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improve price df handling to enable backtesting

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This commit is contained in:
robcaulk
2022-08-17 12:51:14 +02:00
parent 2080ff86ed
commit b90da46b1b
8 changed files with 77 additions and 59 deletions
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15
freqtrade/freqai/prediction_models/ReinforcementLearningTDQN.py
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@@ -10,6 +10,7 @@ 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
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
@@ -21,7 +22,8 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen):
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']
@@ -30,15 +32,10 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
eval_freq = agent_params["eval_cycles"] * len(test_df)
total_timesteps = agent_params["train_cycles"] * len(train_df)
# price data for model training and evaluation
price = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(train_df.index))
price_test = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(
len(test_df.index))
# environments
train_env = MyRLEnv(df=train_df, prices=price, window_size=self.CONV_WIDTH,
train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
reward_kwargs=reward_params)
eval = MyRLEnv(df=test_df, prices=price_test,
eval = MyRLEnv(df=test_df, prices=prices_test,
window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
eval_env = Monitor(eval, ".")
eval_env.reset()
@@ -66,7 +63,7 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
callback=eval_callback
)
best_model = DQN.load(dk.data_path / "best_model.zip")
best_model = DQN.load(dk.data_path / "best_model")
print('Training finished!')