qlib/qlib/data/data.py

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.


from __future__ import division
from __future__ import print_function

import os
import abc
import time
import queue
import bisect
import logging
import importlib
import traceback
import numpy as np
import pandas as pd
from pathlib import Path
from multiprocessing import Pool

from .cache import H
from ..config import C
from .ops import *
from ..log import get_module_logger
from ..utils import parse_field, read_bin, hash_args, normalize_cache_fields
from .base import Feature
from .cache import DiskDatasetCache, DiskExpressionCache
from ..utils import Wrapper, init_instance_by_config, register_wrapper, get_module_by_module_path


class CalendarProvider(abc.ABC):
    """Calendar provider base class

    Provide calendar data.
    """

    @abc.abstractmethod
    def calendar(self, start_time=None, end_time=None, freq="day", future=False):
        """Get calendar of certain market in given time range.

        Parameters
        ----------
        start_time : str
            start of the time range.
        end_time : str
            end of the time range.
        freq : str
            time frequency, available: year/quarter/month/week/day.
        future : bool
            whether including future trading day.

        Returns
        ----------
        list
            calendar list
        """
        raise NotImplementedError("Subclass of CalendarProvider must implement `calendar` method")

    def locate_index(self, start_time, end_time, freq, future):
        """Locate the start time index and end time index in a calendar under certain frequency.

        Parameters
        ----------
        start_time : str
            start of the time range.
        end_time : str
            end of the time range.
        freq : str
            time frequency, available: year/quarter/month/week/day.
        future : bool
            whether including future trading day.

        Returns
        -------
        pd.Timestamp
            the real start time.
        pd.Timestamp
            the real end time.
        int
            the index of start time.
        int
            the index of end time.
        """
        start_time = pd.Timestamp(start_time)
        end_time = pd.Timestamp(end_time)
        calendar, calendar_index = self._get_calendar(freq=freq, future=future)
        if start_time not in calendar_index:
            try:
                start_time = calendar[bisect.bisect_left(calendar, start_time)]
            except IndexError:
                raise IndexError(
                    "`start_time` uses a future date, if you want to get future trading days, you can use: `future=True`"
                )
        start_index = calendar_index[start_time]
        if end_time not in calendar_index:
            end_time = calendar[bisect.bisect_right(calendar, end_time) - 1]
        end_index = calendar_index[end_time]
        return start_time, end_time, start_index, end_index

    def _get_calendar(self, freq, future):
        """Load calendar using memcache.

        Parameters
        ----------
        freq : str
            frequency of read calendar file.
        future : bool
            whether including future trading day.

        Returns
        -------
        list
            list of timestamps.
        dict
            dict composed by timestamp as key and index as value for fast search.
        """
        flag = f"{freq}_future_{future}"
        if flag in H["c"]:
            _calendar, _calendar_index = H["c"][flag]
        else:
            _calendar = np.array(self._load_calendar(freq, future))
            _calendar_index = {x: i for i, x in enumerate(_calendar)}  # for fast search
            H["c"][flag] = _calendar, _calendar_index
        return _calendar, _calendar_index

    def _uri(self, start_time, end_time, freq, future=False):
        """Get the uri of calendar generation task."""
        return hash_args(start_time, end_time, freq, future)


class InstrumentProvider(abc.ABC):
    """Instrument provider base class

    Provide instrument data.
    """

    @staticmethod
    def instruments(market="all", filter_pipe=None):
        """Get the general config dictionary for a base market adding several dynamic filters.

        Parameters
        ----------
        market : str
            market/industry/index shortname, e.g. all/sse/szse/sse50/csi300/csi500.
        filter_pipe : list
            the list of dynamic filters.

        Returns
        ----------
        dict
            dict of stockpool config.
            {`market`=>base market name, `filter_pipe`=>list of filters}

            example :

            .. code-block::

                {'market': 'csi500',
                'filter_pipe': [{'filter_type': 'ExpressionDFilter',
                'rule_expression': '$open<40',
                'filter_start_time': None,
                'filter_end_time': None,
                'keep': False},
                {'filter_type': 'NameDFilter',
                'name_rule_re': 'SH[0-9]{4}55',
                'filter_start_time': None,
                'filter_end_time': None}]}
        """
        if filter_pipe is None:
            filter_pipe = []
        config = {"market": market, "filter_pipe": []}
        # the order of the filters will affect the result, so we need to keep
        # the order
        for filter_t in filter_pipe:
            config["filter_pipe"].append(filter_t.to_config())
        return config

    @abc.abstractmethod
    def list_instruments(self, instruments, start_time=None, end_time=None, freq="day", as_list=False):
        """List the instruments based on a certain stockpool config.

        Parameters
        ----------
        instruments : dict
            stockpool config.
        start_time : str
            start of the time range.
        end_time : str
            end of the time range.
        as_list : bool
            return instruments as list or dict.

        Returns
        -------
        dict or list
            instruments list or dictionary with time spans
        """
        raise NotImplementedError("Subclass of InstrumentProvider must implement `list_instruments` method")

    def _uri(self, instruments, start_time=None, end_time=None, freq="day", as_list=False):
        return hash_args(instruments, start_time, end_time, freq, as_list)

    # instruments type
    LIST = "LIST"
    DICT = "DICT"
    CONF = "CONF"

    @classmethod
    def get_inst_type(cls, inst):
        if "market" in inst:
            return cls.CONF
        if isinstance(inst, dict):
            return cls.DICT
        if isinstance(inst, (list, tuple, pd.Index, np.ndarray)):
            return cls.LIST
        raise ValueError(f"Unknown instrument type {inst}")

    def convert_instruments(self, instrument):
        _instruments_map = getattr(self, "_instruments_map", None)
        if _instruments_map is None:
            _df_list = []
            # FIXME: each process will read these files
            for _path in Path(C.get_data_path()).joinpath("instruments").glob("*.txt"):
                _df = pd.read_csv(_path, sep="\t", names=["inst", "start_datetime", "end_datetime", "save_inst"])
                _df_list.append(_df.iloc[:, [0, -1]])
            df = pd.concat(_df_list, sort=False).sort_values("save_inst")
            df = df.drop_duplicates(subset=["save_inst"], keep="first").fillna(axis=1, method="ffill")
            _instruments_map = df.set_index("inst").iloc[:, 0].to_dict()
            setattr(self, "_instruments_map", _instruments_map)
        return _instruments_map.get(instrument, instrument)


class FeatureProvider(abc.ABC):
    """Feature provider class

    Provide feature data.
    """

    @abc.abstractmethod
    def feature(self, instrument, field, start_time, end_time, freq):
        """Get feature data.

        Parameters
        ----------
        instrument : str
            a certain instrument.
        field : str
            a certain field of feature.
        start_time : str
            start of the time range.
        end_time : str
            end of the time range.
        freq : str
            time frequency, available: year/quarter/month/week/day.

        Returns
        -------
        pd.Series
            data of a certain feature
        """
        raise NotImplementedError("Subclass of FeatureProvider must implement `feature` method")


class ExpressionProvider(abc.ABC):
    """Expression provider class

    Provide Expression data.
    """

    def __init__(self):
        self.expression_instance_cache = {}

    def get_expression_instance(self, field):
        try:
            if field in self.expression_instance_cache:
                expression = self.expression_instance_cache[field]
            else:
                expression = eval(parse_field(field))
                self.expression_instance_cache[field] = expression
        except NameError as e:
            get_module_logger("data").exception(
                "ERROR: field [%s] contains invalid operator/variable [%s]" % (str(field), str(e).split()[1])
            )
            raise
        except SyntaxError:
            get_module_logger("data").exception("ERROR: field [%s] contains invalid syntax" % str(field))
            raise
        return expression

    @abc.abstractmethod
    def expression(self, instrument, field, start_time=None, end_time=None, freq="day"):
        """Get Expression data.

        Parameters
        ----------
        instrument : str
            a certain instrument.
        field : str
            a certain field of feature.
        start_time : str
            start of the time range.
        end_time : str
            end of the time range.
        freq : str
            time frequency, available: year/quarter/month/week/day.

        Returns
        -------
        pd.Series
            data of a certain expression
        """
        raise NotImplementedError("Subclass of ExpressionProvider must implement `Expression` method")


class DatasetProvider(abc.ABC):
    """Dataset provider class

    Provide Dataset data.
    """

    @abc.abstractmethod
    def dataset(self, instruments, fields, start_time=None, end_time=None, freq="day"):
        """Get dataset data.

        Parameters
        ----------
        instruments : list or dict
            list/dict of instruments or dict of stockpool config.
        fields : list
            list of feature instances.
        start_time : str
            start of the time range.
        end_time : str
            end of the time range.
        freq : str
            time frequency.

        Returns
        ----------
        pd.DataFrame
            a pandas dataframe with <instrument, datetime> index.
        """
        raise NotImplementedError("Subclass of DatasetProvider must implement `Dataset` method")

    def _uri(
        self,
        instruments,
        fields,
        start_time=None,
        end_time=None,
        freq="day",
        disk_cache=1,
        **kwargs,
    ):
        """Get task uri, used when generating rabbitmq task in qlib_server

        Parameters
        ----------
        instruments : list or dict
            list/dict of instruments or dict of stockpool config.
        fields : list
            list of feature instances.
        start_time : str
            start of the time range.
        end_time : str
            end of the time range.
        freq : str
            time frequency.
        disk_cache : int
            whether to skip(0)/use(1)/replace(2) disk_cache.

        """
        return DiskDatasetCache._uri(instruments, fields, start_time, end_time, freq, disk_cache)

    @staticmethod
    def get_instruments_d(instruments, freq):
        """
        Parse different types of input instruments to output instruments_d
        Wrong format of input instruments will lead to exception.

        """
        if isinstance(instruments, dict):
            if "market" in instruments:
                # dict of stockpool config
                instruments_d = Inst.list_instruments(instruments=instruments, freq=freq, as_list=False)
            else:
                # dict of instruments and timestamp
                instruments_d = instruments
        elif isinstance(instruments, (list, tuple, pd.Index, np.ndarray)):
            # list or tuple of a group of instruments
            instruments_d = list(instruments)
        else:
            raise ValueError("Unsupported input type for param `instrument`")
        return instruments_d

    @staticmethod
    def get_column_names(fields):
        """
        Get column names from input fields

        """
        if len(fields) == 0:
            raise ValueError("fields cannot be empty")
        fields = fields.copy()
        column_names = [str(f) for f in fields]
        return column_names

    @staticmethod
    def parse_fields(fields):
        # parse and check the input fields
        return [ExpressionD.get_expression_instance(f) for f in fields]

    @staticmethod
    def dataset_processor(instruments_d, column_names, start_time, end_time, freq):
        """
        Load and process the data, return the data set.
        - default using multi-kernel method.

        """
        normalize_column_names = normalize_cache_fields(column_names)
        data = dict()
        # One process for one task, so that the memory will be freed quicker.
        workers = min(C.kernels, len(instruments_d))
        if C.maxtasksperchild is None:
            p = Pool(processes=workers)
        else:
            p = Pool(processes=workers, maxtasksperchild=C.maxtasksperchild)
        if isinstance(instruments_d, dict):
            for inst, spans in instruments_d.items():
                data[inst] = p.apply_async(
                    DatasetProvider.expression_calculator,
                    args=(
                        inst,
                        start_time,
                        end_time,
                        freq,
                        normalize_column_names,
                        spans,
                        C,
                    ),
                )
        else:
            for inst in instruments_d:
                data[inst] = p.apply_async(
                    DatasetProvider.expression_calculator,
                    args=(
                        inst,
                        start_time,
                        end_time,
                        freq,
                        normalize_column_names,
                        None,
                        C,
                    ),
                )

        p.close()
        p.join()

        new_data = dict()
        for inst in sorted(data.keys()):
            if len(data[inst].get()) > 0:
                # NOTE: Python version >= 3.6; in versions after python3.6, dict will always guarantee the insertion order
                new_data[inst] = data[inst].get()

        if len(new_data) > 0:
            data = pd.concat(new_data, names=["instrument"], sort=False)
            data = DiskDatasetCache.cache_to_origin_data(data, column_names)
        else:
            data = pd.DataFrame(columns=column_names)

        return data

    @staticmethod
    def expression_calculator(inst, start_time, end_time, freq, column_names, spans=None, g_config=None):
        """
        Calculate the expressions for one instrument, return a df result.
        If the expression has been calculated before, load from cache.

        return value: A data frame with index 'datetime' and other data columns.

        """
        # FIXME: Windows OS or MacOS using spawn: https://docs.python.org/3.8/library/multiprocessing.html?highlight=spawn#contexts-and-start-methods
        global C
        C = g_config
        # NOTE: This place is compatible with windows, windows multi-process is spawn
        if getattr(ExpressionD, "_provider", None) is None:
            register_all_wrappers()

        obj = dict()
        for field in column_names:
            #  The client does not have expression provider, the data will be loaded from cache using static method.
            obj[field] = ExpressionD.expression(inst, field, start_time, end_time, freq)

        data = pd.DataFrame(obj)
        _calendar = Cal.calendar(freq=freq)
        data.index = _calendar[data.index.values.astype(np.int)]
        data.index.names = ["datetime"]

        if spans is None:
            return data
        else:
            mask = np.zeros(len(data), dtype=np.bool)
            for begin, end in spans:
                mask |= (data.index >= begin) & (data.index <= end)
            return data[mask]


class LocalCalendarProvider(CalendarProvider):
    """Local calendar data provider class

    Provide calendar data from local data source.
    """

    def __init__(self, **kwargs):
        self.remote = kwargs.get("remote", False)

    @property
    def _uri_cal(self):
        """Calendar file uri."""
        return os.path.join(C.get_data_path(), "calendars", "{}.txt")

    def _load_calendar(self, freq, future):
        """Load original calendar timestamp from file.

        Parameters
        ----------
        freq : str
            frequency of read calendar file.

        Returns
        ----------
        list
            list of timestamps
        """
        if future:
            fname = self._uri_cal.format(freq + "_future")
            # if future calendar not exists, return current calendar
            if not os.path.exists(fname):
                get_module_logger("data").warning(f"{freq}_future.txt not exists, return current calendar!")
                fname = self._uri_cal.format(freq)
        else:
            fname = self._uri_cal.format(freq)
        if not os.path.exists(fname):
            raise ValueError("calendar not exists for freq " + freq)
        with open(fname) as f:
            return [pd.Timestamp(x.strip()) for x in f]

    def calendar(self, start_time=None, end_time=None, freq="day", future=False):
        _calendar, _calendar_index = self._get_calendar(freq, future)
        if start_time == "None":
            start_time = None
        if end_time == "None":
            end_time = None
        # strip
        if start_time:
            start_time = pd.Timestamp(start_time)
            if start_time > _calendar[-1]:
                return np.array([])
        else:
            start_time = _calendar[0]
        if end_time:
            end_time = pd.Timestamp(end_time)
            if end_time < _calendar[0]:
                return np.array([])
        else:
            end_time = _calendar[-1]
        _, _, si, ei = self.locate_index(start_time, end_time, freq, future)
        return _calendar[si : ei + 1]


class LocalInstrumentProvider(InstrumentProvider):
    """Local instrument data provider class

    Provide instrument data from local data source.
    """

    def __init__(self):
        pass

    @property
    def _uri_inst(self):
        """Instrument file uri."""
        return os.path.join(C.get_data_path(), "instruments", "{}.txt")

    def _load_instruments(self, market):
        fname = self._uri_inst.format(market)
        if not os.path.exists(fname):
            raise ValueError("instruments not exists for market " + market)
        _instruments = dict()
        df = pd.read_csv(fname, sep="\t", names=["inst", "start_datetime", "end_datetime", "save_inst"])
        df["start_datetime"] = pd.to_datetime(df["start_datetime"])
        df["end_datetime"] = pd.to_datetime(df["end_datetime"])
        for row in df.itertuples(index=False):
            _instruments.setdefault(row[0], []).append((row[1], row[2]))
        return _instruments

    def list_instruments(self, instruments, start_time=None, end_time=None, freq="day", as_list=False):
        market = instruments["market"]
        if market in H["i"]:
            _instruments = H["i"][market]
        else:
            _instruments = self._load_instruments(market)
            H["i"][market] = _instruments
        # strip
        # use calendar boundary
        cal = Cal.calendar(freq=freq)
        start_time = pd.Timestamp(start_time or cal[0])
        end_time = pd.Timestamp(end_time or cal[-1])
        _instruments_filtered = {
            inst: list(
                filter(
                    lambda x: x[0] <= x[1],
                    [(max(start_time, x[0]), min(end_time, x[1])) for x in spans],
                )
            )
            for inst, spans in _instruments.items()
        }
        _instruments_filtered = {key: value for key, value in _instruments_filtered.items() if value}
        # filter
        filter_pipe = instruments["filter_pipe"]
        for filter_config in filter_pipe:
            from . import filter as F

            filter_t = getattr(F, filter_config["filter_type"]).from_config(filter_config)
            _instruments_filtered = filter_t(_instruments_filtered, start_time, end_time, freq)
        # as list
        if as_list:
            return list(_instruments_filtered)
        return _instruments_filtered


class LocalFeatureProvider(FeatureProvider):
    """Local feature data provider class

    Provide feature data from local data source.
    """

    def __init__(self, **kwargs):
        self.remote = kwargs.get("remote", False)

    @property
    def _uri_data(self):
        """Static feature file uri."""
        return os.path.join(C.get_data_path(), "features", "{}", "{}.{}.bin")

    def feature(self, instrument, field, start_index, end_index, freq):
        # validate
        field = str(field).lower()[1:]
        instrument = Inst.convert_instruments(instrument)
        uri_data = self._uri_data.format(instrument.lower(), field, freq)
        if not os.path.exists(uri_data):
            get_module_logger("data").warning("WARN: data not found for %s.%s" % (instrument, field))
            return pd.Series(dtype=np.float32)
            # raise ValueError('uri_data not found: ' + uri_data)
        # load
        series = read_bin(uri_data, start_index, end_index)
        return series


class LocalExpressionProvider(ExpressionProvider):
    """Local expression data provider class

    Provide expression data from local data source.
    """

    def __init__(self):
        super().__init__()

    def expression(self, instrument, field, start_time=None, end_time=None, freq="day"):
        expression = self.get_expression_instance(field)
        start_time = pd.Timestamp(start_time)
        end_time = pd.Timestamp(end_time)
        _, _, start_index, end_index = Cal.locate_index(start_time, end_time, freq, future=False)
        lft_etd, rght_etd = expression.get_extended_window_size()
        series = expression.load(instrument, max(0, start_index - lft_etd), end_index + rght_etd, freq)
        # Ensure that each column type is consistent
        # FIXME:
        # 1) The stock data is currently float. If there is other types of data, this part needs to be re-implemented.
        # 2) The the precision should be configurable
        try:
            series = series.astype(np.float32)
        except ValueError:
            pass
        if not series.empty:
            series = series.loc[start_index:end_index]
        return series


class LocalDatasetProvider(DatasetProvider):
    """Local dataset data provider class

    Provide dataset data from local data source.
    """

    def __init__(self):
        pass

    def dataset(self, instruments, fields, start_time=None, end_time=None, freq="day"):
        instruments_d = self.get_instruments_d(instruments, freq)
        column_names = self.get_column_names(fields)
        cal = Cal.calendar(start_time, end_time, freq)
        if len(cal) == 0:
            return pd.DataFrame(columns=column_names)
        start_time = cal[0]
        end_time = cal[-1]

        data = self.dataset_processor(instruments_d, column_names, start_time, end_time, freq)

        return data

    @staticmethod
    def multi_cache_walker(instruments, fields, start_time=None, end_time=None, freq="day"):
        """
        This method is used to prepare the expression cache for the client.
        Then the client will load the data from expression cache by itself.

        """
        instruments_d = DatasetProvider.get_instruments_d(instruments, freq)
        column_names = DatasetProvider.get_column_names(fields)
        cal = Cal.calendar(start_time, end_time, freq)
        if len(cal) == 0:
            return
        start_time = cal[0]
        end_time = cal[-1]
        workers = min(C.kernels, len(instruments_d))
        if C.maxtasksperchild is None:
            p = Pool(processes=workers)
        else:
            p = Pool(processes=workers, maxtasksperchild=C.maxtasksperchild)

        for inst in instruments_d:
            p.apply_async(
                LocalDatasetProvider.cache_walker,
                args=(
                    inst,
                    start_time,
                    end_time,
                    freq,
                    column_names,
                ),
            )

        p.close()
        p.join()

    @staticmethod
    def cache_walker(inst, start_time, end_time, freq, column_names):
        """
        If the expressions of one instrument haven't been calculated before,
        calculate it and write it into expression cache.

        """
        for field in column_names:
            ExpressionD.expression(inst, field, start_time, end_time, freq)


class ClientCalendarProvider(CalendarProvider):
    """Client calendar data provider class

    Provide calendar data by requesting data from server as a client.
    """

    def __init__(self):
        self.conn = None
        self.queue = queue.Queue()

    def set_conn(self, conn):
        self.conn = conn

    def calendar(self, start_time=None, end_time=None, freq="day", future=False):
        self.conn.send_request(
            request_type="calendar",
            request_content={
                "start_time": str(start_time),
                "end_time": str(end_time),
                "freq": freq,
                "future": future,
            },
            msg_queue=self.queue,
            msg_proc_func=lambda response_content: [pd.Timestamp(c) for c in response_content],
        )
        result = self.queue.get(timeout=C["timeout"])
        return result


class ClientInstrumentProvider(InstrumentProvider):
    """Client instrument data provider class

    Provide instrument data by requesting data from server as a client.
    """

    def __init__(self):
        self.conn = None
        self.queue = queue.Queue()

    def set_conn(self, conn):
        self.conn = conn

    def list_instruments(self, instruments, start_time=None, end_time=None, freq="day", as_list=False):
        def inst_msg_proc_func(response_content):
            if isinstance(response_content, dict):
                instrument = {
                    i: [(pd.Timestamp(s), pd.Timestamp(e)) for s, e in t] for i, t in response_content.items()
                }
            else:
                instrument = response_content
            return instrument

        self.conn.send_request(
            request_type="instrument",
            request_content={
                "instruments": instruments,
                "start_time": str(start_time),
                "end_time": str(end_time),
                "freq": freq,
                "as_list": as_list,
            },
            msg_queue=self.queue,
            msg_proc_func=inst_msg_proc_func,
        )
        result = self.queue.get(timeout=C["timeout"])
        if isinstance(result, Exception):
            raise result
        get_module_logger("data").debug("get result")
        return result


class ClientDatasetProvider(DatasetProvider):
    """Client dataset data provider class

    Provide dataset data by requesting data from server as a client.
    """

    def __init__(self):
        self.conn = None

    def set_conn(self, conn):
        self.conn = conn
        self.queue = queue.Queue()

    def dataset(
        self,
        instruments,
        fields,
        start_time=None,
        end_time=None,
        freq="day",
        disk_cache=0,
        return_uri=False,
    ):
        if Inst.get_inst_type(instruments) == Inst.DICT:
            get_module_logger("data").warning(
                "Getting features from a dict of instruments is not recommended because the features will not be "
                "cached! "
                "The dict of instruments will be cleaned every day."
            )

        if disk_cache == 0:
            """
            Call the server to generate the expression cache.
            Then load the data from the expression cache directly.
            - default using multi-kernel method.

            """
            self.conn.send_request(
                request_type="feature",
                request_content={
                    "instruments": instruments,
                    "fields": fields,
                    "start_time": start_time,
                    "end_time": end_time,
                    "freq": freq,
                    "disk_cache": 0,
                },
                msg_queue=self.queue,
            )
            feature_uri = self.queue.get(timeout=C["timeout"])
            if isinstance(feature_uri, Exception):
                raise feature_uri
            else:
                instruments_d = self.get_instruments_d(instruments, freq)
                column_names = self.get_column_names(fields)
                cal = Cal.calendar(start_time, end_time, freq)
                if len(cal) == 0:
                    return pd.DataFrame(columns=column_names)
                start_time = cal[0]
                end_time = cal[-1]

                data = self.dataset_processor(instruments_d, column_names, start_time, end_time, freq)
                if return_uri:
                    return data, feature_uri
                else:
                    return data
        else:

            """
            Call the server to generate the data-set cache, get the uri of the cache file.
            Then load the data from the file on NFS directly.
            - using single-process implementation.

            """
            self.conn.send_request(
                request_type="feature",
                request_content={
                    "instruments": instruments,
                    "fields": fields,
                    "start_time": start_time,
                    "end_time": end_time,
                    "freq": freq,
                    "disk_cache": 1,
                },
                msg_queue=self.queue,
            )
            # - Done in callback
            feature_uri = self.queue.get(timeout=C["timeout"])
            if isinstance(feature_uri, Exception):
                raise feature_uri
            get_module_logger("data").debug("get result")
            try:
                # pre-mound nfs, used for demo
                mnt_feature_uri = os.path.join(C.get_data_path(), C.dataset_cache_dir_name, feature_uri)
                df = DiskDatasetCache.read_data_from_cache(mnt_feature_uri, start_time, end_time, fields)
                get_module_logger("data").debug("finish slicing data")
                if return_uri:
                    return df, feature_uri
                return df
            except AttributeError:
                raise IOError("Unable to fetch instruments from remote server!")


class BaseProvider:
    """Local provider class

    To keep compatible with old qlib provider.
    """

    def calendar(self, start_time=None, end_time=None, freq="day", future=False):
        return Cal.calendar(start_time, end_time, freq, future=future)

    def instruments(self, market="all", filter_pipe=None, start_time=None, end_time=None):
        if start_time is not None or end_time is not None:
            get_module_logger("Provider").warning(
                "The instruments corresponds to a stock pool. "
                "Parameters `start_time` and `end_time` does not take effect now."
            )
        return InstrumentProvider.instruments(market, filter_pipe)

    def list_instruments(self, instruments, start_time=None, end_time=None, freq="day", as_list=False):
        return Inst.list_instruments(instruments, start_time, end_time, freq, as_list)

    def features(
        self,
        instruments,
        fields,
        start_time=None,
        end_time=None,
        freq="day",
        disk_cache=None,
    ):
        """
        Parameters:
        -----------
        disk_cache : int
            whether to skip(0)/use(1)/replace(2) disk_cache

        This function will try to use cache method which has a keyword `disk_cache`,
        and will use provider method if a type error is raised because the DatasetD instance
        is a provider class.
        """
        disk_cache = C.default_disk_cache if disk_cache is None else disk_cache
        if C.disable_disk_cache:
            disk_cache = False
        try:
            return DatasetD.dataset(instruments, fields, start_time, end_time, freq, disk_cache)
        except TypeError:
            return DatasetD.dataset(instruments, fields, start_time, end_time, freq)


class LocalProvider(BaseProvider):
    def _uri(self, type, **kwargs):
        """_uri
        The server hope to get the uri of the request. The uri will be decided
        by the dataprovider. For ex, different cache layer has different uri.

        :param type: The type of resource for the uri
        :param **kwargs:
        """
        if type == "calendar":
            return Cal._uri(**kwargs)
        elif type == "instrument":
            return Inst._uri(**kwargs)
        elif type == "feature":
            return DatasetD._uri(**kwargs)

    def features_uri(self, instruments, fields, start_time, end_time, freq, disk_cache=1):
        """features_uri

        Return the uri of the generated cache of features/dataset

        :param disk_cache:
        :param instruments:
        :param fields:
        :param start_time:
        :param end_time:
        :param freq:
        """
        return DatasetD._dataset_uri(instruments, fields, start_time, end_time, freq, disk_cache)


class ClientProvider(BaseProvider):
    """Client Provider

    Requesting data from server as a client. Can propose requests:
        - Calendar : Directly respond a list of calendars
        - Instruments (without filter): Directly respond a list/dict of instruments
        - Instruments (with filters):  Respond a list/dict of instruments
        - Features : Respond a cache uri
    The general workflow is described as follows:
    When the user use client provider to propose a request, the client provider will connect the server and send the request. The client will start to wait for the response. The response will be made instantly indicating whether the cache is available. The waiting procedure will terminate only when the client get the reponse saying `feature_available` is true.
    `BUG` : Everytime we make request for certain data we need to connect to the server, wait for the response and disconnect from it. We can't make a sequence of requests within one connection. You can refer to https://python-socketio.readthedocs.io/en/latest/client.html for documentation of python-socketIO client.
    """

    def __init__(self):
        from .client import Client

        self.client = Client(C.flask_server, C.flask_port)
        self.logger = get_module_logger(self.__class__.__name__)
        if isinstance(Cal, ClientCalendarProvider):
            Cal.set_conn(self.client)
        Inst.set_conn(self.client)
        if hasattr(DatasetD, "provider"):
            DatasetD.provider.set_conn(self.client)
        else:
            DatasetD.set_conn(self.client)


Cal = Wrapper()
Inst = Wrapper()
FeatureD = Wrapper()
ExpressionD = Wrapper()
DatasetD = Wrapper()
D = Wrapper()


def register_all_wrappers():
    """register_all_wrappers"""
    logger = get_module_logger("data")
    module = get_module_by_module_path("qlib.data")

    _calendar_provider = init_instance_by_config(C.calendar_provider, module)
    if getattr(C, "calendar_cache", None) is not None:
        _calendar_provider = init_instance_by_config(C.calendar_cache, module, provide=_calendar_provider)
    register_wrapper(Cal, _calendar_provider, "qlib.data")
    logger.debug(f"registering Cal {C.calendar_provider}-{C.calenar_cache}")

    register_wrapper(Inst, C.instrument_provider, "qlib.data")
    logger.debug(f"registering Inst {C.instrument_provider}")

    if getattr(C, "feature_provider", None) is not None:
        feature_provider = init_instance_by_config(C.feature_provider, module)
        register_wrapper(FeatureD, feature_provider, "qlib.data")
        logger.debug(f"registering FeatureD {C.feature_provider}")

    if getattr(C, "expression_provider", None) is not None:
        # This provider is unnecessary in client provider
        _eprovider = init_instance_by_config(C.expression_provider, module)
        if getattr(C, "expression_cache", None) is not None:
            _eprovider = init_instance_by_config(C.expression_cache, module, provider=_eprovider)
        register_wrapper(ExpressionD, _eprovider, "qlib.data")
        logger.debug(f"registering ExpressioneD {C.expression_provider}-{C.expression_cache}")

    _dprovider = init_instance_by_config(C.dataset_provider, module)
    if getattr(C, "dataset_cache", None) is not None:
        _dprovider = init_instance_by_config(C.dataset_cache, module, provider=_dprovider)
    register_wrapper(DatasetD, _dprovider, "qlib.data")
    logger.debug(f"registering DataseteD {C.dataset_provider}-{C.dataset_cache}")

    register_wrapper(D, C.provider, "qlib.data")
    logger.debug(f"registering D {C.provider}")