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persist a single training environment.

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This commit is contained in:
robcaulk 2022-08-18 16:07:19 +02:00
parent 5d4e5e69fe
commit f95602f6bd
6 changed files with 162 additions and 129 deletions
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2
config_examples/config_freqai-rl.example.json
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@ -61,7 +61,7 @@
"train_period_days": 10,
"backtest_period_days": 2,
"identifier": "unique-id",
"data_kitchen_thread_count": 4,
"data_kitchen_thread_count": 2,
"feature_parameters": {
"include_corr_pairlist": [
"BTC/USDT",

12
freqtrade/freqai/RL/Base5ActionRLEnv.py
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@ -7,7 +7,7 @@ import numpy as np
from gym import spaces
from gym.utils import seeding
from pandas import DataFrame
import pandas as pd
logger = logging.getLogger(__name__)
@ -47,6 +47,9 @@ class Base5ActionRLEnv(gym.Env):
self.id = id
self.seed(seed)
self.reset_env(df, prices, window_size, reward_kwargs, starting_point)
def reset_env(self, df, prices, window_size, reward_kwargs, starting_point=True):
self.df = df
self.signal_features = self.df
self.prices = prices
@ -178,10 +181,15 @@ class Base5ActionRLEnv(gym.Env):
return observation, step_reward, self._done, info
def _get_observation(self):
features_and_state = self.signal_features[(
features_window = self.signal_features[(
self._current_tick - self.window_size):self._current_tick]
features_and_state = DataFrame(np.zeros((len(features_window), 2)),
columns=['current_profit_pct', 'position'],
index=features_window.index)
features_and_state['current_profit_pct'] = self.get_unrealized_profit()
features_and_state['position'] = self._position.value
features_and_state = pd.concat([features_window, features_and_state], axis=1)
return features_and_state
def get_unrealized_profit(self):

115
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
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@ -8,9 +8,10 @@ from pandas import DataFrame
from abc import abstractmethod
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.freqai_interface import IFreqaiModel
from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
from freqtrade.freqai.RL.Base5ActionRLEnv import Base5ActionRLEnv, Actions, Positions
from freqtrade.persistence import Trade
import torch.multiprocessing
from stable_baselines3.common.monitor import Monitor
import torch as th
logger = logging.getLogger(__name__)
@ -26,6 +27,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
super().__init__(config=kwargs['config'])
th.set_num_threads(self.freqai_info.get('data_kitchen_thread_count', 4))
self.reward_params = self.freqai_info['rl_config']['model_reward_parameters']
self.train_env: Base5ActionRLEnv = None
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
@ -65,15 +67,37 @@ class BaseReinforcementLearningModel(IFreqaiModel):
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
model = self.fit_rl(data_dictionary, pair, dk, prices_train, prices_test)
self.set_train_and_eval_environments(data_dictionary, prices_train, prices_test)
model = self.fit_rl(data_dictionary, dk)
logger.info(f"--------------------done training {pair}--------------------")
return model
def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test):
"""
User overrides this in their prediction model if they are custom a MyRLEnv. Othwerwise
leaving this will default to Base5ActEnv
"""
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
# 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)
self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params), ".")
else:
self.train_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
self.eval_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
self.train_env.reset()
self.eval_env.reset()
@abstractmethod
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen,
prices_train: DataFrame, prices_test: DataFrame):
def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
"""
Agent customizations and abstract Reinforcement Learning customizations
go in here. Abstract method, so this function must be overridden by
@ -193,66 +217,39 @@ class BaseReinforcementLearningModel(IFreqaiModel):
return
class MyRLEnv(Base3ActionRLEnv):
def step(self, action):
self._done = False
self._current_tick += 1
if self._current_tick == self._end_tick:
self._done = True
self.update_portfolio_log_returns(action)
self._update_profit(action)
step_reward = self._calculate_reward(action)
self.total_reward += step_reward
trade_type = None
if self.is_tradesignal(action): # exclude 3 case not trade
# Update position
class MyRLEnv(Base5ActionRLEnv):
"""
Action: Neutral, position: Long -> Close Long
Action: Neutral, position: Short -> Close Short
Action: Long, position: Neutral -> Open Long
Action: Long, position: Short -> Close Short and Open Long
Action: Short, position: Neutral -> Open Short
Action: Short, position: Long -> Close Long and Open Short
User can override any function in BaseRLEnv and gym.Env. Here the user
Adds 5 actions.
"""
if action == Actions.Neutral.value:
self._position = Positions.Neutral
trade_type = "neutral"
elif action == Actions.Long.value:
self._position = Positions.Long
trade_type = "long"
elif action == Actions.Short.value:
self._position = Positions.Short
trade_type = "short"
else:
print("case not defined")
def calculate_reward(self, action):
# Update last trade tick
self._last_trade_tick = self._current_tick
if self._last_trade_tick is None:
return 0.
if trade_type is not None:
self.trade_history.append(
{'price': self.current_price(), 'index': self._current_tick,
'type': trade_type})
# close long
if action == Actions.Long_sell.value and self._position == Positions.Long:
last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
return float(np.log(current_price) - np.log(last_trade_price))
if self._total_profit < 0.2:
self._done = True
if action == Actions.Long_sell.value and self._position == Positions.Long:
if self.close_trade_profit[-1] > self.profit_aim * self.rr:
last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
return float((np.log(current_price) - np.log(last_trade_price)) * 2)
self._position_history.append(self._position)
observation = self._get_observation()
info = dict(
tick=self._current_tick,
total_reward=self.total_reward,
total_profit=self._total_profit,
position=self._position.value
)
self._update_history(info)
# close short
if action == Actions.Short_buy.value and self._position == Positions.Short:
last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
return float(np.log(last_trade_price) - np.log(current_price))
return observation, step_reward, self._done, info
if action == Actions.Short_buy.value and self._position == Positions.Short:
if self.close_trade_profit[-1] > self.profit_aim * self.rr:
last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
return float((np.log(last_trade_price) - np.log(current_price)) * 2)
return 0.

49
freqtrade/freqai/prediction_models/ReinforcementLearningPPO_multiproc.py
View File

@ -3,9 +3,7 @@ from typing import Any, Dict # , Tuple
import numpy as np
# import numpy.typing as npt
# import pandas as pd
import torch as th
# from pandas import DataFrame
from stable_baselines3.common.monitor import Monitor
from typing import Callable
from stable_baselines3 import PPO
@ -16,7 +14,6 @@ from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Posi
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
import gym
from pandas import DataFrame
logger = logging.getLogger(__name__)
@ -48,26 +45,15 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen,
prices_train: DataFrame, prices_test: DataFrame):
def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
env_id = "train_env"
num_cpu = int(dk.thread_count / 2)
train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train,
self.reward_params, self.CONV_WIDTH) for i in range(num_cpu)])
eval_env_id = 'eval_env'
eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, prices_test,
self.reward_params, self.CONV_WIDTH, monitor=True) for i in
range(num_cpu)])
path = dk.data_path
eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
eval_callback = EvalCallback(self.eval_env, best_model_save_path=f"{path}/",
log_path=f"{path}/ppo/logs/", eval_freq=int(eval_freq),
deterministic=True, render=False)
@ -75,7 +61,7 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
policy_kwargs = dict(activation_fn=th.nn.ReLU,
net_arch=[512, 512, 512])
model = PPO('MlpPolicy', train_env, policy_kwargs=policy_kwargs,
model = PPO('MlpPolicy', self.train_env, policy_kwargs=policy_kwargs,
tensorboard_log=f"{path}/ppo/tensorboard/",
**self.freqai_info['model_training_parameters']
)
@ -87,10 +73,37 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
best_model = PPO.load(dk.data_path / "best_model")
print('Training finished!')
eval_env.close()
return best_model
def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test):
"""
User overrides this in their prediction model if they are custom a MyRLEnv. Othwerwise
leaving this will default to Base5ActEnv
"""
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
# environments
if not self.train_env:
env_id = "train_env"
num_cpu = int(self.freqai_info["data_kitchen_thread_count"] / 2)
self.train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train,
self.reward_params, self.CONV_WIDTH) for i
in range(num_cpu)])
eval_env_id = 'eval_env'
self.eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, prices_test,
self.reward_params, self.CONV_WIDTH, monitor=True) for i
in range(num_cpu)])
else:
self.train_env.env_method('reset_env', train_df, prices_train,
self.CONV_WIDTH, self.reward_params)
self.eval_env.env_method('reset_env', train_df, prices_train,
self.CONV_WIDTH, self.reward_params)
self.train_env.env_method('reset')
self.eval_env.env_method('reset')
class MyRLEnv(Base3ActionRLEnv):
"""

41
freqtrade/freqai/prediction_models/ReinforcementLearningTDQN.py
View File

@ -9,8 +9,7 @@ from freqtrade.freqai.RL.TDQNagent import TDQN
from stable_baselines3 import DQN
from stable_baselines3.common.buffers import ReplayBuffer
import numpy as np
from pandas import DataFrame
import gc
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@ -21,24 +20,15 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen,
prices_train: DataFrame, prices_test: DataFrame):
def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
# environments
train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params)
eval = MyRLEnv(df=test_df, prices=prices_test,
window_size=self.CONV_WIDTH, reward_kwargs=self.reward_params)
eval_env = Monitor(eval, ".")
eval_env.reset()
path = dk.data_path
eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
eval_callback = EvalCallback(self.eval_env, best_model_save_path=f"{path}/",
log_path=f"{path}/tdqn/logs/", eval_freq=int(eval_freq),
deterministic=True, render=False)
@ -46,7 +36,7 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
policy_kwargs = dict(activation_fn=th.nn.ReLU,
net_arch=[256, 256, 128])
model = TDQN('TMultiInputPolicy', train_env,
model = TDQN('TMultiInputPolicy', self.train_env,
tensorboard_log=f"{path}/tdqn/tensorboard/",
policy_kwargs=policy_kwargs,
replay_buffer_class=ReplayBuffer,
@ -58,12 +48,33 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
callback=eval_callback
)
del model
best_model = DQN.load(dk.data_path / "best_model")
print('Training finished!')
gc.collect()
return best_model
def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test):
"""
User overrides this as shown here if they are using a custom MyRLEnv
"""
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
# 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)
self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params), ".")
else:
self.train_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
self.eval_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
self.train_env.reset()
self.eval_env.reset()
# User can inherit and customize 5 action environment
class MyRLEnv(Base5ActionRLEnv):

70
freqtrade/freqai/prediction_models/ReinforcementLearningTDQN_multiproc.py
View File

@ -4,8 +4,8 @@ import torch as th
import numpy as np
import gym
from typing import Callable
from stable_baselines3.common.callbacks import (
EvalCallback, StopTrainingOnNoModelImprovement, StopTrainingOnRewardThreshold)
from stable_baselines3.common.callbacks import EvalCallback
# EvalCallback , StopTrainingOnNoModelImprovement, StopTrainingOnRewardThreshold
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.vec_env import SubprocVecEnv
from stable_baselines3.common.utils import set_random_seed
@ -15,7 +15,6 @@ from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcement
from freqtrade.freqai.RL.TDQNagent import TDQN
from stable_baselines3.common.buffers import ReplayBuffer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from pandas import DataFrame
logger = logging.getLogger(__name__)
@ -47,46 +46,23 @@ class ReinforcementLearningTDQN_multiproc(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen,
prices_train: DataFrame, prices_test: DataFrame):
def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
env_id = "train_env"
num_cpu = int(dk.thread_count / 2)
train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train,
self.reward_params, self.CONV_WIDTH) for i in range(num_cpu)])
eval_env_id = 'eval_env'
eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, prices_test,
self.reward_params, self.CONV_WIDTH, monitor=True) for i in
range(num_cpu)])
path = dk.data_path
stop_train_callback = StopTrainingOnNoModelImprovement(
max_no_improvement_evals=5,
min_evals=10,
verbose=2
)
callback_on_best = StopTrainingOnRewardThreshold(reward_threshold=-200, verbose=2)
eval_callback = EvalCallback(
eval_env, best_model_save_path=f"{path}/",
log_path=f"{path}/tdqn/logs/",
eval_freq=int(eval_freq),
deterministic=True,
render=True,
callback_after_eval=stop_train_callback,
callback_on_new_best=callback_on_best,
verbose=2
)
eval_callback = EvalCallback(self.eval_env, best_model_save_path=f"{path}/",
log_path=f"{path}/tdqn/logs/", eval_freq=int(eval_freq),
deterministic=True, render=False)
# model arch
policy_kwargs = dict(activation_fn=th.nn.ReLU,
net_arch=[512, 512, 512])
model = TDQN('TMultiInputPolicy', train_env,
model = TDQN('TMultiInputPolicy', self.train_env,
policy_kwargs=policy_kwargs,
tensorboard_log=f"{path}/tdqn/tensorboard/",
replay_buffer_class=ReplayBuffer,
@ -100,12 +76,40 @@ class ReinforcementLearningTDQN_multiproc(BaseReinforcementLearningModel):
best_model = DQN.load(dk.data_path / "best_model.zip")
print('Training finished!')
eval_env.close()
return best_model
def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test):
"""
User overrides this in their prediction model if they are custom a MyRLEnv. Othwerwise
leaving this will default to Base5ActEnv
"""
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
# environments
if not self.train_env:
env_id = "train_env"
num_cpu = int(self.freqai_info["data_kitchen_thread_count"] / 2)
self.train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train,
self.reward_params, self.CONV_WIDTH) for i
in range(num_cpu)])
eval_env_id = 'eval_env'
self.eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, prices_test,
self.reward_params, self.CONV_WIDTH, monitor=True) for i
in range(num_cpu)])
else:
self.train_env.env_method('reset_env', train_df, prices_train,
self.CONV_WIDTH, self.reward_params)
self.eval_env.env_method('reset_env', train_df, prices_train,
self.CONV_WIDTH, self.reward_params)
self.train_env.env_method('reset')
self.eval_env.env_method('reset')
# User can inherit and customize 5 action environment
class MyRLEnv(Base5ActionRLEnv):
"""
User can override any function in BaseRLEnv and gym.Env. Here the user