utils.py

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

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 loguru import logger
from yahooquery import Ticker
from tqdm import tqdm
from functools import partial
from concurrent.futures import ProcessPoolExecutor
from bs4 import BeautifulSoup

HS_SYMBOLS_URL = "http://app.finance.ifeng.com/hq/list.php?type=stock_a&class={s_type}"

CALENDAR_URL_BASE = "http://push2his.eastmoney.com/api/qt/stock/kline/get?secid={market}.{bench_code}&fields1=f1%2Cf2%2Cf3%2Cf4%2Cf5&fields2=f51%2Cf52%2Cf53%2Cf54%2Cf55%2Cf56%2Cf57%2Cf58&klt=101&fqt=0&beg=19900101&end=20991231"
SZSE_CALENDAR_URL = "http://www.szse.cn/api/report/exchange/onepersistenthour/monthList?month={month}&random={random}"

CALENDAR_BENCH_URL_MAP = {
    "CSI300": CALENDAR_URL_BASE.format(market=1, bench_code="000300"),
    "CSI500": CALENDAR_URL_BASE.format(market=1, bench_code="000905"),
    "CSI100": CALENDAR_URL_BASE.format(market=1, bench_code="000903"),
    # NOTE: Use the time series of SH600000 as the sequence of all stocks
    "ALL": CALENDAR_URL_BASE.format(market=1, bench_code="000905"),
    # NOTE: Use the time series of ^GSPC(SP500) as the sequence of all stocks
    "US_ALL": "^GSPC",
    "IN_ALL": "^NSEI",
    "BR_ALL": "^BVSP",
}

_BENCH_CALENDAR_LIST = None
_ALL_CALENDAR_LIST = None
_HS_SYMBOLS = None
_US_SYMBOLS = None
_IN_SYMBOLS = None
_BR_SYMBOLS = None
_EN_FUND_SYMBOLS = None
_CALENDAR_MAP = {}

# NOTE: Until 2020-10-20 20:00:00
MINIMUM_SYMBOLS_NUM = 3900


def get_calendar_list(bench_code="CSI300") -> List[pd.Timestamp]:
    """get SH/SZ history calendar list

    Parameters
    ----------
    bench_code: str
        value from ["CSI300", "CSI500", "ALL", "US_ALL"]

    Returns
    -------
        history calendar list
    """

    logger.info(f"get calendar list: {bench_code}......")

    def _get_calendar(url):
        _value_list = requests.get(url, timeout=None).json()["data"]["klines"]
        return sorted(map(lambda x: pd.Timestamp(x.split(",")[0]), _value_list))

    calendar = _CALENDAR_MAP.get(bench_code, None)
    if calendar is None:
        if bench_code.startswith("US_") or bench_code.startswith("IN_") or bench_code.startswith("BR_"):
            print(Ticker(CALENDAR_BENCH_URL_MAP[bench_code]))
            print(Ticker(CALENDAR_BENCH_URL_MAP[bench_code]).history(interval="1d", period="max"))
            df = Ticker(CALENDAR_BENCH_URL_MAP[bench_code]).history(interval="1d", period="max")
            calendar = df.index.get_level_values(level="date").map(pd.Timestamp).unique().tolist()
        else:
            if bench_code.upper() == "ALL":

                @deco_retry
                def _get_calendar(month):
                    _cal = []
                    try:
                        resp = requests.get(
                            SZSE_CALENDAR_URL.format(month=month, random=random.random), timeout=None
                        ).json()
                        for _r in resp["data"]:
                            if int(_r["jybz"]):
                                _cal.append(pd.Timestamp(_r["jyrq"]))
                    except Exception as e:
                        raise ValueError(f"{month}-->{e}") from e
                    return _cal

                month_range = pd.date_range(start="2000-01", end=pd.Timestamp.now() + pd.Timedelta(days=31), freq="M")
                calendar = []
                for _m in month_range:
                    cal = _get_calendar(_m.strftime("%Y-%m"))
                    if cal:
                        calendar += cal
                calendar = list(filter(lambda x: x <= pd.Timestamp.now(), calendar))
            else:
                calendar = _get_calendar(CALENDAR_BENCH_URL_MAP[bench_code])
        _CALENDAR_MAP[bench_code] = calendar
    logger.info(f"end of get calendar list: {bench_code}.")
    return calendar


def return_date_list(date_field_name: str, file_path: Path):
    date_list = pd.read_csv(file_path, sep=",", index_col=0)[date_field_name].to_list()
    return sorted([pd.Timestamp(x) for x in date_list])


def get_calendar_list_by_ratio(
    source_dir: [str, Path],
    date_field_name: str = "date",
    threshold: float = 0.5,
    minimum_count: int = 10,
    max_workers: int = 16,
) -> list:
    """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 day, default 0.5
    minimum_count: int
        minimum count of funds should trade in one day
    max_workers: int
        Concurrent number, default is 16

    Returns
    -------
        history calendar list
    """
    logger.info(f"get calendar list from {source_dir} by threshold = {threshold}......")

    source_dir = Path(source_dir).expanduser()
    file_list = list(source_dir.glob("*.csv"))

    _number_all_funds = len(file_list)

    logger.info(f"count how many funds trade in this day......")
    _dict_count_trade = dict()  # dict{date:count}
    _fun = partial(return_date_list, date_field_name)
    all_oldest_list = []
    with tqdm(total=_number_all_funds) as p_bar:
        with ProcessPoolExecutor(max_workers=max_workers) as executor:
            for date_list in executor.map(_fun, file_list):
                if date_list:
                    all_oldest_list.append(date_list[0])
                for date in date_list:
                    if date not in _dict_count_trade:
                        _dict_count_trade[date] = 0

                    _dict_count_trade[date] += 1

                p_bar.update()

    logger.info(f"count how many funds have founded in this day......")
    _dict_count_founding = {date: _number_all_funds for date in _dict_count_trade}  # dict{date:count}
    with tqdm(total=_number_all_funds) as p_bar:
        for oldest_date in all_oldest_list:
            for date in _dict_count_founding.keys():
                if date < oldest_date:
                    _dict_count_founding[date] -= 1

    calendar = [
        date for date, count in _dict_count_trade.items() if count >= max(int(count * threshold), minimum_count)
    ]

    return calendar


def get_hs_stock_symbols() -> list:
    """get SH/SZ stock symbols

    Returns
    -------
        stock symbols
    """
    global _HS_SYMBOLS  # pylint: disable=W0603

    def _get_symbol():
        """
        Get the stock pool from a web page and process it into the format required by yahooquery.
        Format of data retrieved from the web page: 600519, 000001
        The data format required by yahooquery: 600519.ss, 000001.sz

        Returns
        -------
            set: Returns the set of symbol codes.

        Examples:
        -------
            {600000.ss, 600001.ss, 600002.ss, 600003.ss, ...}
        """
        url = "http://99.push2.eastmoney.com/api/qt/clist/get?pn=1&pz=10000&po=1&np=1&fs=m:0+t:6,m:0+t:80,m:1+t:2,m:1+t:23,m:0+t:81+s:2048&fields=f12"
        try:
            resp = requests.get(url, timeout=None)
            resp.raise_for_status()
        except requests.exceptions.HTTPError as e:
            raise requests.exceptions.HTTPError(f"Request to {url} failed with status code {resp.status_code}") from e

        try:
            _symbols = [_v["f12"] for _v in resp.json()["data"]["diff"]]
        except Exception as e:
            logger.warning("An error occurred while extracting data from the response.")
            raise

        if len(_symbols) < 3900:
            raise ValueError("The complete list of stocks is not available.")

        # Add suffix after the stock code to conform to yahooquery standard, otherwise the data will not be fetched.
        _symbols = [
            _symbol + ".ss" if _symbol.startswith("6") else _symbol + ".sz" if _symbol.startswith(("0", "3")) else None
            for _symbol in _symbols
        ]
        _symbols = [_symbol for _symbol in _symbols if _symbol is not None]

        return set(_symbols)

    if _HS_SYMBOLS is None:
        symbols = set()
        _retry = 60
        # It may take multiple times to get the complete
        while len(symbols) < MINIMUM_SYMBOLS_NUM:
            symbols |= _get_symbol()
            time.sleep(3)

        symbol_cache_path = Path("~/.cache/hs_symbols_cache.pkl").expanduser().resolve()
        symbol_cache_path.parent.mkdir(parents=True, exist_ok=True)
        if symbol_cache_path.exists():
            with symbol_cache_path.open("rb") as fp:
                cache_symbols = pickle.load(fp)
                symbols |= cache_symbols
        with symbol_cache_path.open("wb") as fp:
            pickle.dump(symbols, fp)

        _HS_SYMBOLS = sorted(list(symbols))

    return _HS_SYMBOLS


def get_us_stock_symbols(qlib_data_path: [str, Path] = None) -> list:
    """get US stock symbols

    Returns
    -------
        stock symbols
    """
    global _US_SYMBOLS  # pylint: disable=W0603

    @deco_retry
    def _get_eastmoney():
        url = "http://4.push2.eastmoney.com/api/qt/clist/get?pn=1&pz=10000&fs=m:105,m:106,m:107&fields=f12"
        resp = requests.get(url, timeout=None)
        if resp.status_code != 200:
            raise ValueError("request error")

        try:
            _symbols = [_v["f12"].replace("_", "-P") for _v in resp.json()["data"]["diff"].values()]
        except Exception as e:
            logger.warning(f"request error: {e}")
            raise

        if len(_symbols) < 8000:
            raise ValueError("request error")

        return _symbols

    @deco_retry
    def _get_nasdaq():
        _res_symbols = []
        for _name in ["otherlisted", "nasdaqtraded"]:
            url = f"ftp://ftp.nasdaqtrader.com/SymbolDirectory/{_name}.txt"
            df = pd.read_csv(url, sep="|")
            df = df.rename(columns={"ACT Symbol": "Symbol"})
            _symbols = df["Symbol"].dropna()
            _symbols = _symbols.str.replace("$", "-P", regex=False)
            _symbols = _symbols.str.replace(".W", "-WT", regex=False)
            _symbols = _symbols.str.replace(".U", "-UN", regex=False)
            _symbols = _symbols.str.replace(".R", "-RI", regex=False)
            _symbols = _symbols.str.replace(".", "-", regex=False)
            _res_symbols += _symbols.unique().tolist()
        return _res_symbols

    @deco_retry
    def _get_nyse():
        url = "https://www.nyse.com/api/quotes/filter"
        _parms = {
            "instrumentType": "EQUITY",
            "pageNumber": 1,
            "sortColumn": "NORMALIZED_TICKER",
            "sortOrder": "ASC",
            "maxResultsPerPage": 10000,
            "filterToken": "",
        }
        resp = requests.post(url, json=_parms, timeout=None)
        if resp.status_code != 200:
            raise ValueError("request error")

        try:
            _symbols = [_v["symbolTicker"].replace("-", "-P") for _v in resp.json()]
        except Exception as e:
            logger.warning(f"request error: {e}")
            _symbols = []
        return _symbols

    if _US_SYMBOLS is None:
        _all_symbols = _get_eastmoney() + _get_nasdaq() + _get_nyse()
        if qlib_data_path is not None:
            for _index in ["nasdaq100", "sp500"]:
                ins_df = pd.read_csv(
                    Path(qlib_data_path).joinpath(f"instruments/{_index}.txt"),
                    sep="\t",
                    names=["symbol", "start_date", "end_date"],
                )
                _all_symbols += ins_df["symbol"].unique().tolist()

        def _format(s_):
            s_ = s_.replace(".", "-")
            s_ = s_.strip("$")
            s_ = s_.strip("*")
            return s_

        _US_SYMBOLS = sorted(set(map(_format, filter(lambda x: len(x) < 8 and not x.endswith("WS"), _all_symbols))))

    return _US_SYMBOLS


def get_in_stock_symbols(qlib_data_path: [str, Path] = None) -> list:
    """get IN stock symbols

    Returns
    -------
        stock symbols
    """
    global _IN_SYMBOLS  # pylint: disable=W0603

    @deco_retry
    def _get_nifty():
        url = f"https://www1.nseindia.com/content/equities/EQUITY_L.csv"
        df = pd.read_csv(url)
        df = df.rename(columns={"SYMBOL": "Symbol"})
        df["Symbol"] = df["Symbol"] + ".NS"
        _symbols = df["Symbol"].dropna()
        _symbols = _symbols.unique().tolist()
        return _symbols

    if _IN_SYMBOLS is None:
        _all_symbols = _get_nifty()
        if qlib_data_path is not None:
            for _index in ["nifty"]:
                ins_df = pd.read_csv(
                    Path(qlib_data_path).joinpath(f"instruments/{_index}.txt"),
                    sep="\t",
                    names=["symbol", "start_date", "end_date"],
                )
                _all_symbols += ins_df["symbol"].unique().tolist()

        def _format(s_):
            s_ = s_.replace(".", "-")
            s_ = s_.strip("$")
            s_ = s_.strip("*")
            return s_

        _IN_SYMBOLS = sorted(set(_all_symbols))

    return _IN_SYMBOLS


def get_br_stock_symbols(qlib_data_path: [str, Path] = None) -> list:
    """get Brazil(B3) stock symbols

    Returns
    -------
        B3 stock symbols
    """
    global _BR_SYMBOLS  # pylint: disable=W0603

    @deco_retry
    def _get_ibovespa():
        _symbols = []
        url = "https://www.fundamentus.com.br/detalhes.php?papel="

        # Request
        agent = {"User-Agent": "Mozilla/5.0"}
        page = requests.get(url, headers=agent, timeout=None)

        # BeautifulSoup
        soup = BeautifulSoup(page.content, "html.parser")
        tbody = soup.find("tbody")

        children = tbody.findChildren("a", recursive=True)
        for child in children:
            _symbols.append(str(child).rsplit('"', maxsplit=1)[-1].split(">")[1].split("<")[0])

        return _symbols

    if _BR_SYMBOLS is None:
        _all_symbols = _get_ibovespa()
        if qlib_data_path is not None:
            for _index in ["ibov"]:
                ins_df = pd.read_csv(
                    Path(qlib_data_path).joinpath(f"instruments/{_index}.txt"),
                    sep="\t",
                    names=["symbol", "start_date", "end_date"],
                )
                _all_symbols += ins_df["symbol"].unique().tolist()

        def _format(s_):
            s_ = s_.strip()
            s_ = s_.strip("$")
            s_ = s_.strip("*")
            s_ = s_ + ".SA"
            return s_

        _BR_SYMBOLS = sorted(set(map(_format, _all_symbols)))

    return _BR_SYMBOLS


def get_en_fund_symbols(qlib_data_path: [str, Path] = None) -> list:
    """get en fund symbols

    Returns
    -------
        fund symbols in China
    """
    global _EN_FUND_SYMBOLS  # pylint: disable=W0603

    @deco_retry
    def _get_eastmoney():
        url = "http://fund.eastmoney.com/js/fundcode_search.js"
        resp = requests.get(url, timeout=None)
        if resp.status_code != 200:
            raise ValueError("request error")
        try:
            _symbols = []
            for sub_data in re.findall(r"[\[](.*?)[\]]", resp.content.decode().split("= [")[-1].replace("];", "")):
                data = sub_data.replace('"', "").replace("'", "")
                # TODO: do we need other information, like fund_name from ['000001', 'HXCZHH', '华夏成长混合', '混合型', 'HUAXIACHENGZHANGHUNHE']
                _symbols.append(data.split(",")[0])
        except Exception as e:
            logger.warning(f"request error: {e}")
            raise
        if len(_symbols) < 8000:
            raise ValueError("request error")
        return _symbols

    if _EN_FUND_SYMBOLS is None:
        _all_symbols = _get_eastmoney()

        _EN_FUND_SYMBOLS = sorted(set(_all_symbols))

    return _EN_FUND_SYMBOLS


def symbol_suffix_to_prefix(symbol: str, capital: bool = True) -> str:
    """symbol suffix to prefix

    Parameters
    ----------
    symbol: str
        symbol
    capital : bool
        by default True
    Returns
    -------

    """
    code, exchange = symbol.split(".")
    if exchange.lower() in ["sh", "ss"]:
        res = f"sh{code}"
    else:
        res = f"{exchange}{code}"
    return res.upper() if capital else res.lower()


def symbol_prefix_to_sufix(symbol: str, capital: bool = True) -> str:
    """symbol prefix to sufix

    Parameters
    ----------
    symbol: str
        symbol
    capital : bool
        by default True
    Returns
    -------

    """
    res = f"{symbol[:-2]}.{symbol[-2:]}"
    return res.upper() if capital else res.lower()


def deco_retry(retry: int = 5, retry_sleep: int = 3):
    def deco_func(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            _retry = 5 if callable(retry) else retry
            _result = None
            for _i in range(1, _retry + 1):
                try:
                    _result = func(*args, **kwargs)
                    break

                except Exception as e:
                    logger.warning(f"{func.__name__}: {_i} :{e}")
                    if _i == _retry:
                        raise

                time.sleep(retry_sleep)
            return _result

        return wrapper

    return deco_func(retry) if callable(retry) else deco_func


def get_trading_date_by_shift(trading_list: list, trading_date: pd.Timestamp, shift: int = 1):
    """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
    -------

    """
    trading_date = pd.Timestamp(trading_date)
    left_index = bisect.bisect_left(trading_list, trading_date)
    try:
        res = trading_list[left_index + shift]
    except IndexError:
        res = trading_date
    return res


def generate_minutes_calendar_from_daily(
    calendars: Iterable,
    freq: str = "1min",
    am_range: Tuple[str, str] = ("09:30:00", "11:29:00"),
    pm_range: Tuple[str, str] = ("13:00:00", "14:59:00"),
) -> pd.Index:
    """generate minutes calendar

    Parameters
    ----------
    calendars: Iterable
        daily calendar
    freq: str
        by default 1min
    am_range: Tuple[str, str]
        AM Time Range, by default China-Stock: ("09:30:00", "11:29:00")
    pm_range: Tuple[str, str]
        PM Time Range, by default China-Stock: ("13:00:00", "14:59:00")

    """
    daily_format: str = "%Y-%m-%d"
    res = []
    for _day in calendars:
        for _range in [am_range, pm_range]:
            res.append(
                pd.date_range(
                    f"{pd.Timestamp(_day).strftime(daily_format)} {_range[0]}",
                    f"{pd.Timestamp(_day).strftime(daily_format)} {_range[1]}",
                    freq=freq,
                )
            )

    return pd.Index(sorted(set(np.hstack(res))))


def get_instruments(
    qlib_dir: str,
    index_name: str,
    method: str = "parse_instruments",
    freq: str = "day",
    request_retry: int = 5,
    retry_sleep: int = 3,
    market_index: str = "cn_index",
):
    """

    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
    retry_sleep: int
        request sleep, by default 3
    market_index: str
        Where the files to obtain the index are located,
        for example data_collector.cn_index.collector

    Examples
    -------
        # parse instruments
        $ python collector.py --index_name CSI300 --qlib_dir ~/.qlib/qlib_data/cn_data --method parse_instruments

        # parse new companies
        $ python collector.py --index_name CSI300 --qlib_dir ~/.qlib/qlib_data/cn_data --method save_new_companies

    """
    _cur_module = importlib.import_module("data_collector.{}.collector".format(market_index))
    obj = getattr(_cur_module, f"{index_name.upper()}Index")(
        qlib_dir=qlib_dir, index_name=index_name, freq=freq, request_retry=request_retry, retry_sleep=retry_sleep
    )
    getattr(obj, method)()


def _get_all_1d_data(_date_field_name: str, _symbol_field_name: str, _1d_data_all: pd.DataFrame):
    df = copy.deepcopy(_1d_data_all)
    df.reset_index(inplace=True)
    df.rename(columns={"datetime": _date_field_name, "instrument": _symbol_field_name}, inplace=True)
    df.columns = list(map(lambda x: x[1:] if x.startswith("$") else x, df.columns))
    return df


def get_1d_data(
    _date_field_name: str,
    _symbol_field_name: str,
    symbol: str,
    start: str,
    end: str,
    _1d_data_all: pd.DataFrame,
) -> pd.DataFrame:
    """get 1d data

    Returns
    ------
        data_1d: pd.DataFrame
            data_1d.columns = [_date_field_name, _symbol_field_name, "paused", "volume", "factor", "close"]

    """
    _all_1d_data = _get_all_1d_data(_date_field_name, _symbol_field_name, _1d_data_all)
    return _all_1d_data[
        (_all_1d_data[_symbol_field_name] == symbol.upper())
        & (_all_1d_data[_date_field_name] >= pd.Timestamp(start))
        & (_all_1d_data[_date_field_name] < pd.Timestamp(end))
    ]


def calc_adjusted_price(
    df: pd.DataFrame,
    _1d_data_all: pd.DataFrame,
    _date_field_name: str,
    _symbol_field_name: str,
    frequence: str,
    consistent_1d: bool = True,
    calc_paused: bool = True,
) -> pd.DataFrame:
    """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. Called `calc_paused_num` method to add the `paused_num` field.
        - The `paused_num` is the number of consecutive days of trading suspension.
    """
    # TODO: using daily data factor
    if df.empty:
        return df
    df = df.copy()
    df.drop_duplicates(subset=_date_field_name, inplace=True)
    df.sort_values(_date_field_name, inplace=True)
    symbol = df.iloc[0][_symbol_field_name]
    df[_date_field_name] = pd.to_datetime(df[_date_field_name])
    # get 1d data from qlib
    _start = pd.Timestamp(df[_date_field_name].min()).strftime("%Y-%m-%d")
    _end = (pd.Timestamp(df[_date_field_name].max()) + pd.Timedelta(days=1)).strftime("%Y-%m-%d")
    data_1d: pd.DataFrame = get_1d_data(_date_field_name, _symbol_field_name, symbol, _start, _end, _1d_data_all)
    data_1d = data_1d.copy()
    if data_1d is None or data_1d.empty:
        df["factor"] = 1 / df.loc[df["close"].first_valid_index()]["close"]
        # TODO: np.nan or 1 or 0
        df["paused"] = np.nan
    else:
        # NOTE: volume is np.nan or volume <= 0, paused = 1
        # FIXME: find a more accurate data source
        data_1d["paused"] = 0
        data_1d.loc[(data_1d["volume"].isna()) | (data_1d["volume"] <= 0), "paused"] = 1
        data_1d = data_1d.set_index(_date_field_name)

        # add factor from 1d data
        # NOTE: 1d data info:
        #   - Close price adjusted for splits. Adjusted close price adjusted for both dividends and splits.
        #   - data_1d.adjclose: Adjusted close price adjusted for both dividends and splits.
        #   - data_1d.close: `data_1d.adjclose / (close for the first trading day that is not np.nan)`
        def _calc_factor(df_1d: pd.DataFrame):
            try:
                _date = pd.Timestamp(pd.Timestamp(df_1d[_date_field_name].iloc[0]).date())
                df_1d["factor"] = data_1d.loc[_date]["close"] / df_1d.loc[df_1d["close"].last_valid_index()]["close"]
                df_1d["paused"] = data_1d.loc[_date]["paused"]
            except Exception:
                df_1d["factor"] = np.nan
                df_1d["paused"] = np.nan
            return df_1d

        df = df.groupby([df[_date_field_name].dt.date], group_keys=False).apply(_calc_factor)
        if consistent_1d:
            # the date sequence is consistent with 1d
            df.set_index(_date_field_name, inplace=True)
            df = df.reindex(
                generate_minutes_calendar_from_daily(
                    calendars=pd.to_datetime(data_1d.reset_index()[_date_field_name].drop_duplicates()),
                    freq=frequence,
                    am_range=("09:30:00", "11:29:00"),
                    pm_range=("13:00:00", "14:59:00"),
                )
            )
            df[_symbol_field_name] = df.loc[df[_symbol_field_name].first_valid_index()][_symbol_field_name]
            df.index.names = [_date_field_name]
            df.reset_index(inplace=True)
    for _col in ["open", "close", "high", "low", "volume"]:
        if _col not in df.columns:
            continue
        if _col == "volume":
            df[_col] = df[_col] / df["factor"]
        else:
            df[_col] = df[_col] * df["factor"]
    if calc_paused:
        df = calc_paused_num(df, _date_field_name, _symbol_field_name)
    return df


def calc_paused_num(df: pd.DataFrame, _date_field_name, _symbol_field_name):
    """calc paused num
    This method adds the paused_num field
        - The `paused_num` is the number of consecutive days of trading suspension.
    """
    _symbol = df.iloc[0][_symbol_field_name]
    df = df.copy()
    df["_tmp_date"] = df[_date_field_name].apply(lambda x: pd.Timestamp(x).date())
    # remove data that starts and ends with `np.nan` all day
    all_data = []
    # Record the number of consecutive trading days where the whole day is nan, to remove the last trading day where the whole day is nan
    all_nan_nums = 0
    # Record the number of consecutive occurrences of trading days that are not nan throughout the day
    not_nan_nums = 0
    for _date, _df in df.groupby("_tmp_date"):
        _df["paused"] = 0
        if not _df.loc[_df["volume"] < 0].empty:
            logger.warning(f"volume < 0, will fill np.nan: {_date} {_symbol}")
            _df.loc[_df["volume"] < 0, "volume"] = np.nan

        check_fields = set(_df.columns) - {
            "_tmp_date",
            "paused",
            "factor",
            _date_field_name,
            _symbol_field_name,
        }
        if _df.loc[:, list(check_fields)].isna().values.all() or (_df["volume"] == 0).all():
            all_nan_nums += 1
            not_nan_nums = 0
            _df["paused"] = 1
            if all_data:
                _df["paused_num"] = not_nan_nums
                all_data.append(_df)
        else:
            all_nan_nums = 0
            not_nan_nums += 1
            _df["paused_num"] = not_nan_nums
            all_data.append(_df)
    all_data = all_data[: len(all_data) - all_nan_nums]
    if all_data:
        df = pd.concat(all_data, sort=False)
    else:
        logger.warning(f"data is empty: {_symbol}")
        df = pd.DataFrame()
        return df
    del df["_tmp_date"]
    return df


if __name__ == "__main__":
    assert len(get_hs_stock_symbols()) >= MINIMUM_SYMBOLS_NUM