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from cmath import inf
from typing import Any, List
from easydict import EasyDict
from abc import abstractmethod
from gym import spaces
from gym.utils import seeding
from enum import Enum
import os
import gym
import copy
import pandas as pd
import numpy as np
from ding.envs import BaseEnv, BaseEnvTimestep
from ding.utils import ENV_REGISTRY
from ding.torch_utils import to_ndarray
def load_dataset(name, index_name):
base_dir = os.path.dirname(os.path.abspath(__file__))
path = os.path.join(base_dir, 'data', name + '.csv')
assert os.path.exists(
path
), "You need to put the stock data under the \'DI-engine/dizoo/gym_anytrading/envs/data\' folder.\n \
if using StocksEnv, you can download Google stocks data at \
https://github.com/AminHP/gym-anytrading/blob/master/gym_anytrading/datasets/data/STOCKS_GOOGL.csv"
df = pd.read_csv(path, parse_dates=True, index_col=index_name)
return df
class Actions(int, Enum):
DOUBLE_SELL = 0
SELL = 1
HOLD = 2
BUY = 3
DOUBLE_BUY = 4
class Positions(int, Enum):
SHORT = -1.
FLAT = 0.
LONG = 1.
def transform(position: Positions, action: int) -> Any:
'''
Overview:
used by env.tep().
This func is used to transform the env's position from
the input (position, action) pair according to the status machine.
Arguments:
- position(Positions) : Long, Short or Flat
- action(int) : Doulbe_Sell, Sell, Hold, Buy, Double_Buy
Returns:
- next_position(Positions) : the position after transformation.
'''
if action == Actions.SELL:
if position == Positions.LONG:
return Positions.FLAT, False
if position == Positions.FLAT:
return Positions.SHORT, True
if action == Actions.BUY:
if position == Positions.SHORT:
return Positions.FLAT, False
if position == Positions.FLAT:
return Positions.LONG, True
if action == Actions.DOUBLE_SELL and (position == Positions.LONG or position == Positions.FLAT):
return Positions.SHORT, True
if action == Actions.DOUBLE_BUY and (position == Positions.SHORT or position == Positions.FLAT):
return Positions.LONG, True
return position, False
@ENV_REGISTRY.register('base_trading')
class TradingEnv(BaseEnv):
def __init__(self, cfg: EasyDict) -> None:
self._cfg = cfg
self._env_id = cfg.env_id
#======== param to plot =========
self.cnt = 0
if 'plot_freq' not in self._cfg:
self.plot_freq = 10
else:
self.plot_freq = self._cfg.plot_freq
if 'save_path' not in self._cfg:
self.save_path = './'
else:
self.save_path = self._cfg.save_path
#================================
self.train_range = cfg.train_range
self.test_range = cfg.test_range
self.window_size = cfg.window_size
self.prices = None
self.signal_features = None
self.feature_dim_len = None
self.shape = (cfg.window_size, 3)
#======== param about episode =========
self._start_tick = 0
self._end_tick = 0
self._done = None
self._current_tick = None
self._last_trade_tick = None
self._position = None
self._position_history = None
self._total_reward = None
#======================================
self._init_flag = True
# init the following variables variable at first reset.
self._action_space = None
self._observation_space = None
self._reward_space = None
def seed(self, seed: int, dynamic_seed: bool = True) -> None:
self._seed = seed
self._dynamic_seed = dynamic_seed
np.random.seed(self._seed)
self.np_random, seed = seeding.np_random(seed)
def reset(self, start_idx: int = None) -> Any:
self.cnt += 1
self.prices, self.signal_features, self.feature_dim_len = self._process_data(start_idx)
if self._init_flag:
self.shape = (self.window_size, self.feature_dim_len)
self._action_space = spaces.Discrete(len(Actions))
self._observation_space = spaces.Box(low=-np.inf, high=np.inf, shape=self.shape, dtype=np.float64)
self._reward_space = gym.spaces.Box(-inf, inf, shape=(1, ), dtype=np.float32)
self._init_flag = False
self._done = False
self._current_tick = self._start_tick
self._last_trade_tick = self._current_tick - 1
self._position = Positions.FLAT
self._position_history = [self._position]
self._profit_history = [1.]
self._total_reward = 0.
return self._get_observation()
def random_action(self) -> Any:
return np.array([self.action_space.sample()])
def step(self, action: np.ndarray) -> BaseEnvTimestep:
assert isinstance(action, np.ndarray), type(action)
if action.shape == (1, ):
action = action.item() # 0-dim array
self._done = False
self._current_tick += 1
if self._current_tick >= self._end_tick:
self._done = True
step_reward = self._calculate_reward(action)
self._total_reward += step_reward
self._position, trade = transform(self._position, action)
if trade:
self._last_trade_tick = self._current_tick
self._position_history.append(self._position)
self._profit_history.append(float(np.exp(self._total_reward)))
observation = self._get_observation()
info = dict(
total_reward=self._total_reward,
position=self._position.value,
)
if self._done:
if self._env_id[-1] == 'e' and self.cnt % self.plot_freq == 0:
self.render()
info['max_possible_profit'] = np.log(self.max_possible_profit())
info['eval_episode_return'] = self._total_reward
step_reward = to_ndarray([step_reward]).astype(np.float32)
return BaseEnvTimestep(observation, step_reward, self._done, info)
def _get_observation(self) -> np.ndarray:
obs = to_ndarray(self.signal_features[(self._current_tick - self.window_size + 1):self._current_tick + 1]
).reshape(-1).astype(np.float32)
tick = (self._current_tick - self._last_trade_tick) / self._cfg.eps_length
obs = np.hstack([obs, to_ndarray([self._position.value]), to_ndarray([tick])]).astype(np.float32)
return obs
def render(self) -> None:
import matplotlib.pyplot as plt
plt.clf()
plt.xlabel('trading days')
plt.ylabel('profit')
plt.plot(self._profit_history)
plt.savefig(self.save_path + str(self._env_id) + "-profit.png")
plt.clf()
plt.xlabel('trading days')
plt.ylabel('close price')
window_ticks = np.arange(len(self._position_history))
eps_price = self.raw_prices[self._start_tick:self._end_tick + 1]
plt.plot(eps_price)
short_ticks = []
long_ticks = []
flat_ticks = []
for i, tick in enumerate(window_ticks):
if self._position_history[i] == Positions.SHORT:
short_ticks.append(tick)
elif self._position_history[i] == Positions.LONG:
long_ticks.append(tick)
else:
flat_ticks.append(tick)
plt.plot(long_ticks, eps_price[long_ticks], 'g^', markersize=3, label="Long")
plt.plot(flat_ticks, eps_price[flat_ticks], 'bo', markersize=3, label="Flat")
plt.plot(short_ticks, eps_price[short_ticks], 'rv', markersize=3, label="Short")
plt.legend(loc='upper left', bbox_to_anchor=(0.05, 0.95))
plt.savefig(self.save_path + str(self._env_id) + '-price.png')
def close(self):
import matplotlib.pyplot as plt
plt.close()
# override
def create_collector_env_cfg(cfg: dict) -> List[dict]:
"""
Overview:
Return a list of all of the environment from input config, used in env manager \
(a series of vectorized env), and this method is mainly responsible for envs collecting data.
In TradingEnv, this method will rename every env_id and generate different config.
Arguments:
- cfg (:obj:`dict`): Original input env config, which needs to be transformed into the type of creating \
env instance actually and generated the corresponding number of configurations.
Returns:
- env_cfg_list (:obj:`List[dict]`): List of ``cfg`` including all the config collector envs.
.. note::
Elements(env config) in collector_env_cfg/evaluator_env_cfg can be different, such as server ip and port.
"""
collector_env_num = cfg.pop('collector_env_num')
collector_env_cfg = [copy.deepcopy(cfg) for _ in range(collector_env_num)]
for i in range(collector_env_num):
collector_env_cfg[i]['env_id'] += ('-' + str(i) + 'e')
return collector_env_cfg
# override
def create_evaluator_env_cfg(cfg: dict) -> List[dict]:
"""
Overview:
Return a list of all of the environment from input config, used in env manager \
(a series of vectorized env), and this method is mainly responsible for envs evaluating performance.
In TradingEnv, this method will rename every env_id and generate different config.
Arguments:
- cfg (:obj:`dict`): Original input env config, which needs to be transformed into the type of creating \
env instance actually and generated the corresponding number of configurations.
Returns:
- env_cfg_list (:obj:`List[dict]`): List of ``cfg`` including all the config evaluator envs.
"""
evaluator_env_num = cfg.pop('evaluator_env_num')
evaluator_env_cfg = [copy.deepcopy(cfg) for _ in range(evaluator_env_num)]
for i in range(evaluator_env_num):
evaluator_env_cfg[i]['env_id'] += ('-' + str(i) + 'e')
return evaluator_env_cfg
@abstractmethod
def _process_data(self):
raise NotImplementedError
@abstractmethod
def _calculate_reward(self, action):
raise NotImplementedError
@abstractmethod
def max_possible_profit(self):
raise NotImplementedError
@property
def observation_space(self) -> gym.spaces.Space:
return self._observation_space
@property
def action_space(self) -> gym.spaces.Space:
return self._action_space
@property
def reward_space(self) -> gym.spaces.Space:
return self._reward_space
def __repr__(self) -> str:
return "DI-engine Trading Env"
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