Add BaseCollector

scripts/data_collector/base.py

@@ -0,0 +1,430 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+
+import abc
+import time
+import datetime
+import importlib
+from pathlib import Path
+from typing import Type
+from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
+
+import pandas as pd
+from tqdm import tqdm
+from loguru import logger
+from qlib.utils import code_to_fname
+
+
+class BaseCollector(abc.ABC):
+
+    CACHE_FLAG = "CACHED"
+    NORMAL_FLAG = "NORMAL"
+
+    DEFAULT_START_DATETIME_1D = pd.Timestamp("2000-01-01")
+    DEFAULT_START_DATETIME_1MIN = pd.Timestamp(datetime.datetime.now() - pd.Timedelta(days=5 * 6))
+    DEFAULT_END_DATETIME_1D = pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1))
+    DEFAULT_END_DATETIME_1MIN = pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1))
+
+    INTERVAL_1min = "1min"
+    INTERVAL_1d = "1d"
+
+    def __init__(
+        self,
+        save_dir: [str, Path],
+        start=None,
+        end=None,
+        interval="1d",
+        max_workers=4,
+        max_collector_count=2,
+        delay=0,
+        check_data_length: bool = False,
+        limit_nums: int = None,
+    ):
+        """
+
+        Parameters
+        ----------
+        save_dir: str
+            stock save dir
+        max_workers: int
+            workers, default 4
+        max_collector_count: int
+            default 2
+        delay: float
+            time.sleep(delay), default 0
+        interval: str
+            freq, value from [1min, 1d], default 1d
+        start: str
+            start datetime, default None
+        end: str
+            end datetime, default None
+        check_data_length: bool
+            check data length, by default False
+        limit_nums: int
+            using for debug, by default None
+        """
+        self.save_dir = Path(save_dir).expanduser().resolve()
+        self.save_dir.mkdir(parents=True, exist_ok=True)
+
+        self.delay = delay
+        self.max_workers = max_workers
+        self.max_collector_count = max_collector_count
+        self.mini_symbol_map = {}
+        self.interval = interval
+        self.check_small_data = check_data_length
+
+        self.start_datetime = self.normalize_start_datetime(start)
+        self.end_datetime = self.normalize_end_datetime(end)
+
+        self.stock_list = sorted(set(self.get_stock_list()))
+
+        if limit_nums is not None:
+            try:
+                self.stock_list = self.stock_list[: int(limit_nums)]
+            except Exception as e:
+                logger.warning(f"Cannot use limit_nums={limit_nums}, the parameter will be ignored")
+
+    def normalize_start_datetime(self, start_datetime: [str, pd.Timestamp] = None):
+        return (
+            pd.Timestamp(str(start_datetime))
+            if start_datetime
+            else getattr(self, f"DEFAULT_START_DATETIME_{self.interval.upper()}")
+        )
+
+    def normalize_end_datetime(self, end_datetime: [str, pd.Timestamp] = None):
+        return (
+            pd.Timestamp(str(end_datetime))
+            if end_datetime
+            else getattr(self, f"DEFAULT_END_DATETIME_{self.interval.upper()}")
+        )
+
+    @property
+    @abc.abstractmethod
+    def min_numbers_trading(self):
+        # daily, one year: 252 / 4
+        # us 1min, a week: 6.5 * 60 * 5
+        # cn 1min, a week: 4 * 60 * 5
+        raise NotImplementedError("rewrite min_numbers_trading")
+
+    @abc.abstractmethod
+    def get_stock_list(self):
+        raise NotImplementedError("rewrite get_stock_list")
+
+    @abc.abstractmethod
+    def normalize_symbol(self, symbol: str):
+        """normalize symbol"""
+        raise NotImplementedError("rewrite normalize_symbol")
+
+    @abc.abstractmethod
+    def get_data(
+        self, symbol: str, interval: str, start_datetime: pd.Timestamp, end_datetime: pd.Timestamp
+    ) -> pd.DataFrame:
+        """get data with symbol
+
+        Parameters
+        ----------
+        symbol: str
+        interval: str
+            value from [1min, 1d]
+        start_datetime: pd.Timestamp
+        end_datetime: pd.Timestamp
+
+        Returns
+        ---------
+            pd.DataFrame, "symbol" in pd.columns
+
+        """
+        raise NotImplementedError("rewrite get_timezone")
+
+    def sleep(self):
+        time.sleep(self.delay)
+
+    def _simple_collector(self, symbol: str):
+        """
+
+        Parameters
+        ----------
+        symbol: str
+
+        """
+        self.sleep()
+        df = self.get_data(symbol, self.interval, self.start_datetime, self.end_datetime)
+        _result = self.NORMAL_FLAG
+        if self.check_small_data:
+            _result = self.cache_small_data(symbol, df)
+        if _result == self.NORMAL_FLAG:
+            self.save_instrument(symbol, df)
+        return _result
+
+    def save_instrument(self, symbol, df: pd.DataFrame):
+        """save stock data to file
+
+        Parameters
+        ----------
+        symbol: str
+            stock code
+        df : pd.DataFrame
+            df.columns must contain "symbol" and "datetime"
+        """
+        if df.empty:
+            logger.warning(f"{symbol} is empty")
+            return
+
+        symbol = self.normalize_symbol(symbol)
+        symbol = code_to_fname(symbol)
+        stock_path = self.save_dir.joinpath(f"{symbol}.csv")
+        df["symbol"] = symbol
+        if stock_path.exists():
+            _old_df = pd.read_csv(stock_path)
+            df = _old_df.append(df, sort=False)
+        df.to_csv(stock_path, index=False)
+
+    def cache_small_data(self, symbol, df):
+        if len(df) <= self.min_numbers_trading:
+            logger.warning(f"the number of trading days of {symbol} is less than {self.min_numbers_trading}!")
+            _temp = self.mini_symbol_map.setdefault(symbol, [])
+            _temp.append(df.copy())
+            return self.CACHE_FLAG
+        else:
+            if symbol in self.mini_symbol_map:
+                self.mini_symbol_map.pop(symbol)
+            return self.NORMAL_FLAG
+
+    def _collector(self, stock_list):
+
+        error_symbol = []
+        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
+            with tqdm(total=len(stock_list)) as p_bar:
+                for _symbol, _result in zip(stock_list, executor.map(self._simple_collector, stock_list)):
+                    if _result != self.NORMAL_FLAG:
+                        error_symbol.append(_symbol)
+                    p_bar.update()
+        print(error_symbol)
+        logger.info(f"error symbol nums: {len(error_symbol)}")
+        logger.info(f"current get symbol nums: {len(stock_list)}")
+        error_symbol.extend(self.mini_symbol_map.keys())
+        return sorted(set(error_symbol))
+
+    def collector_data(self):
+        """collector data"""
+        logger.info("start collector data......")
+        stock_list = self.stock_list
+        for i in range(self.max_collector_count):
+            if not stock_list:
+                break
+            logger.info(f"getting data: {i+1}")
+            stock_list = self._collector(stock_list)
+            logger.info(f"{i+1} finish.")
+        for _symbol, _df_list in self.mini_symbol_map.items():
+            self.save_instrument(
+                _symbol, pd.concat(_df_list, sort=False).drop_duplicates(["date"]).sort_values(["date"])
+            )
+        if self.mini_symbol_map:
+            logger.warning(f"less than {self.min_numbers_trading} stock list: {list(self.mini_symbol_map.keys())}")
+        logger.info(f"total {len(self.stock_list)}, error: {len(set(stock_list))}")
+
+
+class BaseNormalize(abc.ABC):
+    def __init__(
+        self,
+        date_field_name: str = "date",
+        symbol_field_name: str = "symbol",
+    ):
+        """
+
+        Parameters
+        ----------
+        date_field_name: str
+            date field name, default is date
+        symbol_field_name: str
+            symbol field name, default is symbol
+        """
+        self._date_field_name = date_field_name
+        self._symbol_field_name = symbol_field_name
+
+        self._calendar_list = self._get_calendar_list()
+
+    @abc.abstractmethod
+    def normalize(self, df: pd.DataFrame) -> pd.DataFrame:
+        # normalize
+        raise NotImplementedError("")
+
+    @abc.abstractmethod
+    def _get_calendar_list(self):
+        """Get benchmark calendar"""
+        raise NotImplementedError("")
+
+
+class Normalize:
+    def __init__(
+        self,
+        source_dir: [str, Path],
+        target_dir: [str, Path],
+        normalize_class: Type[BaseNormalize],
+        max_workers: int = 16,
+        date_field_name: str = "date",
+        symbol_field_name: str = "symbol",
+    ):
+        """
+
+        Parameters
+        ----------
+        source_dir: str or Path
+            The directory where the raw data collected from the Internet is saved
+        target_dir: str or Path
+            Directory for normalize data
+        normalize_class: Type[YahooNormalize]
+            normalize class
+        max_workers: int
+            Concurrent number, default is 16
+        date_field_name: str
+            date field name, default is date
+        symbol_field_name: str
+            symbol field name, default is symbol
+        """
+        if not (source_dir and target_dir):
+            raise ValueError("source_dir and target_dir cannot be None")
+        self._source_dir = Path(source_dir).expanduser()
+        self._target_dir = Path(target_dir).expanduser()
+        self._target_dir.mkdir(parents=True, exist_ok=True)
+
+        self._max_workers = max_workers
+
+        self._normalize_obj = normalize_class(date_field_name=date_field_name, symbol_field_name=symbol_field_name)
+
+    def _executor(self, file_path: Path):
+        file_path = Path(file_path)
+        df = pd.read_csv(file_path)
+        df = self._normalize_obj.normalize(df)
+        if not df.empty:
+            df.to_csv(self._target_dir.joinpath(file_path.name), index=False)
+
+    def normalize(self):
+        logger.info("normalize data......")
+
+        with ProcessPoolExecutor(max_workers=self._max_workers) as worker:
+            file_list = list(self._source_dir.glob("*.csv"))
+            with tqdm(total=len(file_list)) as p_bar:
+                for _ in worker.map(self._executor, file_list):
+                    p_bar.update()
+
+
+class BaseRun(abc.ABC):
+    def __init__(self, source_dir=None, normalize_dir=None, max_workers=4, interval="1d"):
+        """
+
+        Parameters
+        ----------
+        source_dir: str
+            The directory where the raw data collected from the Internet is saved, default "Path(__file__).parent/source"
+        normalize_dir: str
+            Directory for normalize data, default "Path(__file__).parent/normalize"
+        max_workers: int
+            Concurrent number, default is 4
+        interval: str
+            freq, value from [1min, 1d], default 1d
+        """
+        if source_dir is None:
+            source_dir = Path(self.default_base_dir).joinpath("_source")
+        self.source_dir = Path(source_dir).expanduser().resolve()
+        self.source_dir.mkdir(parents=True, exist_ok=True)
+
+        if normalize_dir is None:
+            normalize_dir = Path(self.default_base_dir).joinpath("normalize")
+        self.normalize_dir = Path(normalize_dir).expanduser().resolve()
+        self.normalize_dir.mkdir(parents=True, exist_ok=True)
+
+        self._cur_module = importlib.import_module("collector")
+        self.max_workers = max_workers
+        self.interval = interval
+
+    @property
+    @abc.abstractmethod
+    def collector_class_name(self):
+        raise NotImplementedError("rewrite normalize_symbol")
+
+    @property
+    @abc.abstractmethod
+    def normalize_class_name(self):
+        raise NotImplementedError("rewrite normalize_symbol")
+
+    @property
+    @abc.abstractmethod
+    def default_base_dir(self) -> [Path, str]:
+        raise NotImplementedError("rewrite normalize_symbol")
+
+    def download_data(
+        self,
+        max_collector_count=2,
+        delay=0,
+        start=None,
+        end=None,
+        interval="1d",
+        check_data_length=False,
+        limit_nums=None,
+    ):
+        """download data from Internet
+
+        Parameters
+        ----------
+        max_collector_count: int
+            default 2
+        delay: float
+            time.sleep(delay), default 0
+        interval: str
+            freq, value from [1min, 1d], default 1d
+        start: str
+            start datetime, default "2000-01-01"
+        end: str
+            end datetime, default ``pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1))``
+        check_data_length: bool
+            check data length, by default False
+        limit_nums: int
+            using for debug, by default None
+
+        Examples
+        ---------
+            # get daily data
+            $ python collector.py download_data --source_dir ~/.qlib/stock_data/source --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d
+            # get 1m data
+            $ python collector.py download_data --source_dir ~/.qlib/stock_data/source --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1m
+        """
+
+        _class = getattr(self._cur_module, self.collector_class_name)  # type: Type[BaseCollector]
+        _class(
+            self.source_dir,
+            max_workers=self.max_workers,
+            max_collector_count=max_collector_count,
+            delay=delay,
+            start=start,
+            end=end,
+            interval=interval,
+            check_data_length=check_data_length,
+            limit_nums=limit_nums,
+        ).collector_data()
+
+    def normalize_data(self, date_field_name: str = "date", symbol_field_name: str = "symbol"):
+        """normalize data
+
+        Parameters
+        ----------
+        date_field_name: str
+            date field name, default date
+        symbol_field_name: str
+            symbol field name, default symbol
+
+        Examples
+        ---------
+            $ python collector.py normalize_data --source_dir ~/.qlib/stock_data/source --normalize_dir ~/.qlib/stock_data/normalize --region CN --interval 1d
+        """
+        _class = getattr(self._cur_module, self.normalize_class_name)
+        yc = Normalize(
+            source_dir=self.source_dir,
+            target_dir=self.normalize_dir,
+            normalize_class=_class,
+            max_workers=self.max_workers,
+            date_field_name=date_field_name,
+            symbol_field_name=symbol_field_name,
+        )
+        yc.normalize()

scripts/data_collector/yahoo/collector.py

@@ -10,158 +10,26 @@ import importlib
 from abc import ABC
 from pathlib import Path
 from typing import Iterable, Type
-from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
 
 import fire
 import requests
 import numpy as np
 import pandas as pd
-from tqdm import tqdm
 from loguru import logger
 from yahooquery import Ticker
 from dateutil.tz import tzlocal
 from qlib.utils import code_to_fname, fname_to_code
+from qlib.config import REG_CN as REGION_CN
 
 CUR_DIR = Path(__file__).resolve().parent
 sys.path.append(str(CUR_DIR.parent.parent))
+from data_collector.base import BaseCollector, BaseNormalize, BaseRun
 from data_collector.utils import get_calendar_list, get_hs_stock_symbols, get_us_stock_symbols
 
 INDEX_BENCH_URL = "http://push2his.eastmoney.com/api/qt/stock/kline/get?secid=1.{index_code}&fields1=f1%2Cf2%2Cf3%2Cf4%2Cf5&fields2=f51%2Cf52%2Cf53%2Cf54%2Cf55%2Cf56%2Cf57%2Cf58&klt=101&fqt=0&beg={begin}&end={end}"
-REGION_CN = "CN"
-REGION_US = "US"
 
 
-class YahooData:
-    START_DATETIME = pd.Timestamp("2000-01-01")
-    HIGH_FREQ_START_DATETIME = pd.Timestamp(datetime.datetime.now() - pd.Timedelta(days=5 * 6))
-    END_DATETIME = pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1))
-    INTERVAL_1min = "1min"
-    INTERVAL_1d = "1d"
-
-    def __init__(
-        self,
-        timezone: str = None,
-        start=None,
-        end=None,
-        interval="1d",
-        delay=0,
-        show_1min_logging: bool = False,
-    ):
-        """
-
-        Parameters
-        ----------
-        timezone: str
-            The timezone where the data is located
-        delay: float
-            time.sleep(delay), default 0
-        interval: str
-            freq, value from [1min, 1d], default 1min
-        start: str
-            start datetime, default None
-        end: str
-            end datetime, default None
-        show_1min_logging: bool
-            show 1min logging, by default False; if True, there may be many warning logs
-        """
-        self._timezone = tzlocal() if timezone is None else timezone
-        self._delay = delay
-        self._interval = interval
-        self._show_1min_logging = show_1min_logging
-        self.start_datetime = pd.Timestamp(str(start)) if start else self.START_DATETIME
-        self.end_datetime = min(pd.Timestamp(str(end)) if end else self.END_DATETIME, self.END_DATETIME)
-        if self._interval == self.INTERVAL_1min:
-            self.start_datetime = max(self.start_datetime, self.HIGH_FREQ_START_DATETIME)
-        elif self._interval == self.INTERVAL_1d:
-            pass
-        else:
-            raise ValueError(f"interval error: {self._interval}")
-
-        # using for 1min
-        self._next_datetime = self.convert_datetime(self.start_datetime.date() + pd.Timedelta(days=1), self._timezone)
-        self._latest_datetime = self.convert_datetime(self.end_datetime.date(), self._timezone)
-
-        self.start_datetime = self.convert_datetime(self.start_datetime, self._timezone)
-        self.end_datetime = self.convert_datetime(self.end_datetime, self._timezone)
-
-    @staticmethod
-    def convert_datetime(dt: [pd.Timestamp, datetime.date, str], timezone):
-        try:
-            dt = pd.Timestamp(dt, tz=timezone).timestamp()
-            dt = pd.Timestamp(dt, tz=tzlocal(), unit="s")
-        except ValueError as e:
-            pass
-        return dt
-
-    def _sleep(self):
-        time.sleep(self._delay)
-
-    @staticmethod
-    def get_data_from_remote(symbol, interval, start, end, show_1min_logging: bool = False):
-        error_msg = f"{symbol}-{interval}-{start}-{end}"
-
-        def _show_logging_func():
-            if interval == YahooData.INTERVAL_1min and show_1min_logging:
-                logger.warning(f"{error_msg}:{_resp}")
-
-        interval = "1m" if interval in ["1m", "1min"] else interval
-        try:
-            _resp = Ticker(symbol, asynchronous=False).history(interval=interval, start=start, end=end)
-            if isinstance(_resp, pd.DataFrame):
-                return _resp.reset_index()
-            elif isinstance(_resp, dict):
-                _temp_data = _resp.get(symbol, {})
-                if isinstance(_temp_data, str) or (
-                    isinstance(_resp, dict) and _temp_data.get("indicators", {}).get("quote", None) is None
-                ):
-                    _show_logging_func()
-            else:
-                _show_logging_func()
-        except Exception as e:
-            logger.warning(f"{error_msg}:{e}")
-
-    def get_data(self, symbol: str) -> [pd.DataFrame]:
-        def _get_simple(start_, end_):
-            self._sleep()
-            _remote_interval = "1m" if self._interval == self.INTERVAL_1min else self._interval
-            return self.get_data_from_remote(
-                symbol,
-                interval=_remote_interval,
-                start=start_,
-                end=end_,
-                show_1min_logging=self._show_1min_logging,
-            )
-
-        _result = None
-        if self._interval == self.INTERVAL_1d:
-            _result = _get_simple(self.start_datetime, self.end_datetime)
-        elif self._interval == self.INTERVAL_1min:
-            if self._next_datetime >= self._latest_datetime:
-                _result = _get_simple(self.start_datetime, self.end_datetime)
-            else:
-                _res = []
-
-                def _get_multi(start_, end_):
-                    _resp = _get_simple(start_, end_)
-                    if _resp is not None and not _resp.empty:
-                        _res.append(_resp)
-
-                for _s, _e in (
-                    (self.start_datetime, self._next_datetime),
-                    (self._latest_datetime, self.end_datetime),
-                ):
-                    _get_multi(_s, _e)
-                for _start in pd.date_range(self._next_datetime, self._latest_datetime, closed="left"):
-                    _end = _start + pd.Timedelta(days=1)
-                    _get_multi(_start, _end)
-                if _res:
-                    _result = pd.concat(_res, sort=False).sort_values(["symbol", "date"])
-        else:
-            raise ValueError(f"cannot support {self._interval}")
-        return _result
-
-
-class YahooCollector:
+class YahooCollector(BaseCollector):
     def __init__(
         self,
         save_dir: [str, Path],
@@ -173,7 +41,6 @@ class YahooCollector:
         delay=0,
         check_data_length: bool = False,
         limit_nums: int = None,
-        show_1min_logging: bool = False,
     ):
         """
 
@@ -197,131 +64,118 @@ class YahooCollector:
             check data length, by default False
         limit_nums: int
             using for debug, by default None
-        show_1min_logging: bool
-            show 1m logging, by default False; if True, there may be many warning logs
         """
-        self.save_dir = Path(save_dir).expanduser().resolve()
-        self.save_dir.mkdir(parents=True, exist_ok=True)
-
-        self._delay = delay
-        self.max_workers = max_workers
-        self._max_collector_count = max_collector_count
-        self._mini_symbol_map = {}
-        self._interval = interval
-        self._check_small_data = check_data_length
-
-        self.stock_list = sorted(set(self.get_stock_list()))
-        if limit_nums is not None:
-            try:
-                self.stock_list = self.stock_list[: int(limit_nums)]
-            except Exception as e:
-                logger.warning(f"Cannot use limit_nums={limit_nums}, the parameter will be ignored")
-
-        self.yahoo_data = YahooData(
-            timezone=self._timezone,
+        super(YahooCollector, self).__init__(
+            save_dir=save_dir,
             start=start,
             end=end,
             interval=interval,
+            max_workers=max_workers,
+            max_collector_count=max_collector_count,
             delay=delay,
-            show_1min_logging=show_1min_logging,
+            check_data_length=check_data_length,
+            limit_nums=limit_nums,
         )
 
-    @property
-    @abc.abstractmethod
-    def min_numbers_trading(self):
-        # daily, one year: 252 / 4
-        # us 1min, a week: 6.5 * 60 * 5
-        # cn 1min, a week: 4 * 60 * 5
-        raise NotImplementedError("rewrite min_numbers_trading")
+        self.init_datetime()
 
-    @abc.abstractmethod
-    def get_stock_list(self):
-        raise NotImplementedError("rewrite get_stock_list")
+    def init_datetime(self):
+        if self.interval == self.INTERVAL_1min:
+            self.start_datetime = max(self.start_datetime, self.DEFAULT_START_DATETIME_1MIN)
+        elif self.interval == self.INTERVAL_1d:
+            pass
+        else:
+            raise ValueError(f"interval error: {self.interval}")
+
+        # using for 1min
+        self._next_datetime = self.convert_datetime(self.start_datetime.date() + pd.Timedelta(days=1), self._timezone)
+        self._latest_datetime = self.convert_datetime(self.end_datetime.date(), self._timezone)
+
+        self.start_datetime = self.convert_datetime(self.start_datetime, self._timezone)
+        self.end_datetime = self.convert_datetime(self.end_datetime, self._timezone)
+
+    @staticmethod
+    def convert_datetime(dt: [pd.Timestamp, datetime.date, str], timezone):
+        try:
+            dt = pd.Timestamp(dt, tz=timezone).timestamp()
+            dt = pd.Timestamp(dt, tz=tzlocal(), unit="s")
+        except ValueError as e:
+            pass
+        return dt
 
     @property
     @abc.abstractmethod
     def _timezone(self):
         raise NotImplementedError("rewrite get_timezone")
 
-    def save_stock(self, symbol, df: pd.DataFrame):
-        """save stock data to file
+    @staticmethod
+    def get_data_from_remote(symbol, interval, start, end, show_1min_logging: bool = False):
+        error_msg = f"{symbol}-{interval}-{start}-{end}"
 
-        Parameters
-        ----------
-        symbol: str
-            stock code
-        df : pd.DataFrame
-            df.columns must contain "symbol" and "datetime"
-        """
-        if df.empty:
-            logger.warning(f"{symbol} is empty")
-            return
+        def _show_logging_func():
+            if interval == YahooCollector.INTERVAL_1min and show_1min_logging:
+                logger.warning(f"{error_msg}:{_resp}")
 
-        symbol = self.normalize_symbol(symbol)
-        symbol = code_to_fname(symbol)
-        stock_path = self.save_dir.joinpath(f"{symbol}.csv")
-        df["symbol"] = symbol
-        if stock_path.exists():
-            _old_df = pd.read_csv(stock_path)
-            df = _old_df.append(df, sort=False)
-        df.to_csv(stock_path, index=False)
+        interval = "1m" if interval in ["1m", "1min"] else interval
+        try:
+            _resp = Ticker(symbol, asynchronous=False).history(interval=interval, start=start, end=end)
+            if isinstance(_resp, pd.DataFrame):
+                return _resp.reset_index()
+            elif isinstance(_resp, dict):
+                _temp_data = _resp.get(symbol, {})
+                if isinstance(_temp_data, str) or (
+                    isinstance(_resp, dict) and _temp_data.get("indicators", {}).get("quote", None) is None
+                ):
+                    _show_logging_func()
+            else:
+                _show_logging_func()
+        except Exception as e:
+            logger.warning(f"{error_msg}:{e}")
 
-    def _save_small_data(self, symbol, df):
-        if len(df) <= self.min_numbers_trading:
-            logger.warning(f"the number of trading days of {symbol} is less than {self.min_numbers_trading}!")
-            _temp = self._mini_symbol_map.setdefault(symbol, [])
-            _temp.append(df.copy())
-            return None
-        else:
-            if symbol in self._mini_symbol_map:
-                self._mini_symbol_map.pop(symbol)
-            return symbol
+    def get_data(
+        self, symbol: str, interval: str, start_datetime: pd.Timestamp, end_datetime: pd.Timestamp
+    ) -> pd.DataFrame:
+        def _get_simple(start_, end_):
+            self.sleep()
+            _remote_interval = "1m" if interval == self.INTERVAL_1min else interval
+            return self.get_data_from_remote(
+                symbol,
+                interval=_remote_interval,
+                start=start_,
+                end=end_,
+            )
 
-    def _get_data(self, symbol):
         _result = None
-        df = self.yahoo_data.get_data(symbol)
-        if isinstance(df, pd.DataFrame):
-            if not df.empty:
-                if self._check_small_data:
-                    if self._save_small_data(symbol, df) is not None:
-                        _result = symbol
-                        self.save_stock(symbol, df)
-                else:
-                    _result = symbol
-                    self.save_stock(symbol, df)
-        return _result
+        if interval == self.INTERVAL_1d:
+            _result = _get_simple(start_datetime, end_datetime)
+        elif interval == self.INTERVAL_1min:
+            if self._next_datetime >= self._latest_datetime:
+                _result = _get_simple(start_datetime, end_datetime)
+            else:
+                _res = []
 
-    def _collector(self, stock_list):
+                def _get_multi(start_, end_):
+                    _resp = _get_simple(start_, end_)
+                    if _resp is not None and not _resp.empty:
+                        _res.append(_resp)
 
-        error_symbol = []
-        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
-            with tqdm(total=len(stock_list)) as p_bar:
-                for _symbol, _result in zip(stock_list, executor.map(self._get_data, stock_list)):
-                    if _result is None:
-                        error_symbol.append(_symbol)
-                    p_bar.update()
-        print(error_symbol)
-        logger.info(f"error symbol nums: {len(error_symbol)}")
-        logger.info(f"current get symbol nums: {len(stock_list)}")
-        error_symbol.extend(self._mini_symbol_map.keys())
-        return sorted(set(error_symbol))
+                for _s, _e in (
+                    (self.start_datetime, self._next_datetime),
+                    (self._latest_datetime, self.end_datetime),
+                ):
+                    _get_multi(_s, _e)
+                for _start in pd.date_range(self._next_datetime, self._latest_datetime, closed="left"):
+                    _end = _start + pd.Timedelta(days=1)
+                    _get_multi(_start, _end)
+                if _res:
+                    _result = pd.concat(_res, sort=False).sort_values(["symbol", "date"])
+        else:
+            raise ValueError(f"cannot support {self.interval}")
+        return pd.DataFrame() if _result is None else _result
 
     def collector_data(self):
         """collector data"""
-        logger.info("start collector yahoo data......")
-        stock_list = self.stock_list
-        for i in range(self._max_collector_count):
-            if not stock_list:
-                break
-            logger.info(f"getting data: {i+1}")
-            stock_list = self._collector(stock_list)
-            logger.info(f"{i+1} finish.")
-        for _symbol, _df_list in self._mini_symbol_map.items():
-            self.save_stock(_symbol, pd.concat(_df_list, sort=False).drop_duplicates(["date"]).sort_values(["date"]))
-        if self._mini_symbol_map:
-            logger.warning(f"less than {self.min_numbers_trading} stock list: {list(self._mini_symbol_map.keys())}")
-        logger.info(f"total {len(self.stock_list)}, error: {len(set(stock_list))}")
-
+        super(YahooCollector, self).collector_data()
         self.download_index_data()
 
     @abc.abstractmethod
@@ -329,11 +183,6 @@ class YahooCollector:
         """download index data"""
         raise NotImplementedError("rewrite download_index_data")
 
-    @abc.abstractmethod
-    def normalize_symbol(self, symbol: str):
-        """normalize symbol"""
-        raise NotImplementedError("rewrite normalize_symbol")
-
 
 class YahooCollectorCN(YahooCollector, ABC):
     def get_stock_list(self):
@@ -360,8 +209,8 @@ class YahooCollectorCN1d(YahooCollectorCN):
     def download_index_data(self):
         # TODO: from MSN
         _format = "%Y%m%d"
-        _begin = self.yahoo_data.start_datetime.strftime(_format)
-        _end = (self.yahoo_data.end_datetime + pd.Timedelta(days=-1)).strftime(_format)
+        _begin = self.start_datetime.strftime(_format)
+        _end = (self.end_datetime + pd.Timedelta(days=-1)).strftime(_format)
         for _index_name, _index_code in {"csi300": "000300", "csi100": "000903"}.items():
             logger.info(f"get bench data: {_index_name}({_index_code})......")
             try:
@@ -396,7 +245,7 @@ class YahooCollectorCN1min(YahooCollectorCN):
 
     def download_index_data(self):
         # TODO: 1m
-        logger.warning(f"{self.__class__.__name__} {self._interval} does not support: download_index_data")
+        logger.warning(f"{self.__class__.__name__} {self.interval} does not support: download_index_data")
 
 
 class YahooCollectorUS(YahooCollector, ABC):
@@ -433,29 +282,10 @@ class YahooCollectorUS1min(YahooCollectorUS):
         return 60 * 6.5 * 5
 
 
-class YahooNormalize:
+class YahooNormalize(BaseNormalize):
     COLUMNS = ["open", "close", "high", "low", "volume"]
     DAILY_FORMAT = "%Y-%m-%d"
 
-    def __init__(
-        self,
-        date_field_name: str = "date",
-        symbol_field_name: str = "symbol",
-    ):
-        """
-
-        Parameters
-        ----------
-        date_field_name: str
-            date field name, default is date
-        symbol_field_name: str
-            symbol field name, default is symbol
-        """
-        self._date_field_name = date_field_name
-        self._symbol_field_name = symbol_field_name
-
-        self._calendar_list = self._get_calendar_list()
-
     @staticmethod
     def normalize_yahoo(
         df: pd.DataFrame,
@@ -498,11 +328,6 @@ class YahooNormalize:
         df = self.adjusted_price(df)
         return df
 
-    @abc.abstractmethod
-    def _get_calendar_list(self):
-        """Get benchmark calendar"""
-        raise NotImplementedError("")
-
     @abc.abstractmethod
     def adjusted_price(self, df: pd.DataFrame) -> pd.DataFrame:
         """adjusted price"""
@@ -618,7 +443,9 @@ class YahooNormalize1min(YahooNormalize, ABC):
         # get 1d data from yahoo
         _start = pd.Timestamp(df[self._date_field_name].min()).strftime(self.DAILY_FORMAT)
         _end = (pd.Timestamp(df[self._date_field_name].max()) + pd.Timedelta(days=1)).strftime(self.DAILY_FORMAT)
-        data_1d = YahooData.get_data_from_remote(self.symbol_to_yahoo(symbol), interval="1d", start=_start, end=_end)
+        data_1d = YahooCollector.get_data_from_remote(
+            self.symbol_to_yahoo(symbol), interval="1d", start=_start, end=_end
+        )
         if data_1d is None or data_1d.empty:
             df["factor"] = 1
             # TODO: np.nan or 1 or 0
@@ -723,62 +550,8 @@ class YahooNormalizeCN1min(YahooNormalizeCN, YahooNormalize1min):
         return get_calendar_list("ALL")
 
 
-class Normalize:
-    def __init__(
-        self,
-        source_dir: [str, Path],
-        target_dir: [str, Path],
-        normalize_class: Type[YahooNormalize],
-        max_workers: int = 16,
-        date_field_name: str = "date",
-        symbol_field_name: str = "symbol",
-    ):
-        """
-
-        Parameters
-        ----------
-        source_dir: str or Path
-            The directory where the raw data collected from the Internet is saved
-        target_dir: str or Path
-            Directory for normalize data
-        normalize_class: Type[YahooNormalize]
-            normalize class
-        max_workers: int
-            Concurrent number, default is 16
-        date_field_name: str
-            date field name, default is date
-        symbol_field_name: str
-            symbol field name, default is symbol
-        """
-        if not (source_dir and target_dir):
-            raise ValueError("source_dir and target_dir cannot be None")
-        self._source_dir = Path(source_dir).expanduser()
-        self._target_dir = Path(target_dir).expanduser()
-        self._target_dir.mkdir(parents=True, exist_ok=True)
-
-        self._max_workers = max_workers
-
-        self._normalize_obj = normalize_class(date_field_name=date_field_name, symbol_field_name=symbol_field_name)
-
-    def _executor(self, file_path: Path):
-        file_path = Path(file_path)
-        df = pd.read_csv(file_path)
-        df = self._normalize_obj.normalize(df)
-        if not df.empty:
-            df.to_csv(self._target_dir.joinpath(file_path.name), index=False)
-
-    def normalize(self):
-        logger.info("normalize data......")
-
-        with ProcessPoolExecutor(max_workers=self._max_workers) as worker:
-            file_list = list(self._source_dir.glob("*.csv"))
-            with tqdm(total=len(file_list)) as p_bar:
-                for _ in worker.map(self._executor, file_list):
-                    p_bar.update()
-
-
-class Run:
-    def __init__(self, source_dir=None, normalize_dir=None, max_workers=4, region=REGION_CN):
+class Run(BaseRun):
+    def __init__(self, source_dir=None, normalize_dir=None, max_workers=4, interval="1d", region=REGION_CN):
         """
 
         Parameters
@@ -789,23 +562,26 @@ class Run:
             Directory for normalize data, default "Path(__file__).parent/normalize"
         max_workers: int
             Concurrent number, default is 4
+        interval: str
+            freq, value from [1min, 1d], default 1d
         region: str
             region, value from ["CN", "US"], default "CN"
         """
-        if source_dir is None:
-            source_dir = CUR_DIR.joinpath("source")
-        self.source_dir = Path(source_dir).expanduser().resolve()
-        self.source_dir.mkdir(parents=True, exist_ok=True)
-
-        if normalize_dir is None:
-            normalize_dir = CUR_DIR.joinpath("normalize")
-        self.normalize_dir = Path(normalize_dir).expanduser().resolve()
-        self.normalize_dir.mkdir(parents=True, exist_ok=True)
-
-        self._cur_module = importlib.import_module("collector")
-        self.max_workers = max_workers
+        super().__init__(source_dir, normalize_dir, max_workers, interval)
         self.region = region
 
+    @property
+    def collector_class_name(self):
+        return f"YahooCollector{self.region.upper()}{self.interval}"
+
+    @property
+    def normalize_class_name(self):
+        return f"YahooNormalize{self.region.upper()}{self.interval}"
+
+    @property
+    def default_base_dir(self) -> [Path, str]:
+        return CUR_DIR
+
     def download_data(
         self,
         max_collector_count=2,
@@ -815,7 +591,6 @@ class Run:
         interval="1d",
         check_data_length=False,
         limit_nums=None,
-        show_1min_logging=False,
     ):
         """download data from Internet
 
@@ -835,8 +610,6 @@ class Run:
             check data length, by default False
         limit_nums: int
             using for debug, by default None
-        show_1min_logging: bool
-            show 1m logging, by default False; if True, there may be many warning logs
 
         Examples
         ---------
@@ -846,29 +619,13 @@ class Run:
             $ python collector.py download_data --source_dir ~/.qlib/stock_data/source --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1m
         """
 
-        _class = getattr(
-            self._cur_module, f"YahooCollector{self.region.upper()}{interval}"
-        )  # type: Type[YahooCollector]
-        _class(
-            self.source_dir,
-            max_workers=self.max_workers,
-            max_collector_count=max_collector_count,
-            delay=delay,
-            start=start,
-            end=end,
-            interval=interval,
-            check_data_length=check_data_length,
-            limit_nums=limit_nums,
-            show_1min_logging=show_1min_logging,
-        ).collector_data()
+        super(Run, self).download_data(max_collector_count, delay, start, end, interval, check_data_length, limit_nums)
 
-    def normalize_data(self, interval: str = "1d", date_field_name: str = "date", symbol_field_name: str = "symbol"):
+    def normalize_data(self, date_field_name: str = "date", symbol_field_name: str = "symbol"):
         """normalize data
 
         Parameters
         ----------
-        interval: str
-            freq, value from [1min, 1d], default 1d
         date_field_name: str
             date field name, default date
         symbol_field_name: str
@@ -878,16 +635,7 @@ class Run:
         ---------
             $ python collector.py normalize_data --source_dir ~/.qlib/stock_data/source --normalize_dir ~/.qlib/stock_data/normalize --region CN --interval 1d
         """
-        _class = getattr(self._cur_module, f"YahooNormalize{self.region.upper()}{interval}")
-        yc = Normalize(
-            source_dir=self.source_dir,
-            target_dir=self.normalize_dir,
-            normalize_class=_class,
-            max_workers=self.max_workers,
-            date_field_name=date_field_name,
-            symbol_field_name=symbol_field_name,
-        )
-        yc.normalize()
+        super(Run, self).normalize_data(date_field_name, symbol_field_name)
 
 
 if __name__ == "__main__":