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4933fcefc4
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4933fcefc4 |
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# Changelog
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## [0.9.8](https://github.com/microsoft/qlib/compare/v0.9.7...v0.9.8) (2025-11-13)
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## [0.9.8](https://github.com/microsoft/qlib/compare/v0.9.7...v0.9.8) (2025-10-17)
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### Bug Fixes
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* download orderbook data error ([#1990](https://github.com/microsoft/qlib/issues/1990)) ([136b2dd](https://github.com/microsoft/qlib/commit/136b2ddf9a16e4106d62b8d1336a56273a8abef0))
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* **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))
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* **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))
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* replace deprecated pandas fillna(method=) with ffill()/bfill() ([#1987](https://github.com/microsoft/qlib/issues/1987)) ([7095e75](https://github.com/microsoft/qlib/commit/7095e755fa57e011f0483d24b45fc5bd5a4deaf8))
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* spelling errors ([#1996](https://github.com/microsoft/qlib/issues/1996)) ([f26b341](https://github.com/microsoft/qlib/commit/f26b3417363410531dbbb39e425bce6cf05528a1))
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* 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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2
Makefile
2
Makefile
@@ -113,7 +113,7 @@ dev: prerequisite all
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# Check lint with black.
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black:
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black . -l 120 --check --diff --exclude qlib/_version.py
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black . -l 120 --check --diff
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# Check code folder with pylint.
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# 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
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.. math::
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DEA = EMA(DIF, 9)
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DEA = \frac{EMA(DIF, 9)}{CLOSE}
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Users can use ``Data Handler`` to build formulaic alphas `MACD` in qlib:
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@@ -51,7 +51,7 @@ Users can use ``Data Handler`` to build formulaic alphas `MACD` in qlib:
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.. code-block:: python
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>> from qlib.data.dataset.loader import QlibDataLoader
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>> MACD_EXP = '2 * ((EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9))'
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>> MACD_EXP = '(EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9)/$close'
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>> fields = [MACD_EXP] # MACD
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>> names = ['MACD']
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>> labels = ['Ref($close, -2)/Ref($close, -1) - 1'] # label
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@@ -66,17 +66,17 @@ Users can use ``Data Handler`` to build formulaic alphas `MACD` in qlib:
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feature label
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MACD LABEL
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datetime instrument
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2010-01-04 SH600000 0.008781 -0.019672
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SH600004 0.006699 -0.014721
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SH600006 0.005714 0.002911
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SH600008 0.000798 0.009818
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SH600009 0.017015 -0.017758
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2010-01-04 SH600000 -0.011547 -0.019672
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SH600004 0.002745 -0.014721
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SH600006 0.010133 0.002911
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SH600008 -0.001113 0.009818
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SH600009 0.025878 -0.017758
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... ... ...
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2017-12-29 SZ300124 0.015071 -0.005074
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SZ300136 -0.015466 0.056352
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SZ300144 0.013082 0.011853
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SZ300251 -0.001026 0.021739
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SZ300315 -0.007559 0.012455
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2017-12-29 SZ300124 0.007306 -0.005074
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SZ300136 -0.013492 0.056352
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SZ300144 -0.000966 0.011853
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SZ300251 0.004383 0.021739
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SZ300315 -0.030557 0.012455
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Reference
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=========
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@@ -117,4 +117,3 @@ qrun = "qlib.cli.run:run"
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[tool.setuptools_scm]
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local_scheme = "no-local-version"
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version_scheme = "guess-next-dev"
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write_to = "qlib/_version.py"
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@@ -4,9 +4,6 @@ from pathlib import Path
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from setuptools_scm import get_version
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try:
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from ._version import version as __version__
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except ImportError:
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__version__ = get_version(root="..", relative_to=__file__)
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__version__bak = __version__ # This version is backup for QlibConfig.reset_qlib_version
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import logging
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@@ -51,7 +51,7 @@ class LGBModel(ModelFT, LightGBMFInt):
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w = reweighter.reweight(df)
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else:
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raise ValueError("Unsupported reweighter type.")
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ds_l.append((lgb.Dataset(x.values, label=y, weight=w, free_raw_data=False), key))
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ds_l.append((lgb.Dataset(x.values, label=y, weight=w), key))
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return ds_l
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def fit(
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@@ -109,10 +109,8 @@ class LGBModel(ModelFT, LightGBMFInt):
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verbose level
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"""
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# Based on existing model and finetune by train more rounds
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ds_l = self._prepare_data(dataset, reweighter)
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dtrain, _ = ds_l[0]
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if dtrain.construct().num_data() == 0:
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dtrain, _ = self._prepare_data(dataset, reweighter) # pylint: disable=W0632
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if dtrain.empty:
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raise ValueError("Empty data from dataset, please check your dataset config.")
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verbose_eval_callback = lgb.log_evaluation(period=verbose_eval)
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self.model = lgb.train(
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10
setup.py
10
setup.py
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import numpy
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from setuptools import Extension, setup
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from setuptools_scm import get_version
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def read(rel_path: str) -> str:
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here = os.path.abspath(os.path.dirname(__file__))
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with open(os.path.join(here, rel_path), encoding="utf-8") as fp:
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return fp.read()
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NUMPY_INCLUDE = numpy.get_include()
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VERSION = get_version(root=".", relative_to=__file__)
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setup(
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version=VERSION,
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ext_modules=[
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Extension(
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"qlib.data._libs.rolling",
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