make base 3ac and base 5ac environments. TDQN defaults to 3AC.
This commit is contained in:
robcaulk
parent
096533bcb9
commit
926023935f
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@ -31,7 +31,7 @@ def mean_over_std(x):
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return mean / std if std > 0 else 0
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class BaseRLEnv(gym.Env):
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class Base3ActionRLEnv(gym.Env):
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metadata = {'render.modes': ['human']}
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@ -0,0 +1,364 @@
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import logging
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from enum import Enum
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# from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
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import gym
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import numpy as np
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from gym import spaces
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from gym.utils import seeding
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logger = logging.getLogger(__name__)
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class Actions(Enum):
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Neutral = 0
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Long_buy = 1
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Long_sell = 2
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Short_buy = 3
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Short_sell = 4
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class Positions(Enum):
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Short = 0
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Long = 1
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Neutral = 0.5
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def opposite(self):
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return Positions.Short if self == Positions.Long else Positions.Long
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def mean_over_std(x):
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std = np.std(x, ddof=1)
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mean = np.mean(x)
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return mean / std if std > 0 else 0
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class Base5ActionRLEnv(gym.Env):
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"""
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Base class for a 5 action environment
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"""
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metadata = {'render.modes': ['human']}
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def __init__(self, df, prices, reward_kwargs, window_size=10, starting_point=True, ):
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assert df.ndim == 2
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self.seed()
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self.df = df
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self.signal_features = self.df
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self.prices = prices
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self.window_size = window_size
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self.starting_point = starting_point
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self.rr = reward_kwargs["rr"]
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self.profit_aim = reward_kwargs["profit_aim"]
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self.fee = 0.0015
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# # spaces
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self.shape = (window_size, self.signal_features.shape[1])
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self.action_space = spaces.Discrete(len(Actions))
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self.observation_space = spaces.Box(
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low=-np.inf, high=np.inf, shape=self.shape, dtype=np.float32)
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# episode
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self._start_tick = self.window_size
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self._end_tick = len(self.prices) - 1
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self._done = None
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self._current_tick = None
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self._last_trade_tick = None
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self._position = Positions.Neutral
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self._position_history = None
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self.total_reward = None
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self._total_profit = None
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self._first_rendering = None
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self.history = None
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self.trade_history = []
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# self.A_t, self.B_t = 0.000639, 0.00001954
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self.r_t_change = 0.
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self.returns_report = []
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def seed(self, seed=None):
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self.np_random, seed = seeding.np_random(seed)
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return [seed]
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def reset(self):
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self._done = False
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if self.starting_point is True:
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self._position_history = (self._start_tick * [None]) + [self._position]
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else:
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self._position_history = (self.window_size * [None]) + [self._position]
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self._current_tick = self._start_tick
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self._last_trade_tick = None
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self._position = Positions.Neutral
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self.total_reward = 0.
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self._total_profit = 1. # unit
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self._first_rendering = True
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self.history = {}
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self.trade_history = []
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self.portfolio_log_returns = np.zeros(len(self.prices))
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self._profits = [(self._start_tick, 1)]
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self.close_trade_profit = []
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self.r_t_change = 0.
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self.returns_report = []
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return self._get_observation()
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def step(self, action):
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self._done = False
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self._current_tick += 1
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if self._current_tick == self._end_tick:
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self._done = True
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self.update_portfolio_log_returns(action)
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self._update_profit(action)
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step_reward = self.calculate_reward(action)
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self.total_reward += step_reward
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trade_type = None
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if self.is_tradesignal(action): # exclude 3 case not trade
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# Update position
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"""
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Action: Neutral, position: Long -> Close Long
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Action: Neutral, position: Short -> Close Short
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Action: Long, position: Neutral -> Open Long
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Action: Long, position: Short -> Close Short and Open Long
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Action: Short, position: Neutral -> Open Short
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Action: Short, position: Long -> Close Long and Open Short
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"""
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if action == Actions.Neutral.value:
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self._position = Positions.Neutral
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trade_type = "neutral"
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elif action == Actions.Long_buy.value:
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self._position = Positions.Long
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trade_type = "long"
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elif action == Actions.Short_buy.value:
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self._position = Positions.Short
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trade_type = "short"
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elif action == Actions.Long_sell.value:
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self._position = Positions.Neutral
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trade_type = "neutral"
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elif action == Actions.Short_sell.value:
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self._position = Positions.Neutral
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trade_type = "neutral"
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else:
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print("case not defined")
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# Update last trade tick
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self._last_trade_tick = self._current_tick
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if trade_type is not None:
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self.trade_history.append(
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{'price': self.current_price(), 'index': self._current_tick,
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'type': trade_type})
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if self._total_profit < 0.2:
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self._done = True
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self._position_history.append(self._position)
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observation = self._get_observation()
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info = dict(
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tick=self._current_tick,
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total_reward=self.total_reward,
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total_profit=self._total_profit,
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position=self._position.value
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)
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self._update_history(info)
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return observation, step_reward, self._done, info
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# def processState(self, state):
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# return state.to_numpy()
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# def convert_mlp_Policy(self, obs_):
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# pass
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def _get_observation(self):
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return self.signal_features[(self._current_tick - self.window_size):self._current_tick]
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def get_unrealized_profit(self):
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if self._last_trade_tick is None:
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return 0.
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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_buy_fee(self.prices.iloc[self._current_tick].open)
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last_trade_price = self.add_sell_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_sell_fee(self.prices.iloc[self._current_tick].open)
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last_trade_price = self.add_buy_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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def is_tradesignal(self, action):
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# trade signal
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"""
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not trade signal is :
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Action: Neutral, position: Neutral -> Nothing
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Action: Long, position: Long -> Hold Long
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Action: Short, position: Short -> Hold Short
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"""
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return not ((action == Actions.Neutral.value and self._position == Positions.Neutral) or
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(action == Actions.Short_buy.value and self._position == Positions.Short) or
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(action == Actions.Short_sell.value and self._position == Positions.Short) or
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(action == Actions.Short_buy.value and self._position == Positions.Long) or
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(action == Actions.Short_sell.value and self._position == Positions.Long) or
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(action == Actions.Long_buy.value and self._position == Positions.Long) or
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(action == Actions.Long_sell.value and self._position == Positions.Long) or
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(action == Actions.Long_buy.value and self._position == Positions.Short) or
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(action == Actions.Long_sell.value and self._position == Positions.Short))
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def _is_trade(self, action: Actions):
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return ((action == Actions.Long_buy.value and self._position == Positions.Short) or
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(action == Actions.Short_buy.value and self._position == Positions.Long) or
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(action == Actions.Neutral.value and self._position == Positions.Long) or
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(action == Actions.Neutral.value and self._position == Positions.Short) or
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(action == Actions.Neutral.Short_sell and self._position == Positions.Long) or
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(action == Actions.Neutral.Long_sell and self._position == Positions.Short)
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)
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def is_hold(self, action):
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return ((action == Actions.Short.value and self._position == Positions.Short)
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or (action == Actions.Long.value and self._position == Positions.Long))
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def add_buy_fee(self, price):
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return price * (1 + self.fee)
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def add_sell_fee(self, price):
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return price / (1 + self.fee)
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def _update_history(self, info):
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if not self.history:
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self.history = {key: [] for key in info.keys()}
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for key, value in info.items():
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self.history[key].append(value)
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def get_sharpe_ratio(self):
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return mean_over_std(self.get_portfolio_log_returns())
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def _update_profit(self, action):
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# if self._is_trade(action) or self._done:
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if self._is_trade(action) or self._done:
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pnl = self.get_unrealized_profit()
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if self._position == Positions.Long:
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self._total_profit = self._total_profit + self._total_profit * pnl
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self._profits.append((self._current_tick, self._total_profit))
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self.close_trade_profit.append(pnl)
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if self._position == Positions.Short:
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self._total_profit = self._total_profit + self._total_profit * pnl
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self._profits.append((self._current_tick, self._total_profit))
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self.close_trade_profit.append(pnl)
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def most_recent_return(self, action):
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"""
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We support Long, Neutral and Short positions.
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Return is generated from rising prices in Long
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and falling prices in Short positions.
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The actions Sell/Buy or Hold during a Long position trigger the sell/buy-fee.
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"""
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# Long positions
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if self._position == Positions.Long:
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current_price = self.prices.iloc[self._current_tick].open
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# if action == Actions.Short.value or action == Actions.Neutral.value:
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if action == Actions.Short_buy.value or action == Actions.Neutral.value:
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current_price = self.add_sell_fee(current_price)
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previous_price = self.prices.iloc[self._current_tick - 1].open
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if (self._position_history[self._current_tick - 1] == Positions.Short
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or self._position_history[self._current_tick - 1] == Positions.Neutral):
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previous_price = self.add_buy_fee(previous_price)
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return np.log(current_price) - np.log(previous_price)
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# Short positions
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if self._position == Positions.Short:
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current_price = self.prices.iloc[self._current_tick].open
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# if action == Actions.Long.value or action == Actions.Neutral.value:
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if action == Actions.Long_buy.value or action == Actions.Neutral.value:
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current_price = self.add_buy_fee(current_price)
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previous_price = self.prices.iloc[self._current_tick - 1].open
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if (self._position_history[self._current_tick - 1] == Positions.Long
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or self._position_history[self._current_tick - 1] == Positions.Neutral):
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previous_price = self.add_sell_fee(previous_price)
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return np.log(previous_price) - np.log(current_price)
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return 0
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def get_portfolio_log_returns(self):
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return self.portfolio_log_returns[1:self._current_tick + 1]
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def get_trading_log_return(self):
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return self.portfolio_log_returns[self._start_tick:]
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def update_portfolio_log_returns(self, action):
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self.portfolio_log_returns[self._current_tick] = self.most_recent_return(action)
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def current_price(self) -> float:
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return self.prices.iloc[self._current_tick].open
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def prev_price(self) -> float:
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return self.prices.iloc[self._current_tick - 1].open
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def sharpe_ratio(self):
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if len(self.close_trade_profit) == 0:
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return 0.
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returns = np.array(self.close_trade_profit)
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reward = (np.mean(returns) - 0. + 1e-9) / (np.std(returns) + 1e-9)
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return reward
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def get_bnh_log_return(self):
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return np.diff(np.log(self.prices['open'][self._start_tick:]))
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def calculate_reward(self, action):
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if self._last_trade_tick is None:
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return 0.
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# close long
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if action == Actions.Long_sell.value and self._position == Positions.Long:
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last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
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current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
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return float(np.log(current_price) - np.log(last_trade_price))
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if action == Actions.Long_sell.value and self._position == Positions.Long:
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if self.close_trade_profit[-1] > self.profit_aim * self.rr:
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last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
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current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
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return float((np.log(current_price) - np.log(last_trade_price)) * 2)
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# close short
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if action == Actions.Short_buy.value and self._position == Positions.Short:
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last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
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current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
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return float(np.log(last_trade_price) - np.log(current_price))
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if action == Actions.Short_buy.value and self._position == Positions.Short:
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if self.close_trade_profit[-1] > self.profit_aim * self.rr:
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last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
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current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
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return float((np.log(last_trade_price) - np.log(current_price)) * 2)
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return 0.
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@ -8,7 +8,7 @@ from pandas import DataFrame
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from abc import abstractmethod
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.freqai_interface import IFreqaiModel
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from freqtrade.freqai.RL.BaseRLEnv import BaseRLEnv, Actions, Positions
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from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
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from freqtrade.persistence import Trade
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logger = logging.getLogger(__name__)
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@ -165,7 +165,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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hist_preds_df[return_str] = 0
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class MyRLEnv(BaseRLEnv):
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class MyRLEnv(Base3ActionRLEnv):
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def step(self, action):
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self._done = False
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@ -10,7 +10,7 @@ from stable_baselines3 import PPO
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from stable_baselines3.common.callbacks import EvalCallback
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from stable_baselines3.common.monitor import Monitor
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# from stable_baselines3.common.vec_env import SubprocVecEnv
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from freqtrade.freqai.RL.BaseRLEnv import BaseRLEnv, Actions, Positions
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from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
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from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
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@ -67,7 +67,7 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
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return model
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class MyRLEnv(BaseRLEnv):
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class MyRLEnv(Base3ActionRLEnv):
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"""
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User can override any function in BaseRLEnv and gym.Env
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"""
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@ -1,17 +1,14 @@
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import logging
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from typing import Any, Dict # Optional
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from enum import Enum
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import numpy as np
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import torch as th
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from stable_baselines3.common.callbacks import EvalCallback
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from stable_baselines3.common.monitor import Monitor
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# from stable_baselines3.common.vec_env import SubprocVecEnv
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from freqtrade.freqai.RL.BaseRLEnv import BaseRLEnv
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from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
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from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
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from freqtrade.freqai.RL.TDQNagent import TDQN
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from stable_baselines3.common.buffers import ReplayBuffer
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from gym import spaces
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from gym.utils import seeding
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import numpy as np
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logger = logging.getLogger(__name__)
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@ -71,233 +68,66 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
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return model
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class Actions(Enum):
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Neutral = 0
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Long_buy = 1
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Long_sell = 2
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Short_buy = 3
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Short_sell = 4
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class Positions(Enum):
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Short = 0
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Long = 1
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Neutral = 0.5
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def opposite(self):
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return Positions.Short if self == Positions.Long else Positions.Long
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|
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class MyRLEnv(BaseRLEnv):
|
||||
class MyRLEnv(Base3ActionRLEnv):
|
||||
"""
|
||||
User can override any function in BaseRLEnv and gym.Env. Here the user
|
||||
Adds 5 actions.
|
||||
User can override any function in BaseRLEnv and gym.Env
|
||||
"""
|
||||
|
||||
metadata = {'render.modes': ['human']}
|
||||
|
||||
def __init__(self, df, prices, reward_kwargs, window_size=10, starting_point=True, ):
|
||||
assert df.ndim == 2
|
||||
|
||||
self.seed()
|
||||
self.df = df
|
||||
self.signal_features = self.df
|
||||
self.prices = prices
|
||||
self.window_size = window_size
|
||||
self.starting_point = starting_point
|
||||
self.rr = reward_kwargs["rr"]
|
||||
self.profit_aim = reward_kwargs["profit_aim"]
|
||||
|
||||
self.fee = 0.0015
|
||||
|
||||
# # spaces
|
||||
self.shape = (window_size, self.signal_features.shape[1])
|
||||
self.action_space = spaces.Discrete(len(Actions))
|
||||
self.observation_space = spaces.Box(
|
||||
low=-np.inf, high=np.inf, shape=self.shape, dtype=np.float32)
|
||||
|
||||
# episode
|
||||
self._start_tick = self.window_size
|
||||
self._end_tick = len(self.prices) - 1
|
||||
self._done = None
|
||||
self._current_tick = None
|
||||
self._last_trade_tick = None
|
||||
self._position = Positions.Neutral
|
||||
self._position_history = None
|
||||
self.total_reward = None
|
||||
self._total_profit = None
|
||||
self._first_rendering = None
|
||||
self.history = None
|
||||
self.trade_history = []
|
||||
|
||||
# self.A_t, self.B_t = 0.000639, 0.00001954
|
||||
self.r_t_change = 0.
|
||||
|
||||
self.returns_report = []
|
||||
|
||||
def seed(self, seed=None):
|
||||
self.np_random, seed = seeding.np_random(seed)
|
||||
return [seed]
|
||||
|
||||
def reset(self):
|
||||
|
||||
self._done = False
|
||||
|
||||
if self.starting_point is True:
|
||||
self._position_history = (self._start_tick * [None]) + [self._position]
|
||||
else:
|
||||
self._position_history = (self.window_size * [None]) + [self._position]
|
||||
|
||||
self._current_tick = self._start_tick
|
||||
self._last_trade_tick = None
|
||||
self._position = Positions.Neutral
|
||||
|
||||
self.total_reward = 0.
|
||||
self._total_profit = 1. # unit
|
||||
self._first_rendering = True
|
||||
self.history = {}
|
||||
self.trade_history = []
|
||||
self.portfolio_log_returns = np.zeros(len(self.prices))
|
||||
|
||||
self._profits = [(self._start_tick, 1)]
|
||||
self.close_trade_profit = []
|
||||
self.r_t_change = 0.
|
||||
|
||||
self.returns_report = []
|
||||
|
||||
return self._get_observation()
|
||||
|
||||
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): # exclude 3 case not trade
|
||||
# Update position
|
||||
"""
|
||||
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_buy.value:
|
||||
self._position = Positions.Long
|
||||
trade_type = "long"
|
||||
elif action == Actions.Short_buy.value:
|
||||
self._position = Positions.Short
|
||||
trade_type = "short"
|
||||
elif action == Actions.Long_sell.value:
|
||||
self._position = Positions.Neutral
|
||||
trade_type = "neutral"
|
||||
elif action == Actions.Short_sell.value:
|
||||
self._position = Positions.Neutral
|
||||
trade_type = "neutral"
|
||||
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 _get_observation(self):
|
||||
return self.signal_features[(self._current_tick - self.window_size):self._current_tick]
|
||||
|
||||
def get_unrealized_profit(self):
|
||||
def calculate_reward(self, action):
|
||||
|
||||
if self._last_trade_tick is None:
|
||||
return 0.
|
||||
|
||||
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)
|
||||
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)
|
||||
# close long
|
||||
if (action == Actions.Short.value or
|
||||
action == Actions.Neutral.value) and self._position == Positions.Long:
|
||||
last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
|
||||
return (current_price - last_trade_price) / last_trade_price
|
||||
else:
|
||||
return 0.
|
||||
current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
|
||||
return float(np.log(current_price) - np.log(last_trade_price))
|
||||
|
||||
def is_tradesignal(self, action):
|
||||
# trade signal
|
||||
"""
|
||||
not trade signal is :
|
||||
Action: Neutral, position: Neutral -> Nothing
|
||||
Action: Long, position: Long -> Hold Long
|
||||
Action: Short, position: Short -> Hold Short
|
||||
"""
|
||||
return not ((action == Actions.Neutral.value and self._position == Positions.Neutral) or
|
||||
(action == Actions.Short_buy.value and self._position == Positions.Short) or
|
||||
(action == Actions.Short_sell.value and self._position == Positions.Short) or
|
||||
(action == Actions.Short_buy.value and self._position == Positions.Long) or
|
||||
(action == Actions.Short_sell.value and self._position == Positions.Long) or
|
||||
# close short
|
||||
if (action == Actions.Long.value or
|
||||
action == Actions.Neutral.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))
|
||||
|
||||
(action == Actions.Long_buy.value and self._position == Positions.Long) or
|
||||
(action == Actions.Long_sell.value and self._position == Positions.Long) or
|
||||
(action == Actions.Long_buy.value and self._position == Positions.Short) or
|
||||
(action == Actions.Long_sell.value and self._position == Positions.Short))
|
||||
return 0.
|
||||
|
||||
def _is_trade(self, action):
|
||||
return ((action == Actions.Long_buy.value and self._position == Positions.Short) or
|
||||
(action == Actions.Short_buy.value and self._position == Positions.Long) or
|
||||
(action == Actions.Neutral.value and self._position == Positions.Long) or
|
||||
(action == Actions.Neutral.value and self._position == Positions.Short) or
|
||||
# User can inherit and customize 5 action environment
|
||||
# class MyRLEnv(Base5ActionRLEnv):
|
||||
# """
|
||||
# User can override any function in BaseRLEnv and gym.Env. Here the user
|
||||
# Adds 5 actions.
|
||||
# """
|
||||
|
||||
(action == Actions.Neutral.Short_sell and self._position == Positions.Long) or
|
||||
(action == Actions.Neutral.Long_sell and self._position == Positions.Short)
|
||||
)
|
||||
# def calculate_reward(self, action):
|
||||
|
||||
def is_hold(self, action):
|
||||
return ((action == Actions.Short.value and self._position == Positions.Short)
|
||||
or (action == Actions.Long.value and self._position == Positions.Long))
|
||||
# if self._last_trade_tick is None:
|
||||
# return 0.
|
||||
|
||||
def add_buy_fee(self, price):
|
||||
return price * (1 + self.fee)
|
||||
# # close long
|
||||
# if action == Actions.Long_sell.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))
|
||||
|
||||
def add_sell_fee(self, price):
|
||||
return price / (1 + self.fee)
|
||||
# if action == Actions.Long_sell.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)
|
||||
|
||||
def _update_history(self, info):
|
||||
if not self.history:
|
||||
self.history = {key: [] for key in info.keys()}
|
||||
# # close short
|
||||
# if action == Actions.Short_buy.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))
|
||||
|
||||
for key, value in info.items():
|
||||
self.history[key].append(value)
|
||||
# if action == Actions.Short_buy.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)
|
||||
|
||||
# return 0.
|
||||
|
|
|
|||
Loading…
Reference in New Issue