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base PPO model only customizes reward for 3AC

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This commit is contained in:
robcaulk 2022-08-15 11:24:57 +02:00
parent 6048f60f13
commit 9c78e6c26f
1 changed files with 1 additions and 62 deletions
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63
freqtrade/freqai/prediction_models/ReinforcementLearningPPO.py
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@ -28,7 +28,7 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
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)
eval_freq = agent_params.get("eval_cycles", 4) * len(test_df)
total_timesteps = agent_params["train_cycles"] * len(train_df)
# price data for model training and evaluation
@ -72,67 +72,6 @@ class MyRLEnv(BaseRLEnv):
User can override any function in BaseRLEnv and gym.Env
"""
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):
"""
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
"""
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")
# Update last trade tick
self._last_trade_tick = self._current_tick
if trade_type is not None:
self.trade_history.append(
{'price': self.current_price(), 'index': self._current_tick,
'type': trade_type})
if self._total_profit < 0.2:
self._done = True
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)
return observation, step_reward, self._done, info
def calculate_reward(self, action):
if self._last_trade_tick is None: