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13
CHANGELOG.md
13
CHANGELOG.md
@@ -0,0 +1,13 @@
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# Changelog
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## [0.9.8](https://github.com/microsoft/qlib/compare/v0.9.7...v0.9.8) (2025-11-06)
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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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* **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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* typo in integration documentation: 'userd' -> 'used' ([#2034](https://github.com/microsoft/qlib/issues/2034)) ([3dc5a7d](https://github.com/microsoft/qlib/commit/3dc5a7d299074f0fa45a4b7bb50ab446a8824a32))
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@@ -324,7 +324,7 @@ We recommend users to prepare their own data if they have a high-quality dataset
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```
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2. Start a new Docker container
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```bash
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docker run -it --name <container name> -v <Mounted local directory>:/app qlib_image_stable
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docker run -it --name <container name> -v <Mounted local directory>:/app pyqlib/qlib_image_stable:stable
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```
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3. At this point you are in the docker environment and can run the qlib scripts. An example:
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```bash
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@@ -42,7 +42,7 @@ Example
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.. math::
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DEA = \frac{EMA(DIF, 9)}{CLOSE}
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DEA = EMA(DIF, 9)
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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 = '(EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9)/$close'
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>> MACD_EXP = '2 * ((EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9))'
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>> fields = [MACD_EXP] # MACD
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>> names = ['MACD']
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>> labels = ['Ref($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 -0.011547 -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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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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... ... ...
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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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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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Reference
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=========
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@@ -129,7 +129,7 @@ For example, it looks quite long and complicated:
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But using string is not the only way to implement the expression. You can also implement expression by code.
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Here is an exmaple which does the same thing as above examples.
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Here is an example which does the same thing as above examples.
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.. code-block:: python
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@@ -71,7 +71,7 @@ The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#
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)
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- Override the `predict` method
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- The parameters must include the parameter `dataset`, which will be userd to get the test dataset.
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- The parameters must include the parameter `dataset`, which will be used to get the test dataset.
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- Return the `prediction score`.
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- Please refer to `Model API <../reference/api.html#module-qlib.model.base>`_ for the parameter types of the fit method.
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- 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.
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@@ -140,7 +140,10 @@ def _mount_nfs_uri(provider_uri, mount_path, auto_mount: bool = False):
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_command_log = [line for line in _command_log if _remote_uri in line]
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if len(_command_log) > 0:
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for _c in _command_log:
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_temp_mount = _c.decode("utf-8").split(" ")[2]
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if isinstance(_c, str):
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_temp_mount = _c.split(" ")[2]
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else:
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_temp_mount = _c.decode("utf-8").split(" ")[2]
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_temp_mount = _temp_mount[:-1] if _temp_mount.endswith("/") else _temp_mount
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if _temp_mount == _mount_path:
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_is_mount = True
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@@ -82,7 +82,7 @@ def get_calendar_list(bench_code="CSI300") -> List[pd.Timestamp]:
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if bench_code.upper() == "ALL":
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@deco_retry
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def _get_calendar(month):
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def _get_calendar_from_month(month):
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_cal = []
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try:
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resp = requests.get(
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@@ -98,7 +98,7 @@ def get_calendar_list(bench_code="CSI300") -> List[pd.Timestamp]:
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month_range = pd.date_range(start="2000-01", end=pd.Timestamp.now() + pd.Timedelta(days=31), freq="M")
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calendar = []
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for _m in month_range:
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cal = _get_calendar(_m.strftime("%Y-%m"))
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cal = _get_calendar_from_month(_m.strftime("%Y-%m"))
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if cal:
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calendar += cal
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calendar = list(filter(lambda x: x <= pd.Timestamp.now(), calendar))
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@@ -613,10 +613,6 @@ class YahooNormalize1min(YahooNormalize, ABC):
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def symbol_to_yahoo(self, symbol):
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raise NotImplementedError("rewrite symbol_to_yahoo")
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@abc.abstractmethod
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def _get_1d_calendar_list(self) -> Iterable[pd.Timestamp]:
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raise NotImplementedError("rewrite _get_1d_calendar_list")
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class YahooNormalizeUS:
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def _get_calendar_list(self) -> Iterable[pd.Timestamp]:
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Reference in New Issue
Block a user