231 lines
7.2 KiB
Python
231 lines
7.2 KiB
Python
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from enum import Enum
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import gym
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import matplotlib.pyplot as plt
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import numpy as np
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from gym import spaces
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from gym.utils import seeding
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class Actions(Enum):
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Hold = 0
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Buy = 1
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Sell = 2
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class Positions(Enum):
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Short = 0
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Long = 1
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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 GymAnytrading(gym.Env):
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"""
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Based on https://github.com/AminHP/gym-anytrading
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"""
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metadata = {'render.modes': ['human']}
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def __init__(self, signal_features, prices, window_size, fee=0.0):
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assert signal_features.ndim == 2
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self.seed()
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self.signal_features = signal_features
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self.prices = prices
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self.window_size = window_size
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self.fee = fee
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self.shape = (window_size, self.signal_features.shape[1])
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# spaces
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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 = None
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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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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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self._current_tick = self._start_tick
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self._last_trade_tick = self._current_tick - 1
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self._position = Positions.Short
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self._position_history = (self.window_size * [None]) + [self._position]
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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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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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if self._current_tick == self._end_tick:
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self._done = True
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step_reward = self._calculate_reward(action)
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self._total_reward += step_reward
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self._update_profit(action)
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trade = False
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if ((action == Actions.Buy.value and self._position == Positions.Short) or
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(action == Actions.Sell.value and self._position == Positions.Long)):
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trade = True
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if trade:
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self._position = self._position.opposite()
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self._last_trade_tick = self._current_tick
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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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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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return self.signal_features[(self._current_tick - self.window_size):self._current_tick]
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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 render(self, mode='human'):
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def _plot_position(position, tick):
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color = None
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if position == Positions.Short:
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color = 'red'
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elif position == Positions.Long:
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color = 'green'
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if color:
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plt.scatter(tick, self.prices[tick], color=color)
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if self._first_rendering:
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self._first_rendering = False
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plt.cla()
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plt.plot(self.prices)
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start_position = self._position_history[self._start_tick]
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_plot_position(start_position, self._start_tick)
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_plot_position(self._position, self._current_tick)
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plt.suptitle(
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"Total Reward: %.6f" % self._total_reward + ' ~ ' +
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"Total Profit: %.6f" % self._total_profit
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)
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plt.pause(0.01)
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def render_all(self, mode='human'):
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window_ticks = np.arange(len(self._position_history))
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plt.plot(self.prices)
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short_ticks = []
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long_ticks = []
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for i, tick in enumerate(window_ticks):
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if self._position_history[i] == Positions.Short:
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short_ticks.append(tick)
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elif self._position_history[i] == Positions.Long:
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long_ticks.append(tick)
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plt.plot(short_ticks, self.prices[short_ticks], 'ro')
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plt.plot(long_ticks, self.prices[long_ticks], 'go')
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plt.suptitle(
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"Total Reward: %.6f" % self._total_reward + ' ~ ' +
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"Total Profit: %.6f" % self._total_profit
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)
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def close(self):
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plt.close()
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def save_rendering(self, filepath):
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plt.savefig(filepath)
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def pause_rendering(self):
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plt.show()
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def _calculate_reward(self, action):
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step_reward = 0
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trade = False
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if ((action == Actions.Buy.value and self._position == Positions.Short) or
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(action == Actions.Sell.value and self._position == Positions.Long)):
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trade = True
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if trade:
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current_price = self.prices[self._current_tick]
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last_trade_price = self.prices[self._last_trade_tick]
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price_diff = current_price - last_trade_price
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if self._position == Positions.Long:
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step_reward += price_diff
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return step_reward
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def _update_profit(self, action):
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trade = False
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if ((action == Actions.Buy.value and self._position == Positions.Short) or
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(action == Actions.Sell.value and self._position == Positions.Long)):
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trade = True
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if trade or self._done:
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current_price = self.prices[self._current_tick]
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last_trade_price = self.prices[self._last_trade_tick]
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if self._position == Positions.Long:
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shares = (self._total_profit * (1 - self.fee)) / last_trade_price
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self._total_profit = (shares * (1 - self.fee)) * current_price
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def max_possible_profit(self):
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current_tick = self._start_tick
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last_trade_tick = current_tick - 1
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profit = 1.
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while current_tick <= self._end_tick:
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position = None
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if self.prices[current_tick] < self.prices[current_tick - 1]:
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while (current_tick <= self._end_tick and
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self.prices[current_tick] < self.prices[current_tick - 1]):
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current_tick += 1
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position = Positions.Short
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else:
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while (current_tick <= self._end_tick and
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self.prices[current_tick] >= self.prices[current_tick - 1]):
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current_tick += 1
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position = Positions.Long
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if position == Positions.Long:
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current_price = self.prices[current_tick - 1]
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last_trade_price = self.prices[last_trade_tick]
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shares = profit / last_trade_price
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profit = shares * current_price
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last_trade_tick = current_tick - 1
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print(profit)
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return profit
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