feed.next()["external"]
initial_obs.pop('timestamp', None)
n_features = len(initial_obs.keys())
self._observation_space = Box(low=self._observation_lows,
high=self._observation_highs,
shape=(self.window_size, n_features),
dtype=self._observation_dtype)
self.feed = self.feed.attach(portfolio)
self.renderer_history = []
if self.randomize:
self.num_episodes = 0
while (self.feed.has_next()):
ts = self.feed.next()["external"]["timestamp"]
if ts.time() == self.stop_time:
self.num_episodes += 1
self.feed.reset()
self.warmup()
self.stop = False
def __init__(self,
portfolio: 'Portfolio',
feed: 'DataFeed' = None,
renderer_feed: 'DataFeed' = None,
window_size: int = 1,
min_periods: int = None,
**kwargs) -> None:
internal_group = Stream.group(
_create_internal_streams(portfolio)).rename("internal")
external_group = Stream.group(feed.inputs).rename("external")
# print('---------===0')
# print(feed, '---input feed: ', feed.next() )
# print(renderer_feed, '---input render feed: ', renderer_feed.next())
# print(internal_group.streams , '-------------------------', external_group.streams )
# print('---------0')
if renderer_feed:
renderer_group = Stream.group(
renderer_feed.inputs).rename("renderer")
# print('--- render group: ', renderer_group.streams)
# print('-----------======making push feed in observer')
self.feed = PushFeed(
[internal_group, external_group, renderer_group])
else:
self.feed = DataFeed([internal_group, external_group])
self.window_size = window_size
self.min_periods = min_periods
self._observation_dtype = kwargs.get('dtype', np.float32)
self._observation_lows = kwargs.get('observation_lows', -np.inf)
self._observation_highs = kwargs.get('observation_highs', np.inf)
self.history = ObservationHistory(window_size=window_size)
# print('---------')
# print('---feed: ', self.feed, ' has next')
# print('---rendere: ', self.feed.next()['renderer'])
# print('---external: ', self.feed.next()['external'])
# print('---internal: ', self.feed.next()['internal'])
# print('---------')
# initial_obs = self.feed.next()["external"]
# n_features = len(initial_obs.keys())
n_features = 28
self._observation_space = Box(low=self._observation_lows,
high=self._observation_highs,
shape=(self.window_size, n_features),
dtype=self._observation_dtype)
self.feed = self.feed.attach(portfolio)
self.renderer_history = []
self.feed.reset()
def __init__(self,
portfolio: 'Portfolio',
feed: 'DataFeed' = None,
renderer_feed: 'DataFeed' = None,
window_size: int = 1,
min_periods: int = None,
**kwargs) -> None:
internal_group = Stream.group(_create_internal_streams(portfolio)).rename("internal")
external_group = Stream.group(feed.inputs).rename("external")
if renderer_feed:
renderer_group = Stream.group(renderer_feed.inputs).rename("renderer")
self.feed = DataFeed([
internal_group,
external_group,
renderer_group
])
else:
self.feed = DataFeed([
internal_group,
external_group
])
self.window_size = window_size
self.min_periods = min_periods
self._observation_dtype = kwargs.get('dtype', np.float32)
self._observation_lows = kwargs.get('observation_lows', -np.inf)
self._observation_highs = kwargs.get('observation_highs', np.inf)
self.history = ObservationHistory(window_size=window_size)
initial_obs = self.feed.next()["external"]
n_features = len(initial_obs.keys())
self._observation_space = Box(
low=self._observation_lows,
high=self._observation_highs,
shape=(self.window_size, n_features),
dtype=self._observation_dtype
)
self.feed = self.feed.attach(portfolio)
self.renderer_history = []
self.feed.reset()
self.warmup()
