id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
19,576 | import fire
import pandas as pd
import pathlib
import qlib
import logging
from ...data import D
from ...log import get_module_logger
from ...utils import get_pre_trading_date, is_tradable_date
from ..evaluate import risk_analysis
from ..backtest.backtest import update_account
from .manager import UserManager
from .util... | null |
19,577 | import numpy as np
import pandas as pd
from datetime import datetime
from qlib.data.cache import H
from qlib.data.data import Cal
from qlib.data.ops import ElemOperator, PairOperator
from qlib.utils.time import time_to_day_index
H = MemCache()
Cal: CalendarProviderWrapper = Wrapper()
The provided code snippet includ... | Load High-Freq Calendar Date Using Memcache. !!!NOTE: Loading the calendar is quite slow. So loading calendar before start multiprocessing will make it faster. Parameters ---------- freq : str frequency of read calendar file. future : bool whether including future trading day. Returns ------- _calendar: array of date. |
19,578 | import numpy as np
import pandas as pd
from datetime import datetime
from qlib.data.cache import H
from qlib.data.data import Cal
from qlib.data.ops import ElemOperator, PairOperator
from qlib.utils.time import time_to_day_index
H = MemCache()
Cal: CalendarProviderWrapper = Wrapper()
The provided code snippet includ... | Load High-Freq Calendar Minute Using Memcache |
19,579 | import matplotlib.pyplot as plt
import pandas as pd
The provided code snippet includes necessary dependencies for implementing the `sub_fig_generator` function. Write a Python function `def sub_fig_generator(sub_fs=(3, 3), col_n=10, row_n=1, wspace=None, hspace=None, sharex=False, sharey=False)` to solve the following... | sub_fig_generator. it will return a generator, each row contains <col_n> sub graph FIXME: Known limitation: - The last row will not be plotted automatically, please plot it outside the function Parameters ---------- sub_fs : the figure size of each subgraph in <col_n> * <row_n> subgraphs col_n : the number of subgraph ... |
19,580 | from functools import partial
import pandas as pd
import plotly.graph_objs as go
import statsmodels.api as sm
import matplotlib.pyplot as plt
from scipy import stats
from typing import Sequence
from qlib.typehint import Literal
from ..graph import ScatterGraph, SubplotsGraph, BarGraph, HeatmapGraph
from ..utils import ... | :param pred_label: :param reverse: :param N: :return: |
19,581 | from functools import partial
import pandas as pd
import plotly.graph_objs as go
import statsmodels.api as sm
import matplotlib.pyplot as plt
from scipy import stats
from typing import Sequence
from qlib.typehint import Literal
from ..graph import ScatterGraph, SubplotsGraph, BarGraph, HeatmapGraph
from ..utils import ... | :param pred_label: pd.DataFrame must contain one column of realized return with name `label` and one column of predicted score names `score`. :param methods: Sequence[Literal["IC", "Rank IC"]] IC series to plot. IC is sectional pearson correlation between label and score Rank IC is the spearman correlation between labe... |
19,582 | from functools import partial
import pandas as pd
import plotly.graph_objs as go
import statsmodels.api as sm
import matplotlib.pyplot as plt
from scipy import stats
from typing import Sequence
from qlib.typehint import Literal
from ..graph import ScatterGraph, SubplotsGraph, BarGraph, HeatmapGraph
from ..utils import ... | null |
19,583 | from functools import partial
import pandas as pd
import plotly.graph_objs as go
import statsmodels.api as sm
import matplotlib.pyplot as plt
from scipy import stats
from typing import Sequence
from qlib.typehint import Literal
from ..graph import ScatterGraph, SubplotsGraph, BarGraph, HeatmapGraph
from ..utils import ... | null |
19,584 | from functools import partial
import pandas as pd
import plotly.graph_objs as go
import statsmodels.api as sm
import matplotlib.pyplot as plt
from scipy import stats
from typing import Sequence
from qlib.typehint import Literal
from ..graph import ScatterGraph, SubplotsGraph, BarGraph, HeatmapGraph
from ..utils import ... | r"""Model performance :param pred_label: index is **pd.MultiIndex**, index name is **[instrument, datetime]**; columns names is **[score, label]**. It is usually same as the label of model training(e.g. "Ref($close, -2)/Ref($close, -1) - 1"). .. code-block:: python instrument datetime score label SH600004 2017-12-11 -0... |
19,585 | import copy
from typing import Iterable
import pandas as pd
import plotly.graph_objs as go
from ..graph import BaseGraph, SubplotsGraph
from ..analysis_position.parse_position import get_position_data
def _get_figure_with_position(
position: dict,
report_normal: pd.DataFrame,
label_data: pd.DataFrame,
s... | Backtest buy, sell, and holding cumulative return graph Example: .. code-block:: python from qlib.data import D from qlib.contrib.evaluate import risk_analysis, backtest, long_short_backtest from qlib.contrib.strategy import TopkDropoutStrategy # backtest parameters bparas = {} bparas['limit_threshold'] = 0.095 bparas[... |
19,586 | import copy
from typing import Iterable
import pandas as pd
import plotly.graph_objs as go
from ..graph import ScatterGraph
from ..analysis_position.parse_position import get_position_data
def _get_figure_with_position(
position: dict, label_data: pd.DataFrame, start_date=None, end_date=None
) -> Iterable[go.Figure... | Ranking percentage of stocks buy, sell, and holding on the trading day. Average rank-ratio(similar to **sell_df['label'].rank(ascending=False) / len(sell_df)**) of daily trading Example: .. code-block:: python from qlib.data import D from qlib.contrib.evaluate import backtest from qlib.contrib.strategy import TopkDropo... |
19,587 | import pandas as pd
from ..graph import SubplotsGraph, BaseGraph
def _report_figure(df: pd.DataFrame) -> [list, tuple]:
"""
:param df:
:return:
"""
# Get data
report_df = _calculate_report_data(df)
# Maximum Drawdown
max_start_date, max_end_date = _calculate_maximum(report_df)
ex_max... | display backtest report Example: .. code-block:: python import qlib import pandas as pd from qlib.utils.time import Freq from qlib.utils import flatten_dict from qlib.backtest import backtest, executor from qlib.contrib.evaluate import risk_analysis from qlib.contrib.strategy import TopkDropoutStrategy # init qlib qlib... |
19,588 | from typing import Iterable
import pandas as pd
import plotly.graph_objs as py
from ...evaluate import risk_analysis
from ..graph import SubplotsGraph, ScatterGraph
def _get_risk_analysis_figure(analysis_df: pd.DataFrame) -> Iterable[py.Figure]:
"""Get analysis graph figure
:param analysis_df:
:return:
... | Generate analysis graph and monthly analysis Example: .. code-block:: python import qlib import pandas as pd from qlib.utils.time import Freq from qlib.utils import flatten_dict from qlib.backtest import backtest, executor from qlib.contrib.evaluate import risk_analysis from qlib.contrib.strategy import TopkDropoutStra... |
19,589 | import pandas as pd
from ..graph import ScatterGraph
from ..utils import guess_plotly_rangebreaks
def _get_score_ic(pred_label: pd.DataFrame):
"""
:param pred_label:
:return:
"""
concat_data = pred_label.copy()
concat_data.dropna(axis=0, how="any", inplace=True)
_ic = concat_data.groupby(lev... | score IC Example: .. code-block:: python from qlib.data import D from qlib.contrib.report import analysis_position pred_df_dates = pred_df.index.get_level_values(level='datetime') features_df = D.features(D.instruments('csi500'), ['Ref($close, -2)/Ref($close, -1)-1'], pred_df_dates.min(), pred_df_dates.max()) features_... |
19,590 | import numpy as np
import torch
from torch import nn
from qlib.constant import EPS
from qlib.log import get_module_logger
The provided code snippet includes necessary dependencies for implementing the `preds_to_weight_with_clamp` function. Write a Python function `def preds_to_weight_with_clamp(preds, clip_weight=None... | Clip the weights. Parameters ---------- clip_weight: float The clip threshold. clip_method: str The clip method. Current available: "clamp", "tanh", and "sigmoid". |
19,591 | import pandas as pd
from typing import Tuple
from qlib import get_module_logger
from qlib.utils.paral import complex_parallel, DelayedDict
from joblib import Parallel, delayed
The provided code snippet includes necessary dependencies for implementing the `calc_long_short_prec` function. Write a Python function `def ca... | calculate the precision for long and short operation :param pred/label: index is **pd.MultiIndex**, index name is **[datetime, instruments]**; columns names is **[score]**. .. code-block:: python score datetime instrument 2020-12-01 09:30:00 SH600068 0.553634 SH600195 0.550017 SH600276 0.540321 SH600584 0.517297 SH6007... |
19,592 | import pandas as pd
from typing import Tuple
from qlib import get_module_logger
from qlib.utils.paral import complex_parallel, DelayedDict
from joblib import Parallel, delayed
The provided code snippet includes necessary dependencies for implementing the `calc_long_short_return` function. Write a Python function `def ... | calculate long-short return Note: `label` must be raw stock returns. Parameters ---------- pred : pd.Series stock predictions label : pd.Series stock returns date_col : str datetime index name quantile : float long-short quantile Returns ---------- long_short_r : pd.Series daily long-short returns long_avg_r : pd.Serie... |
19,593 | import pandas as pd
from typing import Tuple
from qlib import get_module_logger
from qlib.utils.paral import complex_parallel, DelayedDict
from joblib import Parallel, delayed
def pred_autocorr(pred: pd.Series, lag=1, inst_col="instrument", date_col="datetime"):
"""pred_autocorr.
Limitation:
- If the dateti... | calculate auto correlation for pred_dict Parameters ---------- pred_dict : dict A dict like {<method_name>: <prediction>} kwargs : all these arguments will be passed into pred_autocorr |
19,594 | import pandas as pd
from typing import Tuple
from qlib import get_module_logger
from qlib.utils.paral import complex_parallel, DelayedDict
from joblib import Parallel, delayed
def calc_ic(pred: pd.Series, label: pd.Series, date_col="datetime", dropna=False) -> (pd.Series, pd.Series):
"""calc_ic.
Parameters
... | calc_all_ic. Parameters ---------- pred_dict_all : A dict like {<method_name>: <prediction>} label: A pd.Series of label values Returns ------- {'Q2+IND_z': {'ic': <ic series like> 2016-01-04 -0.057407 ... 2020-05-28 0.183470 2020-05-29 0.171393 'ric': <rank ic series like> 2016-01-04 -0.040888 ... 2020-05-28 0.236665 ... |
19,595 | import torch.nn as nn
The provided code snippet includes necessary dependencies for implementing the `count_parameters` function. Write a Python function `def count_parameters(models_or_parameters, unit="m")` to solve the following problem:
This function is to obtain the storage size unit of a (or multiple) models. Pa... | This function is to obtain the storage size unit of a (or multiple) models. Parameters ---------- models_or_parameters : PyTorch model(s) or a list of parameters. unit : the storage size unit. Returns ------- The number of parameters of the given model(s) or parameters. |
19,596 | import os
from torch.utils.data import Dataset, DataLoader
import copy
from typing import Text, Union
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Function
from qlib.contrib.model.pytorch_utils import cou... | null |
19,597 | import os
from torch.utils.data import Dataset, DataLoader
import copy
from typing import Text, Union
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Function
from qlib.contrib.model.pytorch_utils import cou... | null |
19,598 | import os
from torch.utils.data import Dataset, DataLoader
import copy
from typing import Text, Union
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Function
from qlib.contrib.model.pytorch_utils import cou... | null |
19,599 | import os
from torch.utils.data import Dataset, DataLoader
import copy
from typing import Text, Union
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Function
from qlib.contrib.model.pytorch_utils import cou... | null |
19,600 | import os
from torch.utils.data import Dataset, DataLoader
import copy
from typing import Text, Union
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Function
from qlib.contrib.model.pytorch_utils import cou... | null |
19,601 | import os
from torch.utils.data import Dataset, DataLoader
import copy
from typing import Text, Union
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Function
from qlib.contrib.model.pytorch_utils import cou... | null |
19,602 | import os
from torch.utils.data import Dataset, DataLoader
import copy
from typing import Text, Union
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Function
from qlib.contrib.model.pytorch_utils import cou... | null |
19,603 | import os
from torch.utils.data import Dataset, DataLoader
import copy
from typing import Text, Union
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Function
from qlib.contrib.model.pytorch_utils import cou... | null |
19,604 | import os
from torch.utils.data import Dataset, DataLoader
import copy
from typing import Text, Union
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Function
from qlib.contrib.model.pytorch_utils import cou... | null |
19,605 | from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
from typing import Text, Union
import copy
from ...utils import get_or_create_path
from ...log import get_module_logger
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional a... | null |
19,606 | from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
import copy
import math
from ...utils import get_or_create_path
from ...log import get_module_logger
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from ..... | null |
19,607 | import io
import os
import copy
import math
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from tqdm import tqdm
from qlib.constant import EPS
from qlib.log import get_module_logger
from ql... | null |
19,608 | import io
import os
import copy
import math
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
try:
from torch.utils.tensorboard import SummaryWriter
except ImportError:
SummaryWriter =... | sample-wise transport Args: all_preds (torch.Tensor): predictions from all predictors, [sample x states] label (torch.Tensor): label, [sample] choice (torch.Tensor): gumbel softmax choice, [sample x states] prob (torch.Tensor): router predicted probility, [sample x states] hist_loss (torch.Tensor): history loss matrix,... |
19,609 | import io
import os
import copy
import math
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
try:
from torch.utils.tensorboard import SummaryWriter
except ImportError:
SummaryWriter =... | daily transport Args: all_preds (torch.Tensor): predictions from all predictors, [sample x states] label (torch.Tensor): label, [sample] choice (torch.Tensor): gumbel softmax choice, [days x states] prob (torch.Tensor): router predicted probility, [days x states] hist_loss (torch.Tensor): history loss matrix, [days x s... |
19,610 | import io
import os
import copy
import math
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from tqdm import tqdm
from qlib.constant import EPS
from qlib.log import get_module_logger
from ql... | Load state dict to provided model while ignore exceptions. |
19,611 | import io
import os
import copy
import math
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from tqdm import tqdm
from qlib.constant import EPS
from qlib.log import get_module_logger
from ql... | null |
19,612 | from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
from typing import Text, Union
import copy
import math
from ...utils import get_or_create_path
from ...log import get_module_logger
import torch
import torch.nn as nn
import torch.optim as optim
from ...model.ba... | null |
19,613 | from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
from scipy.stats import spearmanr, pearsonr
from ..data import D
from collections import OrderedDict
def get_position_list_value(positions):
# generate instrument list and date for whole poitions
instrum... | Parameters generate daily return series from position view positions: positions generated by strategy init_asset_value : init asset value return: pd.Series of daily return , return_series[date] = daily return rate |
19,614 | from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
from scipy.stats import spearmanr, pearsonr
from ..data import D
from collections import OrderedDict
def get_position_value(evaluate_date, position):
"""sum of close*amount
get value of position
use ... | Annualized Returns p_r = (p_end / p_start)^{(250/n)} - 1 p_r annual return p_end final value p_start init value n days of backtest |
19,615 | from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
from scipy.stats import spearmanr, pearsonr
from ..data import D
from collections import OrderedDict
def get_annaul_return_from_return_series(r, method="ci"):
"""Risk Analysis from daily return series
Pa... | Risk Analysis Parameters ---------- r : pandas.Series daily return series method : str interest calculation method, ci(compound interest)/si(simple interest) risk_free_rate : float risk_free_rate, default as 0.00, can set as 0.03 etc |
19,616 | from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
from scipy.stats import spearmanr, pearsonr
from ..data import D
from collections import OrderedDict
The provided code snippet includes necessary dependencies for implementing the `get_max_drawdown_from_series`... | Risk Analysis from asset value cumprod way Parameters ---------- r : pandas.Series daily return series |
19,617 | from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
from scipy.stats import spearmanr, pearsonr
from ..data import D
from collections import OrderedDict
def get_turnover_rate():
# in backtest
pass | null |
19,618 | from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
from scipy.stats import spearmanr, pearsonr
from ..data import D
from collections import OrderedDict
def get_annaul_return_from_return_series(r, method="ci"):
"""Risk Analysis from daily return series
Pa... | null |
19,619 | from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
from scipy.stats import spearmanr, pearsonr
from ..data import D
from collections import OrderedDict
def get_volatility_from_series(r):
return r.std(ddof=1) | null |
19,620 | from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
from scipy.stats import spearmanr, pearsonr
from ..data import D
from collections import OrderedDict
The provided code snippet includes necessary dependencies for implementing the `get_rank_ic` function. Write ... | Rank IC Parameters ---------- r : pandas.Series daily score series of feature b : pandas.Series daily return series |
19,621 | from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
from scipy.stats import spearmanr, pearsonr
from ..data import D
from collections import OrderedDict
def get_normal_ic(a, b):
return pearsonr(a, b)[0] | null |
19,622 | from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
import warnings
from typing import Union
from ..log import get_module_logger
from ..utils import get_date_range
from ..utils.resam import Freq
from ..strategy.base import BaseStrategy
from ..backtest import get_... | analyze statistical time-series indicators of trading Parameters ---------- df : pandas.DataFrame columns: like ['pa', 'pos', 'ffr', 'deal_amount', 'value']. Necessary fields: - 'pa' is the price advantage in trade indicators - 'pos' is the positive rate in trade indicators - 'ffr' is the fulfill rate in trade indicato... |
19,623 | from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
import warnings
from typing import Union
from ..log import get_module_logger
from ..utils import get_date_range
from ..utils.resam import Freq
from ..strategy.base import BaseStrategy
from ..backtest import get_... | A backtest for long-short strategy :param pred: The trading signal produced on day `T`. :param topk: The short topk securities and long topk securities. :param deal_price: The price to deal the trading. :param shift: Whether to shift prediction by one day. The trading day will be T+1 if shift==1. :param open_cost: open... |
19,624 | from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
import warnings
from typing import Union
from ..log import get_module_logger
from ..utils import get_date_range
from ..utils.resam import Freq
from ..strategy.base import BaseStrategy
from ..backtest import get_... | null |
19,625 | import torch
import numpy as np
import pandas as pd
import torch
def data_to_tensor(data, device="cpu", raise_error=False):
if isinstance(data, torch.Tensor):
if device == "cpu":
return data.cpu()
else:
return data.to(device)
if isinstance(data, (pd.DataFrame, pd.Series... | null |
19,626 | from __future__ import annotations
from typing import Any, Generic, TypeVar
import gym
import numpy as np
from gym import spaces
from qlib.typehint import final
from .simulator import ActType, StateType
class GymSpaceValidationError(Exception):
def __init__(self, message: str, space: gym.Space, x: Any) -> None:
... | Strengthened version of gym.Space.contains. Giving more diagnostic information on why validation fails. Throw exception rather than returning true or false. |
19,627 | from __future__ import annotations
import argparse
import os
import random
import sys
import warnings
from pathlib import Path
from typing import cast, List, Optional
import numpy as np
import pandas as pd
import torch
import yaml
from qlib.backtest import Order
from qlib.backtest.decision import OrderDir
from qlib.con... | null |
19,628 | from __future__ import annotations
import argparse
import os
import random
import sys
import warnings
from pathlib import Path
from typing import cast, List, Optional
import numpy as np
import pandas as pd
import torch
import yaml
from qlib.backtest import Order
from qlib.backtest.decision import OrderDir
from qlib.con... | null |
19,629 | from __future__ import annotations
import argparse
import os
import random
import sys
import warnings
from pathlib import Path
from typing import cast, List, Optional
import numpy as np
import pandas as pd
import torch
import yaml
from qlib.backtest import Order
from qlib.backtest.decision import OrderDir
from qlib.con... | null |
19,630 | from __future__ import annotations
from pathlib import Path
import pandas as pd
def read_order_file(order_file: Path | pd.DataFrame) -> pd.DataFrame:
if isinstance(order_file, pd.DataFrame):
return order_file
order_file = Path(order_file)
if order_file.suffix == ".pkl":
order_df = pd.read... | null |
19,631 | import os
import platform
import shutil
import sys
import tempfile
from importlib import import_module
import yaml
def merge_a_into_b(a: dict, b: dict) -> dict:
b = b.copy()
for k, v in a.items():
if isinstance(v, dict) and k in b:
v.pop(DELETE_KEY, False)
b[k] = merge_a_into_b(v... | null |
19,632 | from __future__ import annotations
from typing import Any, Callable, Dict, List, Sequence, cast
from tianshou.policy import BasePolicy
from qlib.rl.interpreter import ActionInterpreter, StateInterpreter
from qlib.rl.reward import Reward
from qlib.rl.simulator import InitialStateType, Simulator
from qlib.rl.utils import... | Train a policy with the parallelism provided by RL framework. Experimental API. Parameters might change shortly. Parameters ---------- simulator_fn Callable receiving initial seed, returning a simulator. state_interpreter Interprets the state of simulators. action_interpreter Interprets the policy actions. initial_stat... |
19,633 | from __future__ import annotations
from typing import Any, Callable, Dict, List, Sequence, cast
from tianshou.policy import BasePolicy
from qlib.rl.interpreter import ActionInterpreter, StateInterpreter
from qlib.rl.reward import Reward
from qlib.rl.simulator import InitialStateType, Simulator
from qlib.rl.utils import... | Backtest with the parallelism provided by RL framework. Experimental API. Parameters might change shortly. Parameters ---------- simulator_fn Callable receiving initial seed, returning a simulator. state_interpreter Interprets the state of simulators. action_interpreter Interprets the policy actions. initial_states Ini... |
19,634 | from __future__ import annotations
import collections
import copy
from contextlib import AbstractContextManager, contextmanager
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, Iterable, List, OrderedDict, Sequence, TypeVar, cast
import torch
from qlib.log import get_module_logger
fr... | Make any object a (possibly dummy) context manager. |
19,635 | from __future__ import annotations
import collections
import copy
from contextlib import AbstractContextManager, contextmanager
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, Iterable, List, OrderedDict, Sequence, TypeVar, cast
import torch
from qlib.log import get_module_logger
fr... | Convert a list into a dict, where each item is named with its type. |
19,636 | from __future__ import annotations
from pathlib import Path
from typing import cast, List
import cachetools
import pandas as pd
import pickle
import os
from qlib.backtest import Exchange, Order
from qlib.backtest.decision import TradeRange, TradeRangeByTime
from qlib.constant import EPS_T
from .base import BaseIntraday... | null |
19,637 | from __future__ import annotations
from pathlib import Path
from typing import cast, List
import cachetools
import pandas as pd
import pickle
import os
from qlib.backtest import Exchange, Order
from qlib.backtest.decision import TradeRange, TradeRangeByTime
from qlib.constant import EPS_T
from .base import BaseIntraday... | null |
19,638 | from __future__ import annotations
from functools import lru_cache
from pathlib import Path
from typing import List, Sequence, cast
import cachetools
import numpy as np
import pandas as pd
from cachetools.keys import hashkey
from qlib.backtest.decision import Order, OrderDir
from qlib.rl.data.base import BaseIntradayBa... | null |
19,639 | from __future__ import annotations
from functools import lru_cache
from pathlib import Path
from typing import List, Sequence, cast
import cachetools
import numpy as np
import pandas as pd
from cachetools.keys import hashkey
from qlib.backtest.decision import Order, OrderDir
from qlib.rl.data.base import BaseIntradayBa... | null |
19,640 | from __future__ import annotations
from functools import lru_cache
from pathlib import Path
from typing import List, Sequence, cast
import cachetools
import numpy as np
import pandas as pd
from cachetools.keys import hashkey
from qlib.backtest.decision import Order, OrderDir
from qlib.rl.data.base import BaseIntradayBa... | null |
19,641 | from __future__ import annotations
from functools import lru_cache
from pathlib import Path
from typing import List, Sequence, cast
import cachetools
import numpy as np
import pandas as pd
from cachetools.keys import hashkey
from qlib.backtest.decision import Order, OrderDir
from qlib.rl.data.base import BaseIntradayBa... | null |
19,642 | from __future__ import annotations
from functools import lru_cache
from pathlib import Path
from typing import List, Sequence, cast
import cachetools
import numpy as np
import pandas as pd
from cachetools.keys import hashkey
from qlib.backtest.decision import Order, OrderDir
from qlib.rl.data.base import BaseIntradayBa... | Load orders, and set start time and end time for the orders. |
19,643 | from __future__ import annotations
from pathlib import Path
import qlib
from qlib.constant import REG_CN
from qlib.contrib.ops.high_freq import BFillNan, Cut, Date, DayCumsum, DayLast, FFillNan, IsInf, IsNull, Select
REG_CN = "cn"
class DayCumsum(ElemOperator):
"""DayCumsum Operator during start time and end time... | Initialize necessary resource to launch the workflow, including data direction, feature columns, etc.. Parameters ---------- qlib_config: Qlib configuration. Example:: { "provider_uri_day": DATA_ROOT_DIR / "qlib_1d", "provider_uri_1min": DATA_ROOT_DIR / "qlib_1min", "feature_root_dir": DATA_ROOT_DIR / "qlib_handler_sto... |
19,644 | from __future__ import annotations
from typing import Any, cast
import numpy as np
import pandas as pd
from qlib.backtest.decision import OrderDir
from qlib.backtest.executor import BaseExecutor, NestedExecutor, SimulatorExecutor
from qlib.constant import float_or_ndarray
def dataframe_append(df: pd.DataFrame, other: ... | null |
19,645 | from __future__ import annotations
from typing import Any, cast
import numpy as np
import pandas as pd
from qlib.backtest.decision import OrderDir
from qlib.backtest.executor import BaseExecutor, NestedExecutor, SimulatorExecutor
from qlib.constant import float_or_ndarray
class OrderDir(IntEnum):
# Order direction... | null |
19,646 | from __future__ import annotations
from typing import Any, cast
import numpy as np
import pandas as pd
from qlib.backtest.decision import OrderDir
from qlib.backtest.executor import BaseExecutor, NestedExecutor, SimulatorExecutor
from qlib.constant import float_or_ndarray
class BaseExecutor:
"""Base executor for t... | null |
19,647 | from __future__ import annotations
import math
from typing import Any, List, Optional, cast
import numpy as np
import pandas as pd
from gym import spaces
from qlib.constant import EPS
from qlib.rl.data.base import ProcessedDataProvider
from qlib.rl.interpreter import ActionInterpreter, StateInterpreter
from qlib.rl.ord... | To 32-bit numeric types. Recursively. |
19,648 | from __future__ import annotations
import math
from typing import Any, List, Optional, cast
import numpy as np
import pandas as pd
from gym import spaces
from qlib.constant import EPS
from qlib.rl.data.base import ProcessedDataProvider
from qlib.rl.interpreter import ActionInterpreter, StateInterpreter
from qlib.rl.ord... | null |
19,649 | from __future__ import annotations
import math
from typing import Any, List, Optional, cast
import numpy as np
import pandas as pd
from gym import spaces
from qlib.constant import EPS
from qlib.rl.data.base import ProcessedDataProvider
from qlib.rl.interpreter import ActionInterpreter, StateInterpreter
from qlib.rl.ord... | null |
19,650 | from __future__ import annotations
from typing import Any, cast, List, Optional
import numpy as np
import pandas as pd
from pathlib import Path
from qlib.backtest.decision import Order, OrderDir
from qlib.constant import EPS, EPS_T, float_or_ndarray
from qlib.rl.data.base import BaseIntradayBacktestData
from qlib.rl.da... | null |
19,651 | from __future__ import annotations
from pathlib import Path
from typing import Any, Dict, Generator, Iterable, Optional, OrderedDict, Tuple, cast
import gym
import numpy as np
import torch
import torch.nn as nn
from gym.spaces import Discrete
from tianshou.data import Batch, ReplayBuffer, to_torch
from tianshou.policy ... | null |
19,652 | from __future__ import annotations
from pathlib import Path
from typing import Any, Dict, Generator, Iterable, Optional, OrderedDict, Tuple, cast
import gym
import numpy as np
import torch
import torch.nn as nn
from gym.spaces import Discrete
from tianshou.data import Batch, ReplayBuffer, to_torch
from tianshou.policy ... | null |
19,653 | from __future__ import annotations
from pathlib import Path
from typing import Any, Dict, Generator, Iterable, Optional, OrderedDict, Tuple, cast
import gym
import numpy as np
import torch
import torch.nn as nn
from gym.spaces import Discrete
from tianshou.data import Batch, ReplayBuffer, to_torch
from tianshou.policy ... | null |
19,654 | from __future__ import annotations
import collections
from types import GeneratorType
from typing import Any, Callable, cast, Dict, Generator, List, Optional, Tuple, Union
import warnings
import numpy as np
import pandas as pd
import torch
from tianshou.data import Batch
from tianshou.policy import BasePolicy
from qlib... | null |
19,655 | from __future__ import annotations
import collections
from types import GeneratorType
from typing import Any, Callable, cast, Dict, Generator, List, Optional, Tuple, Union
import warnings
import numpy as np
import pandas as pd
import torch
from tianshou.data import Batch
from tianshou.policy import BasePolicy
from qlib... | Fill missing data. Parameters ---------- original_data Original data without missing values. fill_method Method used to fill the missing data. Returns ------- The filled data. |
19,656 | from __future__ import annotations
import copy
import warnings
from contextlib import contextmanager
from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Type, Union, cast
import gym
import numpy as np
from tianshou.env import BaseVectorEnv, DummyVectorEnv, ShmemVectorEnv, SubprocVectorEnv
fro... | The NaN observation that indicates the environment receives no seed. We assume that obs is complex and there must be something like float. Otherwise this logic doesn't work. |
19,657 | from __future__ import annotations
import copy
import warnings
from contextlib import contextmanager
from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Type, Union, cast
import gym
import numpy as np
from tianshou.env import BaseVectorEnv, DummyVectorEnv, ShmemVectorEnv, SubprocVectorEnv
fro... | Check whether obs is generated by :func:`generate_nan_observation`. |
19,658 | from __future__ import annotations
import copy
import warnings
from contextlib import contextmanager
from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Type, Union, cast
import gym
import numpy as np
from tianshou.env import BaseVectorEnv, DummyVectorEnv, ShmemVectorEnv, SubprocVectorEnv
fro... | Helper function to create a vector env. Can be used to replace usual VectorEnv. For example, once you wrote: :: DummyVectorEnv([lambda: gym.make(task) for _ in range(env_num)]) Now you can replace it with: :: finite_env_factory(lambda: gym.make(task), "dummy", env_num, my_logger) By doing such replacement, you have two... |
19,659 | from __future__ import division
from __future__ import print_function
import re
import abc
import copy
import queue
import bisect
import numpy as np
import pandas as pd
from typing import List, Union, Optional
from joblib import delayed
from .cache import H
from ..config import C
from .inst_processor import InstProcess... | register_all_wrappers |
19,660 | from __future__ import annotations
import pandas as pd
from typing import Union, List, TYPE_CHECKING
from qlib.utils import init_instance_by_config
def get_level_index(df: pd.DataFrame, level=Union[str, int]) -> int:
"""
get the level index of `df` given `level`
Parameters
----------
df : pd.DataFra... | fetch data from `data` with `selector` and `level` selector are assumed to be well processed. `fetch_df_by_index` is only responsible for get the right level Parameters ---------- selector : Union[pd.Timestamp, slice, str, list] selector level : Union[int, str] the level to use the selector Returns ------- Data of the ... |
19,661 | from __future__ import annotations
import pandas as pd
from typing import Union, List, TYPE_CHECKING
from qlib.utils import init_instance_by_config
class DataHandler(Serializable):
def __init__(
self,
instruments=None,
start_time=None,
end_time=None,
dat... | null |
19,662 | from __future__ import annotations
import pandas as pd
from typing import Union, List, TYPE_CHECKING
from qlib.utils import init_instance_by_config
def get_level_index(df: pd.DataFrame, level=Union[str, int]) -> int:
"""
get the level index of `df` given `level`
Parameters
----------
df : pd.DataFra... | Convert the format of df.MultiIndex according to the following rules: - If `level` is the first level of df.MultiIndex, do nothing - If `level` is the second level of df.MultiIndex, swap the level of index. NOTE: the number of levels of df.MultiIndex should be 2 Parameters ---------- df : Union[pd.DataFrame, pd.Series]... |
19,663 | from __future__ import annotations
import pandas as pd
from typing import Union, List, TYPE_CHECKING
from qlib.utils import init_instance_by_config
class DataHandler(Serializable):
"""
The steps to using a handler
1. initialized data handler (call by `init`).
2. use the data.
The data handler tr... | initialize the handler part of the task **inplace** Parameters ---------- task : dict the task to be handled Returns ------- Union[DataHandler, None]: returns |
19,664 | import abc
from typing import Union, Text, Optional
import numpy as np
import pandas as pd
from qlib.utils.data import robust_zscore, zscore
from ...constant import EPS
from .utils import fetch_df_by_index
from ...utils.serial import Serializable
from ...utils.paral import datetime_groupby_apply
from qlib.data.inst_pro... | get a group of columns from multi-index columns DataFrame Parameters ---------- df : pd.DataFrame with multi of columns. group : str the name of the feature group, i.e. the first level value of the group index. |
19,665 | from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
from typing import Union, List, Type
from scipy.stats import percentileofscore
from .base import Expression, ExpressionOps, Feature, PFeature
from ..log import get_module_logger
from ..utils import get_callable_... | register all operator |
19,666 | import logging
import sys
import os
from pathlib import Path
import qlib
import fire
import ruamel.yaml as yaml
from qlib.config import C
from qlib.model.trainer import task_train
from qlib.utils.data import update_config
from qlib.log import get_module_logger
from qlib.utils import set_log_with_config
def workflow(con... | null |
19,667 | import atexit
import logging
import sys
import traceback
from ..log import get_module_logger
from . import R
from .recorder import Recorder
def experiment_exception_hook(exc_type, value, tb):
"""
End an experiment with status to be "FAILED". This exception tries to catch those uncaught exception
and end the... | Method for handling the experiment when any unusual program ending occurs. The `atexit` handler should be put in the last, since, as long as the program ends, it will be called. Thus, if any exception or user interruption occurs beforehand, we should handle them first. Once `R` is ended, another call of `R.end_exp` wil... |
19,668 | import bisect
from copy import deepcopy
import pandas as pd
from qlib.data import D
from qlib.utils import hash_args
from qlib.utils.mod import init_instance_by_config
from qlib.workflow import R
from qlib.config import C
from qlib.log import get_module_logger
from pymongo import MongoClient
from pymongo.database impor... | Get database in MongoDB, which means you need to declare the address and the name of a database at first. For example: Using qlib.init(): .. code-block:: python mongo_conf = { "task_url": task_url, # your MongoDB url "task_db_name": task_db_name, # database name } qlib.init(..., mongo=mongo_conf) After qlib.init(): .. ... |
19,669 | import bisect
from copy import deepcopy
import pandas as pd
from qlib.data import D
from qlib.utils import hash_args
from qlib.utils.mod import init_instance_by_config
from qlib.workflow import R
from qlib.config import C
from qlib.log import get_module_logger
from pymongo import MongoClient
from pymongo.database impor... | List all recorders which can pass the filter in an experiment. Args: experiment (str or Experiment): the name of an Experiment or an instance rec_filter_func (Callable, optional): return True to retain the given recorder. Defaults to None. Returns: dict: a dict {rid: recorder} after filtering. |
19,670 | import bisect
from copy import deepcopy
import pandas as pd
from qlib.data import D
from qlib.utils import hash_args
from qlib.utils.mod import init_instance_by_config
from qlib.workflow import R
from qlib.config import C
from qlib.log import get_module_logger
from pymongo import MongoClient
from pymongo.database impor... | Replace the handler in task with a cache handler. It will automatically cache the file and save it in cache_dir. >>> import qlib >>> qlib.auto_init() >>> import datetime >>> # it is simplified task >>> task = {"dataset": {"kwargs":{'handler': {'class': 'Alpha158', 'module_path': 'qlib.contrib.data.handler', 'kwargs': {... |
19,671 | import abc
import copy
import pandas as pd
from typing import Dict, List, Union, Callable
from qlib.utils import transform_end_date
from .utils import TimeAdjuster
class TaskGen(metaclass=abc.ABCMeta):
"""
The base class for generating different tasks
Example 1:
input: a specific task template and r... | Use a list of TaskGen and a list of task templates to generate different tasks. For examples: There are 3 task templates a,b,c and 2 TaskGen A,B. A will generates 2 tasks from a template and B will generates 3 tasks from a template. task_generator([a, b, c], [A, B]) will finally generate 3*2*3 = 18 tasks. Parameters --... |
19,672 | import abc
import copy
import pandas as pd
from typing import Dict, List, Union, Callable
from qlib.utils import transform_end_date
from .utils import TimeAdjuster
The provided code snippet includes necessary dependencies for implementing the `handler_mod` function. Write a Python function `def handler_mod(task: dict,... | Help to modify the handler end time when using RollingGen It try to handle the following case - Hander's data end_time is earlier than dataset's test_data's segments. - To handle this, handler's data's end_time is extended. If the handler's end_time is None, then it is not necessary to change it's end time. Args: task ... |
19,673 | import abc
import copy
import pandas as pd
from typing import Dict, List, Union, Callable
from qlib.utils import transform_end_date
from .utils import TimeAdjuster
class TimeAdjuster:
"""
Find appropriate date and adjust date.
"""
def __init__(self, future=True, end_time=None):
self._future = ... | To avoid the leakage of future information, the segments should be truncated according to the test start_time NOTE: This function will change segments **inplace** |
19,674 | import concurrent
import pickle
import time
from contextlib import contextmanager
from typing import Callable, List
import fire
import pymongo
from bson.binary import Binary
from bson.objectid import ObjectId
from pymongo.errors import InvalidDocument
from qlib import auto_init, get_module_logger
from tqdm.cli import t... | r""" While the task pool is not empty (has WAITING tasks), use task_func to fetch and run tasks in task_pool After running this method, here are 4 situations (before_status -> after_status): STATUS_WAITING -> STATUS_DONE: use task["def"] as `task_func` param, it means that the task has not been started STATUS_WAITING -... |
19,675 | from abc import ABCMeta, abstractmethod
from typing import Optional
import pandas as pd
from qlib import get_module_logger
from qlib.data import D
from qlib.data.dataset import Dataset, DatasetH, TSDatasetH
from qlib.data.dataset.handler import DataHandlerLP
from qlib.model import Model
from qlib.utils import get_date_... | null |
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