def __init__(self, mode, initial_invest, session_name, brain, stock):
"""
Initializes a Live_Session.
Args:
mode (string): Whether training, finetuning, or testing.
initial_invest (int): Starting budget.
brain (stable_baselines.ppo2.PPO2 model): Model to use.
stock (string): Ticker representing a chosen stock.
Returns:
None
"""
self.session_name = session_name
self.mode = mode
self.initial_invest = initial_invest
self.portfolio_value = []
self.test_ep_rewards = []
self.actions = generate_actions()
self.timestamp = dir_setup(mode)
self.env = None
self.scaler = joblib.load('saved_scaler.pkl')
self.stock = stock
self.logger = init_logger(__name__, show_debug=True)
self.brain = PPO2.load(brain)
coloredlogs.install(level='TEST', logger=self.logger)
self.logger.info("Bot is live: [{}]".format(datetime.datetime.now()))
def __init__(self, data, init_invest=1000, mode="test"):
"""
Initializes a SimulatedEnv.
Args:
data (pandas.DataFrame): DataFrame consisting of OHLC data of the user's choice.
init_invest (int): Starting budget. Defaults to 1000.
mode (string): Whether we are training, finetuning, or testing the model. Defaults to 'test'.
Returns:
None
"""
super(SimulatedEnv, self).__init__()
self.dataset = data
self.mode = mode
self.n_steps = 180
self.finish_step = 0
self.init_invest = init_invest
self.cur_step = None
self.current_state = []
self.owned_stocks = None
self.cash_in_hand = None
self.scaler = get_scaler(mode, self.dataset)
self.actions = generate_actions()
self.action_space = gym.spaces.Discrete(30)
self.n_features = data.shape[1]
self.obs_shape = (1, self.n_features)
self.observation_space = gym.spaces.Box(low=0,
high=1,
shape=self.obs_shape,
dtype=np.float16)
self.reset()
def __init__(self,
mode,
test_episodes,
initial_invest,
session_name,
stock=None,
brain=None):
"""
Initializes a Static_Session.
Args:
mode (string): Whether training, finetuning, or testing.
test_episodes (int): How many episodes to test the model for.
initial_invest (int): Starting budget.
stock (string): Ticker representing a chosen stock. Defaults to None.
brain (stable_baselines model): Model to use. Defaults to None.
Returns:
None
"""
# SESSION_VARIABLES
self.session_name = session_name
self.mode = mode
self.test_episodes = test_episodes
self.initial_invest = initial_invest
self.portfolio_value = []
self.test_ep_rewards = []
self.losses = 0
self.actions = generate_actions()
self.timestamp = dir_setup(mode)
self.env = None
self.brain = brain
self.n_steps_per_eval = 10000000
self.logger = init_logger(__name__, show_debug=True)
self.stock = stock
coloredlogs.install(level='TEST')
self.train_ds, self.val_ds, self.test_ds = \
train_val_test_split(TOTAL_DATA_PATH)
self.optuna_study = optuna.create_study(
study_name="{}_study".format(self.session_name),
storage="sqlite:///data/params.db",
load_if_exists=True,
pruner=optuna.pruners.MedianPruner())
self.logger.debug(f'Initialized Static Session: {self.session_name}')
self.logger.debug(f'Mode: {self.mode}')
def __init__(self, mode, initial_invest, session_name, brain, stock):
self.session_name = session_name
self.mode = mode
self.initial_invest = initial_invest
self.portfolio_value = []
self.test_ep_rewards = []
self.actions = generate_actions()
self.timestamp = dir_setup(mode)
self.env = None
self.scaler = joblib.load('saved_scaler.pkl')
self.stock = stock
self.logger = init_logger(__name__, show_debug=True)
self.brain = PPO2.load(brain)
coloredlogs.install(level='TEST', logger=self.logger)
n_features = 22
state_size = n_features
action_size = len(ACTIONS) * 10
self.logger.info("Bot is live: [{}]".format(datetime.datetime.now()))
def __init__(self, data, init_invest=1000, mode="test"):
super(SimulatedEnv, self).__init__()
self.dataset = data
self.mode = mode
self.n_steps = 180
self.finish_step = 0
self.init_invest = init_invest
self.cur_step = None
self.current_state = []
self.owned_stocks = None
self.cash_in_hand = None
self.scaler = get_scaler(mode, self.dataset)
self.actions = generate_actions()
self.action_space = gym.spaces.Discrete(30)
self.n_features = data.shape[1]
self.obs_shape = (1, self.n_features)
self.observation_space = gym.spaces.Box(low=0,
high=1,
shape=self.obs_shape,
dtype=np.float16)
self.reset()