remove Base3ActionEnv in favor of Base4Action
This commit is contained in:
robcaulk
@@ -1,332 +0,0 @@
|
||||
# Example of a 3 action environment.
|
||||
|
||||
# import logging
|
||||
# from enum import Enum
|
||||
|
||||
# import gym
|
||||
# import numpy as np
|
||||
# import pandas as pd
|
||||
# from gym import spaces
|
||||
# from gym.utils import seeding
|
||||
# from pandas import DataFrame
|
||||
|
||||
|
||||
# # from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
|
||||
|
||||
# logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# class Actions(Enum):
|
||||
# Short = 0
|
||||
# Long = 1
|
||||
# Neutral = 2
|
||||
|
||||
|
||||
# class Positions(Enum):
|
||||
# Short = 0
|
||||
# Long = 1
|
||||
# Neutral = 0.5
|
||||
|
||||
# def opposite(self):
|
||||
# return Positions.Short if self == Positions.Long else Positions.Long
|
||||
|
||||
|
||||
# def mean_over_std(x):
|
||||
# std = np.std(x, ddof=1)
|
||||
# mean = np.mean(x)
|
||||
# return mean / std if std > 0 else 0
|
||||
|
||||
|
||||
# class Base3ActionRLEnv(gym.Env):
|
||||
|
||||
# metadata = {'render.modes': ['human']}
|
||||
|
||||
# def __init__(self, df: DataFrame = DataFrame(), prices: DataFrame = DataFrame(),
|
||||
# reward_kwargs: dict = {}, window_size=10, starting_point=True,
|
||||
# id: str = 'baseenv-1', seed: int = 1):
|
||||
# assert df.ndim == 2
|
||||
|
||||
# self.id = id
|
||||
# self.seed(seed)
|
||||
# self.reset_env(df, prices, window_size, reward_kwargs, starting_point)
|
||||
|
||||
# def reset_env(self, df, prices, window_size, reward_kwargs, starting_point=True):
|
||||
# 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] + 2)
|
||||
# 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 = []
|
||||
|
||||
# def seed(self, seed: int = 1):
|
||||
# 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 = []
|
||||
|
||||
# return self._get_observation()
|
||||
|
||||
# def step(self, action: int):
|
||||
# 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.value:
|
||||
# self._position = Positions.Long
|
||||
# trade_type = "long"
|
||||
# elif action == Actions.Short.value:
|
||||
# self._position = Positions.Short
|
||||
# trade_type = "short"
|
||||
# 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):
|
||||
# features_window = self.signal_features[(
|
||||
# self._current_tick - self.window_size):self._current_tick]
|
||||
# features_and_state = DataFrame(np.zeros((len(features_window), 2)),
|
||||
# columns=['current_profit_pct', 'position'],
|
||||
# index=features_window.index)
|
||||
|
||||
# features_and_state['current_profit_pct'] = self.get_unrealized_profit()
|
||||
# features_and_state['position'] = self._position.value
|
||||
# features_and_state = pd.concat([features_window, features_and_state], axis=1)
|
||||
# return features_and_state
|
||||
|
||||
# def get_unrealized_profit(self):
|
||||
|
||||
# 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)
|
||||
# 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.
|
||||
|
||||
# def is_tradesignal(self, action: int):
|
||||
# # 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.value and self._position == Positions.Short)
|
||||
# or (action == Actions.Long.value and self._position == Positions.Long))
|
||||
|
||||
# def _is_trade(self, action: Actions):
|
||||
# return ((action == Actions.Long.value and self._position == Positions.Short) or
|
||||
# (action == Actions.Short.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)
|
||||
# )
|
||||
|
||||
# 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))
|
||||
|
||||
# def add_buy_fee(self, price):
|
||||
# return price * (1 + self.fee)
|
||||
|
||||
# def add_sell_fee(self, price):
|
||||
# return price / (1 + self.fee)
|
||||
|
||||
# def _update_history(self, info):
|
||||
# if not self.history:
|
||||
# self.history = {key: [] for key in info.keys()}
|
||||
|
||||
# for key, value in info.items():
|
||||
# self.history[key].append(value)
|
||||
|
||||
# def get_sharpe_ratio(self):
|
||||
# return mean_over_std(self.get_portfolio_log_returns())
|
||||
|
||||
# def calculate_reward(self, action):
|
||||
|
||||
# if self._last_trade_tick is None:
|
||||
# return 0.
|
||||
|
||||
# # 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)
|
||||
# current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
|
||||
# return float(np.log(current_price) - np.log(last_trade_price))
|
||||
|
||||
# # 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))
|
||||
|
||||
# return 0.
|
||||
|
||||
# def _update_profit(self, action):
|
||||
# if self._is_trade(action) or self._done:
|
||||
# pnl = self.get_unrealized_profit()
|
||||
|
||||
# if self._position == Positions.Long:
|
||||
# self._total_profit = self._total_profit + self._total_profit * pnl
|
||||
# self._profits.append((self._current_tick, self._total_profit))
|
||||
# self.close_trade_profit.append(pnl)
|
||||
|
||||
# if self._position == Positions.Short:
|
||||
# self._total_profit = self._total_profit + self._total_profit * pnl
|
||||
# self._profits.append((self._current_tick, self._total_profit))
|
||||
# self.close_trade_profit.append(pnl)
|
||||
|
||||
# def most_recent_return(self, action: int):
|
||||
# """
|
||||
# We support Long, Neutral and Short positions.
|
||||
# Return is generated from rising prices in Long
|
||||
# and falling prices in Short positions.
|
||||
# The actions Sell/Buy or Hold during a Long position trigger the sell/buy-fee.
|
||||
# """
|
||||
# # Long positions
|
||||
# if self._position == Positions.Long:
|
||||
# current_price = self.prices.iloc[self._current_tick].open
|
||||
# if action == Actions.Short.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)
|
||||
|
||||
# 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.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)
|
||||
|
||||
# return np.log(previous_price) - np.log(current_price)
|
||||
|
||||
# return 0
|
||||
|
||||
# def get_portfolio_log_returns(self):
|
||||
# return self.portfolio_log_returns[1:self._current_tick + 1]
|
||||
|
||||
# def update_portfolio_log_returns(self, action):
|
||||
# self.portfolio_log_returns[self._current_tick] = self.most_recent_return(action)
|
||||
|
||||
# def current_price(self) -> float:
|
||||
# return self.prices.iloc[self._current_tick].open
|
||||
|
||||
# def prev_price(self) -> float:
|
||||
# return self.prices.iloc[self._current_tick - 1].open
|
||||
|
||||
# def sharpe_ratio(self) -> float:
|
||||
# if len(self.close_trade_profit) == 0:
|
||||
# return 0.
|
||||
# returns = np.array(self.close_trade_profit)
|
||||
# reward = (np.mean(returns) - 0. + 1e-9) / (np.std(returns) + 1e-9)
|
||||
# return reward
|
||||
Reference in New Issue
Block a user