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