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get TDQN working with 5 action environment

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This commit is contained in:
robcaulk 2022-08-15 11:11:16 +02:00
parent d4db5c3281
commit 6048f60f13
1 changed files with 168 additions and 33 deletions
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201
freqtrade/freqai/prediction_models/ReinforcementLearningTDQN.py
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@ -1,16 +1,17 @@
import logging
from typing import Any, Dict, Optional
from typing import Any, Dict # Optional
from enum import Enum
import numpy as np
import torch as th
from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.monitor import Monitor
# from stable_baselines3.common.vec_env import SubprocVecEnv
from freqtrade.freqai.RL.BaseRLEnv import BaseRLEnv, Actions, Positions
from freqtrade.freqai.RL.BaseRLEnv import BaseRLEnv
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
from freqtrade.freqai.RL.TDQNagent import TDQN
from stable_baselines3.common.buffers import ReplayBuffer
from gym import spaces
from gym.utils import seeding
logger = logging.getLogger(__name__)
@ -57,7 +58,7 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
learning_rate=0.00025, gamma=0.9,
target_update_interval=5000, buffer_size=50000,
exploration_initial_eps=1, exploration_final_eps=0.1,
replay_buffer_class=Optional(ReplayBuffer)
replay_buffer_class=ReplayBuffer
)
model.learn(
@ -70,11 +71,102 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
return model
class Actions(Enum):
Neutral = 0
Long_buy = 1
Long_sell = 2
Short_buy = 3
Short_sell = 4
class Positions(Enum):
Short = 0
Long = 1
Neutral = 0.5
def opposite(self):
return Positions.Short if self == Positions.Long else Positions.Long
class MyRLEnv(BaseRLEnv):
"""
User can override any function in BaseRLEnv and gym.Env
User can override any function in BaseRLEnv and gym.Env. Here the user
Adds 5 actions.
"""
metadata = {'render.modes': ['human']}
def __init__(self, df, prices, reward_kwargs, window_size=10, starting_point=True, ):
assert df.ndim == 2
self.seed()
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])
self.action_space = spaces.Discrete(len(Actions))
self.observation_space = spaces.Box(
low=-np.inf, high=np.inf, shape=self.shape, dtype=np.float32)
# episode
self._start_tick = self.window_size
self._end_tick = len(self.prices) - 1
self._done = None
self._current_tick = None
self._last_trade_tick = None
self._position = Positions.Neutral
self._position_history = None
self.total_reward = None
self._total_profit = None
self._first_rendering = None
self.history = None
self.trade_history = []
# self.A_t, self.B_t = 0.000639, 0.00001954
self.r_t_change = 0.
self.returns_report = []
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def reset(self):
self._done = False
if self.starting_point is True:
self._position_history = (self._start_tick * [None]) + [self._position]
else:
self._position_history = (self.window_size * [None]) + [self._position]
self._current_tick = self._start_tick
self._last_trade_tick = None
self._position = Positions.Neutral
self.total_reward = 0.
self._total_profit = 1. # unit
self._first_rendering = True
self.history = {}
self.trade_history = []
self.portfolio_log_returns = np.zeros(len(self.prices))
self._profits = [(self._start_tick, 1)]
self.close_trade_profit = []
self.r_t_change = 0.
self.returns_report = []
return self._get_observation()
def step(self, action):
self._done = False
self._current_tick += 1
@ -85,11 +177,12 @@ class MyRLEnv(BaseRLEnv):
self.update_portfolio_log_returns(action)
self._update_profit(action)
step_reward = self._calculate_reward(action)
step_reward = self.calculate_reward(action)
self.total_reward += step_reward
trade_type = None
if self.is_tradesignal(action):
if self.is_tradesignal(action): # exclude 3 case not trade
# Update position
"""
Action: Neutral, position: Long -> Close Long
Action: Neutral, position: Short -> Close Short
@ -104,12 +197,18 @@ class MyRLEnv(BaseRLEnv):
if action == Actions.Neutral.value:
self._position = Positions.Neutral
trade_type = "neutral"
elif action == Actions.Long.value:
elif action == Actions.Long_buy.value:
self._position = Positions.Long
trade_type = "long"
elif action == Actions.Short.value:
elif action == Actions.Short_buy.value:
self._position = Positions.Short
trade_type = "short"
elif action == Actions.Long_sell.value:
self._position = Positions.Neutral
trade_type = "neutral"
elif action == Actions.Short_sell.value:
self._position = Positions.Neutral
trade_type = "neutral"
else:
print("case not defined")
@ -136,33 +235,69 @@ class MyRLEnv(BaseRLEnv):
return observation, step_reward, self._done, info
def calculate_reward(self, action):
def _get_observation(self):
return self.signal_features[(self._current_tick - self.window_size):self._current_tick]
def get_unrealized_profit(self):
if self._last_trade_tick is None:
return 0.
# close long
if action == Actions.Long_sell.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))
if action == Actions.Long_sell.value and self._position == Positions.Long:
if self.close_trade_profit[-1] > self.profit_aim * self.rr:
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)) * 2)
# close short
if action == Actions.Short_buy.value and self._position == Positions.Short:
last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
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)
return float(np.log(last_trade_price) - np.log(current_price))
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.
if action == Actions.Short_buy.value and self._position == Positions.Short:
if self.close_trade_profit[-1] > self.profit_aim * self.rr:
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)) * 2)
def is_tradesignal(self, action):
# 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_buy.value and self._position == Positions.Short) or
(action == Actions.Short_sell.value and self._position == Positions.Short) or
(action == Actions.Short_buy.value and self._position == Positions.Long) or
(action == Actions.Short_sell.value and self._position == Positions.Long) or
return 0.
(action == Actions.Long_buy.value and self._position == Positions.Long) or
(action == Actions.Long_sell.value and self._position == Positions.Long) or
(action == Actions.Long_buy.value and self._position == Positions.Short) or
(action == Actions.Long_sell.value and self._position == Positions.Short))
def _is_trade(self, action):
return ((action == Actions.Long_buy.value and self._position == Positions.Short) or
(action == Actions.Short_buy.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) or
(action == Actions.Neutral.Short_sell and self._position == Positions.Long) or
(action == Actions.Neutral.Long_sell 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)