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add test coverage, fix bug in base environment. Ensure proper fee is used.

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This commit is contained in:
robcaulk
2022-11-13 15:31:37 +01:00
parent 81f800a79b
commit af9e400562
4 changed files with 92 additions and 29 deletions
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34
freqtrade/freqai/RL/BaseEnvironment.py
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@@ -10,6 +10,8 @@ from gym import spaces
from gym.utils import seeding
from pandas import DataFrame
from freqtrade.data.dataprovider import DataProvider
logger = logging.getLogger(__name__)
@@ -32,8 +34,21 @@ 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 = {}):
id: str = 'baseenv-1', seed: int = 1, config: dict = {},
dp: Optional[DataProvider] = None):
"""
Initializes the training/eval environment.
:param df: dataframe of features
:param prices: dataframe of prices to be used in the training environment
:param window_size: size of window (temporal) to pass to the agent
:param reward_kwargs: extra config settings assigned by user in `rl_config`
:param starting_point: start at edge of window or not
:param id: string id of the environment (used in backend for multiprocessed env)
:param seed: Sets the seed of the environment higher in the gym.Env object
:param config: Typical user configuration file
:param dp: dataprovider from freqtrade
"""
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
@@ -41,12 +56,23 @@ class BaseEnvironment(gym.Env):
self.reset_env(df, prices, window_size, reward_kwargs, starting_point)
self.max_drawdown = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
self.compound_trades = config['stake_amount'] == 'unlimited'
if self.config.get('fee', None) is not None:
self.fee = self.config['fee']
elif dp is not None:
self.fee = self.dp.exchange.get_fee(symbol=dp.current_whitelist()[0])
else:
self.fee = 0.0015
def reset_env(self, df: DataFrame, prices: DataFrame, window_size: int,
reward_kwargs: dict, starting_point=True):
"""
Resets the environment when the agent fails (in our case, if the drawdown
exceeds the user set max_training_drawdown_pct)
:param df: dataframe of features
:param prices: dataframe of prices to be used in the training environment
:param window_size: size of window (temporal) to pass to the agent
: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
@@ -56,8 +82,6 @@ class BaseEnvironment(gym.Env):
self.rr = reward_kwargs["rr"]
self.profit_aim = reward_kwargs["profit_aim"]
self.fee = 0.0015
# # spaces
if self.add_state_info:
self.total_features = self.signal_features.shape[1] + 3
@@ -233,7 +257,7 @@ class BaseEnvironment(gym.Env):
def _update_total_profit(self):
pnl = self.get_unrealized_profit()
if self.compound_trades:
# assumes unitestake and compounding
# assumes unit stake and compounding
self._total_profit = self._total_profit * (1 + pnl)
else:
# assumes unit stake and no compounding