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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
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4 changed files with 52 additions and 26 deletions
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16
docs/freqai-reinforcement-learning.md
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@ -175,10 +175,20 @@ As you begin to modify the strategy and the prediction model, you will quickly r
pnl = self.get_unrealized_profit() pnl = self.get_unrealized_profit()
factor = 100 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 # reward agent for entering trades
if action in (Actions.Long_enter.value, Actions.Short_enter.value) \ if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
and self._position == Positions.Neutral: 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 # discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral: if action == Actions.Neutral.value and self._position == Positions.Neutral:
return -1 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(), def __init__(self, df: DataFrame = DataFrame(), prices: DataFrame = DataFrame(),
reward_kwargs: dict = {}, window_size=10, starting_point=True, reward_kwargs: dict = {}, window_size=10, starting_point=True,
id: str = 'baseenv-1', seed: int = 1, config: dict = {}, live: bool = False, 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. Initializes the training/eval environment.
:param df: dataframe of features :param df: dataframe of features
@ -60,12 +61,14 @@ class BaseEnvironment(gym.Env):
:param fee: The fee to use for environmental interactions. :param fee: The fee to use for environmental interactions.
:param can_short: Whether or not the environment can short :param can_short: Whether or not the environment can short
""" """
self.config = config self.config: dict = config
self.rl_config = config['freqai']['rl_config'] self.rl_config: dict = config['freqai']['rl_config']
self.add_state_info = self.rl_config.get('add_state_info', False) self.add_state_info: bool = self.rl_config.get('add_state_info', False)
self.id = id self.id: str = id
self.max_drawdown = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8) self.max_drawdown: float = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
self.compound_trades = config['stake_amount'] == 'unlimited' 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: if self.config.get('fee', None) is not None:
self.fee = self.config['fee'] self.fee = self.config['fee']
else: else:
@ -74,8 +77,8 @@ class BaseEnvironment(gym.Env):
# set here to default 5Ac, but all children envs can override this # set here to default 5Ac, but all children envs can override this
self.actions: Type[Enum] = BaseActions self.actions: Type[Enum] = BaseActions
self.tensorboard_metrics: dict = {} self.tensorboard_metrics: dict = {}
self.can_short = can_short self.can_short: bool = can_short
self.live = live self.live: bool = live
if not self.live and self.add_state_info: if not self.live and self.add_state_info:
self.add_state_info = False self.add_state_info = False
logger.warning("add_state_info is not available in backtesting. Deactivating.") 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 reward_kwargs: extra config settings assigned by user in `rl_config`
:param starting_point: start at edge of window or not :param starting_point: start at edge of window or not
""" """
self.df = df self.signal_features: DataFrame = df
self.signal_features = self.df self.prices: DataFrame = prices
self.prices = prices self.window_size: int = window_size
self.window_size = window_size self.starting_point: bool = starting_point
self.starting_point = starting_point self.rr: float = reward_kwargs["rr"]
self.rr = reward_kwargs["rr"] self.profit_aim: float = reward_kwargs["profit_aim"]
self.profit_aim = reward_kwargs["profit_aim"]
# # spaces # # spaces
if self.add_state_info: if self.add_state_info:

24
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
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@ -1,3 +1,4 @@
import copy
import importlib import importlib
import logging import logging
from abc import abstractmethod from abc import abstractmethod
@ -50,6 +51,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
self.eval_callback: Optional[EvalCallback] = None self.eval_callback: Optional[EvalCallback] = None
self.model_type = self.freqai_info['rl_config']['model_type'] self.model_type = self.freqai_info['rl_config']['model_type']
self.rl_config = self.freqai_info['rl_config'] self.rl_config = self.freqai_info['rl_config']
self.df_raw: DataFrame = DataFrame()
self.continual_learning = self.freqai_info.get('continual_learning', False) self.continual_learning = self.freqai_info.get('continual_learning', False)
if self.model_type in SB3_MODELS: if self.model_type in SB3_MODELS:
import_str = 'stable_baselines3' import_str = 'stable_baselines3'
@ -107,6 +109,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
data_dictionary: Dict[str, Any] = dk.make_train_test_datasets( data_dictionary: Dict[str, Any] = dk.make_train_test_datasets(
features_filtered, labels_filtered) 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 dk.fit_labels() # FIXME useless for now, but just satiating append methods
# normalize all data based on train_dataset only # normalize all data based on train_dataset only
@ -143,7 +146,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
train_df = data_dictionary["train_features"] train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_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, self.train_env = self.MyRLEnv(df=train_df,
prices=prices_train, prices=prices_train,
@ -158,7 +161,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
actions = self.train_env.get_actions() actions = self.train_env.get_actions()
self.tensorboard_callback = TensorboardCallback(verbose=1, actions=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 Create dictionary of environment arguments
""" """
@ -166,7 +169,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
"reward_kwargs": self.reward_params, "reward_kwargs": self.reward_params,
"config": self.config, "config": self.config,
"live": self.live, "live": self.live,
"can_short": self.can_short} "can_short": self.can_short,
"pair": pair,
"df_raw": self.df_raw}
if self.data_provider: if self.data_provider:
env_info["fee"] = self.data_provider._exchange \ env_info["fee"] = self.data_provider._exchange \
.get_fee(symbol=self.data_provider.current_whitelist()[0]) # type: ignore .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. 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 An example reward function. This is the one function that users will likely
wish to inject their own creativity into. wish to inject their own creativity into.
@ -363,10 +368,19 @@ class BaseReinforcementLearningModel(IFreqaiModel):
pnl = self.get_unrealized_profit() pnl = self.get_unrealized_profit()
factor = 100. 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 # reward agent for entering trades
if (action in (Actions.Long_enter.value, Actions.Short_enter.value) if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
and self._position == Positions.Neutral): 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 # discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral: if action == Actions.Neutral.value and self._position == Positions.Neutral:
return -1 return -1

2
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
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@ -34,7 +34,7 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
train_df = data_dictionary["train_features"] train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_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" env_id = "train_env"
self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1, self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,