callback function and TDQN model added
This commit is contained in:
MukavaValkku
robcaulk
parent
01232e9a1f
commit
cd3fe44424
@ -5,14 +5,23 @@ import numpy as np
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import numpy.typing as npt
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import pandas as pd
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from pandas import DataFrame
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from stable_baselines.common.callbacks import CallbackList, CheckpointCallback, EvalCallback
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.freqai_interface import IFreqaiModel
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from freqtrade.freqai.prediction_models.RL.RLPrediction_agent import RLPrediction_agent
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from freqtrade.freqai.prediction_models.RL.RLPrediction_agent_v2 import TDQN
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#from freqtrade.freqai.prediction_models.RL.RLPrediction_env import GymAnytrading
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from freqtrade.freqai.prediction_models.RL.RLPrediction_env import DEnv
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from freqtrade.persistence import Trade
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from stable_baselines3.common.vec_env import SubprocVecEnv
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from stable_baselines3.common.monitor import Monitor
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import torch as th
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from stable_baselines3.common.callbacks import CallbackList, CheckpointCallback, EvalCallback, StopTrainingOnRewardThreshold
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from stable_baselines3.common.buffers import ReplayBuffer
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from stable_baselines3 import PPO
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logger = logging.getLogger(__name__)
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@ -74,47 +83,127 @@ class ReinforcementLearningModel(IFreqaiModel):
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def fit(self, data_dictionary: Dict[str, Any], pair: str = ''):
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train_df = data_dictionary["train_features"]
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# train_labels = data_dictionary["train_labels"]
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test_df = data_dictionary["test_features"]
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# test_labels = data_dictionary["test_labels"]
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# train_df = data_dictionary["train_features"]
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# # train_labels = data_dictionary["train_labels"]
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# test_df = data_dictionary["test_features"]
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# # test_labels = data_dictionary["test_labels"]
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# sep = '/'
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# coin = pair.split(sep, 1)[0]
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# price = train_df[f"%-{coin}raw_price_{self.config['timeframe']}"]
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# price.reset_index(inplace=True, drop=True)
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# price = price.to_frame()
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price = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(train_df.index))
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# # sep = '/'
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# # coin = pair.split(sep, 1)[0]
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# # price = train_df[f"%-{coin}raw_price_{self.config['timeframe']}"]
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# # price.reset_index(inplace=True, drop=True)
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# # price = price.to_frame()
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# price = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(train_df.index))
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# price_test = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(test_df.index))
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# #train_env = GymAnytrading(train_df, price, self.CONV_WIDTH)
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# agent_params = self.freqai_info['model_training_parameters']
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# reward_params = self.freqai_info['model_reward_parameters']
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model_name = 'ppo'
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# train_env = DEnv(df=train_df, prices=price, window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
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# #eval_env = DEnv(df=test_df, prices=price_test, window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
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#env_instance = GymAnytrading(train_df, price, self.CONV_WIDTH)
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# #env_instance = SubprocVecEnv([DEnv(df=train_df, prices=price, window_size=self.CONV_WIDTH, reward_kwargs=reward_params)])
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# #train_env.reset()
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# #eval_env.reset()
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# # model
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# #policy_kwargs = dict(net_arch=[512, 512, 512])
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# policy_kwargs = dict(activation_fn=th.nn.Tanh,
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# net_arch=[256, 256, 256])
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# agent = RLPrediction_agent(train_env)
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# #eval_agent = RLPrediction_agent(eval_env)
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# # PPO
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# model_name = 'ppo'
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# model = agent.get_model(model_name, model_kwargs=agent_params, policy_kwargs=policy_kwargs)
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# trained_model = agent.train_model(model=model,
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# tb_log_name=model_name,
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# model_kwargs=agent_params,
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# train_df=train_df,
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# test_df=test_df,
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# price=price,
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# price_test=price_test,
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# window_size=self.CONV_WIDTH)
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# # best_model = eval_agent.train_model(model=model,
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# # tb_log_name=model_name,
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# # model_kwargs=agent_params,
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# # eval=eval_env)
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# # TDQN
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# # model_name = 'TDQN'
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# # model = TDQN('TMultiInputPolicy', train_env, policy_kwargs=policy_kwargs, tensorboard_log='./tensorboard_log/',
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# # learning_rate=agent_params["learning_rate"], gamma=0.9,
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# # target_update_interval=5000, buffer_size=50000,
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# # exploration_initial_eps=1, exploration_final_eps=0.1,
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# # replay_buffer_class=ReplayBuffer
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# # )
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# # trained_model = agent.train_model(model=model,
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# # tb_log_name=model_name,
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# # model_kwargs=agent_params)
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# #model.learn(
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# # total_timesteps=5000,
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# # callback=callback
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# # )
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agent_params = self.freqai_info['model_training_parameters']
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reward_params = self.freqai_info['model_reward_parameters']
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train_df = data_dictionary["train_features"]
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test_df = data_dictionary["test_features"]
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env_instance = DEnv(df=train_df, prices=price, window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
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agent = RLPrediction_agent(env_instance)
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# price data for model training and evaluation
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price = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(train_df.index))
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price_test = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(test_df.index))
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# checkpoint_callback = CheckpointCallback(save_freq=1000, save_path='./logs/')
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# eval_callback = EvalCallback(test_df, best_model_save_path='./models/',
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# log_path='./logs/', eval_freq=10000,
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# deterministic=True, render=False)
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# environments
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train_env = DEnv(df=train_df, prices=price, window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
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eval = DEnv(df=test_df, prices=price_test, window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
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eval_env = Monitor(eval, ".")
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eval_env.reset()
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# #Create the callback list
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# callback = CallbackList([checkpoint_callback, eval_callback])
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# this should be in config - TODO
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agent_type = 'tdqn'
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model = agent.get_model(model_name, model_kwargs=agent_params)
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trained_model = agent.train_model(model=model,
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tb_log_name=model_name,
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model_kwargs=agent_params)
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#eval_callback=callback)
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path = self.dk.data_path
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eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
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log_path=f"{path}/{agent_type}/logs/", eval_freq=10000,
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deterministic=True, render=False)
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# model arch
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policy_kwargs = dict(activation_fn=th.nn.Tanh,
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net_arch=[512, 512, 512])
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if agent_type == 'tdqn':
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model = TDQN('TMultiInputPolicy', train_env, policy_kwargs=policy_kwargs, tensorboard_log=f"{path}/{agent_type}/tensorboard/",
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learning_rate=0.00025, gamma=0.9,
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target_update_interval=5000, buffer_size=50000,
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exploration_initial_eps=1, exploration_final_eps=0.1,
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replay_buffer_class=ReplayBuffer
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)
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elif agent_type == 'ppo':
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model = PPO('MultiInputPolicy', train_env, policy_kwargs=policy_kwargs, tensorboard_log=f"{path}/{agent_type}/tensorboard/",
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learning_rate=0.00025, gamma=0.9
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)
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model.learn(
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total_timesteps=agent_params["total_timesteps"],
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callback=eval_callback
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)
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print('Training finished!')
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return trained_model
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return model
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def get_state_info(self, pair):
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open_trades = Trade.get_trades(trade_filter=Trade.is_open.is_(True))
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