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switch to using FT calc_profi_pct, reverse entry/exit fees

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This commit is contained in:
robcaulk 2022-11-13 13:41:17 +01:00
parent e45d791c19
commit 81f800a79b
4 changed files with 12 additions and 19 deletions
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1
freqtrade/freqai/RL/Base4ActionRLEnv.py
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@ -74,6 +74,7 @@ class Base4ActionRLEnv(BaseEnvironment):
self._last_trade_tick = self._current_tick
elif action == Actions.Exit.value:
self._position = Positions.Neutral
self._update_total_profit()
trade_type = "neutral"
self._last_trade_tick = None
else:

2
freqtrade/freqai/RL/Base5ActionRLEnv.py
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@ -75,8 +75,6 @@ class Base5ActionRLEnv(BaseEnvironment):
if self._current_tick == self._end_tick:
self._done = True
self.update_portfolio_log_returns(action)
self._update_unrealized_total_profit()
step_reward = self.calculate_reward(action)
self.total_reward += step_reward

15
freqtrade/freqai/RL/BaseEnvironment.py
View File

@ -165,12 +165,12 @@ class BaseEnvironment(gym.Env):
if self._position == Positions.Neutral:
return 0.
elif self._position == Positions.Short:
current_price = self.add_entry_fee(self.prices.iloc[self._current_tick].open)
last_trade_price = self.add_exit_fee(self.prices.iloc[self._last_trade_tick].open)
return (last_trade_price - current_price) / last_trade_price
elif self._position == Positions.Long:
current_price = self.add_exit_fee(self.prices.iloc[self._current_tick].open)
last_trade_price = self.add_entry_fee(self.prices.iloc[self._last_trade_tick].open)
return (last_trade_price - current_price) / last_trade_price
elif self._position == Positions.Long:
current_price = self.add_entry_fee(self.prices.iloc[self._current_tick].open)
last_trade_price = self.add_exit_fee(self.prices.iloc[self._last_trade_tick].open)
return (current_price - last_trade_price) / last_trade_price
else:
return 0.
@ -210,9 +210,8 @@ class BaseEnvironment(gym.Env):
"""
An example reward function. This is the one function that users will likely
wish to inject their own creativity into.
:params:
action: int = The action made by the agent for the current candle.
:returns:
:param action: int = The action made by the agent for the current candle.
:return:
float = the reward to give to the agent for current step (used for optimization
of weights in NN)
"""
@ -234,7 +233,7 @@ class BaseEnvironment(gym.Env):
def _update_total_profit(self):
pnl = self.get_unrealized_profit()
if self.compound_trades:
# assumes unite stake and compounding
# assumes unitestake and compounding
self._total_profit = self._total_profit * (1 + pnl)
else:
# assumes unit stake and no compounding

13
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@ -180,17 +180,12 @@ class BaseReinforcementLearningModel(IFreqaiModel):
if self.data_provider._exchange is None: # type: ignore
logger.error('No exchange available.')
else:
current_value = self.data_provider._exchange.get_rate( # type: ignore
current_rate = self.data_provider._exchange.get_rate( # type: ignore
pair, refresh=False, side="exit", is_short=trade.is_short)
openrate = trade.open_rate
now = datetime.now(timezone.utc).timestamp()
trade_duration = int((now - trade.open_date.timestamp()) / self.base_tf_seconds)
if 'long' in str(trade.enter_tag):
market_side = 1
current_profit = (current_value - openrate) / openrate
else:
market_side = 0
current_profit = (openrate - current_value) / openrate
trade_duration = int((now - trade.open_date_utc) / self.base_tf_seconds)
current_profit = trade.calc_profit_ratio(current_rate)
return market_side, current_profit, int(trade_duration)