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add test coverage, fix bug in base environment. Ensure proper fee is used.

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This commit is contained in:
robcaulk
2022-11-13 15:31:37 +01:00
parent 81f800a79b
commit af9e400562
4 changed files with 92 additions and 29 deletions
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37
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
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@@ -74,10 +74,10 @@ class BaseReinforcementLearningModel(IFreqaiModel):
self.ft_params.update({'use_SVM_to_remove_outliers': False})
logger.warning('User tried to use SVM with RL. Deactivating SVM.')
if self.ft_params.get('use_DBSCAN_to_remove_outliers', False):
self.ft_params.update({'use_SVM_to_remove_outliers': False})
self.ft_params.update({'use_DBSCAN_to_remove_outliers': False})
logger.warning('User tried to use DBSCAN with RL. Deactivating DBSCAN.')
if self.freqai_info['data_split_parameters'].get('shuffle', False):
self.freqai_info['data_split_parameters'].update('shuffle', False)
self.freqai_info['data_split_parameters'].update({'shuffle': False})
logger.warning('User tried to shuffle training data. Setting shuffle to False')
def train(
@@ -141,11 +141,18 @@ class BaseReinforcementLearningModel(IFreqaiModel):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
self.train_env = self.MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params, config=self.config)
self.eval_env = Monitor(self.MyRLEnv(df=test_df, prices=prices_test,
window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params, config=self.config))
self.train_env = self.MyRLEnv(df=train_df,
prices=prices_train,
window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params,
config=self.config,
dp=self.data_provider)
self.eval_env = Monitor(self.MyRLEnv(df=test_df,
prices=prices_test,
window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params,
config=self.config,
dp=self.data_provider))
self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
render=False, eval_freq=len(train_df),
best_model_save_path=str(dk.data_path))
@@ -179,12 +186,13 @@ class BaseReinforcementLearningModel(IFreqaiModel):
if trade.pair == pair:
if self.data_provider._exchange is None: # type: ignore
logger.error('No exchange available.')
return 0, 0, 0
else:
current_rate = self.data_provider._exchange.get_rate( # type: ignore
pair, refresh=False, side="exit", is_short=trade.is_short)
now = datetime.now(timezone.utc).timestamp()
trade_duration = int((now - trade.open_date_utc) / self.base_tf_seconds)
trade_duration = int((now - trade.open_date_utc.timestamp()) / self.base_tf_seconds)
current_profit = trade.calc_profit_ratio(current_rate)
return market_side, current_profit, int(trade_duration)
@@ -230,7 +238,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
def _predict(window):
observations = dataframe.iloc[window.index]
if self.live: # self.guard_state_info_if_backtest():
if self.live and self.rl_config('add_state_info', False):
market_side, current_profit, trade_duration = self.get_state_info(dk.pair)
observations['current_profit_pct'] = current_profit
observations['position'] = market_side
@@ -242,17 +250,6 @@ class BaseReinforcementLearningModel(IFreqaiModel):
return output
# def guard_state_info_if_backtest(self):
# """
# Ensure that backtesting mode doesnt try to use state information.
# """
# if self.rl_config('add_state_info', False) and not self.live:
# logger.warning('Backtesting with state info is currently unavailable '
# 'turning it off.')
# self.rl_config['add_state_info'] = False
# return not self.rl_config['add_state_info']
def build_ohlc_price_dataframes(self, data_dictionary: dict,
pair: str, dk: FreqaiDataKitchen) -> Tuple[DataFrame,
DataFrame]: