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fix env bug, allow example strat to short

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This commit is contained in:
robcaulk 2022-08-21 19:43:39 +02:00
parent 8cd4daad0a
commit 8b3a8234ac
2 changed files with 23 additions and 35 deletions
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56
freqtrade/freqai/RL/Base5ActionRLEnv.py
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@ -200,12 +200,12 @@ class Base5ActionRLEnv(gym.Env):
if self._position == Positions.Neutral:
return 0.
elif self._position == Positions.Short:
current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
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_sell_fee(self.prices.iloc[self._current_tick].open)
last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
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 (current_price - last_trade_price) / last_trade_price
else:
return 0.
@ -223,12 +223,12 @@ class Base5ActionRLEnv(gym.Env):
(action == Actions.Neutral.value and self._position == Positions.Long) or
(action == Actions.Short_enter.value and self._position == Positions.Short) or
(action == Actions.Short_enter.value and self._position == Positions.Long) or
(action == Actions.Short_exit.value and self._position == Positions.Short) or
# (action == Actions.Short_exit.value and self._position == Positions.Short) or
(action == Actions.Short_exit.value and self._position == Positions.Long) or
(action == Actions.Short_exit.value and self._position == Positions.Neutral) or
(action == Actions.Long_enter.value and self._position == Positions.Long) or
(action == Actions.Long_enter.value and self._position == Positions.Short) or
(action == Actions.Long_exit.value and self._position == Positions.Long) or
# (action == Actions.Long_exit.value and self._position == Positions.Long) or
(action == Actions.Long_exit.value and self._position == Positions.Short) or
(action == Actions.Long_exit.value and self._position == Positions.Neutral))
@ -243,10 +243,10 @@ class Base5ActionRLEnv(gym.Env):
(action == Actions.Neutral.value and self._position == Positions.Short) or
(action == Actions.Neutral.value and self._position == Positions.Neutral))
def add_buy_fee(self, price):
def add_entry_fee(self, price):
return price * (1 + self.fee)
def add_sell_fee(self, price):
def add_exit_fee(self, price):
return price / (1 + self.fee)
def _update_history(self, info):
@ -266,27 +266,21 @@ class Base5ActionRLEnv(gym.Env):
# close long
if action == Actions.Long_exit.value and self._position == Positions.Long:
last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
return float(np.log(current_price) - np.log(last_trade_price))
if action == Actions.Long_exit.value and self._position == Positions.Long:
if self.close_trade_profit[-1] > self.profit_aim * self.rr:
last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
return float((np.log(current_price) - np.log(last_trade_price)) * 2)
last_trade_price = self.add_entry_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_exit_fee(self.prices.iloc[self._current_tick].open)
factor = 1
if self.close_trade_profit and self.close_trade_profit[-1] > self.profit_aim * self.rr:
factor = 2
return float((np.log(current_price) - np.log(last_trade_price)) * factor)
# close short
if action == Actions.Short_exit.value and self._position == Positions.Short:
last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
return float(np.log(last_trade_price) - np.log(current_price))
if action == Actions.Short_exit.value and self._position == Positions.Short:
if self.close_trade_profit[-1] > self.profit_aim * self.rr:
last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
return float((np.log(last_trade_price) - np.log(current_price)) * 2)
last_trade_price = self.add_exit_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_entry_fee(self.prices.iloc[self._current_tick].open)
factor = 1
if self.close_trade_profit and self.close_trade_profit[-1] > self.profit_aim * self.rr:
factor = 2
return float(np.log(last_trade_price) - np.log(current_price) * factor)
return 0.
@ -315,27 +309,21 @@ class Base5ActionRLEnv(gym.Env):
# Long positions
if self._position == Positions.Long:
current_price = self.prices.iloc[self._current_tick].open
if action == Actions.Short_enter.value or action == Actions.Neutral.value:
current_price = self.add_sell_fee(current_price)
previous_price = self.prices.iloc[self._current_tick - 1].open
if (self._position_history[self._current_tick - 1] == Positions.Short
or self._position_history[self._current_tick - 1] == Positions.Neutral):
previous_price = self.add_buy_fee(previous_price)
previous_price = self.add_entry_fee(previous_price)
return np.log(current_price) - np.log(previous_price)
# Short positions
if self._position == Positions.Short:
current_price = self.prices.iloc[self._current_tick].open
if action == Actions.Long_enter.value or action == Actions.Neutral.value:
current_price = self.add_buy_fee(current_price)
previous_price = self.prices.iloc[self._current_tick - 1].open
if (self._position_history[self._current_tick - 1] == Positions.Long
or self._position_history[self._current_tick - 1] == Positions.Neutral):
previous_price = self.add_sell_fee(previous_price)
previous_price = self.add_exit_fee(previous_price)
return np.log(previous_price) - np.log(current_price)

2
freqtrade/freqai/example_strats/ReinforcementLearningExample5ac.py
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@ -36,7 +36,7 @@ class ReinforcementLearningExample5ac(IStrategy):
stoploss = -0.05
use_exit_signal = True
startup_candle_count: int = 300
can_short = False
can_short = True
linear_roi_offset = DecimalParameter(
0.00, 0.02, default=0.005, space="sell", optimize=False, load=True