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Author SHA1 Message Date
you-n-g
4933fcefc4 chore(main): release 0.9.8 2025-10-17 13:22:09 +08:00
7 changed files with 28 additions and 26 deletions

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@@ -1,13 +1,11 @@
# Changelog
## [0.9.8](https://github.com/microsoft/qlib/compare/v0.9.7...v0.9.8) (2025-11-13)
## [0.9.8](https://github.com/microsoft/qlib/compare/v0.9.7...v0.9.8) (2025-10-17)
### Bug Fixes
* download orderbook data error ([#1990](https://github.com/microsoft/qlib/issues/1990)) ([136b2dd](https://github.com/microsoft/qlib/commit/136b2ddf9a16e4106d62b8d1336a56273a8abef0))
* **gbdt:** correct dtrain assignment in finetune() to use Dataset instead of tuple ([#2049](https://github.com/microsoft/qlib/issues/2049)) ([2b41782](https://github.com/microsoft/qlib/commit/2b41782f0cfb81e8cc065f2915b215758a7838ef))
* **macd:** remove extra division by close in DEA calculation to ensure dimension consistency ([#2046](https://github.com/microsoft/qlib/issues/2046)) ([66c3622](https://github.com/microsoft/qlib/commit/66c36226aafceabe497e5967f67921e5d3c9d497))
* replace deprecated pandas fillna(method=) with ffill()/bfill() ([#1987](https://github.com/microsoft/qlib/issues/1987)) ([7095e75](https://github.com/microsoft/qlib/commit/7095e755fa57e011f0483d24b45fc5bd5a4deaf8))
* spelling errors ([#1996](https://github.com/microsoft/qlib/issues/1996)) ([f26b341](https://github.com/microsoft/qlib/commit/f26b3417363410531dbbb39e425bce6cf05528a1))
* the bug when auto_mount=True ([#2009](https://github.com/microsoft/qlib/issues/2009)) ([213eb6c](https://github.com/microsoft/qlib/commit/213eb6c2cd12342b6ec98f21300217e1659f3d58))

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@@ -113,7 +113,7 @@ dev: prerequisite all
# Check lint with black.
black:
black . -l 120 --check --diff --exclude qlib/_version.py
black . -l 120 --check --diff
# Check code folder with pylint.
# TODO: These problems we will solve in the future. Important among them are: W0221, W0223, W0237, E1102

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@@ -42,7 +42,7 @@ Example
.. math::
DEA = EMA(DIF, 9)
DEA = \frac{EMA(DIF, 9)}{CLOSE}
Users can use ``Data Handler`` to build formulaic alphas `MACD` in qlib:
@@ -51,7 +51,7 @@ Users can use ``Data Handler`` to build formulaic alphas `MACD` in qlib:
.. code-block:: python
>> from qlib.data.dataset.loader import QlibDataLoader
>> MACD_EXP = '2 * ((EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9))'
>> MACD_EXP = '(EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9)/$close'
>> fields = [MACD_EXP] # MACD
>> names = ['MACD']
>> labels = ['Ref($close, -2)/Ref($close, -1) - 1'] # label
@@ -66,17 +66,17 @@ Users can use ``Data Handler`` to build formulaic alphas `MACD` in qlib:
feature label
MACD LABEL
datetime instrument
2010-01-04 SH600000 0.008781 -0.019672
SH600004 0.006699 -0.014721
SH600006 0.005714 0.002911
SH600008 0.000798 0.009818
SH600009 0.017015 -0.017758
2010-01-04 SH600000 -0.011547 -0.019672
SH600004 0.002745 -0.014721
SH600006 0.010133 0.002911
SH600008 -0.001113 0.009818
SH600009 0.025878 -0.017758
... ... ...
2017-12-29 SZ300124 0.015071 -0.005074
SZ300136 -0.015466 0.056352
SZ300144 0.013082 0.011853
SZ300251 -0.001026 0.021739
SZ300315 -0.007559 0.012455
2017-12-29 SZ300124 0.007306 -0.005074
SZ300136 -0.013492 0.056352
SZ300144 -0.000966 0.011853
SZ300251 0.004383 0.021739
SZ300315 -0.030557 0.012455
Reference
=========

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@@ -117,4 +117,3 @@ qrun = "qlib.cli.run:run"
[tool.setuptools_scm]
local_scheme = "no-local-version"
version_scheme = "guess-next-dev"
write_to = "qlib/_version.py"

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@@ -4,9 +4,6 @@ from pathlib import Path
from setuptools_scm import get_version
try:
from ._version import version as __version__
except ImportError:
__version__ = get_version(root="..", relative_to=__file__)
__version__bak = __version__ # This version is backup for QlibConfig.reset_qlib_version
import logging

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@@ -51,7 +51,7 @@ class LGBModel(ModelFT, LightGBMFInt):
w = reweighter.reweight(df)
else:
raise ValueError("Unsupported reweighter type.")
ds_l.append((lgb.Dataset(x.values, label=y, weight=w, free_raw_data=False), key))
ds_l.append((lgb.Dataset(x.values, label=y, weight=w), key))
return ds_l
def fit(
@@ -109,10 +109,8 @@ class LGBModel(ModelFT, LightGBMFInt):
verbose level
"""
# Based on existing model and finetune by train more rounds
ds_l = self._prepare_data(dataset, reweighter)
dtrain, _ = ds_l[0]
if dtrain.construct().num_data() == 0:
dtrain, _ = self._prepare_data(dataset, reweighter) # pylint: disable=W0632
if dtrain.empty:
raise ValueError("Empty data from dataset, please check your dataset config.")
verbose_eval_callback = lgb.log_evaluation(period=verbose_eval)
self.model = lgb.train(

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@@ -2,12 +2,22 @@ import os
import numpy
from setuptools import Extension, setup
from setuptools_scm import get_version
def read(rel_path: str) -> str:
here = os.path.abspath(os.path.dirname(__file__))
with open(os.path.join(here, rel_path), encoding="utf-8") as fp:
return fp.read()
NUMPY_INCLUDE = numpy.get_include()
VERSION = get_version(root=".", relative_to=__file__)
setup(
version=VERSION,
ext_modules=[
Extension(
"qlib.data._libs.rolling",