341 lines
13 KiB
Python
341 lines
13 KiB
Python
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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from pandas import DataFrame
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import pandas as pd
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from abc import abstractmethod
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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_enter = 1
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Long_exit = 2
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Short_enter = 3
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Short_exit = 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: DataFrame = DataFrame(), prices: DataFrame = DataFrame(),
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reward_kwargs: dict = {}, window_size=10, starting_point=True,
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id: str = 'baseenv-1', seed: int = 1, config: dict = {}):
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assert df.ndim == 2
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self.rl_config = config['freqai']['rl_config']
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self.id = id
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self.seed(seed)
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self.reset_env(df, prices, window_size, reward_kwargs, starting_point)
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def reset_env(self, df, prices, window_size, reward_kwargs, starting_point=True):
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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] + 3)
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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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def seed(self, seed: int = 1):
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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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return self._get_observation()
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def step(self, action: int):
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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_enter.value:
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self._position = Positions.Long
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trade_type = "long"
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elif action == Actions.Short_enter.value:
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self._position = Positions.Short
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trade_type = "short"
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elif action == Actions.Long_exit.value:
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self._position = Positions.Neutral
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trade_type = "neutral"
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elif action == Actions.Short_exit.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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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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observation = self._get_observation()
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self._update_history(info)
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return observation, step_reward, self._done, info
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def _get_observation(self):
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features_window = self.signal_features[(
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self._current_tick - self.window_size):self._current_tick]
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features_and_state = DataFrame(np.zeros((len(features_window), 3)),
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columns=['current_profit_pct', 'position', 'trade_duration'],
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index=features_window.index)
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features_and_state['current_profit_pct'] = self.get_unrealized_profit()
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features_and_state['position'] = self._position.value
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features_and_state['trade_duration'] = self.get_trade_duration()
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features_and_state = pd.concat([features_window, features_and_state], axis=1)
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return features_and_state
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def get_trade_duration(self):
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if self._last_trade_tick is None:
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return 0
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else:
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return self._current_tick - self._last_trade_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_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 (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: int):
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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.Neutral.value and self._position == Positions.Short) or
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(action == Actions.Neutral.value and self._position == Positions.Long) or
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(action == Actions.Short_enter.value and self._position == Positions.Short) or
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(action == Actions.Short_enter.value and self._position == Positions.Long) or
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(action == Actions.Short_exit.value and self._position == Positions.Long) or
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(action == Actions.Short_exit.value and self._position == Positions.Neutral) or
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(action == Actions.Long_enter.value and self._position == Positions.Long) or
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(action == Actions.Long_enter.value and self._position == Positions.Short) or
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(action == Actions.Long_exit.value and self._position == Positions.Short) or
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(action == Actions.Long_exit.value and self._position == Positions.Neutral))
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def _is_trade(self, action: Actions):
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return ((action == Actions.Long_enter.value and self._position == Positions.Neutral) or
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(action == Actions.Short_enter.value and self._position == Positions.Neutral))
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def is_hold(self, action):
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return ((action == Actions.Short_enter.value and self._position == Positions.Short) or
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(action == Actions.Long_enter.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.value and self._position == Positions.Neutral))
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def add_entry_fee(self, price):
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return price * (1 + self.fee)
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def add_exit_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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@abstractmethod
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def calculate_reward(self, action):
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"""
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Reward is created by BaseReinforcementLearningModel and can
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be inherited/edited by the user made ReinforcementLearner file.
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"""
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return 0.
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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: int):
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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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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_entry_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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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_exit_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 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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