Fix lint

portfolio_management/database/bases.py

@@ -4,6 +4,7 @@ from sqlalchemy import String
 from sqlalchemy import DateTime
 from sqlalchemy import Integer
 from sqlalchemy import ForeignKey
+
 from sqlalchemy.orm import relationship
 from sqlalchemy.ext.declarative import declarative_base
 

portfolio_management/database/constants.py

@@ -1,12 +1,18 @@
+# Data variables
 OPEN_TIME = 'open_time'
 CLOSE_TIME = 'close_time'
+DATA = 'data'
+
+# Coordinates
 SYMBOL = 'symbol'
 PROPERTY = 'property'
-INTERVAL = 'interval'
-VALUE = 'value'
-DATA = 'data'
 INDEX = 'index'
 
+# Attributes
+INTERVAL = 'interval'
+
+# Others
+VALUE = 'value'
 PROPERTY_LIST = [
     'open',
     'high',

portfolio_management/database/manager.py

@@ -1,10 +1,12 @@
 from typing import List
 from typing import Optional
-from pathlib import Path
+
 from sqlalchemy import inspect
 from sqlalchemy import create_engine
 from sqlalchemy.orm import sessionmaker
 
+import portfolio_management.paths as p
+
 from portfolio_management.io_utilities import write_yaml
 from portfolio_management.io_utilities import create_folders
 
@@ -22,7 +24,6 @@ from portfolio_management.database.utilities import session_scope
 from portfolio_management.database.utilities import get_engine_url
 from portfolio_management.database.utilities import try_insert
 from portfolio_management.database.utilities import silent_bulk_insert
-import portfolio_management.paths as p
 
 
 class Manager:

portfolio_management/database/retrieve.py

@@ -1,11 +1,11 @@
 from typing import List
 from typing import Optional
 
+import numpy as np
 import pandas as pd
 import xarray as xr
-import numpy as np
-
 from pathlib import Path
+
 from sqlalchemy.orm import Session
 
 import portfolio_management.paths as p

portfolio_management/database/utilities.py

@@ -1,6 +1,7 @@
 from typing import Type
 from typing import List
 from pathlib import Path
+
 from contextlib import contextmanager
 
 from sqlalchemy import create_engine

portfolio_management/environment/environment.py

@@ -8,8 +8,9 @@ from gym.utils import seeding
 import numpy as np
 from scipy.special import softmax
 
-from portfolio_management.database import constants as c
 from portfolio_management import paths as p
+from portfolio_management.database import constants as c
+
 from portfolio_management.environment.data import get_dataset
 from portfolio_management.environment.market import Market
 from portfolio_management.environment.portfolio import Portfolio

portfolio_management/environment/portfolio.py

@@ -1,5 +1,6 @@
 from typing import Union
 from typing import List
+
 import numpy as np
 
 from portfolio_management.environment.utilities import loguniform

portfolio_management/soft_actor_critic/agent.py

@@ -1,15 +1,15 @@
 import os
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # noqa Remove 'Successfully opened dynamic library' tensorflow warning
 
-import numpy as np
-from pathlib import Path
-from pathlib import PurePath
-
 from typing import Optional
 from typing import Sequence
 from typing import Union
 from typing import Tuple
 
+import numpy as np
+from pathlib import Path
+from pathlib import PurePath
+
 import torch
 import torch.optim as optim
 import torch.nn.functional as F  # noqa : N812

portfolio_management/soft_actor_critic/critic.py

@@ -1,11 +1,11 @@
-import torch
-import torch.nn as nn
-
 from typing import List
 from typing import Tuple
 from typing import Optional
 from typing import Sequence
 
+import torch
+import torch.nn as nn
+
 from portfolio_management.soft_actor_critic.utilities import weight_initialization
 from portfolio_management.soft_actor_critic.utilities import get_multilayer_perceptron
 

portfolio_management/soft_actor_critic/evaluate.py

@@ -1,17 +1,27 @@
+from typing import Optional
+from typing import Sequence
+
 import gym
 import torch
 import numpy as np
 from pathlib import Path
-from typing import Optional
-from typing import Sequence
-
 
 from portfolio_management.soft_actor_critic.agent import Agent
 
 
-def evaluate(env_name: str, run_name: str, env_kwargs: Optional[dict] = None, num_episodes: int = 5, seed: int = 0,
-             hidden_units: Optional[Sequence[int]] = None, directory: str = '../runs/',
-             deterministic: bool = False, render: bool = False, record: bool = False, **kwargs):
+def evaluate(
+        env_name: str,
+        run_name: str,
+        env_kwargs: Optional[dict] = None,
+        num_episodes: int = 5,
+        seed: int = 0,
+        hidden_units: Optional[Sequence[int]] = None,
+        directory: str = '../runs/',
+        deterministic: bool = False,
+        render: bool = False,
+        record: bool = False,
+        **kwargs
+):
 
     if kwargs:
         print(f'Unrecognized kwargs : {kwargs}')

portfolio_management/soft_actor_critic/memory.py

@@ -1,11 +1,11 @@
-import numpy as np
-
 from typing import Tuple
 from typing import Union
 from typing import Sequence
 
+import numpy as np
 
-class ReplayBuffer:  # todo maybe do more clean in the future
+
+class ReplayBuffer:  # todo maybe do more clean in the future + random deletion
     def __init__(self, max_size: int, input_shape: Union[Sequence[int], int], num_actions: int):
         self.memory_counter = 0
         self.memory_size = int(max_size)

portfolio_management/soft_actor_critic/policy.py

@@ -1,12 +1,13 @@
+from typing import Optional
+from typing import Sequence
+
+import numpy as np
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.distributions.normal import Normal
 
-import numpy as np
-from typing import Optional
-from typing import Sequence
-
 from portfolio_management.soft_actor_critic.utilities import weight_initialization
 from portfolio_management.soft_actor_critic.utilities import get_multilayer_perceptron
 

portfolio_management/soft_actor_critic/train.py

@@ -1,11 +1,12 @@
+from typing import Optional
+from typing import Sequence
+
 import gym
 import torch
 import datetime
 import numpy as np
 from pathlib import Path
 from itertools import count
-from typing import Optional
-from typing import Sequence
 
 from torch.utils.tensorboard import SummaryWriter
 
@@ -17,11 +18,26 @@ from portfolio_management.soft_actor_critic.utilities import save_to_writer
 from portfolio_management.soft_actor_critic.utilities import get_timedelta_formatted
 
 
-def train(env_name: str, env_kwargs: Optional[dict] = None, batch_size: int = 256, memory_size: int = 10e6,
-          learning_rate: float = 3e-4, alpha: float = 0.05, gamma: float = 0.99, tau: float = 0.005,
-          num_steps: int = 1_000_000, hidden_units: Optional[Sequence[int]] = None, load_models: bool = False,
-          saving_frequency: int = 20, run_name: Optional[str] = None, start_step: int = 1_000, seed: int = 0,
-          updates_per_step: int = 1, directory: str = '../runs/', **kwargs):
+def train(
+        env_name: str,
+        env_kwargs: Optional[dict] = None,
+        batch_size: int = 256,
+        memory_size: int = 10e6,
+        learning_rate: float = 3e-4,
+        alpha: float = 0.05,
+        gamma: float = 0.99,
+        tau: float = 0.005,
+        num_steps: int = 1_000_000,
+        hidden_units: Optional[Sequence[int]] = None,
+        load_models: bool = False,
+        saving_frequency: int = 20,
+        run_name: Optional[str] = None,
+        start_step: int = 1_000,
+        seed: int = 0,
+        updates_per_step: int = 1,
+        directory: str = '../runs/',
+        **kwargs
+):
 
     if kwargs:
         print(f'Unrecognized kwargs : {kwargs}')

portfolio_management/soft_actor_critic/utilities.py

@@ -1,3 +1,7 @@
+from typing import List
+from typing import Union
+from typing import Optional
+
 import datetime
 from pathlib import Path
 from contextlib import contextmanager
@@ -5,10 +9,6 @@ from contextlib import contextmanager
 import torch
 import torch.nn as nn
 
-from typing import List
-from typing import Union
-from typing import Optional
-
 
 def get_run_name(*arguments, **keyword_arguments) -> str:
     name = f'{datetime.datetime.now().strftime("%Y%m%d_%H%M%S")}'
@@ -69,6 +69,7 @@ def filter_info(dictionary: dict) -> dict:
             filtered_dictionary[key] = value
     return filtered_dictionary
 
+
 def filter_info_step(dictionary: dict) -> dict:
     filtered_dictionary = {}
     for key, value in dictionary.items():

requirements.txt

@@ -8,3 +8,5 @@ gym
 numpy
 scipy
 python-binance
+torch
+torchsummary