get TDQN working with 5 action environment
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robcaulk
parent
d4db5c3281
commit
6048f60f13
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@ -1,16 +1,17 @@
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import logging
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from typing import Any, Dict, Optional
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from typing import Any, Dict # Optional
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from enum import Enum
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import numpy as np
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import torch as th
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from stable_baselines3.common.callbacks import EvalCallback
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from stable_baselines3.common.monitor import Monitor
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# from stable_baselines3.common.vec_env import SubprocVecEnv
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from freqtrade.freqai.RL.BaseRLEnv import BaseRLEnv, Actions, Positions
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from freqtrade.freqai.RL.BaseRLEnv import BaseRLEnv
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from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
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from freqtrade.freqai.RL.TDQNagent import TDQN
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from stable_baselines3.common.buffers import ReplayBuffer
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from gym import spaces
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from gym.utils import seeding
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logger = logging.getLogger(__name__)
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@ -57,7 +58,7 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
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learning_rate=0.00025, gamma=0.9,
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target_update_interval=5000, buffer_size=50000,
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exploration_initial_eps=1, exploration_final_eps=0.1,
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replay_buffer_class=Optional(ReplayBuffer)
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replay_buffer_class=ReplayBuffer
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)
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model.learn(
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@ -70,11 +71,102 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
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return model
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class Actions(Enum):
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Neutral = 0
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Long_buy = 1
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Long_sell = 2
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Short_buy = 3
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Short_sell = 4
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class Positions(Enum):
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Short = 0
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Long = 1
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Neutral = 0.5
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def opposite(self):
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return Positions.Short if self == Positions.Long else Positions.Long
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class MyRLEnv(BaseRLEnv):
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"""
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User can override any function in BaseRLEnv and gym.Env
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User can override any function in BaseRLEnv and gym.Env. Here the user
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Adds 5 actions.
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"""
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metadata = {'render.modes': ['human']}
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def __init__(self, df, prices, reward_kwargs, window_size=10, starting_point=True, ):
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assert df.ndim == 2
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self.seed()
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self.df = df
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self.signal_features = self.df
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self.prices = prices
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self.window_size = window_size
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self.starting_point = starting_point
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self.rr = reward_kwargs["rr"]
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self.profit_aim = reward_kwargs["profit_aim"]
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self.fee = 0.0015
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# # spaces
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self.shape = (window_size, self.signal_features.shape[1])
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self.action_space = spaces.Discrete(len(Actions))
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self.observation_space = spaces.Box(
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low=-np.inf, high=np.inf, shape=self.shape, dtype=np.float32)
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# episode
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self._start_tick = self.window_size
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self._end_tick = len(self.prices) - 1
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self._done = None
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self._current_tick = None
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self._last_trade_tick = None
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self._position = Positions.Neutral
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self._position_history = None
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self.total_reward = None
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self._total_profit = None
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self._first_rendering = None
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self.history = None
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self.trade_history = []
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# self.A_t, self.B_t = 0.000639, 0.00001954
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self.r_t_change = 0.
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self.returns_report = []
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def seed(self, seed=None):
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self.np_random, seed = seeding.np_random(seed)
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return [seed]
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def reset(self):
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self._done = False
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if self.starting_point is True:
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self._position_history = (self._start_tick * [None]) + [self._position]
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else:
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self._position_history = (self.window_size * [None]) + [self._position]
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self._current_tick = self._start_tick
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self._last_trade_tick = None
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self._position = Positions.Neutral
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self.total_reward = 0.
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self._total_profit = 1. # unit
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self._first_rendering = True
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self.history = {}
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self.trade_history = []
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self.portfolio_log_returns = np.zeros(len(self.prices))
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self._profits = [(self._start_tick, 1)]
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self.close_trade_profit = []
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self.r_t_change = 0.
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self.returns_report = []
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return self._get_observation()
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def step(self, action):
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self._done = False
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self._current_tick += 1
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@ -85,11 +177,12 @@ class MyRLEnv(BaseRLEnv):
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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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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):
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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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@ -104,12 +197,18 @@ class MyRLEnv(BaseRLEnv):
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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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elif action == Actions.Long_buy.value:
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self._position = Positions.Long
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trade_type = "long"
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elif action == Actions.Short.value:
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elif action == Actions.Short_buy.value:
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self._position = Positions.Short
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trade_type = "short"
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elif action == Actions.Long_sell.value:
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self._position = Positions.Neutral
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trade_type = "neutral"
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elif action == Actions.Short_sell.value:
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self._position = Positions.Neutral
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trade_type = "neutral"
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else:
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print("case not defined")
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@ -136,33 +235,69 @@ class MyRLEnv(BaseRLEnv):
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return observation, step_reward, self._done, info
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def calculate_reward(self, action):
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def _get_observation(self):
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return self.signal_features[(self._current_tick - self.window_size):self._current_tick]
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def get_unrealized_profit(self):
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if self._last_trade_tick is None:
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return 0.
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# close long
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if action == Actions.Long_sell.value and self._position == Positions.Long:
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last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
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current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
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return float(np.log(current_price) - np.log(last_trade_price))
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if action == Actions.Long_sell.value and self._position == Positions.Long:
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if self.close_trade_profit[-1] > self.profit_aim * self.rr:
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last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
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current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
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return float((np.log(current_price) - np.log(last_trade_price)) * 2)
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# close short
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if action == Actions.Short_buy.value and self._position == Positions.Short:
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last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
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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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return float(np.log(last_trade_price) - np.log(current_price))
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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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if action == Actions.Short_buy.value and self._position == Positions.Short:
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if self.close_trade_profit[-1] > self.profit_aim * self.rr:
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last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
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current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
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return float((np.log(last_trade_price) - np.log(current_price)) * 2)
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def is_tradesignal(self, action):
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# trade signal
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"""
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not trade signal is :
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Action: Neutral, position: Neutral -> Nothing
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Action: Long, position: Long -> Hold Long
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Action: Short, position: Short -> Hold Short
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"""
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return not ((action == Actions.Neutral.value and self._position == Positions.Neutral) or
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(action == Actions.Short_buy.value and self._position == Positions.Short) or
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(action == Actions.Short_sell.value and self._position == Positions.Short) or
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(action == Actions.Short_buy.value and self._position == Positions.Long) or
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(action == Actions.Short_sell.value and self._position == Positions.Long) or
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return 0.
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(action == Actions.Long_buy.value and self._position == Positions.Long) or
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(action == Actions.Long_sell.value and self._position == Positions.Long) or
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(action == Actions.Long_buy.value and self._position == Positions.Short) or
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(action == Actions.Long_sell.value and self._position == Positions.Short))
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def _is_trade(self, action):
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return ((action == Actions.Long_buy.value and self._position == Positions.Short) or
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(action == Actions.Short_buy.value and self._position == Positions.Long) or
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(action == Actions.Neutral.value and self._position == Positions.Long) or
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(action == Actions.Neutral.value and self._position == Positions.Short) or
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(action == Actions.Neutral.Short_sell and self._position == Positions.Long) or
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(action == Actions.Neutral.Long_sell and self._position == Positions.Short)
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)
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def is_hold(self, action):
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return ((action == Actions.Short.value and self._position == Positions.Short)
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or (action == Actions.Long.value and self._position == Positions.Long))
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def add_buy_fee(self, price):
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return price * (1 + self.fee)
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def add_sell_fee(self, price):
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return price / (1 + self.fee)
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def _update_history(self, info):
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if not self.history:
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self.history = {key: [] for key in info.keys()}
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for key, value in info.items():
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self.history[key].append(value)
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