fix: gen_training_orders.py bugs

CHANGELOG.md

@@ -1,14 +0,0 @@
-# Changelog
-
-## [0.9.8](https://github.com/microsoft/qlib/compare/v0.9.7...v0.9.8) (2025-11-13)
-
-
-### 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))
-* typo in integration documentation: 'userd' -> 'used' ([#2034](https://github.com/microsoft/qlib/issues/2034)) ([3dc5a7d](https://github.com/microsoft/qlib/commit/3dc5a7d299074f0fa45a4b7bb50ab446a8824a32))

Makefile

@@ -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

docs/advanced/alpha.rst

@@ -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
+    2010-01-04 SH600000   -0.011547 -0.019672
-               SH600004    0.006699 -0.014721
+               SH600004    0.002745 -0.014721
-               SH600006    0.005714  0.002911
+               SH600006    0.010133  0.002911
-               SH600008    0.000798  0.009818
+               SH600008   -0.001113  0.009818
-               SH600009    0.017015 -0.017758
+               SH600009    0.025878 -0.017758
     ...                         ...       ...
-    2017-12-29 SZ300124    0.015071 -0.005074
+    2017-12-29 SZ300124    0.007306 -0.005074
-               SZ300136   -0.015466  0.056352
+               SZ300136   -0.013492  0.056352
-               SZ300144    0.013082  0.011853
+               SZ300144   -0.000966  0.011853
-               SZ300251   -0.001026  0.021739
+               SZ300251    0.004383  0.021739
-               SZ300315   -0.007559  0.012455
+               SZ300315   -0.030557  0.012455
 
 Reference
 =========

docs/start/getdata.rst

@@ -129,7 +129,7 @@ For example, it looks quite long and complicated:
 
 
 But using string is not the only way to implement the expression. You can also implement expression by code.
-Here is an example which does the same thing as above examples.
+Here is an exmaple which does the same thing as above examples.
 
 
 .. code-block:: python

docs/start/integration.rst

@@ -71,7 +71,7 @@ The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#
                 )
 
 - Override the `predict` method
-    - The parameters must include the parameter `dataset`, which will be used to get the test dataset.
+    - The parameters must include the parameter `dataset`, which will be userd to get the test dataset.
     - Return the `prediction score`.
     - Please refer to `Model API <../reference/api.html#module-qlib.model.base>`_ for the parameter types of the fit method.
     - Code Example: In the following example, users need to use `LightGBM` to predict the label(such as `preds`) of test data `x_test` and return it.

examples/rl_order_execution/scripts/gen_training_orders.py

@@ -17,11 +17,11 @@ def generate_order(stock: str, start_idx: int, end_idx: int) -> bool:
     if len(df) == 0 or df.isnull().values.any() or min(df["$volume0"]) < 1e-5:
         return False
 
-    df["date"] = df["datetime"].dt.date.astype("datetime64")
+    df["date"] = df["datetime"].dt.date.astype("datetime64[ns]")
     df = df.set_index(["instrument", "datetime", "date"])
-    df = df.groupby("date", group_keys=False).take(range(start_idx, end_idx)).droplevel(level=0)
+    df = df.groupby("date", group_keys=True).take(range(start_idx, end_idx)).droplevel(level=0)
 
-    order_all = pd.DataFrame(df.groupby(level=(2, 0), group_keys=False).mean().dropna())
+    order_all = pd.DataFrame(df.groupby(level=(2, 0), group_keys=True).mean().dropna())
     order_all["amount"] = np.random.lognormal(-3.28, 1.14) * order_all["$volume0"]
     order_all = order_all[order_all["amount"] > 0.0]
     order_all["order_type"] = 0

pyproject.toml

@@ -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"

qlib/__init__.py

@@ -4,10 +4,7 @@ from pathlib import Path
 
 from setuptools_scm import get_version
 
-try:
+__version__ = get_version(root="..", relative_to=__file__)
-    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
 import os

qlib/contrib/model/gbdt.py

@@ -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, _ = self._prepare_data(dataset, reweighter)  # pylint: disable=W0632
-        dtrain, _ = ds_l[0]
+        if dtrain.empty:
-
-        if dtrain.construct().num_data() == 0:
             raise ValueError("Empty data from dataset, please check your dataset config.")
         verbose_eval_callback = lgb.log_evaluation(period=verbose_eval)
         self.model = lgb.train(

scripts/data_collector/utils.py

@@ -82,7 +82,7 @@ def get_calendar_list(bench_code="CSI300") -> List[pd.Timestamp]:
             if bench_code.upper() == "ALL":
 
                 @deco_retry
-                def _get_calendar_from_month(month):
+                def _get_calendar(month):
                     _cal = []
                     try:
                         resp = requests.get(
@@ -98,7 +98,7 @@ def get_calendar_list(bench_code="CSI300") -> List[pd.Timestamp]:
                 month_range = pd.date_range(start="2000-01", end=pd.Timestamp.now() + pd.Timedelta(days=31), freq="M")
                 calendar = []
                 for _m in month_range:
-                    cal = _get_calendar_from_month(_m.strftime("%Y-%m"))
+                    cal = _get_calendar(_m.strftime("%Y-%m"))
                     if cal:
                         calendar += cal
                 calendar = list(filter(lambda x: x <= pd.Timestamp.now(), calendar))

scripts/data_collector/yahoo/collector.py

@@ -613,6 +613,10 @@ class YahooNormalize1min(YahooNormalize, ABC):
     def symbol_to_yahoo(self, symbol):
         raise NotImplementedError("rewrite symbol_to_yahoo")
 
+    @abc.abstractmethod
+    def _get_1d_calendar_list(self) -> Iterable[pd.Timestamp]:
+        raise NotImplementedError("rewrite _get_1d_calendar_list")
+
 
 class YahooNormalizeUS:
     def _get_calendar_list(self) -> Iterable[pd.Timestamp]:

setup.py

@@ -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",