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Merge pull request #8147 from freqtrade/add-pair-to-env

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Add pair to environment for access inside calculate_reward
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Matthias 2023-02-10 19:38:10 +01:00 committed by GitHub
commit 42c76d9e0c
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4 changed files with 52 additions and 26 deletions
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16
docs/freqai-reinforcement-learning.md
View File

@ -175,10 +175,20 @@ As you begin to modify the strategy and the prediction model, you will quickly r
pnl = self.get_unrealized_profit()
factor = 100
# you can use feature values from dataframe
rsi_now = self.raw_features[f"%-rsi-period-10_shift-1_{self.pair}_"
f"{self.config['timeframe']}"].iloc[self._current_tick]
# reward agent for entering trades
if action in (Actions.Long_enter.value, Actions.Short_enter.value) \
and self._position == Positions.Neutral:
return 25
if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
and self._position == Positions.Neutral):
if rsi_now < 40:
factor = 40 / rsi_now
else:
factor = 1
return 25 * factor
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
return -1

34
freqtrade/freqai/RL/BaseEnvironment.py
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@ -45,7 +45,8 @@ 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 = "",
df_raw: DataFrame = DataFrame()):
"""
Initializes the training/eval environment.
:param df: dataframe of features
@ -60,12 +61,14 @@ 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
self.raw_features: DataFrame = df_raw
if self.config.get('fee', None) is not None:
self.fee = self.config['fee']
else:
@ -74,8 +77,8 @@ 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 +96,12 @@ 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.signal_features: DataFrame = 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:

24
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
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@ -1,3 +1,4 @@
import copy
import importlib
import logging
from abc import abstractmethod
@ -50,6 +51,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
self.eval_callback: Optional[EvalCallback] = None
self.model_type = self.freqai_info['rl_config']['model_type']
self.rl_config = self.freqai_info['rl_config']
self.df_raw: DataFrame = DataFrame()
self.continual_learning = self.freqai_info.get('continual_learning', False)
if self.model_type in SB3_MODELS:
import_str = 'stable_baselines3'
@ -107,6 +109,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
data_dictionary: Dict[str, Any] = dk.make_train_test_datasets(
features_filtered, labels_filtered)
self.df_raw = copy.deepcopy(data_dictionary["train_features"])
dk.fit_labels() # FIXME useless for now, but just satiating append methods
# normalize all data based on train_dataset only
@ -143,7 +146,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
env_info = self.pack_env_dict()
env_info = self.pack_env_dict(dk.pair)
self.train_env = self.MyRLEnv(df=train_df,
prices=prices_train,
@ -158,7 +161,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
actions = self.train_env.get_actions()
self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
def pack_env_dict(self) -> Dict[str, Any]:
def pack_env_dict(self, pair: str) -> Dict[str, Any]:
"""
Create dictionary of environment arguments
"""
@ -166,7 +169,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
"reward_kwargs": self.reward_params,
"config": self.config,
"live": self.live,
"can_short": self.can_short}
"can_short": self.can_short,
"pair": pair,
"df_raw": self.df_raw}
if self.data_provider:
env_info["fee"] = self.data_provider._exchange \
.get_fee(symbol=self.data_provider.current_whitelist()[0]) # type: ignore
@ -347,7 +352,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
sets a custom reward based on profit and trade duration.
"""
def calculate_reward(self, action: int) -> float:
def calculate_reward(self, action: int) -> float: # noqa: C901
"""
An example reward function. This is the one function that users will likely
wish to inject their own creativity into.
@ -363,10 +368,19 @@ class BaseReinforcementLearningModel(IFreqaiModel):
pnl = self.get_unrealized_profit()
factor = 100.
# you can use feature values from dataframe
rsi_now = self.raw_features[f"%-rsi-period-10_shift-1_{self.pair}_"
f"{self.config['timeframe']}"].iloc[self._current_tick]
# reward agent for entering trades
if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
and self._position == Positions.Neutral):
return 25
if rsi_now < 40:
factor = 40 / rsi_now
else:
factor = 1
return 25 * factor
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
return -1

2
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
View File

@ -34,7 +34,7 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
env_info = self.pack_env_dict()
env_info = self.pack_env_dict(dk.pair)
env_id = "train_env"
self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,