add continual retraining feature, handly mypy typing reqs, improve docstrings
This commit is contained in:
robcaulk
parent
b708134c1a
commit
c0cee5df07
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@ -85,12 +85,13 @@
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"verbose": 1
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},
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"rl_config": {
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"train_cycles": 10,
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"train_cycles": 3,
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"eval_cycles": 3,
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"thread_count": 4,
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"max_trade_duration_candles": 100,
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"model_type": "PPO",
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"policy_type": "MlpPolicy",
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"continual_retraining": true,
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"model_reward_parameters": {
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"rr": 1,
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"profit_aim": 0.02,
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@ -1,330 +1,330 @@
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import logging
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from enum import Enum
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# import logging
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# from enum import Enum
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import gym
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import numpy as np
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import pandas as pd
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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 gym
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# import numpy as np
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# import pandas as pd
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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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# from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
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# # from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
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logger = logging.getLogger(__name__)
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# logger = logging.getLogger(__name__)
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class Actions(Enum):
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Short = 0
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Long = 1
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Neutral = 2
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# class Actions(Enum):
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# Short = 0
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# Long = 1
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# Neutral = 2
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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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# 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 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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# 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 Base3ActionRLEnv(gym.Env):
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# class Base3ActionRLEnv(gym.Env):
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metadata = {'render.modes': ['human']}
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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):
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assert df.ndim == 2
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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):
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# assert df.ndim == 2
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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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# 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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# 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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# self.fee = 0.0015
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# # spaces
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self.shape = (window_size, self.signal_features.shape[1] + 2)
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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.value:
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self._position = Positions.Long
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trade_type = "long"
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elif action == Actions.Short.value:
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self._position = Positions.Short
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trade_type = "short"
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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 _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), 2)),
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columns=['current_profit_pct', 'position'],
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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 = pd.concat([features_window, features_and_state], axis=1)
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return features_and_state
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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: 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)
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or (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 _is_trade(self, action: Actions):
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return ((action == Actions.Long.value and self._position == Positions.Short) or
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(action == Actions.Short.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)
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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 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.Short.value or
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action == Actions.Neutral.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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# close short
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if (action == Actions.Long.value or
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action == Actions.Neutral.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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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 == 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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if action == Actions.Short.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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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 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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# # # spaces
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# self.shape = (window_size, self.signal_features.shape[1] + 2)
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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.value:
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# self._position = Positions.Long
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# trade_type = "long"
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# elif action == Actions.Short.value:
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# self._position = Positions.Short
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# trade_type = "short"
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# else:
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# 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 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
|
||||
# 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
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
import logging
|
||||
from enum import Enum
|
||||
# from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
|
||||
from typing import Optional
|
||||
|
||||
import gym
|
||||
import numpy as np
|
||||
|
|
@ -44,14 +44,14 @@ class Base5ActionRLEnv(gym.Env):
|
|||
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 = {}):
|
||||
assert df.ndim == 2
|
||||
|
||||
self.rl_config = config['freqai']['rl_config']
|
||||
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):
|
||||
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
|
||||
|
|
@ -69,18 +69,18 @@ class Base5ActionRLEnv(gym.Env):
|
|||
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._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 = None
|
||||
self.total_reward = None
|
||||
self._total_profit = None
|
||||
self._first_rendering = None
|
||||
self.history = None
|
||||
self.trade_history = []
|
||||
self._position_history: list = [None]
|
||||
self.total_reward: float = 0
|
||||
self._total_profit: float = 0
|
||||
self._first_rendering: bool = False
|
||||
self.history: dict = {}
|
||||
self.trade_history: list = []
|
||||
|
||||
def seed(self, seed: int = 1):
|
||||
self.np_random, seed = seeding.np_random(seed)
|
||||
|
|
@ -125,8 +125,7 @@ class Base5ActionRLEnv(gym.Env):
|
|||
self.total_reward += step_reward
|
||||
|
||||
trade_type = None
|
||||
if self.is_tradesignal(action): # exclude 3 case not trade
|
||||
# Update position
|
||||
if self.is_tradesignal(action):
|
||||
"""
|
||||
Action: Neutral, position: Long -> Close Long
|
||||
Action: Neutral, position: Short -> Close Short
|
||||
|
|
@ -223,9 +222,8 @@ class Base5ActionRLEnv(gym.Env):
|
|||
# trade signal
|
||||
"""
|
||||
not trade signal is :
|
||||
Action: Neutral, position: Neutral -> Nothing
|
||||
Action: Long, position: Long -> Hold Long
|
||||
Action: Short, position: Short -> Hold Short
|
||||
Determine if the signal is non sensical
|
||||
e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
|
||||
"""
|
||||
return not ((action == Actions.Neutral.value and self._position == Positions.Neutral) or
|
||||
(action == Actions.Neutral.value and self._position == Positions.Short) or
|
||||
|
|
@ -292,7 +290,7 @@ class Base5ActionRLEnv(gym.Env):
|
|||
|
||||
def most_recent_return(self, action: int):
|
||||
"""
|
||||
We support Long, Neutral and Short positions.
|
||||
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.
|
||||
|
|
|
|||
|
|
@ -19,6 +19,7 @@ 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')
|
||||
|
|
@ -40,6 +41,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
|
|||
self.eval_env: Base5ActionRLEnv = None
|
||||
self.eval_callback: EvalCallback = None
|
||||
self.model_type = self.freqai_info['rl_config']['model_type']
|
||||
self.rl_config = self.freqai_info['rl_config']
|
||||
self.continual_retraining = self.rl_config['continual_retraining']
|
||||
if self.model_type in SB3_MODELS:
|
||||
import_str = 'stable_baselines3'
|
||||
elif self.model_type in SB3_CONTRIB_MODELS:
|
||||
|
|
@ -68,7 +71,6 @@ class BaseReinforcementLearningModel(IFreqaiModel):
|
|||
|
||||
logger.info("--------------------Starting training " f"{pair} --------------------")
|
||||
|
||||
# filter the features requested by user in the configuration file and elegantly handle NaNs
|
||||
features_filtered, labels_filtered = dk.filter_features(
|
||||
unfiltered_dataframe,
|
||||
dk.training_features_list,
|
||||
|
|
@ -78,19 +80,19 @@ class BaseReinforcementLearningModel(IFreqaiModel):
|
|||
|
||||
data_dictionary: Dict[str, Any] = dk.make_train_test_datasets(
|
||||
features_filtered, labels_filtered)
|
||||
dk.fit_labels() # useless for now, but just satiating append methods
|
||||
dk.fit_labels() # FIXME useless for now, but just satiating append methods
|
||||
|
||||
# normalize all data based on train_dataset only
|
||||
prices_train, prices_test = self.build_ohlc_price_dataframes(dk.data_dictionary, pair, dk)
|
||||
data_dictionary = dk.normalize_data(data_dictionary)
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
# data cleaning/analysis
|
||||
self.data_cleaning_train(dk)
|
||||
|
||||
logger.info(
|
||||
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
|
||||
f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
|
||||
f' features and {len(data_dictionary["train_features"])} data points'
|
||||
)
|
||||
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
|
||||
|
||||
self.set_train_and_eval_environments(data_dictionary, prices_train, prices_test, dk)
|
||||
|
||||
|
|
@ -100,9 +102,11 @@ class BaseReinforcementLearningModel(IFreqaiModel):
|
|||
|
||||
return model
|
||||
|
||||
def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test, dk):
|
||||
def set_train_and_eval_environments(self, data_dictionary: Dict[str, DataFrame],
|
||||
prices_train: DataFrame, prices_test: DataFrame,
|
||||
dk: FreqaiDataKitchen):
|
||||
"""
|
||||
User overrides this as shown here if they are using a custom MyRLEnv
|
||||
User can override this if they are using a custom MyRLEnv
|
||||
"""
|
||||
train_df = data_dictionary["train_features"]
|
||||
test_df = data_dictionary["test_features"]
|
||||
|
|
@ -114,18 +118,22 @@ class BaseReinforcementLearningModel(IFreqaiModel):
|
|||
reward_kwargs=self.reward_params, config=self.config)
|
||||
self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
|
||||
window_size=self.CONV_WIDTH,
|
||||
reward_kwargs=self.reward_params, config=self.config), ".")
|
||||
reward_kwargs=self.reward_params, config=self.config),
|
||||
str(Path(dk.data_path / 'monitor')))
|
||||
self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
|
||||
render=False, eval_freq=eval_freq,
|
||||
best_model_save_path=dk.data_path)
|
||||
best_model_save_path=str(dk.data_path))
|
||||
else:
|
||||
self.train_env.reset()
|
||||
self.eval_env.reset()
|
||||
self.train_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
|
||||
self.eval_env.reset_env(test_df, prices_test, self.CONV_WIDTH, self.reward_params)
|
||||
# self.eval_callback.eval_env = self.eval_env
|
||||
# self.eval_callback.best_model_save_path = str(dk.data_path)
|
||||
# self.eval_callback._init_callback()
|
||||
self.eval_callback.__init__(self.eval_env, deterministic=True,
|
||||
render=False, eval_freq=eval_freq,
|
||||
best_model_save_path=dk.data_path)
|
||||
best_model_save_path=str(dk.data_path))
|
||||
|
||||
@abstractmethod
|
||||
def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
|
||||
|
|
@ -137,19 +145,20 @@ class BaseReinforcementLearningModel(IFreqaiModel):
|
|||
|
||||
return
|
||||
|
||||
def get_state_info(self, pair):
|
||||
def get_state_info(self, pair: str):
|
||||
open_trades = Trade.get_trades_proxy(is_open=True)
|
||||
market_side = 0.5
|
||||
current_profit = 0
|
||||
current_profit: float = 0
|
||||
trade_duration = 0
|
||||
for trade in open_trades:
|
||||
if trade.pair == pair:
|
||||
# FIXME: mypy typing doesnt like that strategy may be "None" (it never will be)
|
||||
current_value = self.strategy.dp._exchange.get_rate(
|
||||
pair, refresh=False, side="exit", is_short=trade.is_short)
|
||||
openrate = trade.open_rate
|
||||
now = datetime.now(timezone.utc).timestamp()
|
||||
trade_duration = (now - trade.open_date.timestamp()) / self.base_tf_seconds
|
||||
if 'long' in trade.enter_tag:
|
||||
trade_duration = int((now - trade.open_date.timestamp()) / self.base_tf_seconds)
|
||||
if 'long' in str(trade.enter_tag):
|
||||
market_side = 1
|
||||
current_profit = (current_value - openrate) / openrate
|
||||
else:
|
||||
|
|
@ -245,8 +254,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
|
|||
return
|
||||
|
||||
|
||||
def make_env(env_id: str, rank: int, seed: int, train_df, price,
|
||||
reward_params, window_size, monitor=False, config={}) -> Callable:
|
||||
def make_env(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:
|
||||
"""
|
||||
Utility function for multiprocessed env.
|
||||
|
||||
|
|
|
|||
|
|
@ -22,6 +22,12 @@ class ReinforcementLearnerCustomAgent(BaseReinforcementLearningModel):
|
|||
"""
|
||||
User can customize agent by defining the class and using it directly.
|
||||
Here the example is "TDQN"
|
||||
|
||||
Warning!
|
||||
This is an advanced example of how a user may create and use a highly
|
||||
customized model class (which can inherit from existing classes,
|
||||
similar to how the example below inherits from DQN).
|
||||
This file is for example purposes only, and should not be run.
|
||||
"""
|
||||
|
||||
def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
|
||||
|
|
@ -34,7 +40,7 @@ class ReinforcementLearnerCustomAgent(BaseReinforcementLearningModel):
|
|||
|
||||
# TDQN is a custom agent defined below
|
||||
model = TDQN(self.policy_type, self.train_env,
|
||||
tensorboard_log=Path(dk.data_path / "tensorboard"),
|
||||
tensorboard_log=str(Path(dk.data_path / "tensorboard")),
|
||||
policy_kwargs=policy_kwargs,
|
||||
**self.freqai_info['model_training_parameters']
|
||||
)
|
||||
|
|
@ -217,7 +223,7 @@ class TDQN(DQN):
|
|||
exploration_initial_eps: float = 1.0,
|
||||
exploration_final_eps: float = 0.05,
|
||||
max_grad_norm: float = 10,
|
||||
tensorboard_log: Optional[Path] = None,
|
||||
tensorboard_log: Optional[str] = None,
|
||||
create_eval_env: bool = False,
|
||||
policy_kwargs: Optional[Dict[str, Any]] = None,
|
||||
verbose: int = 1,
|
||||
|
|
@ -485,6 +485,10 @@ class FreqaiDataDrawer:
|
|||
f"Unable to load model, ensure model exists at " f"{dk.data_path} "
|
||||
)
|
||||
|
||||
# load it into ram if it was loaded from disk
|
||||
if coin not in self.model_dictionary:
|
||||
self.model_dictionary[coin] = model
|
||||
|
||||
if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
|
||||
dk.pca = cloudpickle.load(
|
||||
open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb")
|
||||
|
|
|
|||
|
|
@ -76,7 +76,8 @@ class ReinforcementLearningExample5ac(IStrategy):
|
|||
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
|
||||
informative[f"%-{coin}raw_volume"] = informative["volume"]
|
||||
|
||||
# The following features are necessary for RL models
|
||||
# FIXME: add these outside the user strategy?
|
||||
# The following columns are necessary for RL models.
|
||||
informative[f"%-{coin}raw_close"] = informative["close"]
|
||||
informative[f"%-{coin}raw_open"] = informative["open"]
|
||||
informative[f"%-{coin}raw_high"] = informative["high"]
|
||||
|
|
|
|||
|
|
@ -57,9 +57,9 @@ class BaseClassifierModel(IFreqaiModel):
|
|||
self.data_cleaning_train(dk)
|
||||
|
||||
logger.info(
|
||||
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
|
||||
f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
|
||||
f' features and {len(data_dictionary["train_features"])} data points'
|
||||
)
|
||||
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
|
||||
|
||||
model = self.fit(data_dictionary)
|
||||
|
||||
|
|
|
|||
|
|
@ -56,9 +56,9 @@ class BaseRegressionModel(IFreqaiModel):
|
|||
self.data_cleaning_train(dk)
|
||||
|
||||
logger.info(
|
||||
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
|
||||
f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
|
||||
f' features and {len(data_dictionary["train_features"])} data points'
|
||||
)
|
||||
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
|
||||
|
||||
model = self.fit(data_dictionary)
|
||||
|
||||
|
|
|
|||
|
|
@ -53,9 +53,9 @@ class BaseTensorFlowModel(IFreqaiModel):
|
|||
self.data_cleaning_train(dk)
|
||||
|
||||
logger.info(
|
||||
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
|
||||
f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
|
||||
f' features and {len(data_dictionary["train_features"])} data points'
|
||||
)
|
||||
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
|
||||
|
||||
model = self.fit(data_dictionary)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
import logging
|
||||
from typing import Any, Dict # , Tuple
|
||||
from typing import Any, Dict
|
||||
|
||||
# import numpy.typing as npt
|
||||
import torch as th
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
|
||||
|
|
@ -22,12 +21,18 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
|
|||
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
|
||||
|
||||
policy_kwargs = dict(activation_fn=th.nn.ReLU,
|
||||
net_arch=[256, 256, 128])
|
||||
net_arch=[512, 512, 256])
|
||||
|
||||
model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
|
||||
tensorboard_log=Path(dk.data_path / "tensorboard"),
|
||||
**self.freqai_info['model_training_parameters']
|
||||
)
|
||||
if dk.pair not in self.dd.model_dictionary or not self.continual_retraining:
|
||||
model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
|
||||
tensorboard_log=Path(dk.data_path / "tensorboard"),
|
||||
**self.freqai_info['model_training_parameters']
|
||||
)
|
||||
else:
|
||||
logger.info('Continual training activated - starting training from previously '
|
||||
'trained agent.')
|
||||
model = self.dd.model_dictionary[dk.pair]
|
||||
model.set_env(self.train_env)
|
||||
|
||||
model.learn(
|
||||
total_timesteps=int(total_timesteps),
|
||||
|
|
|
|||
Loading…
Reference in New Issue