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add normalize 1min to use local data && change the default parameters for collecting 1min

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This commit is contained in:
zhupr
2021-06-08 14:45:20 +08:00
parent 554b9c7826
commit a845a2271b
9 changed files with 328 additions and 70 deletions
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196
scripts/data_collector/yahoo/collector.py
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@@ -137,7 +137,7 @@ 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=1)
@deco_retry(retry_sleep=self.delay)
def _get_simple(start_, end_):
self.sleep()
_remote_interval = "1m" if interval == self.INTERVAL_1min else interval
@@ -200,10 +200,6 @@ class YahooCollectorCN(YahooCollector, ABC):
class YahooCollectorCN1d(YahooCollectorCN):
@property
def min_numbers_trading(self):
return 252 / 4
def download_index_data(self):
# TODO: from MSN
_format = "%Y%m%d"
@@ -237,10 +233,6 @@ class YahooCollectorCN1d(YahooCollectorCN):
class YahooCollectorCN1min(YahooCollectorCN):
@property
def min_numbers_trading(self):
return 60 * 4 * 5
def download_index_data(self):
# TODO: 1m
logger.warning(f"{self.__class__.__name__} {self.interval} does not support: download_index_data")
@@ -269,15 +261,11 @@ class YahooCollectorUS(YahooCollector, ABC):
class YahooCollectorUS1d(YahooCollectorUS):
@property
def min_numbers_trading(self):
return 252 / 4
pass
class YahooCollectorUS1min(YahooCollectorUS):
@property
def min_numbers_trading(self):
return 60 * 6.5 * 5
pass
class YahooNormalize(BaseNormalize):
@@ -514,7 +502,17 @@ class YahooNormalize1min(YahooNormalize, ABC):
)
def get_1d_data(self, symbol: str, start: str, end: str) -> pd.DataFrame:
"""get 1d data
Returns
------
data_1d: pd.DataFrame
set(data_1d.columns) - set([self._date_field_name, self._symbol_field_name, "paused", "volume", "factor"]) == {}
"""
data_1d = YahooCollector.get_data_from_remote(self.symbol_to_yahoo(symbol), interval="1d", start=start, end=end)
if not (data_1d is None or data_1d.empty):
data_1d = self.data_1d_obj.normalize(data_1d)
return data_1d
def adjusted_price(self, df: pd.DataFrame) -> pd.DataFrame:
@@ -526,13 +524,12 @@ 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 = self.get_1d_data(symbol, _start, _end)
data_1d: pd.DataFrame = self.get_1d_data(symbol, _start, _end)
if data_1d is None or data_1d.empty:
df["factor"] = 1
# TODO: np.nan or 1 or 0
df["paused"] = np.nan
else:
data_1d = self.data_1d_obj.normalize(data_1d) # type: pd.DataFrame
# NOTE: volume is np.nan or volume <= 0, paused = 1
# FIXME: find a more accurate data source
data_1d["paused"] = 0
@@ -621,12 +618,12 @@ class YahooNormalize1min(YahooNormalize, ABC):
raise NotImplementedError("rewrite symbol_to_yahoo")
@abc.abstractmethod
def _get_1d_calendar_list(self):
def _get_1d_calendar_list(self) -> Iterable[pd.Timestamp]:
raise NotImplementedError("rewrite _get_1d_calendar_list")
class YahooNormalizeUS:
def _get_calendar_list(self):
def _get_calendar_list(self) -> Iterable[pd.Timestamp]:
# TODO: from MSN
return get_calendar_list("US_ALL")
@@ -638,7 +635,7 @@ class YahooNormalizeUS1d(YahooNormalizeUS, YahooNormalize1d):
class YahooNormalizeUS1min(YahooNormalizeUS, YahooNormalize1min):
CONSISTENT_1d = False
def _get_calendar_list(self):
def _get_calendar_list(self) -> Iterable[pd.Timestamp]:
# TODO: support 1min
raise ValueError("Does not support 1min")
@@ -650,7 +647,7 @@ class YahooNormalizeUS1min(YahooNormalizeUS, YahooNormalize1min):
class YahooNormalizeCN:
def _get_calendar_list(self):
def _get_calendar_list(self) -> Iterable[pd.Timestamp]:
# TODO: from MSN
return get_calendar_list("ALL")
@@ -670,7 +667,7 @@ class YahooNormalizeCN1min(YahooNormalizeCN, YahooNormalize1min):
CONSISTENT_1d = True
CALC_PAUSED_NUM = True
def _get_calendar_list(self):
def _get_calendar_list(self) -> Iterable[pd.Timestamp]:
return self.generate_1min_from_daily(self.calendar_list_1d)
def symbol_to_yahoo(self, symbol):
@@ -680,10 +677,67 @@ class YahooNormalizeCN1min(YahooNormalizeCN, YahooNormalize1min):
symbol = symbol[2:] + "." + _exchange
return symbol
def _get_1d_calendar_list(self):
def _get_1d_calendar_list(self) -> Iterable[pd.Timestamp]:
return get_calendar_list("ALL")
class YahooNormalizeCN1minOffline(YahooNormalizeCN1min):
"""Normalised to 1min using local 1d data
1d data usually from: Normalised to 1min using local 1d data
"""
def __init__(
self, qlib_data_1d_dir: [str, Path], date_field_name: str = "date", symbol_field_name: str = "symbol", **kwargs
):
"""
Parameters
----------
qlib_data_1d_dir: str, Path
the qlib data to be updated for yahoo, usually from: Normalised to 1min using local 1d data
date_field_name: str
date field name, default is date
symbol_field_name: str
symbol field name, default is symbol
"""
super(YahooNormalizeCN1minOffline, self).__init__(date_field_name, symbol_field_name)
self.qlib_data_1d_dir = qlib_data_1d_dir
self._all_1d_data = self._get_all_1d_data()
def _get_1d_calendar_list(self) -> Iterable[pd.Timestamp]:
import qlib
from qlib.data import D
qlib.init(provider_uri=self.qlib_data_1d_dir)
return list(D.calendar(freq="day"))
def _get_all_1d_data(self):
import qlib
from qlib.data import D
qlib.init(provider_uri=self.qlib_data_1d_dir)
df = D.features(D.instruments("all"), ["$paused", "$volume", "$factor"], freq="day")
df.reset_index(inplace=True)
df.rename(columns={"datetime": self._date_field_name, "instrument": self._symbol_field_name}, inplace=True)
df.columns = list(map(lambda x: x[1:] if x.startswith("$") else x, df.columns))
return df
def get_1d_data(self, symbol: str, start: str, end: str) -> pd.DataFrame:
"""get 1d data
Returns
------
data_1d: pd.DataFrame
set(data_1d.columns) - set([self._date_field_name, self._symbol_field_name, "paused", "volume", "factor"]) == {}
"""
return self._all_1d_data[
(self._all_1d_data[self._symbol_field_name] == symbol.upper())
& (self._all_1d_data[self._date_field_name] >= pd.Timestamp(start))
& (self._all_1d_data[self._date_field_name] < pd.Timestamp(end))
]
class Run(BaseRun):
def __init__(self, source_dir=None, normalize_dir=None, max_workers=1, interval="1d", region=REGION_CN):
"""
@@ -722,7 +776,7 @@ class Run(BaseRun):
delay=0,
start=None,
end=None,
check_data_length=False,
check_data_length=None,
limit_nums=None,
):
"""download data from Internet
@@ -734,14 +788,21 @@ class Run(BaseRun):
delay: float
time.sleep(delay), default 0
start: str
start datetime, default "2000-01-01"
start datetime, default "2000-01-01"; closed interval(including start)
end: str
end datetime, default ``pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1))``
check_data_length: bool
check data length, by default False
end datetime, default ``pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1))``; open interval(excluding end)
check_data_length: int
check data length, if not None and greater than 0, each symbol will be considered complete if its data length is greater than or equal to this value, otherwise it will be fetched again, the maximum number of fetches being (max_collector_count). By default None.
limit_nums: int
using for debug, by default None
Notes
-----
check_data_length, example:
daily, one year: 252 // 4
us 1min, a week: 6.5 * 60 * 5
cn 1min, a week: 4 * 60 * 5
Examples
---------
# get daily data
@@ -813,6 +874,85 @@ class Run(BaseRun):
)
yc.normalize()
def normalize_data_1min_cn_offline(
self, qlib_data_1d_dir, date_field_name: str = "date", symbol_field_name: str = "symbol"
):
"""Normalised to 1min using local 1d data
Parameters
----------
qlib_data_1d_dir: str
the qlib data to be updated for yahoo, usually from: https://github.com/microsoft/qlib/tree/main/scripts#download-cn-data
date_field_name: str
date field name, default date
symbol_field_name: str
symbol field name, default symbol
Examples
---------
$ python collector.py normalize_data_1min_cn_offline --qlib_data_1d_dir ~/.qlib/qlib_data/cn_1d --source_dir ~/.qlib/stock_data/source_cn_1min --normalize_dir ~/.qlib/stock_data/normalize_cn_1min --region CN --interval 1min
"""
_class = getattr(self._cur_module, f"{self.normalize_class_name}Offline")
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,
qlib_data_1d_dir=qlib_data_1d_dir,
)
yc.normalize()
def download_today_data(
self,
max_collector_count=2,
delay=0,
check_data_length=None,
limit_nums=None,
):
"""download today data from Internet
Parameters
----------
max_collector_count: int
default 2
delay: float
time.sleep(delay), default 0
check_data_length: int
check data length, if not None and greater than 0, each symbol will be considered complete if its data length is greater than or equal to this value, otherwise it will be fetched again, the maximum number of fetches being (max_collector_count). By default None.
limit_nums: int
using for debug, by default None
Notes
-----
Download today's data:
start_time = datetime.datetime.now().date(); closed interval(including start)
end_time = pd.Timestamp(start_time + pd.Timedelta(days=1)).date(); open interval(excluding end)
check_data_length, example:
daily, one year: 252 // 4
us 1min, a week: 6.5 * 60 * 5
cn 1min, a week: 4 * 60 * 5
Examples
---------
# get daily data
$ python collector.py download_today_data --source_dir ~/.qlib/stock_data/source --region CN --delay 0.1 --interval 1d
# get 1m data
$ python collector.py download_today_data --source_dir ~/.qlib/stock_data/source --region CN --delay 0.1 --interval 1m
"""
start = datetime.datetime.now().date()
end = pd.Timestamp(start + pd.Timedelta(days=1)).date()
self.download_data(
max_collector_count,
delay,
start.strftime("%Y-%m-%d"),
end.strftime("%Y-%m-%d"),
check_data_length,
limit_nums,
)
if __name__ == "__main__":
fire.Fire(Run)