Ibovespa index support (#990)

* feat: download ibovespa index historic composition

ibovespa(ibov) is the largest index in Brazil's stocks exchange.
The br_index folder has support for downloading new companies for the current index composition.
And has support, as well, for downloading companies from historic composition of ibov index.

Partially resolves issue #956

* fix: typo error instead of end_date, it was written end_ate

* feat: adds support for downloading stocks historic prices from Brazil's stocks exchange (B3)

Together with commit c2f933 it resolves issue #956

* fix: code formatted with black.

* wip: Creating code logic for brazils stock market data normalization

* docs: brazils stock market data normalization code documentation

* fix: code formatted the with black

* docs: fixed typo

* docs: more info about python version used to generate requirements.txt file

* docs: added BeautifulSoup requirements

* feat: removed debug prints

* feat: added ibov_index_composition variable as a class attribute of IBOVIndex

* feat: added increment to generate the four month period used by the ibov index

* refactor: Added get_instruments() method inside utils.py for better code usability.

Message in the PR request to understand the context of the change

In the course of reviewing this PR we found two issues.

    1. there are multiple places where the get_instruments() method is used,
	and we feel that scripts.index.py is the best place for the
	get_instruments() method to go.
    2. data_collector.utils has some very generic stuff put inside it.

* refactor: improve brazils stocks download speed

The reason to use retry=2 is due to the fact that
Yahoo Finance unfortunately does not keep track of the majority
of Brazilian stocks.

Therefore, the decorator deco_retry with retry argument
set to 5 will keep trying to get the stock data 5 times,
which makes the code to download Brazilians stocks very slow.

In future, this may change, but for now
I suggest to leave retry argument to 1 or 2 in
order to improve download speed.

In order to achieve this code logic an argument called retry_config
was added into YahooCollectorBR1d and YahooCollectorBR1min

* fix: added __main__ at the bottom of the script

* refactor: changed interface inside each index

Using partial as `fire.Fire(partial(get_instruments, market_index="br_index" ))`
will make the interface easier for the user to execute the script.
Then all the collector.py CLI in each folder can remove a redundant arguments.

* refactor: implemented  class interface retry into YahooCollectorBR

* docs: added BR as a possible region into the documentation

* refactor: make retry attribute part of the interface

This way we don't have to use hasattr to access the retry attribute as previously done

scripts/data_collector/br_index/README.md

@@ -0,0 +1,61 @@
+# iBOVESPA History Companies Collection
+
+## Requirements
+
+- Install the libs from the file `requirements.txt`
+
+    ```bash
+    pip install -r requirements.txt
+    ```
+- `requirements.txt` file was generated using python3.8
+
+## For the ibovespa (IBOV) index, we have:
+
+<hr/>
+
+### Method `get_new_companies`
+
+#### <b>Index start date</b>
+
+- The ibovespa index started on 2 January 1968 ([wiki](https://en.wikipedia.org/wiki/%C3%8Dndice_Bovespa)).  In order to use this start date in our `bench_start_date(self)` method, two conditions must be satisfied:
+    1) APIs used to download brazilian stocks (B3) historical prices must keep track of such historic data since 2 January 1968
+
+    2) Some website or API must provide, from that date, the historic index composition. In other words, the companies used to build the index .
+
+    As a consequence, the method `bench_start_date(self)` inside `collector.py` was implemented using `pd.Timestamp("2003-01-03")` due to two reasons
+
+    1) The earliest ibov composition that have been found was from the first quarter of 2003. More informations about such composition can be seen on the sections below.
+
+    2) Yahoo finance, one of the libraries used to download symbols historic prices, keeps track from this date forward.
+
+- Within the `get_new_companies` method, a logic was implemented to get, for each ibovespa component stock, the start date that yahoo finance keeps track of.
+
+#### <b>Code Logic</b>
+
+The code does a web scrapping into the B3's [website](https://sistemaswebb3-listados.b3.com.br/indexPage/day/IBOV?language=pt-br), which keeps track of the ibovespa stocks composition on the current day. 
+
+Other approaches, such as `request` and `Beautiful Soup` could have been used. However, the website shows the table with the stocks with some delay, since it uses a script inside of it to obtain such compositions.
+Alternatively, `selenium` was used to download this stocks' composition in order to overcome this problem.
+
+Futhermore, the data downloaded from the selenium script  was preprocessed so it could be saved into the `csv` format stablished by `scripts/data_collector/index.py`.
+
+<hr/>
+
+### Method `get_changes` 
+
+No suitable data source that keeps track of ibovespa's history stocks composition has been found. Except from this [repository](https://github.com/igor17400/IBOV-HCI) which provide such information have been used, however it only provides the data from the 1st quarter of 2003 to 3rd quarter of 2021.
+
+With that reference, the index's composition can be compared quarter by quarter and year by year and then generate a file that keeps track of which stocks have been removed and which have been added each quarter and year.
+
+<hr/>
+
+### Collector Data
+
+```bash
+# parse instruments, using in qlib/instruments.
+python collector.py --index_name IBOV --qlib_dir ~/.qlib/qlib_data/br_data --method parse_instruments
+
+# parse new companies
+python collector.py --index_name IBOV --qlib_dir ~/.qlib/qlib_data/br_data --method save_new_companies
+```
+

scripts/data_collector/br_index/collector.py

@@ -0,0 +1,277 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+from functools import partial
+import sys
+from pathlib import Path
+import importlib
+import datetime
+
+import fire
+import pandas as pd
+from tqdm import tqdm
+from loguru import logger
+
+CUR_DIR = Path(__file__).resolve().parent
+sys.path.append(str(CUR_DIR.parent.parent))
+
+from data_collector.index import IndexBase
+from data_collector.utils import get_instruments
+
+quarter_dict = {"1Q": "01-03", "2Q": "05-01", "3Q": "09-01"}
+
+
+class IBOVIndex(IndexBase):
+
+    ibov_index_composition = "https://raw.githubusercontent.com/igor17400/IBOV-HCI/main/historic_composition/{}.csv"
+    years_4_month_periods = []
+
+    def __init__(
+        self,
+        index_name: str,
+        qlib_dir: [str, Path] = None,
+        freq: str = "day",
+        request_retry: int = 5,
+        retry_sleep: int = 3,
+    ):
+        super(IBOVIndex, self).__init__(
+            index_name=index_name, qlib_dir=qlib_dir, freq=freq, request_retry=request_retry, retry_sleep=retry_sleep
+        )
+
+        self.today: datetime = datetime.date.today()
+        self.current_4_month_period = self.get_current_4_month_period(self.today.month)
+        self.year = str(self.today.year)
+        self.years_4_month_periods = self.get_four_month_period()
+
+    @property
+    def bench_start_date(self) -> pd.Timestamp:
+        """
+        The ibovespa index started on 2 January 1968 (wiki), however,
+        no suitable data source that keeps track of ibovespa's history
+        stocks composition has been found. Except from the repo indicated
+        in README. Which keeps track of such information starting from
+        the first quarter of 2003
+        """
+        return pd.Timestamp("2003-01-03")
+
+    def get_current_4_month_period(self, current_month: int):
+        """
+        This function is used to calculated what is the current 
+        four month period for the current month. For example, 
+        If the current month is August 8, its four month period
+        is 2Q.
+
+        OBS: In english Q is used to represent *quarter*
+        which means a three month period. However, in 
+        portuguese we use Q to represent a four month period.
+        In other words,
+
+        Jan, Feb, Mar, Apr: 1Q
+        May, Jun, Jul, Aug: 2Q
+        Sep, Oct, Nov, Dez: 3Q
+
+        Parameters
+        ----------
+        month : int
+            Current month (1 <= month <= 12)
+
+        Returns
+        -------
+        current_4m_period:str
+            Current Four Month Period (1Q or 2Q or 3Q)
+        """
+        if current_month < 5:
+            return "1Q"
+        if current_month < 9:
+            return "2Q"
+        if current_month <= 12:
+            return "3Q"
+        else:
+            return -1
+
+    def get_four_month_period(self):
+        """
+        The ibovespa index is updated every four months. 
+        Therefore, we will represent each time period as 2003_1Q 
+        which means 2003 first four mount period (Jan, Feb, Mar, Apr)
+        """
+        four_months_period = ["1Q", "2Q", "3Q"]
+        init_year = 2003
+        now = datetime.datetime.now()
+        current_year = now.year
+        current_month = now.month
+        for year in [item for item in range(init_year, current_year)]:
+            for el in four_months_period:
+                self.years_4_month_periods.append(str(year)+"_"+el)
+        # For current year the logic must be a little different
+        current_4_month_period = self.get_current_4_month_period(current_month)
+        for i in range(int(current_4_month_period[0])):
+            self.years_4_month_periods.append(str(current_year) + "_" + str(i+1) + "Q")
+        return self.years_4_month_periods
+
+
+    def format_datetime(self, inst_df: pd.DataFrame) -> pd.DataFrame:
+        """formatting the datetime in an instrument
+
+        Parameters
+        ----------
+        inst_df: pd.DataFrame
+            inst_df.columns = [self.SYMBOL_FIELD_NAME, self.START_DATE_FIELD, self.END_DATE_FIELD]
+
+        Returns
+        -------
+        inst_df: pd.DataFrame
+
+        """
+        logger.info("Formatting Datetime")
+        if self.freq != "day":
+            inst_df[self.END_DATE_FIELD] = inst_df[self.END_DATE_FIELD].apply(
+                lambda x: (pd.Timestamp(x) + pd.Timedelta(hours=23, minutes=59)).strftime("%Y-%m-%d %H:%M:%S")
+            )
+        else:
+            inst_df[self.START_DATE_FIELD] = inst_df[self.START_DATE_FIELD].apply(
+                lambda x: (pd.Timestamp(x)).strftime("%Y-%m-%d")
+            )
+
+            inst_df[self.END_DATE_FIELD] = inst_df[self.END_DATE_FIELD].apply(
+                lambda x: (pd.Timestamp(x)).strftime("%Y-%m-%d")
+            )
+        return inst_df
+
+    def format_quarter(self, cell: str):
+        """
+        Parameters
+        ----------
+        cell: str
+            It must be on the format 2003_1Q --> years_4_month_periods
+
+        Returns
+        ----------
+        date: str
+            Returns date in format 2003-03-01
+        """
+        cell_split = cell.split("_")
+        return cell_split[0] + "-" + quarter_dict[cell_split[1]]
+
+    def get_changes(self):
+        """
+        Access the index historic composition and compare it quarter
+        by quarter and year by year in order to generate a file that
+        keeps track of which stocks have been removed and which have
+        been added.
+
+        The Dataframe used as reference will provided the index
+        composition for each year an quarter:
+        pd.DataFrame:
+            symbol
+            SH600000
+            SH600001
+            .
+            .
+            .
+
+        Parameters
+        ----------
+        self: is used to represent the instance of the class.
+
+        Returns
+        ----------
+        pd.DataFrame:
+            symbol      date        type
+            SH600000  2019-11-11    add
+            SH600001  2020-11-10    remove
+            dtypes:
+                symbol: str
+                date: pd.Timestamp
+                type: str, value from ["add", "remove"]
+        """
+        logger.info("Getting companies changes in {} index ...".format(self.index_name))
+
+        try:
+            df_changes_list = []
+            for i in tqdm(range(len(self.years_4_month_periods) - 1)):
+                df = pd.read_csv(self.ibov_index_composition.format(self.years_4_month_periods[i]), on_bad_lines="skip")["symbol"]
+                df_ = pd.read_csv(self.ibov_index_composition.format(self.years_4_month_periods[i + 1]), on_bad_lines="skip")["symbol"]
+
+                ## Remove Dataframe
+                remove_date = self.years_4_month_periods[i].split("_")[0] + "-" + quarter_dict[self.years_4_month_periods[i].split("_")[1]]
+                list_remove = list(df[~df.isin(df_)])
+                df_removed = pd.DataFrame(
+                    {
+                        "date": len(list_remove) * [remove_date],
+                        "type": len(list_remove) * ["remove"],
+                        "symbol": list_remove,
+                    }
+                )
+
+                ## Add Dataframe
+                add_date = self.years_4_month_periods[i + 1].split("_")[0] + "-" + quarter_dict[self.years_4_month_periods[i + 1].split("_")[1]]
+                list_add = list(df_[~df_.isin(df)])
+                df_added = pd.DataFrame(
+                    {"date": len(list_add) * [add_date], "type": len(list_add) * ["add"], "symbol": list_add}
+                )
+
+                df_changes_list.append(pd.concat([df_added, df_removed], sort=False))
+                df = pd.concat(df_changes_list).reset_index(drop=True)
+                df["symbol"] = df["symbol"].astype(str) + ".SA"
+
+            return df
+
+        except Exception as E:
+            logger.error("An error occured while downloading 2008 index composition - {}".format(E))
+
+    def get_new_companies(self):
+        """
+        Get latest index composition.
+        The repo indicated on README has implemented a script
+        to get the latest index composition from B3 website using
+        selenium. Therefore, this method will download the file
+        containing such composition
+
+        Parameters
+        ----------
+        self: is used to represent the instance of the class.
+
+        Returns
+        ----------
+        pd.DataFrame:
+            symbol      start_date  end_date
+            RRRP3	    2020-11-13	2022-03-02
+            ALPA4	    2008-01-02	2022-03-02
+            dtypes:
+                symbol: str
+                start_date: pd.Timestamp
+                end_date: pd.Timestamp
+        """
+        logger.info("Getting new companies in {} index ...".format(self.index_name))
+
+        try:
+            ## Get index composition
+
+            df_index = pd.read_csv(
+                self.ibov_index_composition.format(self.year + "_" + self.current_4_month_period), on_bad_lines="skip"
+            )
+            df_date_first_added = pd.read_csv(
+                self.ibov_index_composition.format("date_first_added_" + self.year + "_" + self.current_4_month_period),
+                on_bad_lines="skip",
+            )
+            df = df_index.merge(df_date_first_added, on="symbol")[["symbol", "Date First Added"]]
+            df[self.START_DATE_FIELD] = df["Date First Added"].map(self.format_quarter)
+
+            # end_date will be our current quarter + 1, since the IBOV index updates itself every quarter
+            df[self.END_DATE_FIELD] = self.year + "-" + quarter_dict[self.current_4_month_period]
+            df = df[["symbol", self.START_DATE_FIELD, self.END_DATE_FIELD]]
+            df["symbol"] = df["symbol"].astype(str) + ".SA"
+
+            return df
+
+        except Exception as E:
+            logger.error("An error occured while getting new companies - {}".format(E))
+
+    def filter_df(self, df: pd.DataFrame) -> pd.DataFrame:
+        if "Código" in df.columns:
+            return df.loc[:, ["Código"]].copy()
+
+
+
+if __name__ == "__main__":
+    fire.Fire(partial(get_instruments, market_index="br_index" ))

scripts/data_collector/br_index/requirements.txt

@@ -0,0 +1,34 @@
+async-generator==1.10
+attrs==21.4.0
+certifi==2021.10.8
+cffi==1.15.0
+charset-normalizer==2.0.12
+cryptography==36.0.1
+fire==0.4.0
+h11==0.13.0
+idna==3.3
+loguru==0.6.0
+lxml==4.8.0
+multitasking==0.0.10
+numpy==1.22.2
+outcome==1.1.0
+pandas==1.4.1
+pycoingecko==2.2.0
+pycparser==2.21
+pyOpenSSL==22.0.0
+PySocks==1.7.1
+python-dateutil==2.8.2
+pytz==2021.3
+requests==2.27.1
+requests-futures==1.0.0
+six==1.16.0
+sniffio==1.2.0
+sortedcontainers==2.4.0
+termcolor==1.1.0
+tqdm==4.63.0
+trio==0.20.0
+trio-websocket==0.9.2
+urllib3==1.26.8
+wget==3.2
+wsproto==1.1.0
+yahooquery==2.2.15

scripts/data_collector/cn_index/collector.py

@@ -21,6 +21,7 @@ sys.path.append(str(CUR_DIR.parent.parent))
 
 from data_collector.index import IndexBase
 from data_collector.utils import get_calendar_list, get_trading_date_by_shift, deco_retry
+from data_collector.utils import get_instruments
 
 
 NEW_COMPANIES_URL = "https://csi-web-dev.oss-cn-shanghai-finance-1-pub.aliyuncs.com/static/html/csindex/public/uploads/file/autofile/cons/{index_code}cons.xls"
@@ -315,7 +316,7 @@ class CSIIndex(IndexBase):
         return df
 
 
-class CSI300(CSIIndex):
+class CSI300Index(CSIIndex):
     @property
     def index_code(self):
         return "000300"
@@ -458,46 +459,5 @@ class CSI500(CSIIndex):
         return df
 
 
-def get_instruments(
-    qlib_dir: str,
-    index_name: str,
-    method: str = "parse_instruments",
-    freq: str = "day",
-    request_retry: int = 5,
-    retry_sleep: int = 3,
-):
-    """
-
-    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
-
-    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.cn_index.collector")
-    obj = getattr(_cur_module, f"{index_name.upper()}")(
-        qlib_dir=qlib_dir, index_name=index_name, freq=freq, request_retry=request_retry, retry_sleep=retry_sleep
-    )
-    getattr(obj, method)()
-
-
 if __name__ == "__main__":
     fire.Fire(get_instruments)

scripts/data_collector/index.py

@@ -19,7 +19,7 @@ class IndexBase:
     SYMBOL_FIELD_NAME = "symbol"
     DATE_FIELD_NAME = "date"
     START_DATE_FIELD = "start_date"
-    END_DATE_FIELD = "end_ate"
+    END_DATE_FIELD = "end_date"
     CHANGE_TYPE_FIELD = "type"
     INSTRUMENTS_COLUMNS = [SYMBOL_FIELD_NAME, START_DATE_FIELD, END_DATE_FIELD]
     REMOVE = "remove"

scripts/data_collector/us_index/collector.py

@@ -2,6 +2,7 @@
 # Licensed under the MIT License.
 
 import abc
+from functools import partial
 import sys
 import importlib
 from pathlib import Path
@@ -20,6 +21,7 @@ sys.path.append(str(CUR_DIR.parent.parent))
 
 from data_collector.index import IndexBase
 from data_collector.utils import deco_retry, get_calendar_list, get_trading_date_by_shift
+from data_collector.utils import get_instruments
 
 
 WIKI_URL = "https://en.wikipedia.org/wiki"
@@ -269,46 +271,6 @@ class SP400Index(WIKIIndex):
         logger.warning(f"No suitable data source has been found!")
 
 
-def get_instruments(
-    qlib_dir: str,
-    index_name: str,
-    method: str = "parse_instruments",
-    freq: str = "day",
-    request_retry: int = 5,
-    retry_sleep: int = 3,
-):
-    """
-
-    Parameters
-    ----------
-    qlib_dir: str
-        qlib data dir, default "Path(__file__).parent/qlib_data"
-    index_name: str
-        index name, value from ["SP500", "NASDAQ100", "DJIA", "SP400"]
-    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
-
-    Examples
-    -------
-        # parse instruments
-        $ python collector.py --index_name SP500 --qlib_dir ~/.qlib/qlib_data/us_data --method parse_instruments
-
-        # parse new companies
-        $ python collector.py --index_name SP500 --qlib_dir ~/.qlib/qlib_data/us_data --method save_new_companies
-
-    """
-    _cur_module = importlib.import_module("data_collector.us_index.collector")
-    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)()
-
 
 if __name__ == "__main__":
-    fire.Fire(get_instruments)
+    fire.Fire(partial(get_instruments, market_index="us_index"))

scripts/data_collector/utils.py

@@ -2,6 +2,7 @@
 #  Licensed under the MIT License.
 
 import re
+import importlib
 import time
 import bisect
 import pickle
@@ -19,6 +20,7 @@ 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}"
 
@@ -34,6 +36,7 @@ CALENDAR_BENCH_URL_MAP = {
     # 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
@@ -41,6 +44,7 @@ _ALL_CALENDAR_LIST = None
 _HS_SYMBOLS = None
 _US_SYMBOLS = None
 _IN_SYMBOLS = None
+_BR_SYMBOLS = None
 _EN_FUND_SYMBOLS = None
 _CALENDAR_MAP = {}
 
@@ -69,7 +73,9 @@ def get_calendar_list(bench_code="CSI300") -> List[pd.Timestamp]:
 
     calendar = _CALENDAR_MAP.get(bench_code, None)
     if calendar is None:
-        if bench_code.startswith("US_") or bench_code.startswith("IN_"):
+        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:
@@ -345,6 +351,57 @@ def get_in_stock_symbols(qlib_data_path: [str, Path] = None) -> list:
     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
+
+    @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)
+
+        # 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).split('"')[-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
 
@@ -502,6 +559,50 @@ def generate_minutes_calendar_from_daily(
 
     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)()
+
 
 if __name__ == "__main__":
-    assert len(get_hs_stock_symbols()) >= MINIMUM_SYMBOLS_NUM
+    assert len(get_hs_stock_symbols()) >= MINIMUM_SYMBOLS_NUM

scripts/data_collector/yahoo/README.md

@@ -66,7 +66,7 @@ pip install -r requirements.txt
           - `source_dir`: save the directory
           - `interval`: `1d` or `1min`, by default `1d`
             > **due to the limitation of the *YahooFinance API*, only the last month's data is available in `1min`**
-          - `region`: `CN` or `US` or `IN`, by default `CN`
+          - `region`: `CN` or `US` or `IN` or `BR`, by default `CN`
           - `delay`: `time.sleep(delay)`, by default *0.5*
           - `start`: start datetime, by default *"2000-01-01"*; *closed interval(including start)*
           - `end`: end datetime, by default `pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1))`; *open interval(excluding end)*
@@ -80,14 +80,21 @@ pip install -r requirements.txt
           python collector.py download_data --source_dir ~/.qlib/stock_data/source/cn_data --start 2020-01-01 --end 2020-12-31 --delay 1 --interval 1d --region CN
           # cn 1min data
           python collector.py download_data --source_dir ~/.qlib/stock_data/source/cn_data_1min --delay 1 --interval 1min --region CN
+
           # us 1d data
           python collector.py download_data --source_dir ~/.qlib/stock_data/source/us_data --start 2020-01-01 --end 2020-12-31 --delay 1 --interval 1d --region US
           # us 1min data
           python collector.py download_data --source_dir ~/.qlib/stock_data/source/us_data_1min --delay 1 --interval 1min --region US
+
           # in 1d data
           python collector.py download_data --source_dir ~/.qlib/stock_data/source/in_data --start 2020-01-01 --end 2020-12-31 --delay 1 --interval 1d --region IN
           # in 1min data
           python collector.py download_data --source_dir ~/.qlib/stock_data/source/in_data_1min --delay 1 --interval 1min --region IN
+
+          # br 1d data
+          python collector.py download_data --source_dir ~/.qlib/stock_data/source/br_data --start 2003-01-03 --end 2022-03-01 --delay 1 --interval 1d --region BR
+          # br 1min data
+          python collector.py download_data --source_dir ~/.qlib/stock_data/source/br_data_1min --delay 1 --interval 1min --region BR
           ```
   2. normalize data: `python scripts/data_collector/yahoo/collector.py normalize_data`
      
@@ -116,8 +123,15 @@ pip install -r requirements.txt
         ```bash
         # normalize 1d cn
         python collector.py normalize_data --source_dir ~/.qlib/stock_data/source/cn_data --normalize_dir ~/.qlib/stock_data/source/cn_1d_nor --region CN --interval 1d
+
         # normalize 1min cn
         python collector.py normalize_data --qlib_data_1d_dir ~/.qlib/qlib_data/cn_data --source_dir ~/.qlib/stock_data/source/cn_data_1min --normalize_dir ~/.qlib/stock_data/source/cn_1min_nor --region CN --interval 1min
+
+        # normalize 1d br
+        python scripts/data_collector/yahoo/collector.py normalize_data --source_dir ~/.qlib/stock_data/source/br_data --normalize_dir ~/.qlib/stock_data/source/br_1d_nor --region BR --interval 1d
+
+        # normalize 1min br
+        python collector.py normalize_data --qlib_data_1d_dir ~/.qlib/qlib_data/br_data --source_dir ~/.qlib/stock_data/source/br_data_1min --normalize_dir ~/.qlib/stock_data/source/br_1min_nor --region BR --interval 1min
         ```
   3. dump data: `python scripts/dump_bin.py dump_all`
     

scripts/data_collector/yahoo/collector.py

@@ -2,6 +2,7 @@
 # Licensed under the MIT License.
 
 import abc
+from re import I
 import sys
 import copy
 import time
@@ -35,6 +36,7 @@ from data_collector.utils import (
     get_hs_stock_symbols,
     get_us_stock_symbols,
     get_in_stock_symbols,
+    get_br_stock_symbols,
     generate_minutes_calendar_from_daily,
 )
 
@@ -42,6 +44,8 @@ INDEX_BENCH_URL = "http://push2his.eastmoney.com/api/qt/stock/kline/get?secid=1.
 
 
 class YahooCollector(BaseCollector):
+    retry = 5  # Configuration attribute.  How many times will it try to re-request the data if the network fails.
+
     def __init__(
         self,
         save_dir: [str, Path],
@@ -146,7 +150,7 @@ class YahooCollector(BaseCollector):
     def get_data(
         self, symbol: str, interval: str, start_datetime: pd.Timestamp, end_datetime: pd.Timestamp
     ) -> pd.DataFrame:
-        @deco_retry(retry_sleep=self.delay)
+        @deco_retry(retry_sleep=self.delay, retry=self.retry)
         def _get_simple(start_, end_):
             self.sleep()
             _remote_interval = "1m" if interval == self.INTERVAL_1min else interval
@@ -311,6 +315,55 @@ class YahooCollectorIN1min(YahooCollectorIN):
     pass
 
 
+class YahooCollectorBR(YahooCollector, ABC):
+    def retry(cls):
+        """"
+            The reason to use retry=2 is due to the fact that
+            Yahoo Finance unfortunately does not keep track of some
+            Brazilian stocks. 
+            
+            Therefore, the decorator deco_retry with retry argument
+            set to 5 will keep trying to get the stock data up to 5 times, 
+            which makes the code to download Brazilians stocks very slow. 
+            
+            In future, this may change, but for now 
+            I suggest to leave retry argument to 1 or 2 in 
+            order to improve download speed.
+
+            To achieve this goal an abstract attribute (retry)
+            was added into YahooCollectorBR base class
+        """
+        raise NotImplementedError
+        
+    def get_instrument_list(self):
+        logger.info("get BR stock symbols......")
+        symbols = get_br_stock_symbols() + [
+            "^BVSP",
+        ]
+        logger.info(f"get {len(symbols)} symbols.")
+        return symbols
+
+    def download_index_data(self):
+        pass
+
+    def normalize_symbol(self, symbol):
+        return code_to_fname(symbol).upper()
+
+    @property
+    def _timezone(self):
+        return "Brazil/East"
+
+
+class YahooCollectorBR1d(YahooCollectorBR):
+    retry = 2
+    pass
+
+
+class YahooCollectorBR1min(YahooCollectorBR):
+    retry = 2
+    pass
+
+
 class YahooNormalize(BaseNormalize):
     COLUMNS = ["open", "close", "high", "low", "volume"]
     DAILY_FORMAT = "%Y-%m-%d"
@@ -833,6 +886,29 @@ class YahooNormalizeCN1min(YahooNormalizeCN, YahooNormalize1minOffline):
         return get_calendar_list("ALL")
 
 
+class YahooNormalizeBR:
+    def _get_calendar_list(self) -> Iterable[pd.Timestamp]:
+        return get_calendar_list("BR_ALL")
+
+
+class YahooNormalizeBR1d(YahooNormalizeBR, YahooNormalize1d):
+    pass
+
+
+class YahooNormalizeBR1min(YahooNormalizeBR, YahooNormalize1minOffline):
+    CALC_PAUSED_NUM = False
+
+    def _get_calendar_list(self) -> Iterable[pd.Timestamp]:
+        # TODO: support 1min
+        raise ValueError("Does not support 1min")
+
+    def _get_1d_calendar_list(self):
+        return get_calendar_list("BR_ALL")
+
+    def symbol_to_yahoo(self, symbol):
+        return fname_to_code(symbol)
+
+
 class Run(BaseRun):
     def __init__(self, source_dir=None, normalize_dir=None, max_workers=1, interval="1d", region=REGION_CN):
         """
@@ -848,7 +924,7 @@ class Run(BaseRun):
         interval: str
             freq, value from [1min, 1d], default 1d
         region: str
-            region, value from ["CN", "US"], default "CN"
+            region, value from ["CN", "US", "BR"], default "CN"
         """
         super().__init__(source_dir, normalize_dir, max_workers, interval)
         self.region = region

scripts/data_collector/yahoo/requirements.txt

@@ -7,3 +7,6 @@ tqdm
 lxml
 yahooquery
 joblib
+beautifulsoup4
+bs4
+soupsieve