import logging
from typing import Any, Dict # Optional
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.monitor import Monitor
from stable_baselines3.common.vec_env import SubprocVecEnv
from stable_baselines3.common.utils import set_random_seed
from stable_baselines3 import DQN
from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
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__)
def make_env(env_id: str, rank: int, seed: int, train_df, price,
reward_params, window_size, monitor=False) -> Callable:
"""
Utility function for multiprocessed env.
:param env_id: (str) the environment ID
:param num_env: (int) the number of environment you wish to have in subprocesses
:param seed: (int) the inital seed for RNG
:param rank: (int) index of the subprocess
:return: (Callable)
"""
def _init() -> gym.Env:
env = MyRLEnv(df=train_df, prices=price, window_size=window_size,
reward_kwargs=reward_params, id=env_id, seed=seed + rank)
if monitor:
env = Monitor(env, ".")
return env
set_random_seed(seed)
return _init
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):
agent_params = self.freqai_info['model_training_parameters']
reward_params = self.freqai_info['model_reward_parameters']
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
eval_freq = agent_params["eval_cycles"] * len(test_df)
total_timesteps = agent_params["train_cycles"] * len(train_df)
learning_rate = agent_params["learning_rate"]
env_id = "train_env"
num_cpu = int(dk.thread_count / 2)
train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train, 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, 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
)
# model arch
policy_kwargs = dict(activation_fn=th.nn.ReLU,
net_arch=[512, 512, 512])
model = TDQN('TMultiInputPolicy', train_env,
policy_kwargs=policy_kwargs,
tensorboard_log=f"{path}/tdqn/tensorboard/",
learning_rate=learning_rate, gamma=0.9,
target_update_interval=5000, buffer_size=50000,
exploration_initial_eps=1, exploration_final_eps=0.1,
replay_buffer_class=ReplayBuffer
)
model.learn(
total_timesteps=int(total_timesteps),
callback=eval_callback
)
best_model = DQN.load(dk.data_path / "best_model.zip")
print('Training finished!')
eval_env.close()
return best_model
class MyRLEnv(Base3ActionRLEnv):
"""
User can override any function in BaseRLEnv and gym.Env
"""
def calculate_reward(self, action):
if self._last_trade_tick is None:
return 0.
# close long
if (action == Actions.Short.value or
action == Actions.Neutral.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))
# close short
if (action == Actions.Long.value or
action == Actions.Neutral.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 0.
# User can inherit and customize 5 action environment
# class MyRLEnv(Base5ActionRLEnv):
# """
# User can override any function in BaseRLEnv and gym.Env. Here the user
# Adds 5 actions.
# """
# def calculate_reward(self, action):
# if self._last_trade_tick is None:
# return 0.
# # 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 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)
# # 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))
# 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.