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improve default reward, fix bugs in environment

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This commit is contained in:
robcaulk 2022-08-24 18:32:40 +02:00
parent a61821e1c6
commit d1bee29b1e
3 changed files with 102 additions and 53 deletions
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40
freqtrade/freqai/RL/Base5ActionRLEnv.py
View File

@ -140,30 +140,32 @@ class Base5ActionRLEnv(gym.Env):
if action == Actions.Neutral.value:
self._position = Positions.Neutral
trade_type = "neutral"
self._last_trade_tick = None
elif action == Actions.Long_enter.value:
self._position = Positions.Long
trade_type = "long"
self._last_trade_tick = self._current_tick
elif action == Actions.Short_enter.value:
self._position = Positions.Short
trade_type = "short"
self._last_trade_tick = self._current_tick
elif action == Actions.Long_exit.value:
self._position = Positions.Neutral
trade_type = "neutral"
self._last_trade_tick = None
elif action == Actions.Short_exit.value:
self._position = Positions.Neutral
trade_type = "neutral"
self._last_trade_tick = None
else:
print("case not defined")
# Update last trade tick
self._last_trade_tick = self._current_tick
if trade_type is not None:
self.trade_history.append(
{'price': self.current_price(), 'index': self._current_tick,
'type': trade_type})
if self._total_profit < 0.2:
if self._total_profit < 0.5:
self._done = True
self._position_history.append(self._position)
@ -221,8 +223,7 @@ class Base5ActionRLEnv(gym.Env):
def is_tradesignal(self, action: int):
# trade signal
"""
not trade signal is :
Determine if the signal is non sensical
Determine if the signal is a trade signal
e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
"""
return not ((action == Actions.Neutral.value and self._position == Positions.Neutral) or
@ -237,6 +238,24 @@ class Base5ActionRLEnv(gym.Env):
(action == Actions.Long_exit.value and self._position == Positions.Short) or
(action == Actions.Long_exit.value and self._position == Positions.Neutral))
def _is_valid(self, action: int):
# trade signal
"""
Determine if the signal is valid.
e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
"""
# Agent should only try to exit if it is in position
if action in (Actions.Short_exit.value, Actions.Long_exit.value):
if self._position not in (Positions.Short, Positions.Long):
return False
# Agent should only try to enter if it is not in position
if action in (Actions.Short_enter.value, Actions.Long_enter.value):
if self._position != Positions.Neutral:
return False
return True
def _is_trade(self, action: Actions):
return ((action == Actions.Long_enter.value and self._position == Positions.Neutral) or
(action == Actions.Short_enter.value and self._position == Positions.Neutral))
@ -278,13 +297,8 @@ class Base5ActionRLEnv(gym.Env):
if self._is_trade(action) or self._done:
pnl = self.get_unrealized_profit()
if self._position == Positions.Long:
self._total_profit = self._total_profit + self._total_profit * pnl
self._profits.append((self._current_tick, self._total_profit))
self.close_trade_profit.append(pnl)
if self._position == Positions.Short:
self._total_profit = self._total_profit + self._total_profit * pnl
if self._position in (Positions.Long, Positions.Short):
self._total_profit *= (1 + pnl)
self._profits.append((self._current_tick, self._total_profit))
self.close_trade_profit.append(pnl)

61
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@ -19,7 +19,6 @@ from typing import Callable
from datetime import datetime, timezone
from stable_baselines3.common.utils import set_random_seed
import gym
from pathlib import Path
logger = logging.getLogger(__name__)
torch.multiprocessing.set_sharing_strategy('file_system')
@ -112,27 +111,14 @@ class BaseReinforcementLearningModel(IFreqaiModel):
test_df = data_dictionary["test_features"]
eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
# environments
if not self.train_env:
self.train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params, config=self.config)
self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params, config=self.config))
self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
render=False, eval_freq=eval_freq,
best_model_save_path=str(dk.data_path))
else:
self.train_env.reset()
self.eval_env.reset()
self.train_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
self.eval_env.reset_env(test_df, prices_test, self.CONV_WIDTH, self.reward_params)
# self.eval_callback.eval_env = self.eval_env
# self.eval_callback.best_model_save_path = str(dk.data_path)
# self.eval_callback._init_callback()
self.eval_callback.__init__(self.eval_env, deterministic=True,
render=False, eval_freq=eval_freq,
best_model_save_path=str(dk.data_path))
self.train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params, config=self.config)
self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params, config=self.config))
self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
render=False, eval_freq=eval_freq,
best_model_save_path=str(dk.data_path))
@abstractmethod
def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
@ -284,30 +270,43 @@ class MyRLEnv(Base5ActionRLEnv):
def calculate_reward(self, action):
if self._last_trade_tick is None:
return 0.
# first, penalize if the action is not valid
if not self._is_valid(action):
return -15
pnl = self.get_unrealized_profit()
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 100)
rew = np.sign(pnl) * (pnl + 1)
factor = 100
# reward agent for entering trades
if action in (Actions.Long_enter.value, Actions.Short_enter.value):
return 25
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
return -15
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
trade_duration = self._current_tick - self._last_trade_tick
factor = 1
if trade_duration <= max_trade_duration:
factor *= 1.5
elif trade_duration > max_trade_duration:
factor *= 0.5
# discourage sitting in position
if self._position in (Positions.Short, Positions.Long):
return -50 * trade_duration / max_trade_duration
# close long
if action == Actions.Long_exit.value and self._position == Positions.Long:
if self.close_trade_profit and self.close_trade_profit[-1] > self.profit_aim * self.rr:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(pnl * factor)
return float(rew * factor)
# close short
if action == Actions.Short_exit.value and self._position == Positions.Short:
factor = 1
if self.close_trade_profit and self.close_trade_profit[-1] > self.profit_aim * self.rr:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(pnl * factor)
return float(rew * factor)
return 0.

54
freqtrade/freqai/prediction_models/ReinforcementLearner.py
View File

@ -6,6 +6,10 @@ from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
from pathlib import Path
from pandas import DataFrame
from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.monitor import Monitor
import numpy as np
logger = logging.getLogger(__name__)
@ -49,6 +53,25 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
return model
def set_train_and_eval_environments(self, data_dictionary: Dict[str, DataFrame],
prices_train: DataFrame, prices_test: DataFrame,
dk: FreqaiDataKitchen):
"""
User can override this if they are using a custom MyRLEnv
"""
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
self.train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params, config=self.config)
self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params, config=self.config))
self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
render=False, eval_freq=eval_freq,
best_model_save_path=str(dk.data_path))
class MyRLEnv(Base5ActionRLEnv):
"""
@ -58,30 +81,43 @@ class MyRLEnv(Base5ActionRLEnv):
def calculate_reward(self, action):
if self._last_trade_tick is None:
return 0.
# first, penalize if the action is not valid
if not self._is_valid(action):
return -15
pnl = self.get_unrealized_profit()
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 100)
rew = np.sign(pnl) * (pnl + 1)
factor = 100
# reward agent for entering trades
if action in (Actions.Long_enter.value, Actions.Short_enter.value):
return 25
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
return -15
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
trade_duration = self._current_tick - self._last_trade_tick
factor = 1
if trade_duration <= max_trade_duration:
factor *= 1.5
elif trade_duration > max_trade_duration:
factor *= 0.5
# discourage sitting in position
if self._position in (Positions.Short, Positions.Long):
return -50 * trade_duration / max_trade_duration
# close long
if action == Actions.Long_exit.value and self._position == Positions.Long:
if self.close_trade_profit and self.close_trade_profit[-1] > self.profit_aim * self.rr:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(pnl * factor)
return float(rew * factor)
# close short
if action == Actions.Short_exit.value and self._position == Positions.Short:
factor = 1
if self.close_trade_profit and self.close_trade_profit[-1] > self.profit_aim * self.rr:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
return float(pnl * factor)
return float(rew * factor)
return 0.