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Merge pull request #463 from zhupr/support_extend_data

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Support extend data
This commit is contained in:
you-n-g
2021-06-24 13:53:30 +08:00
committed by GitHub
parent b261f7b501 39340fbf06
commit 8ebdb1e873
19 changed files with 979 additions and 284 deletions
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22
README.md
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@@ -159,6 +159,28 @@ Users could create the same dataset with it.
*Please pay **ATTENTION** that the data is collected from [Yahoo Finance](https://finance.yahoo.com/lookup), and the data might not be perfect.
We recommend users to prepare their own data if they have a high-quality dataset. For more information, users can refer to the [related document](https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format)*.
### Automatic update of daily frequency data(from yahoo finance)
> It is recommended that users update the data manually once (--trading_date 2021-05-25) and then set it to update automatically.
> For more information refer to: [yahoo collector](https://github.com/microsoft/qlib/tree/main/scripts/data_collector/yahoo#automatic-update-of-daily-frequency-datafrom-yahoo-finance)
* Automatic update of data to the "qlib" directory each trading day(Linux)
* use *crontab*: `crontab -e`
* set up timed tasks:
```
* * * * 1-5 python <script path> update_data_to_bin --qlib_data_1d_dir <user data dir>
```
* **script path**: *scripts/data_collector/yahoo/collector.py*
* Manual update of data
```
python scripts/data_collector/yahoo/collector.py update_data_to_bin --qlib_data_1d_dir <user data dir> --trading_date <start date> --end_date <end date>
```
* *trading_date*: start of trading day
* *end_date*: end of trading day(not included)
<!--
- Run the initialization code and get stock data:

28
docs/component/data.rst
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@@ -67,6 +67,34 @@ After running the above command, users can find china-stock and us-stock data in
When ``Qlib`` is initialized with this dataset, users could build and evaluate their own models with it. Please refer to `Initialization <../start/initialization.html>`_ for more details.
Automatic update of daily frequency data
----------------------------------------
**It is recommended that users update the data manually once (\-\-trading_date 2021-05-25) and then set it to update automatically.**
For more information refer to: `yahoo collector <https://github.com/microsoft/qlib/tree/main/scripts/data_collector/yahoo#Automatic-update-of-daily-frequency-data>`_
- Automatic update of data to the "qlib" directory each trading day(Linux)
- use *crontab*: `crontab -e`
- set up timed tasks:
.. code-block:: bash
* * * * 1-5 python <script path> update_data_to_bin --qlib_data_1d_dir <user data dir>
- **script path**: *scripts/data_collector/yahoo/collector.py*
- Manual update of data
.. code-block:: bash
python scripts/data_collector/yahoo/collector.py update_data_to_bin --qlib_data_1d_dir <user data dir> --trading_date <start date> --end_date <end date>
- *trading_date*: start of trading day
- *end_date*: end of trading day(not included)
Converting CSV Format into Qlib Format
-------------------------------------------

4
examples/benchmarks/README.md
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@@ -4,6 +4,10 @@ Here are the results of each benchmark model running on Qlib's `Alpha360` and `A
The numbers shown below demonstrate the performance of the entire `workflow` of each model. We will update the `workflow` as well as models in the near future for better results.
> If you need to reproduce the results below, please use the **v1** dataset: `python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_cn_1d --region cn --version v1`
>
> In the new version of qlib, the default dataset is **v2**. Since the data is collected from the YahooFinance API (which is not very stable), the results of *v2* and *v1* may differ
## Alpha360 dataset
| Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown |
|---|---|---|---|---|---|---|---|---|

33
qlib/contrib/report/analysis_model/analysis_model_performance.py
View File

@@ -3,7 +3,6 @@
import pandas as pd
import plotly.tools as tls
import plotly.graph_objs as go
import statsmodels.api as sm
@@ -80,9 +79,35 @@ def _plot_qq(data: pd.Series = None, dist=stats.norm) -> go.Figure:
:param dist:
:return:
"""
fig, ax = plt.subplots(figsize=(8, 5))
_mpl_fig = sm.qqplot(data.dropna(), dist, fit=True, line="45", ax=ax)
return tls.mpl_to_plotly(_mpl_fig)
# NOTE: plotly.tools.mpl_to_plotly not actively maintained, resulting in errors in the new version of matplotlib,
# ref: https://github.com/plotly/plotly.py/issues/2913#issuecomment-730071567
# removing plotly.tools.mpl_to_plotly for greater compatibility with matplotlib versions
_plt_fig = sm.qqplot(data.dropna(), dist=dist, fit=True, line="45")
plt.close(_plt_fig)
qqplot_data = _plt_fig.gca().lines
fig = go.Figure()
fig.add_trace(
{
"type": "scatter",
"x": qqplot_data[0].get_xdata(),
"y": qqplot_data[0].get_ydata(),
"mode": "markers",
"marker": {"color": "#19d3f3"},
}
)
fig.add_trace(
{
"type": "scatter",
"x": qqplot_data[1].get_xdata(),
"y": qqplot_data[1].get_ydata(),
"mode": "lines",
"line": {"color": "#636efa"},
}
)
del qqplot_data
return fig
def _pred_ic(pred_label: pd.DataFrame = None, rank: bool = False, **kwargs) -> tuple:

94
scripts/data_collector/base.py
View File

@@ -7,12 +7,13 @@ import time
import datetime
import importlib
from pathlib import Path
from typing import Type
from typing import Type, Iterable
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import pandas as pd
from tqdm import tqdm
from loguru import logger
from joblib import Parallel, delayed
from qlib.utils import code_to_fname
@@ -22,9 +23,9 @@ class BaseCollector(abc.ABC):
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))
DEFAULT_START_DATETIME_1MIN = pd.Timestamp(datetime.datetime.now() - pd.Timedelta(days=5 * 6 - 1)).date()
DEFAULT_END_DATETIME_1D = pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1)).date()
DEFAULT_END_DATETIME_1MIN = DEFAULT_END_DATETIME_1D
INTERVAL_1min = "1min"
INTERVAL_1d = "1d"
@@ -35,10 +36,10 @@ class BaseCollector(abc.ABC):
start=None,
end=None,
interval="1d",
max_workers=4,
max_workers=1,
max_collector_count=2,
delay=0,
check_data_length: bool = False,
check_data_length: int = None,
limit_nums: int = None,
):
"""
@@ -48,7 +49,7 @@ class BaseCollector(abc.ABC):
save_dir: str
instrument save dir
max_workers: int
workers, default 4
workers, default 1; Concurrent number, default is 1; when collecting data, it is recommended that max_workers be set to 1
max_collector_count: int
default 2
delay: float
@@ -59,8 +60,8 @@ class BaseCollector(abc.ABC):
start datetime, default None
end: str
end datetime, default None
check_data_length: bool
check data length, by default False
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
"""
@@ -72,7 +73,7 @@ class BaseCollector(abc.ABC):
self.max_collector_count = max_collector_count
self.mini_symbol_map = {}
self.interval = interval
self.check_small_data = check_data_length
self.check_data_length = max(int(check_data_length) if check_data_length is not None else 0, 0)
self.start_datetime = self.normalize_start_datetime(start)
self.end_datetime = self.normalize_end_datetime(end)
@@ -99,14 +100,6 @@ class BaseCollector(abc.ABC):
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_instrument_list(self):
raise NotImplementedError("rewrite get_instrument_list")
@@ -132,7 +125,7 @@ class BaseCollector(abc.ABC):
Returns
---------
pd.DataFrame, "symbol" in pd.columns
pd.DataFrame, "symbol" and "date"in pd.columns
"""
raise NotImplementedError("rewrite get_timezone")
@@ -151,7 +144,7 @@ class BaseCollector(abc.ABC):
self.sleep()
df = self.get_data(symbol, self.interval, self.start_datetime, self.end_datetime)
_result = self.NORMAL_FLAG
if self.check_small_data:
if self.check_data_length > 0:
_result = self.cache_small_data(symbol, df)
if _result == self.NORMAL_FLAG:
self.save_instrument(symbol, df)
@@ -181,8 +174,8 @@ class BaseCollector(abc.ABC):
df.to_csv(instrument_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}!")
if len(df) < self.check_data_length:
logger.warning(f"the number of trading days of {symbol} is less than {self.check_data_length}!")
_temp = self.mini_symbol_map.setdefault(symbol, [])
_temp.append(df.copy())
return self.CACHE_FLAG
@@ -194,12 +187,12 @@ class BaseCollector(abc.ABC):
def _collector(self, instrument_list):
error_symbol = []
with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
with tqdm(total=len(instrument_list)) as p_bar:
for _symbol, _result in zip(instrument_list, executor.map(self._simple_collector, instrument_list)):
if _result != self.NORMAL_FLAG:
error_symbol.append(_symbol)
p_bar.update()
res = Parallel(n_jobs=self.max_workers)(
delayed(self._simple_collector)(_inst) for _inst in tqdm(instrument_list)
)
for _symbol, _result in zip(instrument_list, res):
if _result != self.NORMAL_FLAG:
error_symbol.append(_symbol)
print(error_symbol)
logger.info(f"error symbol nums: {len(error_symbol)}")
logger.info(f"current get symbol nums: {len(instrument_list)}")
@@ -217,20 +210,16 @@ class BaseCollector(abc.ABC):
instrument_list = self._collector(instrument_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"])
)
_df = pd.concat(_df_list, sort=False)
if not _df.empty:
self.save_instrument(_symbol, _df.drop_duplicates(["date"]).sort_values(["date"]))
if self.mini_symbol_map:
logger.warning(f"less than {self.min_numbers_trading} instrument list: {list(self.mini_symbol_map.keys())}")
logger.warning(f"less than {self.check_data_length} instrument list: {list(self.mini_symbol_map.keys())}")
logger.info(f"total {len(self.instrument_list)}, error: {len(set(instrument_list))}")
class BaseNormalize(abc.ABC):
def __init__(
self,
date_field_name: str = "date",
symbol_field_name: str = "symbol",
):
def __init__(self, date_field_name: str = "date", symbol_field_name: str = "symbol", **kwargs):
"""
Parameters
@@ -242,7 +231,7 @@ class BaseNormalize(abc.ABC):
"""
self._date_field_name = date_field_name
self._symbol_field_name = symbol_field_name
self.kwargs = kwargs
self._calendar_list = self._get_calendar_list()
@abc.abstractmethod
@@ -251,7 +240,7 @@ class BaseNormalize(abc.ABC):
raise NotImplementedError("")
@abc.abstractmethod
def _get_calendar_list(self):
def _get_calendar_list(self) -> Iterable[pd.Timestamp]:
"""Get benchmark calendar"""
raise NotImplementedError("")
@@ -265,6 +254,7 @@ class Normalize:
max_workers: int = 16,
date_field_name: str = "date",
symbol_field_name: str = "symbol",
**kwargs,
):
"""
@@ -288,16 +278,23 @@ class Normalize:
self._source_dir = Path(source_dir).expanduser()
self._target_dir = Path(target_dir).expanduser()
self._target_dir.mkdir(parents=True, exist_ok=True)
self._date_field_name = date_field_name
self._symbol_field_name = symbol_field_name
self._end_date = kwargs.get("end_date", None)
self._max_workers = max_workers
self._normalize_obj = normalize_class(date_field_name=date_field_name, symbol_field_name=symbol_field_name)
self._normalize_obj = normalize_class(
date_field_name=date_field_name, symbol_field_name=symbol_field_name, **kwargs
)
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:
if df is not None and not df.empty:
if self._end_date is not None:
_mask = pd.to_datetime(df[self._date_field_name]) <= pd.Timestamp(self._end_date)
df = df[_mask]
df.to_csv(self._target_dir.joinpath(file_path.name), index=False)
def normalize(self):
@@ -311,7 +308,7 @@ class Normalize:
class BaseRun(abc.ABC):
def __init__(self, source_dir=None, normalize_dir=None, max_workers=4, interval="1d"):
def __init__(self, source_dir=None, normalize_dir=None, max_workers=1, interval="1d"):
"""
Parameters
@@ -321,7 +318,7 @@ class BaseRun(abc.ABC):
normalize_dir: str
Directory for normalize data, default "Path(__file__).parent/normalize"
max_workers: int
Concurrent number, default is 4
Concurrent number, default is 1; Concurrent number, default is 1; when collecting data, it is recommended that max_workers be set to 1
interval: str
freq, value from [1min, 1d], default 1d
"""
@@ -361,7 +358,7 @@ class BaseRun(abc.ABC):
start=None,
end=None,
interval="1d",
check_data_length=False,
check_data_length: int = None,
limit_nums=None,
):
"""download data from Internet
@@ -378,8 +375,8 @@ class BaseRun(abc.ABC):
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
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
@@ -404,7 +401,7 @@ class BaseRun(abc.ABC):
limit_nums=limit_nums,
).collector_data()
def normalize_data(self, date_field_name: str = "date", symbol_field_name: str = "symbol"):
def normalize_data(self, date_field_name: str = "date", symbol_field_name: str = "symbol", **kwargs):
"""normalize data
Parameters
@@ -426,5 +423,6 @@ class BaseRun(abc.ABC):
max_workers=self.max_workers,
date_field_name=date_field_name,
symbol_field_name=symbol_field_name,
**kwargs,
)
yc.normalize()

34
scripts/data_collector/cn_index/collector.py
View File

@@ -19,12 +19,31 @@ 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_calendar_list, get_trading_date_by_shift
from data_collector.utils import get_calendar_list, get_trading_date_by_shift, deco_retry
NEW_COMPANIES_URL = "http://www.csindex.com.cn/uploads/file/autofile/cons/{index_code}cons.xls"
INDEX_CHANGES_URL = "http://www.csindex.com.cn/zh-CN/search/total?key=%E5%85%B3%E4%BA%8E%E8%B0%83%E6%95%B4%E6%B2%AA%E6%B7%B1300%E5%92%8C%E4%B8%AD%E8%AF%81%E9%A6%99%E6%B8%AF100%E7%AD%89%E6%8C%87%E6%95%B0%E6%A0%B7%E6%9C%AC%E8%82%A1%E7%9A%84%E5%85%AC%E5%91%8A"
# INDEX_CHANGES_URL = "http://www.csindex.com.cn/zh-CN/search/total?key=%E5%85%B3%E4%BA%8E%E8%B0%83%E6%95%B4%E6%B2%AA%E6%B7%B1300%E5%92%8C%E4%B8%AD%E8%AF%81%E9%A6%99%E6%B8%AF100%E7%AD%89%E6%8C%87%E6%95%B0%E6%A0%B7%E6%9C%AC%E8%82%A1%E7%9A%84%E5%85%AC%E5%91%8A"
# 2020-11-27 Announcement title change
INDEX_CHANGES_URL = "http://www.csindex.com.cn/zh-CN/search/total?key=%E5%85%B3%E4%BA%8E%E8%B0%83%E6%95%B4%E6%B2%AA%E6%B7%B1300%E5%92%8C%E4%B8%AD%E8%AF%81%E9%A6%99%E6%B8%AF100%E7%AD%89"
REQ_HEADERS = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.101 Safari/537.36 Edg/91.0.864.48"
}
@deco_retry
def retry_request(url: str, method: str = "get", exclude_status: List = None):
if exclude_status is None:
exclude_status = []
method_func = getattr(requests, method)
_resp = method_func(url, headers=REQ_HEADERS)
_status = _resp.status_code
if _status not in exclude_status and _status != 200:
raise ValueError(f"response status: {_status}, url={url}")
return _resp
class CSIIndex(IndexBase):
@@ -134,9 +153,8 @@ class CSIIndex(IndexBase):
date: pd.Timestamp
type: str, value from ["add", "remove"]
"""
resp = requests.get(url)
resp = retry_request(url)
_text = resp.text
date_list = re.findall(r"(\d{4}).*?年.*?(\d+).*?月.*?(\d+).*?日", _text)
if len(date_list) >= 2:
add_date = pd.Timestamp("-".join(date_list[0]))
@@ -147,7 +165,7 @@ class CSIIndex(IndexBase):
logger.info(f"get {add_date} changes")
try:
excel_url = re.findall('.*href="(.*?xls.*?)".*', _text)[0]
content = requests.get(f"http://www.csindex.com.cn{excel_url}").content
content = retry_request(f"http://www.csindex.com.cn{excel_url}", exclude_status=[404]).content
_io = BytesIO(content)
df_map = pd.read_excel(_io, sheet_name=None)
with self.cache_dir.joinpath(
@@ -201,7 +219,7 @@ class CSIIndex(IndexBase):
-------
[url1, url2]
"""
resp = requests.get(self.changes_url)
resp = retry_request(self.changes_url)
html = etree.HTML(resp.text)
return html.xpath("//*[@id='itemContainer']//li/a/@href")
@@ -221,7 +239,7 @@ class CSIIndex(IndexBase):
end_date: pd.Timestamp
"""
logger.info("get new companies......")
context = requests.get(self.new_companies_url).content
context = retry_request(self.new_companies_url).content
with self.cache_dir.joinpath(
f"{self.index_name.lower()}_new_companies.{self.new_companies_url.split('.')[-1]}"
).open("wb") as fp:
@@ -292,7 +310,7 @@ def get_instruments(
$ python collector.py --index_name CSI300 --qlib_dir ~/.qlib/qlib_data/cn_data --method save_new_companies
"""
_cur_module = importlib.import_module("collector")
_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, request_retry=request_retry, retry_sleep=retry_sleep
)

23
scripts/data_collector/contrib/fill_cn_1min_data/README.md Normal file
View File

@@ -0,0 +1,23 @@
# Use 1d data to fill in the missing symbols relative to 1min
## Requirements
```bash
pip install -r requirements.txt
```
## fill 1min data
```bash
python fill_1min_using_1d.py --data_1min_dir ~/.qlib/csv_data/cn_data_1min --qlib_data_1d_dir ~/.qlib/qlib_data/cn_data
```
## Parameters
- ata_1min_dir: csv data
- qlib_data_1d_dir: qlib data directory
- max_workers: `ThreadPoolExecutor(max_workers=max_workers)`, by default *16*
- date_field_name: date field name, by default *date*
- symbol_field_name: symbol field name, by default *symbol*

100
scripts/data_collector/contrib/fill_cn_1min_data/fill_cn_1min_data.py Normal file
View File

@@ -0,0 +1,100 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import sys
from pathlib import Path
from concurrent.futures import ThreadPoolExecutor
import fire
import qlib
import pandas as pd
from tqdm import tqdm
from qlib.data import D
from loguru import logger
CUR_DIR = Path(__file__).resolve().parent
sys.path.append(str(CUR_DIR.parent.parent.parent))
from data_collector.utils import generate_minutes_calendar_from_daily
def get_date_range(data_1min_dir: Path, max_workers: int = 16, date_field_name: str = "date"):
csv_files = list(data_1min_dir.glob("*.csv"))
min_date = None
max_date = None
with tqdm(total=len(csv_files)) as p_bar:
with ThreadPoolExecutor(max_workers=max_workers) as executor:
for _file, _result in zip(csv_files, executor.map(pd.read_csv, csv_files)):
if not _result.empty:
_dates = pd.to_datetime(_result[date_field_name])
_tmp_min = _dates.min()
min_date = min(min_date, _tmp_min) if min_date is not None else _tmp_min
_tmp_max = _dates.max()
max_date = max(max_date, _tmp_max) if max_date is not None else _tmp_max
p_bar.update()
return min_date, max_date
def get_symbols(data_1min_dir: Path):
return list(map(lambda x: x.name[:-4].upper(), data_1min_dir.glob("*.csv")))
def fill_1min_using_1d(
data_1min_dir: [str, Path],
qlib_data_1d_dir: [str, Path],
max_workers: int = 16,
date_field_name: str = "date",
symbol_field_name: str = "symbol",
):
"""Use 1d data to fill in the missing symbols relative to 1min
Parameters
----------
data_1min_dir: str
1min data dir
qlib_data_1d_dir: str
1d qlib data(bin data) dir, from: https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format
max_workers: int
ThreadPoolExecutor(max_workers), by default 16
date_field_name: str
date field name, by default date
symbol_field_name: str
symbol field name, by default symbol
"""
data_1min_dir = Path(data_1min_dir).expanduser().resolve()
qlib_data_1d_dir = Path(qlib_data_1d_dir).expanduser().resolve()
min_date, max_date = get_date_range(data_1min_dir, max_workers, date_field_name)
symbols_1min = get_symbols(data_1min_dir)
qlib.init(provider_uri=str(qlib_data_1d_dir))
data_1d = D.features(D.instruments("all"), ["$close"], min_date, max_date, freq="day")
miss_symbols = set(data_1d.index.get_level_values(level="instrument").unique()) - set(symbols_1min)
if not miss_symbols:
logger.warning("More symbols in 1min than 1d, no padding required")
return
logger.info(f"miss_symbols {len(miss_symbols)}: {miss_symbols}")
tmp_df = pd.read_csv(list(data_1min_dir.glob("*.csv"))[0])
columns = tmp_df.columns
_si = tmp_df[symbol_field_name].first_valid_index()
is_lower = tmp_df.loc[_si][symbol_field_name].islower()
for symbol in tqdm(miss_symbols):
if is_lower:
symbol = symbol.lower()
index_1d = data_1d.loc(axis=0)[symbol.upper()].index
index_1min = generate_minutes_calendar_from_daily(index_1d)
index_1min.name = date_field_name
_df = pd.DataFrame(columns=columns, index=index_1min)
if date_field_name in _df.columns:
del _df[date_field_name]
_df.reset_index(inplace=True)
_df[symbol_field_name] = symbol
_df["paused_num"] = 0
_df.to_csv(data_1min_dir.joinpath(f"{symbol}.csv"), index=False)
if __name__ == "__main__":
fire.Fire(fill_1min_using_1d)

5
scripts/data_collector/contrib/fill_cn_1min_data/requirements.txt Normal file
View File

@@ -0,0 +1,5 @@
fire
pandas
loguru
tqdm
pyqlib

0
scripts/data_collector/contrib/README.md → scripts/data_collector/contrib/future_trading_date_collector/README.md
View File

2
scripts/data_collector/contrib/future_trading_date_collector.py → scripts/data_collector/contrib/future_trading_date_collector/future_trading_date_collector.py
View File

@@ -14,7 +14,7 @@ from loguru import logger
import baostock as bs
CUR_DIR = Path(__file__).resolve().parent
sys.path.append(str(CUR_DIR.parent.parent))
sys.path.append(str(CUR_DIR.parent.parent.parent))
from data_collector.utils import generate_minutes_calendar_from_daily

0
scripts/data_collector/contrib/requirements.txt → scripts/data_collector/contrib/future_trading_date_collector/requirements.txt
View File

21
scripts/data_collector/fund/collector.py
View File

@@ -3,18 +3,13 @@
import abc
import sys
import copy
import time
import datetime
import importlib
import json
from abc import ABC
from pathlib import Path
from typing import Iterable, Type
import fire
import requests
import numpy as np
import pandas as pd
from loguru import logger
from dateutil.tz import tzlocal
@@ -38,7 +33,7 @@ class FundCollector(BaseCollector):
max_workers=4,
max_collector_count=2,
delay=0,
check_data_length: bool = False,
check_data_length: int = None,
limit_nums: int = None,
):
"""
@@ -59,8 +54,8 @@ class FundCollector(BaseCollector):
start datetime, default None
end: str
end datetime, default None
check_data_length: bool
check data length, by default False
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
"""
@@ -168,9 +163,7 @@ class FundollectorCN(FundCollector, ABC):
class FundCollectorCN1d(FundollectorCN):
@property
def min_numbers_trading(self):
return 252 / 4
pass
class FundNormalize(BaseNormalize):
@@ -261,7 +254,7 @@ class Run(BaseRun):
start=None,
end=None,
interval="1d",
check_data_length=False,
check_data_length: int = None,
limit_nums=None,
):
"""download data from Internet
@@ -278,8 +271,8 @@ class Run(BaseRun):
start datetime, default "2000-01-01"
end: str
end datetime, default ``pd.Timestamp(datetime.datetime.now() + pd.Timedelta(days=1))``
check_data_length: bool # if this param useful?
check data length, by default False
check_data_length: int # if this param useful?
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

2
scripts/data_collector/us_index/collector.py
View File

@@ -271,7 +271,7 @@ def get_instruments(
$ python collector.py --index_name SP500 --qlib_dir ~/.qlib/qlib_data/cn_data --method save_new_companies
"""
_cur_module = importlib.import_module("collector")
_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, request_retry=request_retry, retry_sleep=retry_sleep
)

5
scripts/data_collector/utils.py
View File

@@ -2,7 +2,6 @@
# Licensed under the MIT License.
import re
import os
import time
import bisect
import pickle
@@ -10,7 +9,7 @@ import random
import requests
import functools
from pathlib import Path
from typing import Iterable, Tuple
from typing import Iterable, Tuple, List
import numpy as np
import pandas as pd
@@ -47,7 +46,7 @@ _CALENDAR_MAP = {}
MINIMUM_SYMBOLS_NUM = 3900
def get_calendar_list(bench_code="CSI300") -> list:
def get_calendar_list(bench_code="CSI300") -> List[pd.Timestamp]:
"""get SH/SZ history calendar list
Parameters

261
scripts/data_collector/yahoo/README.md
View File

@@ -1,3 +1,11 @@
- [Collector Data](#collector-data)
- [Get Qlib data](#get-qlib-databin-file)
- [Collector *YahooFinance* data to qlib](#collector-yahoofinance-data-to-qlib)
- [Automatic update of daily frequency data](#automatic-update-of-daily-frequency-datafrom-yahoo-finance)
- [Using qlib data](#using-qlib-data)
# Collect Data From Yahoo Finance
> *Please pay **ATTENTION** that the data is collected from [Yahoo Finance](https://finance.yahoo.com/lookup) and the data might not be perfect. We recommend users to prepare their own data if they have high-quality dataset. For more information, users can refer to the [related document](https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format)*
@@ -18,113 +26,170 @@ pip install -r requirements.txt
## Collector Data
### Get Qlib data(`bin file`)
> `qlib-data` from *YahooFinance*, is the data that has been dumped and can be used directly in `qlib`
### CN Data
- get data: `python scripts/get_data.py qlib_data`
- parameters:
- `target_dir`: save dir, by default *~/.qlib/qlib_data/cn_data*
- `version`: dataset version, value from [`v1`, `v2`], by default `v1`
- `v2` end date is *2021-06*, `v1` end date is *2020-09*
- user can append data to `v2`: [automatic update of daily frequency data](#automatic-update-of-daily-frequency-datafrom-yahoo-finance)
- **the [benchmarks](https://github.com/microsoft/qlib/tree/main/examples/benchmarks) for qlib use `v1`**, *due to the unstable access to historical data by YahooFinance, there are some differences between `v2` and `v1`*
- `interval`: `1d` or `1min`, by default `1d`
- `region`: `cn` or `us`, by default `cn`
- `delete_old`: delete existing data from `target_dir`(*features, calendars, instruments, dataset_cache, features_cache*), value from [`True`, `False`], by default `True`
- `exists_skip`: traget_dir data already exists, skip `get_data`, value from [`True`, `False`], by default `False`
- examples:
```bash
# cn 1d
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_cn_1d --region cn
# cn 1min
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_cn_1min --region cn --interval 1min
# us 1d
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_us_1d --region us --interval 1d
# us 1min
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_us_1min --region us --interval 1min
```
#### 1d from yahoo
### Collector *YahooFinance* data to qlib
> collector *YahooFinance* data and *dump* into `qlib` format
1. download data to csv: `python scripts/data_collector/yahoo/collector.py download_data`
```bash
- parameters:
- `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`, 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)*
- `max_workers`: get the number of concurrent symbols, it is not recommended to change this parameter in order to maintain the integrity of the symbol data, by default *1*
- `check_data_length`: check the number of rows per *symbol*, by default `None`
> if `len(symbol_df) < check_data_length`, it will be re-fetched, with the number of re-fetches coming from the `max_collector_count` parameter
- `max_collector_count`: number of *"failed"* symbol retries, by default 2
- examples:
```bash
# cn 1d data
python collector.py download_data --source_dir ~/.qlib/stock_data/source/cn_1d --start 2020-01-01 --end 2020-12-31 --delay 1 --interval 1d --region US
# cn 1min data
python collector.py download_data --source_dir ~/.qlib/stock_data/source/cn_1min --delay 1 --interval 1min --region CN
# us 1d data
python collector.py download_data --source_dir ~/.qlib/stock_data/source/us_1d --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_1min --delay 1 --interval 1min --region US
```
2. normalize data: `python scripts/data_collector/yahoo/collector.py normalize_data`
- parameters:
- `source_dir`: csv directory
- `normalize_dir`: result directory
- `max_workers`: number of concurrent, by default *1*
- `interval`: `1d` or `1min`, by default `1d`
> if **`interval == 1min`**, `qlib_data_1d_dir` cannot be `None`
- `region`: `CN` or `US`, by default `CN`
- `date_field_name`: column *name* identifying time in csv files, by default `date`
- `symbol_field_name`: column *name* identifying symbol in csv files, by default `symbol`
- `end_date`: if not `None`, normalize the last date saved (*including end_date*); if `None`, it will ignore this parameter; by default `None`
- `qlib_data_1d_dir`: qlib directory(1d data)
```
if interval==1min, qlib_data_1d_dir cannot be None, normalize 1min needs to use 1d data;
qlib_data_1d can be obtained like this:
$ python scripts/get_data.py qlib_data --target_dir <qlib_data_1d_dir> --interval 1d
$ python scripts/data_collector/yahoo/collector.py update_data_to_bin --qlib_data_1d_dir <qlib_data_1d_dir> --trading_date 2021-06-01
or:
download 1d data from YahooFinance
```
- examples:
```bash
# normalize 1d cn
python collector.py normalize_data --source_dir ~/.qlib/stock_data/source/cn_1d --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/qlib_cn_1d --source_dir ~/.qlib/stock_data/source/cn_1min --normalize_dir ~/.qlib/stock_data/source/cn_1min_nor --region CN --interval 1min
```
3. dump data: `python scripts/dump_bin.py dump_all`
- parameters:
- `csv_path`: stock data path or directory, **normalize result(normalize_dir)**
- `qlib_dir`: qlib(dump) data director
- `freq`: transaction frequency, by default `day`
> `freq_map = {1d:day, 1mih: 1min}`
- `max_workers`: number of threads, by default *16*
- `include_fields`: dump fields, by default `""`
- `exclude_fields`: fields not dumped, by default `"""
> dump_fields = `include_fields if include_fields else set(symbol_df.columns) - set(exclude_fields) exclude_fields else symbol_df.columns`
- `symbol_field_name`: column *name* identifying symbol in csv files, by default `symbol`
- `date_field_name`: column *name* identifying time in csv files, by default `date`
- examples:
```bash
# dump 1d cn
python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/cn_1d_nor --qlib_dir ~/.qlib/qlib_data/qlib_cn_1d --freq day --exclude_fields date,symbol
# dump 1min cn
python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/cn_1min_nor --qlib_dir ~/.qlib/qlib_data/qlib_cn_1min --freq 1min --exclude_fields date,symbol
```
# download from yahoo finance
python collector.py download_data --source_dir ~/.qlib/stock_data/source/cn_1d --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d
### Automatic update of daily frequency data(from yahoo finance)
> It is recommended that users update the data manually once (--trading_date 2021-05-25) and then set it to update automatically.
# normalize
python collector.py normalize_data --source_dir ~/.qlib/stock_data/source/cn_1d --normalize_dir ~/.qlib/stock_data/source/cn_1d_nor --region CN --interval 1d
* Automatic update of data to the "qlib" directory each trading day(Linux)
* use *crontab*: `crontab -e`
* set up timed tasks:
# dump data
cd qlib/scripts
python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/cn_1d_nor --qlib_dir ~/.qlib/qlib_data/qlib_cn_1d --freq day --exclude_fields date,adjclose,dividends,splits,symbol
```
* * * * 1-5 python <script path> update_data_to_bin --qlib_data_1d_dir <user data dir>
```
* **script path**: *scripts/data_collector/yahoo/collector.py*
```
* Manual update of data
```
python scripts/data_collector/yahoo/collector.py update_data_to_bin --qlib_data_1d_dir <user data dir> --trading_date <start date> --end_date <end date>
```
* `trading_date`: start of trading day
* `end_date`: end of trading day(not included)
* `check_data_length`: check the number of rows per *symbol*, by default `None`
> if `len(symbol_df) < check_data_length`, it will be re-fetched, with the number of re-fetches coming from the `max_collector_count` parameter
### 1d from qlib
```bash
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_cn_1d --region cn
```
### using data
```python
import qlib
from qlib.data import D
qlib.init(provider_uri="~/.qlib/qlib_data/qlib_cn_1d", region="cn")
df = D.features(D.instruments("all"), ["$close"], freq="day")
```
#### 1min from yahoo
```bash
# download from yahoo finance
python collector.py download_data --source_dir ~/.qlib/stock_data/source/cn_1min --region CN --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1min
# normalize
python collector.py normalize_data --source_dir ~/.qlib/stock_data/source/cn_1min --normalize_dir ~/.qlib/stock_data/source/cn_1min_nor --region CN --interval 1min
# dump data
cd qlib/scripts
python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/cn_1min_nor --qlib_dir ~/.qlib/qlib_data/qlib_cn_1min --freq 1min --exclude_fields date,adjclose,dividends,splits,symbol
```
### 1min from qlib
```bash
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_cn_1min --interval 1min --region cn
```
### using data
```python
import qlib
from qlib.data import D
qlib.init(provider_uri="~/.qlib/qlib_data/qlib_cn_1min", region="cn")
df = D.features(D.instruments("all"), ["$close"], freq="1min")
```
### US Data
#### 1d from yahoo
```bash
# download from yahoo finance
python collector.py download_data --source_dir ~/.qlib/stock_data/source/us_1d --region US --start 2020-11-01 --end 2020-11-10 --delay 0.1 --interval 1d
# normalize
python collector.py normalize_data --source_dir ~/.qlib/stock_data/source/us_1d --normalize_dir ~/.qlib/stock_data/source/us_1d_nor --region US --interval 1d
# dump data
cd qlib/scripts
python dump_bin.py dump_all --csv_path ~/.qlib/stock_data/source/us_1d_nor --qlib_dir ~/.qlib/stock_data/source/qlib_us_1d --freq day --exclude_fields date,adjclose,dividends,splits,symbol
```
#### 1d from qlib
```bash
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_us_1d --region us
```
### using data
```python
# using
import qlib
from qlib.data import D
qlib.init(provider_uri="~/.qlib/qlib_data/qlib_us_1d", region="us")
df = D.features(D.instruments("all"), ["$close"], freq="day")
```
* `scripts/data_collector/yahoo/collector.py update_data_to_bin` parameters:
* `source_dir`: The directory where the raw data collected from the Internet is saved, default "Path(__file__).parent/source"
* `normalize_dir`: Directory for normalize data, default "Path(__file__).parent/normalize"
* `qlib_data_1d_dir`: the qlib data to be updated for yahoo, usually from: [download qlib data](https://github.com/microsoft/qlib/tree/main/scripts#download-cn-data)
* `trading_date`: trading days to be updated, by default ``datetime.datetime.now().strftime("%Y-%m-%d")``
* `end_date`: end datetime, default ``pd.Timestamp(trading_date + pd.Timedelta(days=1))``; open interval(excluding end)
* `region`: region, value from ["CN", "US"], default "CN"
### Help
```bash
python collector.py collector_data --help
```
## Using qlib data
## Parameters
```python
import qlib
from qlib.data import D
# 1d data cn
# freq=day, freq default day
qlib.init(provider_uri="~/.qlib/qlib_data/qlib_cn_1d", region="cn")
df = D.features(D.instruments("all"), ["$close"], freq="day")
# 1min data cn
# freq=1min
qlib.init(provider_uri="~/.qlib/qlib_data/qlib_cn_1min", region="cn")
inst = D.list_instruments(D.instruments("all"), freq="1min", as_list=True)
# get 100 symbols
df = D.features(inst[:100], ["$close"], freq="1min")
# get all symbol data
# df = D.features(D.instruments("all"), ["$close"], freq="1min")
# 1d data us
qlib.init(provider_uri="~/.qlib/qlib_data/qlib_us_1d", region="us")
df = D.features(D.instruments("all"), ["$close"], freq="day")
# 1min data us
qlib.init(provider_uri="~/.qlib/qlib_data/qlib_us_1min", region="cn")
inst = D.list_instruments(D.instruments("all"), freq="1min", as_list=True)
# get 100 symbols
df = D.features(inst[:100], ["$close"], freq="1min")
# get all symbol data
# df = D.features(D.instruments("all"), ["$close"], freq="1min")
```
- interval: 1min or 1d
- region: CN or US

613
scripts/data_collector/yahoo/collector.py
View File

@@ -8,8 +8,9 @@ import time
import datetime
import importlib
from abc import ABC
import multiprocessing
from pathlib import Path
from typing import Iterable, Type
from typing import Iterable
import fire
import requests
@@ -18,13 +19,18 @@ import pandas as pd
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.tests.data import GetData
from qlib.utils import code_to_fname, fname_to_code, exists_qlib_data
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 dump_bin import DumpDataUpdate
from data_collector.base import BaseCollector, BaseNormalize, BaseRun, Normalize
from data_collector.utils import (
deco_retry,
get_calendar_list,
get_hs_stock_symbols,
get_us_stock_symbols,
@@ -44,7 +50,7 @@ class YahooCollector(BaseCollector):
max_workers=4,
max_collector_count=2,
delay=0,
check_data_length: bool = False,
check_data_length: int = None,
limit_nums: int = None,
):
"""
@@ -65,8 +71,8 @@ class YahooCollector(BaseCollector):
start datetime, default None
end: str
end datetime, default None
check_data_length: bool
check data length, by default False
check_data_length: int
check data length, by default None
limit_nums: int
using for debug, by default None
"""
@@ -92,10 +98,6 @@ class YahooCollector(BaseCollector):
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)
@@ -140,40 +142,39 @@ 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)
def _get_simple(start_, end_):
self.sleep()
_remote_interval = "1m" if interval == self.INTERVAL_1min else interval
return self.get_data_from_remote(
resp = self.get_data_from_remote(
symbol,
interval=_remote_interval,
start=start_,
end=end_,
)
if resp is None or resp.empty:
raise ValueError(f"get data error: {symbol}--{start_}--{end_}")
return resp
_result = None
if interval == self.INTERVAL_1d:
_result = _get_simple(start_datetime, end_datetime)
elif interval == self.INTERVAL_1min:
if self._next_datetime >= self._latest_datetime:
try:
_result = _get_simple(start_datetime, 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"])
except ValueError as e:
pass
elif interval == self.INTERVAL_1min:
_res = []
_start = self.start_datetime
while _start < self.end_datetime:
_tmp_end = min(_start + pd.Timedelta(days=7), self.end_datetime)
try:
_resp = _get_simple(_start, _tmp_end)
_res.append(_resp)
except ValueError as e:
pass
_start = _tmp_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
@@ -207,10 +208,6 @@ 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"
@@ -244,13 +241,12 @@ class YahooCollectorCN1d(YahooCollectorCN):
class YahooCollectorCN1min(YahooCollectorCN):
@property
def min_numbers_trading(self):
return 60 * 4 * 5
def get_instrument_list(self):
symbols = super(YahooCollectorCN1min, self).get_instrument_list()
return symbols + ["000300.ss", "000905.ss", "000903.ss"]
def download_index_data(self):
# TODO: 1m
logger.warning(f"{self.__class__.__name__} {self.interval} does not support: download_index_data")
pass
class YahooCollectorUS(YahooCollector, ABC):
@@ -276,15 +272,11 @@ 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):
@@ -297,6 +289,7 @@ class YahooNormalize(BaseNormalize):
calendar_list: list = None,
date_field_name: str = "date",
symbol_field_name: str = "symbol",
last_close: float = None,
):
if df.empty:
return df
@@ -318,7 +311,10 @@ class YahooNormalize(BaseNormalize):
df.sort_index(inplace=True)
df.loc[(df["volume"] <= 0) | np.isnan(df["volume"]), set(df.columns) - {symbol_field_name}] = np.nan
_tmp_series = df["close"].fillna(method="ffill")
df["change"] = _tmp_series / _tmp_series.shift(1) - 1
_tmp_shift_series = _tmp_series.shift(1)
if last_close is not None:
_tmp_shift_series.iloc[0] = float(last_close)
df["change"] = _tmp_series / _tmp_shift_series - 1
columns += ["change"]
df.loc[(df["volume"] <= 0) | np.isnan(df["volume"]), columns] = np.nan
@@ -367,6 +363,17 @@ class YahooNormalize1d(YahooNormalize, ABC):
df = self._manual_adj_data(df)
return df
def _get_first_close(self, df: pd.DataFrame) -> float:
"""get first close value
Notes
-----
For incremental updates(append) to Yahoo 1D data, user need to use a close that is not 0 on the first trading day of the existing data
"""
df = df.loc[df["close"].first_valid_index() :]
_close = df["close"].iloc[0]
return _close
def _manual_adj_data(self, df: pd.DataFrame) -> pd.DataFrame:
"""manual adjust data: All fields (except change) are standardized according to the close of the first day"""
if df.empty:
@@ -374,45 +381,112 @@ class YahooNormalize1d(YahooNormalize, ABC):
df = df.copy()
df.sort_values(self._date_field_name, inplace=True)
df = df.set_index(self._date_field_name)
df = df.loc[df["close"].first_valid_index() :]
_close = df["close"].iloc[0]
_close = self._get_first_close(df)
for _col in df.columns:
if _col == self._symbol_field_name:
# NOTE: retain original adjclose, required for incremental updates
if _col in [self._symbol_field_name, "adjclose", "change"]:
continue
if _col == "volume":
df[_col] = df[_col] * _close
elif _col != "change":
df[_col] = df[_col] / _close
else:
pass
df[_col] = df[_col] / _close
return df.reset_index()
class YahooNormalize1dExtend(YahooNormalize1d):
def __init__(
self, old_qlib_data_dir: [str, Path], date_field_name: str = "date", symbol_field_name: str = "symbol", **kwargs
):
"""
Parameters
----------
old_qlib_data_dir: str, Path
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 is date
symbol_field_name: str
symbol field name, default is symbol
"""
super(YahooNormalize1dExtend, self).__init__(date_field_name, symbol_field_name)
self._first_close_field = "first_close"
self._ori_close_field = "ori_close"
self.old_qlib_data = self._get_old_data(old_qlib_data_dir)
def _get_old_data(self, qlib_data_dir: [str, Path]):
import qlib
from qlib.data import D
qlib_data_dir = str(Path(qlib_data_dir).expanduser().resolve())
qlib.init(provider_uri=qlib_data_dir, expression_cache=None, dataset_cache=None)
df = D.features(D.instruments("all"), ["$close/$factor", "$adjclose/$close"])
df.columns = [self._ori_close_field, self._first_close_field]
return df
def _get_close(self, df: pd.DataFrame, field_name: str):
_symbol = df.loc[df[self._symbol_field_name].first_valid_index()][self._symbol_field_name].upper()
_df = self.old_qlib_data.loc(axis=0)[_symbol]
_close = _df.loc[_df.last_valid_index()][field_name]
return _close
def _get_first_close(self, df: pd.DataFrame) -> float:
try:
_close = self._get_close(df, field_name=self._first_close_field)
except KeyError:
_close = super(YahooNormalize1dExtend, self)._get_first_close(df)
return _close
def _get_last_close(self, df: pd.DataFrame) -> float:
try:
_close = self._get_close(df, field_name=self._ori_close_field)
except KeyError:
_close = None
return _close
def _get_last_date(self, df: pd.DataFrame) -> pd.Timestamp:
_symbol = df.loc[df[self._symbol_field_name].first_valid_index()][self._symbol_field_name].upper()
try:
_df = self.old_qlib_data.loc(axis=0)[_symbol]
_date = _df.index.max()
except KeyError:
_date = None
return _date
def normalize(self, df: pd.DataFrame) -> pd.DataFrame:
_last_close = self._get_last_close(df)
# reindex
_last_date = self._get_last_date(df)
if _last_date is not None:
df = df.set_index(self._date_field_name)
df.index = pd.to_datetime(df.index)
df = df[~df.index.duplicated(keep="first")]
_max_date = df.index.max()
df = df.reindex(self._calendar_list).loc[:_max_date].reset_index()
df = df[df[self._date_field_name] > _last_date]
if df.empty:
return pd.DataFrame()
_si = df["close"].first_valid_index()
if _si > df.index[0]:
logger.warning(
f"{df.loc[_si][self._symbol_field_name]} missing data: {df.loc[:_si - 1][self._date_field_name].to_list()}"
)
# normalize
df = self.normalize_yahoo(
df, self._calendar_list, self._date_field_name, self._symbol_field_name, last_close=_last_close
)
# adjusted price
df = self.adjusted_price(df)
df = self._manual_adj_data(df)
return df
class YahooNormalize1min(YahooNormalize, ABC):
AM_RANGE = None # type: tuple # eg: ("09:30:00", "11:29:00")
PM_RANGE = None # type: tuple # eg: ("13:00:00", "14:59:00")
# Whether the trading day of 1min data is consistent with 1d
CONSISTENT_1d = False
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
"""
super(YahooNormalize1min, self).__init__(date_field_name, symbol_field_name)
_class_name = self.__class__.__name__.replace("min", "d")
_class = getattr(importlib.import_module("collector"), _class_name) # type: Type[YahooNormalize]
self.data_1d_obj = _class(self._date_field_name, self._symbol_field_name)
CONSISTENT_1d = True
CALC_PAUSED_NUM = True
@property
def calendar_list_1d(self):
@@ -427,24 +501,40 @@ class YahooNormalize1min(YahooNormalize, ABC):
calendars, freq="1min", am_range=self.AM_RANGE, pm_range=self.PM_RANGE
)
def get_1d_data(self, symbol: str, start: str, end: str) -> pd.DataFrame:
"""get 1d data
Returns
------
data_1d: pd.DataFrame
data_1d.columns = [self._date_field_name, self._symbol_field_name, "paused", "volume", "factor", "close"]
"""
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):
_class_name = self.__class__.__name__.replace("min", "d")
_class: type(YahooNormalize) = getattr(importlib.import_module("collector"), _class_name)
data_1d_obj = _class(self._date_field_name, self._symbol_field_name)
data_1d = data_1d_obj.normalize(data_1d)
return data_1d
def adjusted_price(self, df: pd.DataFrame) -> pd.DataFrame:
# TODO: using daily data factor
if df.empty:
return df
df = df.copy()
df = df.sort_values(self._date_field_name)
symbol = df.iloc[0][self._symbol_field_name]
# 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 = YahooCollector.get_data_from_remote(
self.symbol_to_yahoo(symbol), interval="1d", start=_start, end=_end
)
data_1d: pd.DataFrame = self.get_1d_data(symbol, _start, _end)
data_1d = data_1d.copy()
if data_1d is None or data_1d.empty:
df["factor"] = 1
df["factor"] = 1 / df.loc[df["close"].first_valid_index()]["close"]
# 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
@@ -452,9 +542,13 @@ class YahooNormalize1min(YahooNormalize, ABC):
data_1d = data_1d.set_index(self._date_field_name)
# add factor from 1d data
# NOTE: yahoo 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)`
df["date_tmp"] = df[self._date_field_name].apply(lambda x: pd.Timestamp(x).date())
df.set_index("date_tmp", inplace=True)
df.loc[:, "factor"] = data_1d["factor"]
df.loc[:, "factor"] = data_1d["close"] / df["close"]
df.loc[:, "paused"] = data_1d["paused"]
df.reset_index("date_tmp", drop=True, inplace=True)
@@ -478,6 +572,54 @@ class YahooNormalize1min(YahooNormalize, ABC):
df[_col] = df[_col] / df["factor"]
else:
df[_col] = df[_col] * df["factor"]
if self.CALC_PAUSED_NUM:
df = self.calc_paused_num(df)
return df
def calc_paused_num(self, df: pd.DataFrame):
_symbol = df.iloc[0][self._symbol_field_name]
df = df.copy()
df["_tmp_date"] = df[self._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",
self._date_field_name,
self._symbol_field_name,
}
if _df.loc[:, 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
@abc.abstractmethod
@@ -485,12 +627,67 @@ 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 YahooNormalize1minOffline(YahooNormalize1min):
"""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
"""
self.qlib_data_1d_dir = qlib_data_1d_dir
super(YahooNormalize1minOffline, self).__init__(date_field_name, symbol_field_name)
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", "$factor", "$close"], 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
data_1d.columns = [self._date_field_name, self._symbol_field_name, "paused", "volume", "factor", "close"]
"""
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 YahooNormalizeUS:
def _get_calendar_list(self):
def _get_calendar_list(self) -> Iterable[pd.Timestamp]:
# TODO: from MSN
return get_calendar_list("US_ALL")
@@ -499,10 +696,10 @@ class YahooNormalizeUS1d(YahooNormalizeUS, YahooNormalize1d):
pass
class YahooNormalizeUS1min(YahooNormalizeUS, YahooNormalize1min):
CONSISTENT_1d = False
class YahooNormalizeUS1min(YahooNormalizeUS, YahooNormalize1minOffline):
CALC_PAUSED_NUM = False
def _get_calendar_list(self):
def _get_calendar_list(self) -> Iterable[pd.Timestamp]:
# TODO: support 1min
raise ValueError("Does not support 1min")
@@ -514,7 +711,7 @@ 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")
@@ -523,28 +720,30 @@ class YahooNormalizeCN1d(YahooNormalizeCN, YahooNormalize1d):
pass
class YahooNormalizeCN1min(YahooNormalizeCN, YahooNormalize1min):
class YahooNormalizeCN1dExtend(YahooNormalizeCN, YahooNormalize1dExtend):
pass
class YahooNormalizeCN1min(YahooNormalizeCN, YahooNormalize1minOffline):
AM_RANGE = ("09:30:00", "11:29:00")
PM_RANGE = ("13:00:00", "14:59:00")
CONSISTENT_1d = 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):
if "." not in symbol:
_exchange = symbol[:2]
_exchange = "ss" if _exchange == "sh" else _exchange
_exchange = ("ss" if _exchange.islower() else "SS") if _exchange.lower() == "sh" else _exchange
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 Run(BaseRun):
def __init__(self, source_dir=None, normalize_dir=None, max_workers=4, interval="1d", region=REGION_CN):
def __init__(self, source_dir=None, normalize_dir=None, max_workers=1, interval="1d", region=REGION_CN):
"""
Parameters
@@ -554,7 +753,7 @@ class Run(BaseRun):
normalize_dir: str
Directory for normalize data, default "Path(__file__).parent/normalize"
max_workers: int
Concurrent number, default is 4
Concurrent number, default is 1; when collecting data, it is recommended that max_workers be set to 1
interval: str
freq, value from [1min, 1d], default 1d
region: str
@@ -578,10 +777,10 @@ class Run(BaseRun):
def download_data(
self,
max_collector_count=2,
delay=0,
delay=0.5,
start=None,
end=None,
check_data_length=False,
check_data_length=None,
limit_nums=None,
):
"""download data from Internet
@@ -591,16 +790,23 @@ class Run(BaseRun):
max_collector_count: int
default 2
delay: float
time.sleep(delay), default 0
time.sleep(delay), default 0.5
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
@@ -612,7 +818,13 @@ class Run(BaseRun):
max_collector_count, delay, start, end, self.interval, check_data_length, limit_nums
)
def normalize_data(self, date_field_name: str = "date", symbol_field_name: str = "symbol"):
def normalize_data(
self,
date_field_name: str = "date",
symbol_field_name: str = "symbol",
end_date: str = None,
qlib_data_1d_dir: str = None,
):
"""normalize data
Parameters
@@ -621,12 +833,205 @@ class Run(BaseRun):
date field name, default date
symbol_field_name: str
symbol field name, default symbol
end_date: str
if not None, normalize the last date saved (including end_date); if None, it will ignore this parameter; by default None
qlib_data_1d_dir: str
if interval==1min, qlib_data_1d_dir cannot be None, normalize 1min needs to use 1d data;
qlib_data_1d can be obtained like this:
$ python scripts/get_data.py qlib_data --target_dir <qlib_data_1d_dir> --interval 1d
$ python scripts/data_collector/yahoo/collector.py update_data_to_bin --qlib_data_1d_dir <qlib_data_1d_dir> --trading_date 2021-06-01
or:
download 1d data, reference: https://github.com/microsoft/qlib/tree/main/scripts/data_collector/yahoo#1d-from-yahoo
Examples
---------
$ python collector.py normalize_data --source_dir ~/.qlib/stock_data/source --normalize_dir ~/.qlib/stock_data/normalize --region CN --interval 1d
$ python collector.py normalize_data --source_dir ~/.qlib/stock_data/source --normalize_dir ~/.qlib/stock_data/normalize --region cn --interval 1d
$ python collector.py normalize_data --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
"""
super(Run, self).normalize_data(date_field_name, symbol_field_name)
if self.interval.lower() == "1min":
if qlib_data_1d_dir is None or not Path(qlib_data_1d_dir).expanduser().exists():
raise ValueError(
"If normalize 1min, the qlib_data_1d_dir parameter must be set: --qlib_data_1d_dir <user qlib 1d data >, Reference: https://github.com/zhupr/qlib/tree/support_extend_data/scripts/data_collector/yahoo#automatic-update-of-daily-frequency-datafrom-yahoo-finance"
)
super(Run, self).normalize_data(
date_field_name, symbol_field_name, end_date=end_date, qlib_data_1d_dir=qlib_data_1d_dir
)
def normalize_data_1d_extend(
self, old_qlib_data_dir, date_field_name: str = "date", symbol_field_name: str = "symbol"
):
"""normalize data extend; extending yahoo qlib data(from: https://github.com/microsoft/qlib/tree/main/scripts#download-cn-data)
Notes
-----
Steps to extend yahoo qlib data:
1. download qlib data: https://github.com/microsoft/qlib/tree/main/scripts#download-cn-data; save to <dir1>
2. collector source data: https://github.com/microsoft/qlib/tree/main/scripts/data_collector/yahoo#collector-data; save to <dir2>
3. normalize new source data(from step 2): python scripts/data_collector/yahoo/collector.py normalize_data_1d_extend --old_qlib_dir <dir1> --source_dir <dir2> --normalize_dir <dir3> --region CN --interval 1d
4. dump data: python scripts/dump_bin.py dump_update --csv_path <dir3> --qlib_dir <dir1> --freq day --date_field_name date --symbol_field_name symbol --exclude_fields symbol,date
5. update instrument(eg. csi300): python python scripts/data_collector/cn_index/collector.py --index_name CSI300 --qlib_dir <dir1> --method parse_instruments
Parameters
----------
old_qlib_data_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_1d_extend --old_qlib_dir ~/.qlib/qlib_data/cn_1d --source_dir ~/.qlib/stock_data/source --normalize_dir ~/.qlib/stock_data/normalize --region CN --interval 1d
"""
_class = getattr(self._cur_module, f"{self.normalize_class_name}Extend")
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,
old_qlib_data_dir=old_qlib_data_dir,
)
yc.normalize()
def download_today_data(
self,
max_collector_count=2,
delay=0.5,
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 0.5
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,
)
def update_data_to_bin(
self,
qlib_data_1d_dir: str,
trading_date: str = None,
end_date: str = None,
check_data_length: int = None,
delay: float = 1,
):
"""update yahoo data to bin
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
trading_date: str
trading days to be updated, by default ``datetime.datetime.now().strftime("%Y-%m-%d")``
end_date: str
end datetime, default ``pd.Timestamp(trading_date + 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.
delay: float
time.sleep(delay), default 1
Notes
-----
If the data in qlib_data_dir is incomplete, np.nan will be populated to trading_date for the previous trading day
Examples
-------
$ python collector.py update_data_to_bin --qlib_data_1d_dir <user data dir> --trading_date <start date> --end_date <end date>
# get 1m data
"""
if self.interval.lower() != "1d":
logger.warning(f"currently supports 1d data updates: --interval 1d")
# start/end date
if trading_date is None:
trading_date = datetime.datetime.now().strftime("%Y-%m-%d")
logger.warning(f"trading_date is None, use the current date: {trading_date}")
if end_date is None:
end_date = (pd.Timestamp(trading_date) + pd.Timedelta(days=1)).strftime("%Y-%m-%d")
# download qlib 1d data
qlib_data_1d_dir = str(Path(qlib_data_1d_dir).expanduser().resolve())
if not exists_qlib_data(qlib_data_1d_dir):
GetData().qlib_data(target_dir=qlib_data_1d_dir, interval=self.interval, region=self.region)
# download data from yahoo
# NOTE: when downloading data from YahooFinance, max_workers is recommended to be 1
self.download_data(delay=delay, start=trading_date, end=end_date, check_data_length=check_data_length)
# NOTE: a larger max_workers setting here would be faster
self.max_workers = (
max(multiprocessing.cpu_count() - 2, 1)
if self.max_workers is None or self.max_workers <= 1
else self.max_workers
)
# normalize data
self.normalize_data_1d_extend(qlib_data_1d_dir)
# dump bin
_dump = DumpDataUpdate(
csv_path=self.normalize_dir,
qlib_dir=qlib_data_1d_dir,
exclude_fields="symbol,date",
max_workers=self.max_workers,
)
_dump.dump()
# parse index
_region = self.region.lower()
if _region not in ["cn", "us"]:
logger.warning(f"Unsupported region: region={_region}, component downloads will be ignored")
return
index_list = ["CSI100", "CSI300"] if _region == "cn" else ["SP500", "NASDAQ100", "DJIA", "SP400"]
get_instruments = getattr(
importlib.import_module(f"data_collector.{_region}_index.collector"), "get_instruments"
)
for _index in index_list:
get_instruments(str(qlib_data_1d_dir), _index)
if __name__ == "__main__":

1
scripts/data_collector/yahoo/requirements.txt
View File

@@ -6,3 +6,4 @@ pandas
tqdm
lxml
yahooquery
joblib

15
scripts/dump_bin.py
View File

@@ -401,6 +401,8 @@ class DumpDataUpdate(DumpDataBase):
)
self._mode = self.UPDATE_MODE
self._old_calendar_list = self._read_calendars(self._calendars_dir.joinpath(f"{self.freq}.txt"))
# NOTE: all.txt only exists once for each stock
# NOTE: if a stock corresponds to multiple different time ranges, user need to modify self._update_instruments
self._update_instruments = (
self._read_instruments(self._instruments_dir.joinpath(self.INSTRUMENTS_FILE_NAME))
.set_index([self.symbol_field_name])
@@ -409,10 +411,9 @@ class DumpDataUpdate(DumpDataBase):
# load all csv files
self._all_data = self._load_all_source_data() # type: pd.DataFrame
self._update_calendars = sorted(
self._new_calendar_list = self._old_calendar_list + sorted(
filter(lambda x: x > self._old_calendar_list[-1], self._all_data[self.date_field_name].unique())
)
self._new_calendar_list = self._old_calendar_list + self._update_calendars
def _load_all_source_data(self):
# NOTE: Need more memory
@@ -452,8 +453,16 @@ class DumpDataUpdate(DumpDataBase):
if not (isinstance(_start, pd.Timestamp) and isinstance(_end, pd.Timestamp)):
continue
if _code in self._update_instruments:
# exists stock, will append data
_update_calendars = (
_df[_df[self.date_field_name] > self._update_instruments[_code][self.INSTRUMENTS_START_FIELD]][
self.date_field_name
]
.sort_values()
.to_list()
)
self._update_instruments[_code][self.INSTRUMENTS_END_FIELD] = self._format_datetime(_end)
futures[executor.submit(self._dump_bin, _df, self._update_calendars)] = _code
futures[executor.submit(self._dump_bin, _df, _update_calendars)] = _code
else:
# new stock
_dt_range = self._update_instruments.setdefault(_code, dict())