refactor environment inheritence tree to accommodate flexible action types/counts. fix bug in train profit handling
This commit is contained in:
robcaulk
parent
8c313b431d
commit
7766350c15
@ -1,14 +1,11 @@
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import logging
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from enum import Enum
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from typing import Optional
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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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from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
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logger = logging.getLogger(__name__)
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@ -19,95 +16,13 @@ class Actions(Enum):
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Short_enter = 3
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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 Base4ActionRLEnv(gym.Env):
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class Base4ActionRLEnv(BaseEnvironment):
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"""
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Base class for a 5 action environment
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Base class for a 4 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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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: DataFrame, prices: DataFrame, window_size: int,
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reward_kwargs: dict, 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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def set_action_space(self):
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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: int = self.window_size
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self._end_tick: int = len(self.prices) - 1
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self._done: bool = False
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self._current_tick: int = self._start_tick
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self._last_trade_tick: Optional[int] = None
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self._position = Positions.Neutral
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self._position_history: list = [None]
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self.total_reward: float = 0
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self._total_profit: float = 1
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self.history: dict = {}
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self.trade_history: list = []
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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.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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@ -181,43 +96,6 @@ class Base4ActionRLEnv(gym.Env):
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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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@ -240,7 +118,7 @@ class Base4ActionRLEnv(gym.Env):
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e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
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"""
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# Agent should only try to exit if it is in position
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if action in (Actions.Exit.value):
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if action == Actions.Exit.value:
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if self._position not in (Positions.Short, Positions.Long):
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return False
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@ -250,97 +128,3 @@ class Base4ActionRLEnv(gym.Env):
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return False
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return True
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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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pnl = self.get_unrealized_profit()
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if self._position in (Positions.Long, Positions.Short):
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self._total_profit *= (1 + 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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Calculate the tick to tick return if in a trade.
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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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@ -1,14 +1,14 @@
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import logging
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from enum import Enum
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from typing import Optional
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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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from gym import spaces
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from pandas import DataFrame
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from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
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logger = logging.getLogger(__name__)
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@ -20,70 +20,19 @@ class Actions(Enum):
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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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class Base5ActionRLEnv(BaseEnvironment):
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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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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: DataFrame, prices: DataFrame, window_size: int,
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reward_kwargs: dict, 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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def set_action_space(self):
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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: int = self.window_size
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self._end_tick: int = len(self.prices) - 1
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self._done: bool = False
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self._current_tick: int = self._start_tick
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self._last_trade_tick: Optional[int] = None
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self._position = Positions.Neutral
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self._position_history: list = [None]
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self.total_reward: float = 0
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self._total_profit: float = 1
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self.history: dict = {}
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self.trade_history: list = []
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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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@ -106,6 +55,7 @@ class Base5ActionRLEnv(gym.Env):
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self._profits = [(self._start_tick, 1)]
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self.close_trade_profit = []
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self._total_unrealized_profit = 1
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return self._get_observation()
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@ -118,7 +68,7 @@ class Base5ActionRLEnv(gym.Env):
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self.update_portfolio_log_returns(action)
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self._update_profit(action)
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self._update_unrealized_total_profit()
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step_reward = self.calculate_reward(action)
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self.total_reward += step_reward
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@ -148,10 +98,12 @@ class Base5ActionRLEnv(gym.Env):
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trade_type = "short"
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self._last_trade_tick = self._current_tick
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elif action == Actions.Long_exit.value:
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self._update_total_profit()
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self._position = Positions.Neutral
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trade_type = "neutral"
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self._last_trade_tick = None
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elif action == Actions.Short_exit.value:
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self._update_total_profit()
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self._position = Positions.Neutral
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trade_type = "neutral"
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self._last_trade_tick = None
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@ -163,7 +115,8 @@ class Base5ActionRLEnv(gym.Env):
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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 < 1 - self.rl_config.get('max_training_drawdown_pct', 0.8):
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if (self._total_profit < self.max_drawdown or
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self._total_unrealized_profit < self.max_drawdown):
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self._done = True
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self._position_history.append(self._position)
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@ -200,24 +153,6 @@ class Base5ActionRLEnv(gym.Env):
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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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@ -253,97 +188,3 @@ class Base5ActionRLEnv(gym.Env):
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return False
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return True
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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
|
||||
be inherited/edited by the user made ReinforcementLearner file.
|
||||
"""
|
||||
|
||||
return 0.
|
||||
|
||||
def _update_profit(self, action):
|
||||
if self._is_trade(action) or self._done:
|
||||
pnl = self.get_unrealized_profit()
|
||||
|
||||
if self._position in (Positions.Long, Positions.Short):
|
||||
self._total_profit *= (1 + pnl)
|
||||
self._profits.append((self._current_tick, self._total_profit))
|
||||
self.close_trade_profit.append(pnl)
|
||||
|
||||
def most_recent_return(self, action: int):
|
||||
"""
|
||||
Calculate the tick to tick return if in a trade.
|
||||
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
|
||||
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_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
|
||||
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_exit_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):
|
||||
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
|
||||
|
||||
270
freqtrade/freqai/RL/BaseEnvironment.py
Normal file
270
freqtrade/freqai/RL/BaseEnvironment.py
Normal file
@ -0,0 +1,270 @@
|
||||
import logging
|
||||
from abc import abstractmethod
|
||||
from enum import Enum
|
||||
from typing import Optional
|
||||
|
||||
import gym
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from gym import spaces
|
||||
from gym.utils import seeding
|
||||
from pandas import DataFrame
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class Positions(Enum):
|
||||
Short = 0
|
||||
Long = 1
|
||||
Neutral = 0.5
|
||||
|
||||
def opposite(self):
|
||||
return Positions.Short if self == Positions.Long else Positions.Long
|
||||
|
||||
|
||||
class BaseEnvironment(gym.Env):
|
||||
"""
|
||||
Base class for environments. This class is agnostic to action count.
|
||||
Inherited classes customize this to include varying action counts/types,
|
||||
See RL/Base5ActionRLEnv.py and RL/Base4ActionRLEnv.py
|
||||
"""
|
||||
|
||||
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, config: dict = {}):
|
||||
|
||||
self.rl_config = config['freqai']['rl_config']
|
||||
self.id = id
|
||||
self.seed(seed)
|
||||
self.reset_env(df, prices, window_size, reward_kwargs, starting_point)
|
||||
self.max_drawdown = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
|
||||
self.compound_trades = config['stake_amount'] == 'unlimited'
|
||||
|
||||
def reset_env(self, df: DataFrame, prices: DataFrame, window_size: int,
|
||||
reward_kwargs: dict, 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] + 3)
|
||||
self.set_action_space()
|
||||
self.observation_space = spaces.Box(
|
||||
low=-np.inf, high=np.inf, shape=self.shape, dtype=np.float32)
|
||||
|
||||
# episode
|
||||
self._start_tick: int = self.window_size
|
||||
self._end_tick: int = len(self.prices) - 1
|
||||
self._done: bool = False
|
||||
self._current_tick: int = self._start_tick
|
||||
self._last_trade_tick: Optional[int] = None
|
||||
self._position = Positions.Neutral
|
||||
self._position_history: list = [None]
|
||||
self.total_reward: float = 0
|
||||
self._total_profit: float = 1
|
||||
self._total_unrealized_profit: float = 1
|
||||
self.history: dict = {}
|
||||
self.trade_history: list = []
|
||||
|
||||
@abstractmethod
|
||||
def set_action_space(self):
|
||||
"""
|
||||
Unique to the environment action count. Must be inherited.
|
||||
"""
|
||||
|
||||
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.history = {}
|
||||
self.trade_history = []
|
||||
self.portfolio_log_returns = np.zeros(len(self.prices))
|
||||
|
||||
self._profits = [(self._start_tick, 1)]
|
||||
self.close_trade_profit = []
|
||||
self._total_unrealized_profit = 1
|
||||
|
||||
return self._get_observation()
|
||||
|
||||
@abstractmethod
|
||||
def step(self, action: int):
|
||||
"""
|
||||
Step depeneds on action types, this must be inherited.
|
||||
"""
|
||||
return
|
||||
|
||||
def _get_observation(self):
|
||||
"""
|
||||
This may or may not be independent of action types, user can inherit
|
||||
this in their custom "MyRLEnv"
|
||||
"""
|
||||
features_window = self.signal_features[(
|
||||
self._current_tick - self.window_size):self._current_tick]
|
||||
features_and_state = DataFrame(np.zeros((len(features_window), 3)),
|
||||
columns=['current_profit_pct', 'position', 'trade_duration'],
|
||||
index=features_window.index)
|
||||
|
||||
features_and_state['current_profit_pct'] = self.get_unrealized_profit()
|
||||
features_and_state['position'] = self._position.value
|
||||
features_and_state['trade_duration'] = self.get_trade_duration()
|
||||
features_and_state = pd.concat([features_window, features_and_state], axis=1)
|
||||
return features_and_state
|
||||
|
||||
def get_trade_duration(self):
|
||||
if self._last_trade_tick is None:
|
||||
return 0
|
||||
else:
|
||||
return self._current_tick - self._last_trade_tick
|
||||
|
||||
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_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_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.
|
||||
|
||||
@abstractmethod
|
||||
def is_tradesignal(self, action: int):
|
||||
# trade signal
|
||||
"""
|
||||
Determine if the signal is a trade signal. This is
|
||||
unique to the actions in the environment, and therefore must be
|
||||
inherited.
|
||||
"""
|
||||
return
|
||||
|
||||
def _is_valid(self, action: int):
|
||||
# trade signal
|
||||
"""
|
||||
Determine if the signal is valid.This is
|
||||
unique to the actions in the environment, and therefore must be
|
||||
inherited.
|
||||
"""
|
||||
return
|
||||
|
||||
def add_entry_fee(self, price):
|
||||
return price * (1 + self.fee)
|
||||
|
||||
def add_exit_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)
|
||||
|
||||
@abstractmethod
|
||||
def calculate_reward(self, action):
|
||||
"""
|
||||
Reward is created by BaseReinforcementLearningModel and can
|
||||
be inherited/edited by the user made ReinforcementLearner file.
|
||||
"""
|
||||
|
||||
return 0.
|
||||
|
||||
def _update_unrealized_total_profit(self):
|
||||
"""
|
||||
Update the unrealized total profit incase of episode end.
|
||||
"""
|
||||
if self._position in (Positions.Long, Positions.Short):
|
||||
pnl = self.get_unrealized_profit()
|
||||
if self.compound_trades:
|
||||
# assumes unit stake and compounding
|
||||
unrl_profit = self._total_profit * (1 + pnl)
|
||||
else:
|
||||
# assumes unit stake and no compounding
|
||||
unrl_profit = self._total_profit + pnl
|
||||
self._total_unrealized_profit = unrl_profit
|
||||
|
||||
def _update_total_profit(self):
|
||||
pnl = self.get_unrealized_profit()
|
||||
if self.compound_trades:
|
||||
# assumes unite stake and compounding
|
||||
self._total_profit = self._total_profit * (1 + pnl)
|
||||
else:
|
||||
# assumes unit stake and no compounding
|
||||
self._total_profit += pnl
|
||||
|
||||
def most_recent_return(self, action: int):
|
||||
"""
|
||||
Calculate the tick to tick return if in a trade.
|
||||
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
|
||||
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_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
|
||||
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_exit_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):
|
||||
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
|
||||
@ -1,25 +1,28 @@
|
||||
import logging
|
||||
from typing import Any, Dict, Tuple
|
||||
from abc import abstractmethod
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from typing import Any, Callable, Dict, Tuple
|
||||
|
||||
import gym
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
import pandas as pd
|
||||
import torch as th
|
||||
import torch.multiprocessing
|
||||
from pandas import DataFrame
|
||||
from abc import abstractmethod
|
||||
from stable_baselines3.common.callbacks import EvalCallback
|
||||
from stable_baselines3.common.monitor import Monitor
|
||||
from stable_baselines3.common.utils import set_random_seed
|
||||
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.freqai.freqai_interface import IFreqaiModel
|
||||
from freqtrade.freqai.RL.Base5ActionRLEnv import Base5ActionRLEnv, Actions, Positions
|
||||
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv
|
||||
from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
|
||||
from freqtrade.persistence import Trade
|
||||
import torch.multiprocessing
|
||||
from stable_baselines3.common.callbacks import EvalCallback
|
||||
from stable_baselines3.common.monitor import Monitor
|
||||
import torch as th
|
||||
from typing import Callable
|
||||
from datetime import datetime, timezone
|
||||
from stable_baselines3.common.utils import set_random_seed
|
||||
import gym
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
torch.multiprocessing.set_sharing_strategy('file_system')
|
||||
@ -37,8 +40,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
|
||||
super().__init__(config=kwargs['config'])
|
||||
th.set_num_threads(self.freqai_info['rl_config'].get('thread_count', 4))
|
||||
self.reward_params = self.freqai_info['rl_config']['model_reward_parameters']
|
||||
self.train_env: Base5ActionRLEnv = None
|
||||
self.eval_env: Base5ActionRLEnv = None
|
||||
self.train_env: BaseEnvironment = None
|
||||
self.eval_env: BaseEnvironment = None
|
||||
self.eval_callback: EvalCallback = None
|
||||
self.model_type = self.freqai_info['rl_config']['model_type']
|
||||
self.rl_config = self.freqai_info['rl_config']
|
||||
@ -194,7 +197,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
|
||||
def _predict(window):
|
||||
market_side, current_profit, trade_duration = self.get_state_info(dk.pair)
|
||||
observations = dataframe.iloc[window.index]
|
||||
observations['current_profit'] = current_profit
|
||||
observations['current_profit_pct'] = current_profit
|
||||
observations['position'] = market_side
|
||||
observations['trade_duration'] = trade_duration
|
||||
res, _ = model.predict(observations, deterministic=True)
|
||||
@ -306,7 +309,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
|
||||
return
|
||||
|
||||
|
||||
def make_env(MyRLEnv: Base5ActionRLEnv, env_id: str, rank: int,
|
||||
def make_env(MyRLEnv: BaseEnvironment, env_id: str, rank: int,
|
||||
seed: int, train_df: DataFrame, price: DataFrame,
|
||||
reward_params: Dict[str, int], window_size: int, monitor: bool = False,
|
||||
config: Dict[str, Any] = {}) -> Callable:
|
||||
|
||||
@ -1,19 +1,20 @@
|
||||
import logging
|
||||
import torch as th
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Optional, Tuple, Type, Union
|
||||
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
|
||||
|
||||
import gym
|
||||
import torch as th
|
||||
from stable_baselines3 import DQN
|
||||
from stable_baselines3.common.buffers import ReplayBuffer
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from pathlib import Path
|
||||
from stable_baselines3.dqn.policies import (CnnPolicy, DQNPolicy, MlpPolicy,
|
||||
QNetwork)
|
||||
from torch import nn
|
||||
import gym
|
||||
from stable_baselines3.common.torch_layers import (BaseFeaturesExtractor,
|
||||
FlattenExtractor)
|
||||
from stable_baselines3.common.type_aliases import GymEnv, Schedule
|
||||
from stable_baselines3.common.policies import BasePolicy
|
||||
from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor
|
||||
from stable_baselines3.common.type_aliases import GymEnv, Schedule
|
||||
from stable_baselines3.dqn.policies import CnnPolicy, DQNPolicy, MlpPolicy, QNetwork
|
||||
from torch import nn
|
||||
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@ -7,7 +7,7 @@ import time
|
||||
from abc import ABC, abstractmethod
|
||||
from pathlib import Path
|
||||
from threading import Lock
|
||||
from typing import Any, Dict, Tuple, Optional
|
||||
from typing import Any, Dict, Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
@ -1,15 +1,14 @@
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict
|
||||
|
||||
import torch as th
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Positions
|
||||
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
|
||||
from pathlib import Path
|
||||
# from pandas import DataFrame
|
||||
# from stable_baselines3.common.callbacks import EvalCallback
|
||||
# from stable_baselines3.common.monitor import Monitor
|
||||
import numpy as np
|
||||
import torch as th
|
||||
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
|
||||
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@ -53,7 +52,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
|
||||
|
||||
return model
|
||||
|
||||
class MyRLEnv(BaseReinforcementLearningModel.MyRLEnv):
|
||||
class MyRLEnv(Base5ActionRLEnv):
|
||||
"""
|
||||
User can override any function in BaseRLEnv and gym.Env. Here the user
|
||||
sets a custom reward based on profit and trade duration.
|
||||
|
||||
@ -1,15 +1,16 @@
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict # , Tuple
|
||||
|
||||
# import numpy.typing as npt
|
||||
import torch as th
|
||||
from stable_baselines3.common.callbacks import EvalCallback
|
||||
from stable_baselines3.common.vec_env import SubprocVecEnv
|
||||
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.freqai.RL.BaseReinforcementLearningModel import (BaseReinforcementLearningModel,
|
||||
make_env)
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@ -26,7 +27,7 @@ class ReinforcementLearner_multiproc(BaseReinforcementLearningModel):
|
||||
|
||||
# model arch
|
||||
policy_kwargs = dict(activation_fn=th.nn.ReLU,
|
||||
net_arch=[256, 256])
|
||||
net_arch=[256, 256, 128])
|
||||
|
||||
if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
|
||||
model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
|
||||
@ -64,9 +65,9 @@ class ReinforcementLearner_multiproc(BaseReinforcementLearningModel):
|
||||
test_df = data_dictionary["test_features"]
|
||||
|
||||
env_id = "train_env"
|
||||
num_cpu = int(self.freqai_info["rl_config"]["thread_count"] / 2)
|
||||
num_cpu = int(self.freqai_info["rl_config"]["thread_count"])
|
||||
self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1, train_df, prices_train,
|
||||
self.reward_params, self.CONV_WIDTH,
|
||||
self.reward_params, self.CONV_WIDTH, monitor=True,
|
||||
config=self.config) for i
|
||||
in range(num_cpu)])
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user