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import pytest |
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import numpy as np |
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from easydict import EasyDict |
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from dizoo.gym_anytrading.envs import StocksEnv |
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@pytest.mark.envtest |
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class TestStocksEnv: |
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def test_naive(self): |
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env = StocksEnv(EasyDict({"env_id": 'stocks-v0', "eps_length": 300,\ |
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"window_size": 20, "train_range": None, "test_range": None, "stocks_data_filename": 'STOCKS_GOOGL'})) |
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env.seed(314, dynamic_seed=False) |
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assert env._seed == 314 |
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obs = env.reset() |
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assert obs.shape == (62, ) |
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for _ in range(5): |
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env.reset() |
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np.random.seed(314) |
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print('=' * 60) |
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for i in range(10): |
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if i < 5: |
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random_action = np.array([env.action_space.sample()]) |
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else: |
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random_action = env.random_action() |
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timestep = env.step(random_action) |
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print(timestep) |
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assert isinstance(timestep.obs, np.ndarray) |
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assert isinstance(timestep.done, bool) |
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assert timestep.obs.shape == (62, ) |
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assert timestep.reward.shape == (1, ) |
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assert timestep.reward >= env.reward_space.low |
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assert timestep.reward <= env.reward_space.high |
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print(env.observation_space, env.action_space, env.reward_space) |
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env.close() |
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