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add pair to environment for access inside calculate_reward

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robcaulk
2023-02-10 14:45:50 +01:00
parent d47d8c135b
commit 4fc0edb8b7
5 changed files with 48 additions and 25 deletions
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33
freqtrade/freqai/RL/BaseEnvironment.py
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@@ -45,7 +45,7 @@ class BaseEnvironment(gym.Env):
def __init__(self, df: DataFrame = DataFrame(), prices: DataFrame = DataFrame(),
reward_kwargs: dict = {}, window_size=10, starting_point=True,
id: str = 'baseenv-1', seed: int = 1, config: dict = {}, live: bool = False,
fee: float = 0.0015, can_short: bool = False):
fee: float = 0.0015, can_short: bool = False, pair: str = ""):
"""
Initializes the training/eval environment.
:param df: dataframe of features
@@ -60,12 +60,13 @@ class BaseEnvironment(gym.Env):
:param fee: The fee to use for environmental interactions.
:param can_short: Whether or not the environment can short
"""
self.config = config
self.rl_config = config['freqai']['rl_config']
self.add_state_info = self.rl_config.get('add_state_info', False)
self.id = id
self.max_drawdown = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
self.compound_trades = config['stake_amount'] == 'unlimited'
self.config: dict = config
self.rl_config: dict = config['freqai']['rl_config']
self.add_state_info: bool = self.rl_config.get('add_state_info', False)
self.id: str = id
self.max_drawdown: float = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
self.compound_trades: bool = config['stake_amount'] == 'unlimited'
self.pair: str = pair
if self.config.get('fee', None) is not None:
self.fee = self.config['fee']
else:
@@ -74,8 +75,8 @@ class BaseEnvironment(gym.Env):
# set here to default 5Ac, but all children envs can override this
self.actions: Type[Enum] = BaseActions
self.tensorboard_metrics: dict = {}
self.can_short = can_short
self.live = live
self.can_short: bool = can_short
self.live: bool = live
if not self.live and self.add_state_info:
self.add_state_info = False
logger.warning("add_state_info is not available in backtesting. Deactivating.")
@@ -93,13 +94,13 @@ class BaseEnvironment(gym.Env):
:param reward_kwargs: extra config settings assigned by user in `rl_config`
:param starting_point: start at edge of window or not
"""
self.df = df
self.signal_features = self.df
self.prices = prices
self.window_size = window_size
self.starting_point = starting_point
self.rr = reward_kwargs["rr"]
self.profit_aim = reward_kwargs["profit_aim"]
self.df: DataFrame = df
self.signal_features: DataFrame = self.df
self.prices: DataFrame = prices
self.window_size: int = window_size
self.starting_point: bool = starting_point
self.rr: float = reward_kwargs["rr"]
self.profit_aim: float = reward_kwargs["profit_aim"]
# # spaces
if self.add_state_info: