id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
19,676 | import logging
from typing import Optional, Text, Dict, Any
import re
from logging import config as logging_config
from time import time
from contextlib import contextmanager
from .config import C
The provided code snippet includes necessary dependencies for implementing the `set_log_with_config` function. Write a Pyt... | set log with config :param log_config: :return: |
19,677 | import logging
from typing import Optional, Text, Dict, Any
import re
from logging import config as logging_config
from time import time
from contextlib import contextmanager
from .config import C
def set_global_logger_level(level: int, return_orig_handler_level: bool = False):
"""set qlib.xxx logger handlers level... | set qlib.xxx logger handlers level to use contextmanager Parameters ---------- level: int logger level Examples --------- .. code-block:: python import qlib import logging from qlib.log import get_module_logger, set_global_logger_level_cm qlib.init() tmp_logger_01 = get_module_logger("tmp_logger_01", level=logging.INFO... |
19,678 | import socket
from typing import Callable, List, Optional
from tqdm.auto import tqdm
from qlib.config import C
from qlib.data.dataset import Dataset
from qlib.data.dataset.weight import Reweighter
from qlib.log import get_module_logger
from qlib.model.base import Model
from qlib.utils import (
auto_filter_kwargs,
... | Begin task training to start a recorder and save the task config. Args: task_config (dict): the config of a task experiment_name (str): the name of experiment recorder_name (str): the given name will be the recorder name. None for using rid. Returns: Recorder: the model recorder |
19,679 | import socket
from typing import Callable, List, Optional
from tqdm.auto import tqdm
from qlib.config import C
from qlib.data.dataset import Dataset
from qlib.data.dataset.weight import Reweighter
from qlib.log import get_module_logger
from qlib.model.base import Model
from qlib.utils import (
auto_filter_kwargs,
... | Finish task training with real model fitting and saving. Args: rec (Recorder): the recorder will be resumed experiment_name (str): the name of experiment Returns: Recorder: the model recorder |
19,680 | import bisect
from datetime import datetime, time, date, timedelta
from typing import List, Optional, Tuple, Union
import functools
import re
import pandas as pd
from qlib.config import C
from qlib.constant import REG_CN, REG_TW, REG_US
REG_CN = "cn"
REG_US = "us"
REG_TW = "tw"
The provided code snippet includes nece... | Is there only one piece of data for stock market. Parameters ---------- start_time : Union[pd.Timestamp, str] closed start time for data. end_time : Union[pd.Timestamp, str] closed end time for data. freq : region: str Region, for example, "cn", "us" Returns ------- bool True means one piece of data to obtain. |
19,681 | import bisect
from datetime import datetime, time, date, timedelta
from typing import List, Optional, Tuple, Union
import functools
import re
import pandas as pd
from qlib.config import C
from qlib.constant import REG_CN, REG_TW, REG_US
CN_TIME = [
datetime.strptime("9:30", "%H:%M"),
datetime.strptime("11:30", ... | null |
19,682 | import bisect
from datetime import datetime, time, date, timedelta
from typing import List, Optional, Tuple, Union
import functools
import re
import pandas as pd
from qlib.config import C
from qlib.constant import REG_CN, REG_TW, REG_US
def get_min_cal(shift: int = 0, region: str = REG_CN) -> List[time]:
"""
ge... | get the min-bar index in a day for a time range (both left and right is closed) given a fixed frequency Parameters ---------- start : str e.g. "9:30" end : str e.g. "14:30" freq : str "1min" Returns ------- Tuple[int, int]: The index of start and end in the calendar. Both left and right are **closed** |
19,683 | import bisect
from datetime import datetime, time, date, timedelta
from typing import List, Optional, Tuple, Union
import functools
import re
import pandas as pd
from qlib.config import C
from qlib.constant import REG_CN, REG_TW, REG_US
The provided code snippet includes necessary dependencies for implementing the `ep... | change the time by infinitely small quantity. Parameters ---------- date_time : pd.Timestamp the original time direction : str the direction the time are going to - "backward" for going to history - "forward" for going to the future Returns ------- pd.Timestamp: the shifted time |
19,684 | from __future__ import annotations
from typing import Dict, Tuple, Union, Callable, List
import bisect
import numpy as np
import pandas as pd
class SingleData(IndexData):
def __init__(
self, data: Union[int, float, np.number, list, dict, pd.Series] = [], index: Union[List, pd.Index, Index] = []
):
... | concat all SingleData by index. TODO: now just for SingleData. Parameters ---------- data_list : List[SingleData] the list of all SingleData to concat. Returns ------- MultiData the MultiData with ndim == 2 |
19,685 | from __future__ import annotations
from typing import Dict, Tuple, Union, Callable, List
import bisect
import numpy as np
import pandas as pd
class SingleData(IndexData):
def __init__(
self, data: Union[int, float, np.number, list, dict, pd.Series] = [], index: Union[List, pd.Index, Index] = []
):
... | concat all SingleData by new index. Parameters ---------- data_list : List[SingleData] the list of all SingleData to sum. new_index : list the new_index of new SingleData. fill_value : float fill the missing values or replace np.NaN. Returns ------- SingleData the SingleData with new_index and values after sum. |
19,686 | from __future__ import annotations
from typing import Dict, Tuple, Union, Callable, List
import bisect
import numpy as np
import pandas as pd
class BinaryOps:
def __init__(self, method_name):
self.method_name = method_name
def __get__(self, obj, *args):
# bind object
self.obj = obj
... | meta class for auto generating operations for index data. |
19,687 | import contextlib
import importlib
import os
from pathlib import Path
import pickle
import pkgutil
import re
import sys
from types import ModuleType
from typing import Any, Dict, List, Tuple, Union
from urllib.parse import urlparse
from qlib.typehint import InstConf
The provided code snippet includes necessary depende... | Python doesn't provide the downcasting mechanism. We use the trick here to downcast the class Parameters ---------- obj : object the object to be cast cls : type the target class type |
19,688 | import contextlib
import importlib
import os
from pathlib import Path
import pickle
import pkgutil
import re
import sys
from types import ModuleType
from typing import Any, Dict, List, Tuple, Union
from urllib.parse import urlparse
from qlib.typehint import InstConf
The provided code snippet includes necessary depende... | Find all the classes recursively that inherit from `cls` in a given module. - `cls` itself is also included >>> from qlib.data.dataset.handler import DataHandler >>> find_all_classes("qlib.contrib.data.handler", DataHandler) [<class 'qlib.contrib.data.handler.Alpha158'>, <class 'qlib.contrib.data.handler.Alpha158vwap'>... |
19,689 | import os
import shutil
import tempfile
import contextlib
from typing import Optional, Text, IO, Union
from pathlib import Path
from qlib.log import get_module_logger
The provided code snippet includes necessary dependencies for implementing the `get_or_create_path` function. Write a Python function `def get_or_create... | Create or get a file or directory given the path and return_dir. Parameters ---------- path: a string indicates the path or None indicates creating a temporary path. return_dir: if True, create and return a directory; otherwise c&r a file. |
19,690 | import os
import shutil
import tempfile
import contextlib
from typing import Optional, Text, IO, Union
from pathlib import Path
from qlib.log import get_module_logger
The provided code snippet includes necessary dependencies for implementing the `save_multiple_parts_file` function. Write a Python function `def save_mu... | Save multiple parts file Implementation process: 1. get the absolute path to 'filename' 2. create a 'filename' directory 3. user does something with file_path('filename/') 4. remove 'filename' directory 5. make_archive 'filename' directory, and rename 'archive file' to filename :param filename: result model path :param... |
19,691 | import os
import shutil
import tempfile
import contextlib
from typing import Optional, Text, IO, Union
from pathlib import Path
from qlib.log import get_module_logger
log = get_module_logger("utils.file")
The provided code snippet includes necessary dependencies for implementing the `unpack_archive_with_buffer` functi... | Unpack archive with archive buffer After the call is finished, the archive file and directory will be deleted. Implementation process: 1. create 'tempfile' in '~/tmp/' and directory 2. 'buffer' write to 'tempfile' 3. unpack archive file('tempfile') 4. user does something with file_path('tempfile/') 5. remove 'tempfile'... |
19,692 | import os
import shutil
import tempfile
import contextlib
from typing import Optional, Text, IO, Union
from pathlib import Path
from qlib.log import get_module_logger
def get_tmp_file_with_buffer(buffer):
temp_dir = os.path.expanduser("~/tmp")
if not os.path.exists(temp_dir):
os.makedirs(temp_dir)
... | null |
19,693 | import os
import shutil
import tempfile
import contextlib
from typing import Optional, Text, IO, Union
from pathlib import Path
from qlib.log import get_module_logger
The provided code snippet includes necessary dependencies for implementing the `get_io_object` function. Write a Python function `def get_io_object(file... | providing a easy interface to get an IO object Parameters ---------- file : Union[IO, str, Path] a object representing the file Returns ------- IO: a IO-like object Raises ------ NotImplementedError: |
19,694 | from copy import deepcopy
from typing import List, Union
import pandas as pd
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `robust_zscore` function. Write a Python function `def robust_zscore(x: pd.Series, zscore=False)` to solve the following problem:
Robust ZScore ... | Robust ZScore Normalization Use robust statistics for Z-Score normalization: mean(x) = median(x) std(x) = MAD(x) * 1.4826 Reference: https://en.wikipedia.org/wiki/Median_absolute_deviation. |
19,695 | from copy import deepcopy
from typing import List, Union
import pandas as pd
import numpy as np
def zscore(x: Union[pd.Series, pd.DataFrame]):
return (x - x.mean()).div(x.std()) | null |
19,696 | from copy import deepcopy
from typing import List, Union
import pandas as pd
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `deepcopy_basic_type` function. Write a Python function `def deepcopy_basic_type(obj: object) -> object` to solve the following problem:
deepcop... | deepcopy an object without copy the complicated objects. This is useful when you want to generate Qlib tasks and share the handler NOTE: - This function can't handle recursive objects!!!!! Parameters ---------- obj : object the object to be copied Returns ------- object: The copied object |
19,697 | from functools import partial
from threading import Thread
from typing import Callable, Text, Union
from joblib import Parallel, delayed
from joblib._parallel_backends import MultiprocessingBackend
import pandas as pd
from queue import Queue
import concurrent
from qlib.config import C, QlibConfig
class ParallelExt(Para... | datetime_groupby_apply This function will apply the `apply_func` on the datetime level index. Parameters ---------- df : DataFrame for processing apply_func : Union[Callable, Text] apply_func for processing the data if a string is given, then it is treated as naive pandas function axis : which axis is the datetime leve... |
19,698 | import numpy as np
import pandas as pd
from functools import partial
from typing import Union, Callable
from . import lazy_sort_index
from .time import Freq, cal_sam_minute
from ..config import C
class Freq:
NORM_FREQ_MONTH = "month"
NORM_FREQ_WEEK = "week"
NORM_FREQ_DAY = "day"
NORM_FREQ_MINUTE = "min... | Resample the calendar with frequency freq_raw into the calendar with frequency freq_sam Assumption: - Fix length (240) of the calendar in each day. Parameters ---------- calendar_raw : np.ndarray The calendar with frequency freq_raw freq_raw : str Frequency of the raw calendar freq_sam : str Sample frequency region: st... |
19,699 | import numpy as np
import pandas as pd
from functools import partial
from typing import Union, Callable
from . import lazy_sort_index
from .time import Freq, cal_sam_minute
from ..config import C
class Freq:
NORM_FREQ_MONTH = "month"
NORM_FREQ_WEEK = "week"
NORM_FREQ_DAY = "day"
NORM_FREQ_MINUTE = "min... | get the feature with higher or equal frequency than `freq`. Returns ------- pd.DataFrame the feature with higher or equal frequency |
19,700 | import numpy as np
import pandas as pd
from functools import partial
from typing import Union, Callable
from . import lazy_sort_index
from .time import Freq, cal_sam_minute
from ..config import C
def get_level_index(df: pd.DataFrame, level=Union[str, int]) -> int:
"""
get the level index of `df` given `level`... | Resample value from time-series data - If `feature` has MultiIndex[instrument, datetime], apply the `method` to each instruemnt data with datetime in [start_time, end_time] Example: .. code-block:: print(feature) $close $volume instrument datetime SH600000 2010-01-04 86.778313 16162960.0 2010-01-05 87.433578 28117442.0... |
19,701 | import numpy as np
import pandas as pd
from functools import partial
from typing import Union, Callable
from . import lazy_sort_index
from .time import Freq, cal_sam_minute
from ..config import C
def get_valid_value(series, last=True):
"""get the first/last not nan value of pd.Series with single level index
Par... | get the first/last not nan value of pd.Series|DataFrame with single level index |
19,702 | import os
import numpy as np
import pandas as pd
from pathlib import Path
DATA_PATH = Path(os.path.join("data", "pickle", "backtest"))
OUTPUT_PATH = Path(os.path.join("data", "orders"))
np.random.seed(1234)
np.random.shuffle(stocks)
def generate_order(stock: str, start_idx: int, end_idx: int) -> bool:
dataset = pd... | null |
19,703 | import qlib
import optuna
from qlib.constant import REG_CN
from qlib.utils import init_instance_by_config
from qlib.tests.data import GetData
from qlib.tests.config import get_dataset_config, CSI300_MARKET, DATASET_ALPHA360_CLASS
def objective(trial):
task = {
"model": {
"class": "LGBModel",
... | null |
19,704 | import qlib
import optuna
from qlib.constant import REG_CN
from qlib.utils import init_instance_by_config
from qlib.tests.config import CSI300_DATASET_CONFIG
from qlib.tests.data import GetData
def objective(trial):
task = {
"model": {
"class": "LGBModel",
"module_path": "qlib.contr... | null |
19,705 | import os
import sys
import fire
import time
import glob
import yaml
import shutil
import signal
import inspect
import tempfile
import functools
import statistics
import subprocess
from datetime import datetime
from pathlib import Path
from operator import xor
from pprint import pprint
import qlib
from qlib.workflow im... | null |
19,706 | import os
import sys
import fire
import time
import glob
import yaml
import shutil
import signal
import inspect
import tempfile
import functools
import statistics
import subprocess
from datetime import datetime
from pathlib import Path
from operator import xor
from pprint import pprint
import qlib
from qlib.workflow im... | null |
19,707 | import os
import sys
import fire
import time
import glob
import yaml
import shutil
import signal
import inspect
import tempfile
import functools
import statistics
import subprocess
from datetime import datetime
from pathlib import Path
from operator import xor
from pprint import pprint
import qlib
from qlib.workflow im... | null |
19,708 | import os
import sys
import fire
import time
import glob
import yaml
import shutil
import signal
import inspect
import tempfile
import functools
import statistics
import subprocess
from datetime import datetime
from pathlib import Path
from operator import xor
from pprint import pprint
import qlib
from qlib.workflow im... | null |
19,709 | import os
import sys
import fire
import time
import glob
import yaml
import shutil
import signal
import inspect
import tempfile
import functools
import statistics
import subprocess
from datetime import datetime
from pathlib import Path
from operator import xor
from pprint import pprint
import qlib
from qlib.workflow im... | null |
19,710 | import os
import sys
import fire
import time
import glob
import yaml
import shutil
import signal
import inspect
import tempfile
import functools
import statistics
import subprocess
from datetime import datetime
from pathlib import Path
from operator import xor
from pprint import pprint
import qlib
from qlib.workflow im... | null |
19,711 | import os
import sys
import fire
import time
import glob
import yaml
import shutil
import signal
import inspect
import tempfile
import functools
import statistics
import subprocess
from datetime import datetime
from pathlib import Path
from operator import xor
from pprint import pprint
import qlib
from qlib.workflow im... | null |
19,712 | import os
import sys
import fire
import time
import glob
import yaml
import shutil
import signal
import inspect
import tempfile
import functools
import statistics
import subprocess
from datetime import datetime
from pathlib import Path
from operator import xor
from pprint import pprint
import qlib
from qlib.workflow im... | null |
19,713 | import os
import sys
import fire
import time
import glob
import yaml
import shutil
import signal
import inspect
import tempfile
import functools
import statistics
import subprocess
from datetime import datetime
from pathlib import Path
from operator import xor
from pprint import pprint
import qlib
from qlib.workflow im... | null |
19,714 | from pathlib import Path
from typing import Union
import numpy as np
import pandas as pd
import tensorflow.compat.v1 as tf
import data_formatters.base
import expt_settings.configs
import libs.hyperparam_opt
import libs.tft_model
import libs.utils as utils
import os
import datetime as dte
from qlib.model.base import Mod... | Prepare data to fit the TFT model. Args: df: Original DataFrame. fillna: Whether to fill the data with the mean values. Returns: Transformed DataFrame. |
19,715 | from pathlib import Path
from typing import Union
import numpy as np
import pandas as pd
import tensorflow.compat.v1 as tf
import data_formatters.base
import expt_settings.configs
import libs.hyperparam_opt
import libs.tft_model
import libs.utils as utils
import os
import datetime as dte
from qlib.model.base import Mod... | null |
19,716 | from pathlib import Path
from typing import Union
import numpy as np
import pandas as pd
import tensorflow.compat.v1 as tf
import data_formatters.base
import expt_settings.configs
import libs.hyperparam_opt
import libs.tft_model
import libs.utils as utils
import os
import datetime as dte
from qlib.model.base import Mod... | null |
19,717 | import os
import pathlib
import numpy as np
import tensorflow as tf
from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file
The provided code snippet includes necessary dependencies for implementing the `get_single_col_by_input_type` function. Write a Python function `def get_single_col... | Returns name of single column. Args: input_type: Input type of column to extract column_definition: Column definition list for experiment |
19,718 | import os
import pathlib
import numpy as np
import tensorflow as tf
from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file
The provided code snippet includes necessary dependencies for implementing the `extract_cols_from_data_type` function. Write a Python function `def extract_cols_fr... | Extracts the names of columns that correspond to a define data_type. Args: data_type: DataType of columns to extract. column_definition: Column definition to use. excluded_input_types: Set of input types to exclude Returns: List of names for columns with data type specified. |
19,719 | import os
import pathlib
import numpy as np
import tensorflow as tf
from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file
The provided code snippet includes necessary dependencies for implementing the `tensorflow_quantile_loss` function. Write a Python function `def tensorflow_quantil... | Computes quantile loss for tensorflow. Standard quantile loss as defined in the "Training Procedure" section of the main TFT paper Args: y: Targets y_pred: Predictions quantile: Quantile to use for loss calculations (between 0 & 1) Returns: Tensor for quantile loss. |
19,720 | import os
import pathlib
import numpy as np
import tensorflow as tf
from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file
The provided code snippet includes necessary dependencies for implementing the `numpy_normalised_quantile_loss` function. Write a Python function `def numpy_normal... | Computes normalised quantile loss for numpy arrays. Uses the q-Risk metric as defined in the "Training Procedure" section of the main TFT paper. Args: y: Targets y_pred: Predictions quantile: Quantile to use for loss calculations (between 0 & 1) Returns: Float for normalised quantile loss. |
19,721 | import os
import pathlib
import numpy as np
import tensorflow as tf
from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file
The provided code snippet includes necessary dependencies for implementing the `create_folder_if_not_exist` function. Write a Python function `def create_folder_if... | Creates folder if it doesn't exist. Args: directory: Folder path to create. |
19,722 | import os
import pathlib
import numpy as np
import tensorflow as tf
from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file
The provided code snippet includes necessary dependencies for implementing the `get_default_tensorflow_config` function. Write a Python function `def get_default_t... | Creates tensorflow config for graphs to run on CPU or GPU. Specifies whether to run graph on gpu or cpu and which GPU ID to use for multi GPU machines. Args: tf_device: 'cpu' or 'gpu' gpu_id: GPU ID to use if relevant Returns: Tensorflow config. |
19,723 | import os
import pathlib
import numpy as np
import tensorflow as tf
from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file
The provided code snippet includes necessary dependencies for implementing the `save` function. Write a Python function `def save(tf_session, model_folder, cp_name... | Saves Tensorflow graph to checkpoint. Saves all trainiable variables under a given variable scope to checkpoint. Args: tf_session: Session containing graph model_folder: Folder to save models cp_name: Name of Tensorflow checkpoint scope: Variable scope containing variables to save |
19,724 | import os
import pathlib
import numpy as np
import tensorflow as tf
from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file
def print_weights_in_checkpoint(model_folder, cp_name):
"""Prints all weights in Tensorflow checkpoint.
Args:
model_folder: Folder containing checkpoi... | Loads Tensorflow graph from checkpoint. Args: tf_session: Session to load graph into model_folder: Folder containing serialised model cp_name: Name of Tensorflow checkpoint scope: Variable scope to use. verbose: Whether to print additional debugging information. |
19,725 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gc
import json
import os
import shutil
import data_formatters.base
import libs.utils as utils
import numpy as np
import pandas as pd
import tensorflow as tf
Dense = tf.keras.layers.Dense
The provided cod... | Applies simple feed-forward network to an input. Args: inputs: MLP inputs hidden_size: Hidden state size output_size: Output size of MLP output_activation: Activation function to apply on output hidden_activation: Activation function to apply on input use_time_distributed: Whether to apply across time Returns: Tensor f... |
19,726 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gc
import json
import os
import shutil
import data_formatters.base
import libs.utils as utils
import numpy as np
import pandas as pd
import tensorflow as tf
Dense = tf.keras.layers.Dense
Activation = tf.k... | Applies the gated residual network (GRN) as defined in paper. Args: x: Network inputs hidden_layer_size: Internal state size output_size: Size of output layer dropout_rate: Dropout rate if dropout is applied use_time_distributed: Whether to apply network across time dimension additional_context: Additional context vect... |
19,727 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gc
import json
import os
import shutil
import data_formatters.base
import libs.utils as utils
import numpy as np
import pandas as pd
import tensorflow as tf
K = tf.keras.backend
The provided code snippet... | Returns causal mask to apply for self-attention layer. Args: self_attn_inputs: Inputs to self attention layer to determine mask shape |
19,728 | import os
import copy
import math
import json
import collections
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from tqdm import tqdm
from qlib.utils import get_or_create_path
from qlib.log import get_module_logger
from qlib.model.ba... | null |
19,729 | import os
import copy
import math
import json
import collections
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from tqdm import tqdm
from qlib.utils import get_or_create_path
from qlib.log import get_module_logger
from qlib.model.ba... | null |
19,730 | import os
import copy
import math
import json
import collections
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from tqdm import tqdm
from qlib.utils import get_or_create_path
from qlib.log import get_module_logger
from qlib.model.ba... | null |
19,731 | import copy
import torch
import numpy as np
import pandas as pd
from qlib.data.dataset import DatasetH
device = "cuda" if torch.cuda.is_available() else "cpu"
def _to_tensor(x):
if not isinstance(x, torch.Tensor):
return torch.tensor(x, dtype=torch.float, device=device)
return x | null |
19,732 | import copy
import torch
import numpy as np
import pandas as pd
from qlib.data.dataset import DatasetH
The provided code snippet includes necessary dependencies for implementing the `_create_ts_slices` function. Write a Python function `def _create_ts_slices(index, seq_len)` to solve the following problem:
create time... | create time series slices from pandas index Args: index (pd.MultiIndex): pandas multiindex with <instrument, datetime> order seq_len (int): sequence length |
19,733 | import copy
import torch
import numpy as np
import pandas as pd
from qlib.data.dataset import DatasetH
The provided code snippet includes necessary dependencies for implementing the `_get_date_parse_fn` function. Write a Python function `def _get_date_parse_fn(target)` to solve the following problem:
get date parse fu... | get date parse function This method is used to parse date arguments as target type. Example: get_date_parse_fn('20120101')('2017-01-01') => '20170101' get_date_parse_fn(20120101)('2017-01-01') => 20170101 |
19,734 | import os
import numpy as np
import pandas as pd
from qlib.data import D
from qlib.model.riskmodel import StructuredCovEstimator
def prepare_data(riskdata_root="./riskdata", T=240, start_time="2016-01-01"):
universe = D.features(D.instruments("csi300"), ["$close"], start_time=start_time).swaplevel().sort_index()
... | null |
19,735 | from datetime import date, datetime as dt
import os
from pathlib import Path
import random
import shutil
import time
import traceback
from arctic import Arctic, chunkstore
import arctic
from arctic import Arctic, CHUNK_STORE
from arctic.chunkstore.chunkstore import CHUNK_SIZE
import fire
from joblib import Parallel, de... | null |
19,736 | from datetime import date, datetime as dt
import os
from pathlib import Path
import random
import shutil
import time
import traceback
from arctic import Arctic, chunkstore
import arctic
from arctic import Arctic, CHUNK_STORE
from arctic.chunkstore.chunkstore import CHUNK_SIZE
import fire
from joblib import Parallel, de... | null |
19,737 | import pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
plt.rcParams["font.sans-serif"] = "SimHei"
plt.rcParams["axes.unicode_minus"] = False
from tqdm.auto import tqdm
plt.figure(figsize=(40, 20))
sns.heatmap(data_sim)
plt.figure(figsize=(40,... | null |
19,738 | import abc
import importlib
from pathlib import Path
from typing import Union, Iterable, List
import fire
import numpy as np
import pandas as pd
import baostock as bs
from loguru import logger
The provided code snippet includes necessary dependencies for implementing the `run` function. Write a Python function `def ru... | Collect future calendar(day) Parameters ---------- qlib_dir: qlib data directory region: cn/CN or us/US start_date start date end_date end date Examples ------- # get cn future calendar $ python future_calendar_collector.py --qlib_data_1d_dir <user data dir> --region cn |
19,739 | import re
import copy
import importlib
import time
import bisect
import pickle
import random
import requests
import functools
from pathlib import Path
from typing import Iterable, Tuple, List
import numpy as np
import pandas as pd
from lxml import etree
from loguru import logger
from yahooquery import Ticker
from tqdm ... | get SH/SZ history calendar list Parameters ---------- bench_code: str value from ["CSI300", "CSI500", "ALL", "US_ALL"] Returns ------- history calendar list |
19,740 | import re
import copy
import importlib
import time
import bisect
import pickle
import random
import requests
import functools
from pathlib import Path
from typing import Iterable, Tuple, List
import numpy as np
import pandas as pd
from lxml import etree
from loguru import logger
from yahooquery import Ticker
from tqdm ... | get calendar list by selecting the date when few funds trade in this day Parameters ---------- source_dir: str or Path The directory where the raw data collected from the Internet is saved date_field_name: str date field name, default is date threshold: float threshold to exclude some days when few funds trade in this ... |
19,741 | import re
import copy
import importlib
import time
import bisect
import pickle
import random
import requests
import functools
from pathlib import Path
from typing import Iterable, Tuple, List
import numpy as np
import pandas as pd
from lxml import etree
from loguru import logger
from yahooquery import Ticker
from tqdm ... | get SH/SZ stock symbols Returns ------- stock symbols |
19,742 | import re
import copy
import importlib
import time
import bisect
import pickle
import random
import requests
import functools
from pathlib import Path
from typing import Iterable, Tuple, List
import numpy as np
import pandas as pd
from lxml import etree
from loguru import logger
from yahooquery import Ticker
from tqdm ... | get US stock symbols Returns ------- stock symbols |
19,743 | import re
import copy
import importlib
import time
import bisect
import pickle
import random
import requests
import functools
from pathlib import Path
from typing import Iterable, Tuple, List
import numpy as np
import pandas as pd
from lxml import etree
from loguru import logger
from yahooquery import Ticker
from tqdm ... | get IN stock symbols Returns ------- stock symbols |
19,744 | import re
import copy
import importlib
import time
import bisect
import pickle
import random
import requests
import functools
from pathlib import Path
from typing import Iterable, Tuple, List
import numpy as np
import pandas as pd
from lxml import etree
from loguru import logger
from yahooquery import Ticker
from tqdm ... | get Brazil(B3) stock symbols Returns ------- B3 stock symbols |
19,745 | import re
import copy
import importlib
import time
import bisect
import pickle
import random
import requests
import functools
from pathlib import Path
from typing import Iterable, Tuple, List
import numpy as np
import pandas as pd
from lxml import etree
from loguru import logger
from yahooquery import Ticker
from tqdm ... | get en fund symbols Returns ------- fund symbols in China |
19,746 | import re
import copy
import importlib
import time
import bisect
import pickle
import random
import requests
import functools
from pathlib import Path
from typing import Iterable, Tuple, List
import numpy as np
import pandas as pd
from lxml import etree
from loguru import logger
from yahooquery import Ticker
from tqdm ... | symbol suffix to prefix Parameters ---------- symbol: str symbol capital : bool by default True Returns ------- |
19,747 | import re
import copy
import importlib
import time
import bisect
import pickle
import random
import requests
import functools
from pathlib import Path
from typing import Iterable, Tuple, List
import numpy as np
import pandas as pd
from lxml import etree
from loguru import logger
from yahooquery import Ticker
from tqdm ... | symbol prefix to sufix Parameters ---------- symbol: str symbol capital : bool by default True Returns ------- |
19,748 | import re
import copy
import importlib
import time
import bisect
import pickle
import random
import requests
import functools
from pathlib import Path
from typing import Iterable, Tuple, List
import numpy as np
import pandas as pd
from lxml import etree
from loguru import logger
from yahooquery import Ticker
from tqdm ... | get trading date by shift Parameters ---------- trading_list: list trading calendar list shift : int shift, default is 1 trading_date : pd.Timestamp trading date Returns ------- |
19,749 | import re
import copy
import importlib
import time
import bisect
import pickle
import random
import requests
import functools
from pathlib import Path
from typing import Iterable, Tuple, List
import numpy as np
import pandas as pd
from lxml import etree
from loguru import logger
from yahooquery import Ticker
from tqdm ... | Parameters ---------- qlib_dir: str qlib data dir, default "Path(__file__).parent/qlib_data" index_name: str index name, value from ["csi100", "csi300"] method: str method, value from ["parse_instruments", "save_new_companies"] freq: str freq, value from ["day", "1min"] request_retry: int request retry, by default 5 re... |
19,750 | import re
import copy
import importlib
import time
import bisect
import pickle
import random
import requests
import functools
from pathlib import Path
from typing import Iterable, Tuple, List
import numpy as np
import pandas as pd
from lxml import etree
from loguru import logger
from yahooquery import Ticker
from tqdm ... | calc adjusted price This method does 4 things. 1. Adds the `paused` field. - The added paused field comes from the paused field of the 1d data. 2. Aligns the time of the 1d data. 3. The data is reweighted. - The reweighting method: - volume / factor - open * factor - high * factor - low * factor - close * factor 4. Cal... |
19,751 | import sys
from pathlib import Path
from concurrent.futures import ThreadPoolExecutor
import fire
import qlib
import pandas as pd
from tqdm import tqdm
from qlib.data import D
from loguru import logger
from data_collector.utils import generate_minutes_calendar_from_daily
def get_date_range(data_1min_dir: Path, max_work... | Use 1d data to fill in the missing symbols relative to 1min Parameters ---------- data_1min_dir: str 1min data dir qlib_data_1d_dir: str 1d qlib data(bin data) dir, from: https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format max_workers: int ThreadPoolExecutor(max_workers), by... |
19,752 | import sys
from typing import List
from pathlib import Path
import fire
import numpy as np
import pandas as pd
from loguru import logger
import baostock as bs
from data_collector.utils import generate_minutes_calendar_from_daily
def read_calendar_from_qlib(qlib_dir: Path) -> pd.DataFrame:
calendar_path = qlib_dir.j... | get future calendar Parameters ---------- qlib_dir: str or Path qlib data directory freq: str value from ["day", "1min"], by default day |
19,753 | import abc
import sys
import datetime
from abc import ABC
from pathlib import Path
import fire
import pandas as pd
from loguru import logger
from dateutil.tz import tzlocal
from data_collector.base import BaseCollector, BaseNormalize, BaseRun
from data_collector.utils import deco_retry
from pycoingecko import CoinGecko... | get crypto symbols in coingecko Returns ------- crypto symbols in given exchanges list of coingecko |
19,754 | import re
import abc
import sys
from io import BytesIO
from typing import List, Iterable
from pathlib import Path
import fire
import requests
import pandas as pd
import baostock as bs
from tqdm import tqdm
from loguru import logger
from data_collector.index import IndexBase
from data_collector.utils import get_calendar... | null |
19,755 | import mysql.connector
def print_user(user):
config = {
"host": "127.0.0.1",
"port": "3306",
"database": "hello_mysql",
"user": "root",
"password": "root1234"
}
# config = {
# "host": "bpw0hq9h09e7mqicjhtl-mysql.services.clever-cloud.com",
# "port":... | null |
19,756 | from hatchway import QueryOrBody, api_view
from api import schemas
from api.decorators import scope_required
from api.models import Application
def add_app(
request,
client_name: QueryOrBody[str],
redirect_uris: QueryOrBody[str],
scopes: QueryOrBody[None | str] = None,
website: QueryOrBody[None | s... | null |
19,757 | from hatchway import QueryOrBody, api_view
from api import schemas
from api.decorators import scope_required
from api.models import Application
def verify_credentials(
request,
) -> schemas.Application:
return schemas.Application.from_application_no_keys(request.token.application) | null |
19,758 | from django.core.files import File
from django.shortcuts import get_object_or_404
from hatchway import ApiError, QueryOrBody, api_view
from activities.models import PostAttachment, PostAttachmentStates
from api import schemas
from core.files import blurhash_image, resize_image
from ..decorators import scope_required
d... | null |
19,759 | from django.core.files import File
from django.shortcuts import get_object_or_404
from hatchway import ApiError, QueryOrBody, api_view
from activities.models import PostAttachment, PostAttachmentStates
from api import schemas
from core.files import blurhash_image, resize_image
from ..decorators import scope_required
d... | null |
19,760 | from django.core.files import File
from django.shortcuts import get_object_or_404
from hatchway import ApiError, QueryOrBody, api_view
from activities.models import PostAttachment, PostAttachmentStates
from api import schemas
from core.files import blurhash_image, resize_image
from ..decorators import scope_required
d... | null |
19,761 | from hatchway import api_view
from activities.models import Emoji
from api.schemas import CustomEmoji
class CustomEmoji(Schema):
def from_emoji(cls, emoji: activities_models.Emoji) -> "CustomEmoji":
def emojis(request) -> list[CustomEmoji]:
return [
CustomEmoji.from_emoji(e) for e in Emoji.objects.us... | null |
19,762 | from django.http import HttpRequest
from django.shortcuts import get_object_or_404
from hatchway import ApiResponse, api_view
from activities.models import PostInteraction, TimelineEvent
from activities.services import TimelineService
from api import schemas
from api.decorators import scope_required
from api.pagination... | null |
19,763 | from django.http import HttpRequest
from django.shortcuts import get_object_or_404
from hatchway import ApiResponse, api_view
from activities.models import PostInteraction, TimelineEvent
from activities.services import TimelineService
from api import schemas
from api.decorators import scope_required
from api.pagination... | null |
19,764 | from django.http import HttpRequest
from django.shortcuts import get_object_or_404
from hatchway import ApiResponse, api_view
from activities.models import PostInteraction, TimelineEvent
from activities.services import TimelineService
from api import schemas
from api.decorators import scope_required
from api.pagination... | null |
19,765 | from django.http import HttpRequest
from django.shortcuts import get_object_or_404
from hatchway import api_view
from activities.models import Hashtag
from api import schemas
from api.decorators import scope_required
from api.pagination import MastodonPaginator, PaginatingApiResponse, PaginationResult
from users.models... | null |
19,766 | from django.http import HttpRequest
from django.shortcuts import get_object_or_404
from hatchway import api_view
from activities.models import Hashtag
from api import schemas
from api.decorators import scope_required
from api.pagination import MastodonPaginator, PaginatingApiResponse, PaginationResult
from users.models... | null |
19,767 | from django.http import HttpRequest
from django.shortcuts import get_object_or_404
from hatchway import api_view
from activities.models import Hashtag
from api import schemas
from api.decorators import scope_required
from api.pagination import MastodonPaginator, PaginatingApiResponse, PaginationResult
from users.models... | null |
19,768 | from django.http import HttpRequest
from hatchway import api_view
from activities.models import Post
from activities.services import TimelineService
from api import schemas
from api.decorators import scope_required
from api.pagination import MastodonPaginator, PaginatingApiResponse, PaginationResult
class PaginatingAp... | null |
19,769 | from django.http import HttpRequest
from django.shortcuts import get_object_or_404
from hatchway import api_view
from api import schemas
from api.decorators import scope_required
from api.pagination import MastodonPaginator, PaginatingApiResponse, PaginationResult
from users.models.identity import Identity
from users.s... | null |
19,770 | from django.http import HttpRequest
from django.shortcuts import get_object_or_404
from hatchway import api_view
from api import schemas
from api.decorators import scope_required
from api.pagination import MastodonPaginator, PaginatingApiResponse, PaginationResult
from users.models.identity import Identity
from users.s... | null |
19,771 | from django.http import HttpRequest
from django.shortcuts import get_object_or_404
from hatchway import api_view
from api import schemas
from api.decorators import scope_required
from api.pagination import MastodonPaginator, PaginatingApiResponse, PaginationResult
from users.models.identity import Identity
from users.s... | null |
19,772 | from typing import Any
from django.core.files import File
from django.http import HttpRequest
from django.shortcuts import get_object_or_404
from hatchway import ApiResponse, QueryOrBody, api_view
from activities.models import Post, PostInteraction, PostInteractionStates
from activities.services import SearchService
fr... | null |
19,773 | from typing import Any
from django.core.files import File
from django.http import HttpRequest
from django.shortcuts import get_object_or_404
from hatchway import ApiResponse, QueryOrBody, api_view
from activities.models import Post, PostInteraction, PostInteractionStates
from activities.services import SearchService
fr... | null |
19,774 | from typing import Any
from django.core.files import File
from django.http import HttpRequest
from django.shortcuts import get_object_or_404
from hatchway import ApiResponse, QueryOrBody, api_view
from activities.models import Post, PostInteraction, PostInteractionStates
from activities.services import SearchService
fr... | null |
19,775 | from typing import Any
from django.core.files import File
from django.http import HttpRequest
from django.shortcuts import get_object_or_404
from hatchway import ApiResponse, QueryOrBody, api_view
from activities.models import Post, PostInteraction, PostInteractionStates
from activities.services import SearchService
fr... | Returns people you follow that also follow given account IDs |
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