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docs/component/data.rst
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@@ -1,7 +1,7 @@
.. _data:
================================
Data Layer: Data Framework&Usage
Data Layer: Data Framework & Usage
================================
Introduction
@@ -15,7 +15,9 @@ The introduction of ``Data Layer`` includes the following parts.
- Data Preparation
- Data API
- Data Loader
- Data Handler
- Dataset
- Cache
- Data and Cache File Structure
@@ -146,43 +148,161 @@ Filter
To know more about ``Filter``, please refer to `Filter API <../reference/api.html#module-qlib.data.filter>`_.
Reference
-------------
To know more about ``Data API``, please refer to `Data API <../reference/api.html#data>`_.
Data Loader
=================
``Data Loader`` in ``Qlib`` is designed to load raw data from the original data source. It will be loaded and used in the ``Data Handler`` module.
The ``QlibDataLoader`` class in ``Qlib`` is such an interface that allows users to load raw data from the data source.
Interface
------------
Here are some interfaces of the ``QlibDataLoader`` class:
- `load(instruments, start_time=None, end_time=None)`
- This method loads the data as pd.DataFrame
- Parameters:
- `instruments` : str or dict
it can either be the market name or the config file of instruments generated by InstrumentProvider.
- `start_time` : str
start of the time range.
- `end_time` : str
end of the time range.
- Returns:
- The data being loaded with type `pd.DataFrame`
- `load_group_df(instruments, exprs: list, names: list, start_time=None, end_time=None)`
- This method loads the dataframe for specific group.
- Parameters:
- `instruments` : str or dict
it can either be the market name or the config file of instruments generated by InstrumentProvider.
- `exprs` : list
the expressions to describe the content of the data.
- `names` : list
the name of the data.
- `start_time` : str
start of the time range.
- `end_time` : str
end of the time range.
- Returns:
- The queried data in type `pd.DataFrame`.
API
-----------
To know more about ``Data Loader``, please refer to `Data Loader API <../reference/api.html#module-qlib.data.dataset.loader>`_.
Data Handler
=================
Users can use ``Data Handler`` in an automatic workflow by ``Estimator``, refer to `Estimator: Workflow Management <estimator.html>`_ for more details.
The ``Data Handler`` module in ``Qlib`` is designed to handler those common data processing methods which will be used by most of the models.
Users can use ``Data Handler`` in an automatic workflow by ``qrun``, refer to `Workflow: Workflow Management <workflow.html>`_ for more details.
Also, ``Data Handler`` can be used as an independent module, by which users can easily preprocess data(standardization, remove NaN, etc.) and build datasets. It is a subclass of ``qlib.data.dataset.handler.DataHandlerLP``, which provides some interfaces as follows.
Base Class & Interface
----------------------
Qlib provides a base class `qlib.data.dataset.DataHandlerLP <../reference/api.html#qlib.data.dataset.handler.DataHandlerLP>`_, which provides the following interfaces:
In addition to use ``Data Handler`` in an automatic workflow with ``qrun``, ``Data Handler`` can be used as an independent module, by which users can easily preprocess data (standardization, remove NaN, etc.) and build datasets.
- `load_feature`
Implement the interface to load the data features.
In order to achieve so, ``Qlib`` provides a base class `qlib.data.dataset.DataHandlerLP <../reference/api.html#qlib.data.dataset.handler.DataHandlerLP>`_. The core idea of this class is that: we will have some leanable ``Processors`` which can learn the parameters of data processing. When new data comes in, these `trained` ``Processors`` can then infer on the new data and thus processing real-time data in an efficient way. More information about ``Processors`` will be listed in the next subsection.
- `load_label`
Implement the interface to load the data labels and calculate the users' labels.
Here are some important interfaces that ``DataHandlerLP`` provides:
- `setup_processed_data`
Implement the interface for data preprocessing, such as preparing feature columns, discarding blank lines, and so on.
- `__init__(instruments=None, start_time=None, end_time=None, data_loader: Tuple[dict, str, DataLoader] = None, infer_processors=[], learn_processors=[], process_type=PTYPE_A, **kwargs)`
- Initialization of the class.
- Parameters:
- `infer_processors` : list
- list of <description info> of processors to generate data for inference
- example of <description info>:
Qlib also provides two functions to help users init the data handler, users can override them for users' needs.
.. code-block::
1) classname & kwargs:
{
"class": "MinMaxNorm",
"kwargs": {
"fit_start_time": "20080101",
"fit_end_time": "20121231"
}
}
2) Only classname:
"DropnaFeature"
3) object instance of Processor
- `_init_raw_data`
Users can init the raw df, feature names, and label names of data handler in this function.
If the index of feature df and label df are not the same, users need to override this method to merge them (e.g. inner, left, right merge).
- `learn_processors` : list
similar to infer_processors, but for generating data for learning models
- `process_type`: str
- PTYPE_I = 'independent'
- self._infer will processed by infer_processors
- self._learn will be processed by learn_processors
- PTYPE_A = 'append'
- self._infer will processed by infer_processors
- self._learn will be processed by infer_processors + learn_processors
- (e.g. self._infer processed by learn_processors )
- `fetch(selector: Union[pd.Timestamp, slice, str] = slice(None, None), level: Union[str, int] = "datetime", col_set=DataHandler.CS_ALL, data_key: str = DK_I)`
- This method fetches data from underlying data source
- Parameters:
- `selector` : Union[pd.Timestamp, slice, str]
describe how to select data by index.
- `level` : Union[str, int]
which index level to select the data.
- `col_set` : str
select a set of meaningful columns.(e.g. features, columns).
- `data_key` : str
The data to fetch: DK_*.
- Returns:
- The retrieved results in the type: `pd.DataFrame`.
- `get_cols(col_set=DataHandler.CS_ALL, data_key: str = DK_I)`
- This method gets the column names.
- Parameters:
- `col_set` : str
select a set of meaningful columns.(e.g. features, columns).
- `data_key` : str
the data to fetch: DK_*.
- Returns:
- A list of column names.
If users want to load features and labels by config, users can inherit ``qlib.data.dataset.handler.ConfigDataHandler``, ``Qlib`` also provides some preprocess method in this subclass.
If users want to use qlib data, `QLibDataHandler` is recommended. Users can inherit their custom class from `QLibDataHandler`, which is also a subclass of `ConfigDataHandler`.
Processor
----------
The ``Processor`` module in ``Qlib`` is designed to be learnable and it is responsible for handling data processing such as `normalization` and `drop none/nan features/labels`.
``Qlib`` provides the following ``Processors``:
- ``DropnaProcessor``: `processor` that drops N/A features.
- ``DropnaLabel``: `processor` that drops N/A labels.
- ``TanhProcess``: `processor` that uses `tanh` to process noise data.
- ``ProcessInf``: `processor` that handles infinity values, it will be replaces by the mean of the column.
- ``Fillna``: `processor` that handles N/A values, which will fill the N/A value by 0 or other given number.
- ``MinMaxNorm``: `processor` that applies min-max normalization.
- ``ZscoreNorm``: `processor` that applies z-score normalization.
- ``CSZScoreNorm``: `processor` that applies cross sectional z-score normalization.
- ``CSRankNorm``: `processor` that applies cross sectional rank normalization.
Users can also create their own `processor` by inheriting the base class of ``Processor``. Please refer to the implementation of all the processors for more information (`Processor Link <https://github.com/microsoft/qlib/blob/main/qlib/data/dataset/processor.py>`_).
API
---------
To know more about ``Processor``, please refer to `Processor API <../reference/api.html#module-qlib.data.dataset.processor>`_.
Usage
--------------
@@ -194,15 +314,12 @@ Usage
- `get_rolling_data`
- According to the start and end dates, and `rolling_period`, an iterator is returned, which can be used to traverse the features and labels used for rolling.
Example
--------------
``Data Handler`` can be run with ``estimator`` by modifying the configuration file, and can also be used as a single module.
``Data Handler`` can be run with ``qrun`` by modifying the configuration file, and can also be used as a single module.
Know more about how to run ``Data Handler`` with ``Estimator``, please refer to `Estimator: Workflow Management <estimator.html>`_
Know more about how to run ``Data Handler`` with ``qrun``, please refer to `Workflow: Workflow Management <workflow.html>`_
Qlib provides implemented data handler `Alpha158`. The following example shows how to run `Alpha158` as a single module.
@@ -211,45 +328,70 @@ Qlib provides implemented data handler `Alpha158`. The following example shows h
.. code-block:: Python
import qlib
from qlib.contrib.data.handler import Alpha158
from qlib.contrib.model.gbdt import LGBModel
DATA_HANDLER_CONFIG = {
"dropna_label": True,
"start_date": "2007-01-01",
"end_date": "2020-08-01",
"market": "csi300",
data_handler_config = {
"start_time": "2008-01-01",
"end_time": "2020-08-01",
"fit_start_time": "2008-01-01",
"fit_end_time": "2014-12-31",
"instruments": "csi300",
}
TRAINER_CONFIG = {
"train_start_date": "2007-01-01",
"train_end_date": "2014-12-31",
"validate_start_date": "2015-01-01",
"validate_end_date": "2016-12-31",
"test_start_date": "2017-01-01",
"test_end_date": "2020-08-01",
}
if __name__ == "__main__":
qlib.init()
h = Alpha158(**data_handler_config)
exampleDataHandler = Alpha158(**DATA_HANDLER_CONFIG)
# get all the columns of the data
print(h.get_cols())
# example of 'get_split_data'
x_train, y_train, x_validate, y_validate, x_test, y_test = exampleDataHandler.get_split_data(**TRAINER_CONFIG)
# fetch all the labels
print(h.fetch(col_set="label"))
# example of 'get_rolling_data'
for (x_train, y_train, x_validate, y_validate, x_test, y_test) in exampleDataHandler.get_rolling_data(**TRAINER_CONFIG):
print(x_train, y_train, x_validate, y_validate, x_test, y_test)
.. note:: (x_train, y_train, x_validate, y_validate, x_test, y_test) can be used as arguments for the `fit`, `predic``, and `score` methods of the ``Interday Model`` , please refer to `Model <model.html#base-class-interface>`_.
Also, the above example has been given in ``examples.estimator.train_backtest_analyze.ipynb``.
# fetch all the features
print(h.fetch(col_set="feature"))
API
---------
To know more about ``Data Handler``, please refer to `Data Handler API <../reference/api.html#module-qlib.data.dataset.handler>`_.
Dataset
=================
The ``Dataset`` module in ``Qlib`` aims to prepare data for model training and inferencing.
The motivation of this module is that we want to maximize the flexibility of of different models to handle data that are suitable for themselves. This module gives the model the rights to process their data in an unique way. For instance, models such as ``GBDT`` may work well on data that contains `nan` or `None` value, while neural networks such as ``DNN`` will break down on such data.
The ``DatasetH`` class is the `dataset` with `Data Handler`. Here is the most important interface of the class:
- `prepare(segments: Union[List[str], Tuple[str], str, slice], col_set=DataHandler.CS_ALL, data_key=DataHandlerLP.DK_I, **kwargs)`
- This method prepares the data for learning and inference.
- Parameters:
- `segments` : Union[List[str], Tuple[str], str, slice]
Describe the scope of the data to be prepared
Here are some examples:
- 'train'
- ['train', 'valid']
- `col_set` : str
The col_set will be passed to self._handler when fetching data.
- `data_key` : str
The data to fetch: DK_*
Default is DK_I, which indicate fetching data for **inference**.
API
---------
To know more about ``Dataset``, please refer to `Dataset API <../reference/api.html#module-qlib.data.dataset.__init__>`_.
Cache
==========

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docs/component/model.rst
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@@ -7,7 +7,7 @@ Interday Model: Model Training & Prediction
Introduction
===================
``Interday Model`` is designed to make the `prediction score` about stocks. Users can use the ``Interday Model`` in an automatic workflow by ``Estimator``, please refer to `Estimator: Workflow Management <estimator.html>`_.
``Interday Model`` is designed to make the `prediction score` about stocks. Users can use the ``Interday Model`` in an automatic workflow by ``qrun``, please refer to `Workflow: Workflow Management <workflow.html>`_.
Because the components in ``Qlib`` are designed in a loosely-coupled way, ``Interday Model`` can be used as an independent module also.
@@ -20,151 +20,125 @@ The base class provides the following interfaces:
- `__init__(**kwargs)`
- Initialization.
- If users use ``Estimator`` to start an `experiment`, the parameter of `__init__` method shoule be consistent with the hyperparameters in the configuration file.
- `fit(self, x_train, y_train, x_valid, y_valid, w_train=None, w_valid=None, **kwargs)`
- `fit(self, dataset, **kwargs)`
- Train model.
- Parameter:
- `x_train`, pd.DataFrame type, train feature
The following example explains the value of `x_train`:
- `dataset`, ``Qlib``'s ``DatasetH`` type. For more information about ``DatasetH``, users can refer to the related document: `Qlib Dataset <../component/data.html#dataset>`_.
The `dataset` is passed into the `model`'s method because there are some unique data preprocessing procedures for each, we want to give each model maximum flexibility to handle the data that is suitable for their own.
The following code example shows how to retrieve `x_train`, `y_train` and `w_train` from the `dataset`:
.. code-block:: YAML
KMID KLEN KMID2 KUP KUP2
instrument datetime
SH600004 2012-01-04 0.000000 0.017685 0.000000 0.012862 0.727275
2012-01-05 -0.006473 0.025890 -0.250001 0.012945 0.499998
2012-01-06 0.008117 0.019481 0.416666 0.008117 0.416666
2012-01-09 0.016051 0.025682 0.624998 0.006421 0.250001
2012-01-10 0.017323 0.026772 0.647057 0.003150 0.117648
... ... ... ... ... ...
SZ300273 2014-12-25 -0.005295 0.038697 -0.136843 0.016293 0.421052
2014-12-26 -0.022486 0.041701 -0.539215 0.002453 0.058824
2014-12-29 -0.031526 0.039092 -0.806451 0.000000 0.000000
2014-12-30 -0.010000 0.032174 -0.310811 0.013913 0.432433
2014-12-31 0.010917 0.020087 0.543479 0.001310 0.065216
.. code-block:: Python
`x_train` is a pandas DataFrame, whose index is MultiIndex <instrument(str), datetime(pd.Timestamp)>. Each column of `x_train` corresponds to a feature, and the column name is the feature name.
.. note::
The number and names of the columns are determined by the data handler, please refer to `Data Handler <data.html#data-handler>`_ and `Estimator Data Section <estimator.html#data-section>`_.
- `y_train`, pd.DataFrame type, train label
The following example explains the value of `y_train`:
# get features and labels
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
)
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]
.. code-block:: YAML
LABEL
instrument datetime
SH600004 2012-01-04 -0.798456
2012-01-05 -1.366716
2012-01-06 -0.491026
2012-01-09 0.296900
2012-01-10 0.501426
... ...
SZ300273 2014-12-25 -0.465540
2014-12-26 0.233864
2014-12-29 0.471368
2014-12-30 0.411914
2014-12-31 1.342723
`y_train` is a pandas DataFrame, whose index is MultiIndex <instrument(str), datetime(pd.Timestamp)>. The `LABEL` column represents the value of train label.
.. note::
The number and names of the columns are determined by the ``Data Handler``, please refer to `Data Handler <data.html#data-handler>`_.
- `x_valid`, pd.DataFrame type, validation feature
The format of `x_valid` is same as `x_train`
- `y_valid`, pd.DataFrame type, validation label
The format of `y_valid` is same as `y_train`
- `w_train`(Optional args, default is None), pd.DataFrame type, train weight
`w_train` is a pandas DataFrame, whose shape and index is same as `x_train`. The float value in `w_train` represents the weight of the feature at the same position in `x_train`.
- `w_train`(Optional args, default is None), pd.DataFrame type, validation weight
`w_train` is a pandas DataFrame, whose shape and index is the same as `x_valid`. The float value in `w_train` represents the weight of the feature at the same position in `x_train`.
- `predict(self, x_test, **kwargs)`
- Predict test data 'x_test'
- Parameter:
- `x_test`, pd.DataFrame type, test features
The form of `x_test` is same as `x_train` in 'fit' method.
- Return:
- `label`, np.ndarray type, test label
The label of `x_test` that predicted by model.
- `score(self, x_test, y_test, w_test=None, **kwargs)`
- Evaluate model with test feature/label
- Parameter:
- `x_test`, pd.DataFrame type, test feature
The format of `x_test` is same as `x_train` in `fit` method.
# get weights
try:
wdf_train, wdf_valid = dataset.prepare(["train", "valid"], col_set=["weight"], data_key=DataHandlerLP.DK_L)
w_train, w_valid = wdf_train["weight"], wdf_valid["weight"]
except KeyError as e:
w_train = pd.DataFrame(np.ones_like(y_train.values), index=y_train.index)
w_valid = pd.DataFrame(np.ones_like(y_valid.values), index=y_valid.index)
- `x_test`, pd.DataFrame type, test label
The format of `y_test` is same as `y_train` in `fit` method.
- `predict(self, dataset, **kwargs)`
- Predict test data.
- Parameter:
- `dataset`, ``Qlib``'s ``DatasetH`` type. The usage is similar to the example above.
- Returns:
- Predic results with type: `pandas.Series`.
- `w_test`, pd.DataFrame type, test weight
The format of `w_test` is same as `w_train` in `fit` method.
- Return: float type, evaluation score
- `finetune(self, dataset, **kwargs)`
- Finetune the model.
- Parameter:
- `dataset`, ``Qlib``'s ``DatasetH`` type. The usage is similar to the example above.
For other interfaces such as `save`, `load`, `finetune`, please refer to `Model API <../reference/api.html#module-qlib.model.base>`_.
For other interfaces such as `finetune`, please refer to `Model API <../reference/api.html#module-qlib.model.base>`_.
Example
==================
``Qlib`` provides ``LightGBM`` and ``DNN`` models as the baseline, the following steps show how to run`` LightGBM`` as an independent module.
``Qlib``'s `Model Zoo` includes models such as ``LightGBM``, ``DNN``, ``LSTM``, etc.. These models are treated as the baselines of ``Interday Model``. The following steps show how to run`` LightGBM`` as an independent module.
- Initialize ``Qlib`` with `qlib.init` first, please refer to `Initialization <../start/initialization.html>`_.
- Run the following code to get the `prediction score` `pred_score`
.. code-block:: Python
from qlib.contrib.data.handler import Alpha158
from qlib.contrib.model.gbdt import LGBModel
from qlib.contrib.data.handler import Alpha158
from qlib.utils import init_instance_by_config, flatten_dict
from qlib.workflow import R
from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
DATA_HANDLER_CONFIG = {
"dropna_label": True,
"start_date": "2007-01-01",
"end_date": "2020-08-01",
"market": MARKET,
market = "csi300"
benchmark = "SH000300"
data_handler_config = {
"start_time": "2008-01-01",
"end_time": "2020-08-01",
"fit_start_time": "2008-01-01",
"fit_end_time": "2014-12-31",
"instruments": market,
}
TRAINER_CONFIG = {
"train_start_date": "2007-01-01",
"train_end_date": "2014-12-31",
"validate_start_date": "2015-01-01",
"validate_end_date": "2016-12-31",
"test_start_date": "2017-01-01",
"test_end_date": "2020-08-01",
task = {
"model": {
"class": "LGBModel",
"module_path": "qlib.contrib.model.gbdt",
"kwargs": {
"loss": "mse",
"colsample_bytree": 0.8879,
"learning_rate": 0.0421,
"subsample": 0.8789,
"lambda_l1": 205.6999,
"lambda_l2": 580.9768,
"max_depth": 8,
"num_leaves": 210,
"num_threads": 20,
},
},
"dataset": {
"class": "DatasetH",
"module_path": "qlib.data.dataset",
"kwargs": {
"handler": {
"class": "Alpha158",
"module_path": "qlib.contrib.data.handler",
"kwargs": data_handler_config,
},
"segments": {
"train": ("2008-01-01", "2014-12-31"),
"valid": ("2015-01-01", "2016-12-31"),
"test": ("2017-01-01", "2020-08-01"),
},
},
},
}
# model initiaiton
model = init_instance_by_config(task["model"])
dataset = init_instance_by_config(task["dataset"])
x_train, y_train, x_validate, y_validate, x_test, y_test = Alpha158(
**DATA_HANDLER_CONFIG
).get_split_data(**TRAINER_CONFIG)
# start exp
with R.start(experiment_name="workflow"):
# train
R.log_params(**flatten_dict(task))
model.fit(dataset)
# prediction
recorder = R.get_recorder()
sr = SignalRecord(model, dataset, recorder)
sr.generate()
MODEL_CONFIG = {
"loss": "mse",
"colsample_bytree": 0.8879,
"learning_rate": 0.0421,
"subsample": 0.8789,
"lambda_l1": 205.6999,
"lambda_l2": 580.9768,
"max_depth": 8,
"num_leaves": 210,
"num_threads": 20,
}
# use default model
model = LGBModel(**MODEL_CONFIG)
model.fit(x_train, y_train, x_validate, y_validate)
_pred = model.predict(x_test)
pred_score = pd.DataFrame(index=_pred.index)
pred_score["score"] = _pred.iloc(axis=1)[0]
.. note:: `Alpha158` is the data handler provided by ``Qlib``, please refer to `Data Handler <data.html#data-handler>`_.
.. note::
`Alpha158` is the data handler provided by ``Qlib``, please refer to `Data Handler <data.html#data-handler>`_.
`SignalRecord` is the `Record Template` in ``Qlib``, please refer to `Workflow <recorder.html#record-template>`_.
Also, the above example has been given in ``examples/train_backtest_analyze.ipynb``.

6
docs/component/recorder.rst
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@@ -402,8 +402,8 @@ Record Template
The ``RecordTemp`` class is a class that enables generate experiment results such as IC and backtest in a certain format. We have provided three different `Record Template` class:
- ``SignalRecord``: This class generates the `preidction` of the model.
- ``SigAnaRecord``: This class generates the `IC`, `ICIR`, `Rank IC` and `Rank ICIR`.
- ``SignalRecord``: This class generates the `preidction` results of the model.
- ``SigAnaRecord``: This class generates the `IC`, `ICIR`, `Rank IC` and `Rank ICIR` of the model.
- ``PortAnaRecord``: This class generates the results of `backtest`. The detailed information about `backtest` as well as the available `strategy`, users can refer to `Strategy <../component/strategy.html>`_ and `Backtest <../component/backtest.html>`_.
For more information, please refer to `Record Template API <../reference/api.html#module-qlib.workflow.record_temp>`_.
For more information about the APIs, please refer to `Record Template API <../reference/api.html#module-qlib.workflow.record_temp>`_.

14
docs/reference/api.rst
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@@ -60,12 +60,26 @@ Cache
Contrib
====================
Data Loader
---------------
.. automodule:: qlib.data.dataset.loader
:members:
Data Handler
---------------
.. automodule:: qlib.data.dataset.handler
:members:
Processor
---------------
.. automodule:: qlib.data.dataset.processor
:members:
Dataset
---------------
.. automodule:: qlib.data.dataset.__init__
:members:
Model
--------------------
.. automodule:: qlib.model.base

103
docs/start/integration.rst
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@@ -5,7 +5,7 @@ Custom Model Integration
Introduction
===================
``Qlib`` provides ``lightGBM`` and ``Dnn`` model as the baseline of ``Interday Model``. In addition to the default model, users can integrate their own custom models into ``Qlib``.
``Qlib``'s `Model Zoo` includes models such as ``LightGBM``, ``DNN``, ``LSTM``, etc.. These models are treated as the baselines of ``Interday Model``. In addition to the default models ``Qlib`` provide, users can integrate their own custom models into ``Qlib``.
Users can integrate their own custom models according to the following steps.
@@ -32,79 +32,76 @@ The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#
- Override the `fit` method
- ``Qlib`` calls the fit method to train the model
- The parameters must include training feature `x_train`, training label `y_train`, test feature `x_valid`, test label `y_valid` at least.
- The parameters could include some optional parameters with default values, such as train weight `w_train`, test weight `w_valid` and `num_boost_round = 1000`.
- The parameters must include training feature `dataset`.
- The parameters could include some optional parameters with default values, such as `num_boost_round = 1000` for `GBDT`.
- Code Example: In the following example, `num_boost_round = 1000` is an optional parameter.
.. code-block:: Python
def fit(self, x_train:pd.DataFrame, y_train:pd.DataFrame, x_valid:pd.DataFrame, y_valid:pd.DataFrame,
w_train:pd.DataFrame = None, w_valid:pd.DataFrame = None, num_boost_round = 1000, **kwargs):
def fit(self, dataset: DatasetH, num_boost_round = 1000, **kwargs):
# prepare dataset for lgb training and evaluation
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
)
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]
# Lightgbm need 1D array as its label
if y_train.values.ndim == 2 and y_train.values.shape[1] == 1:
y_train_1d, y_valid_1d = np.squeeze(y_train.values), np.squeeze(y_valid.values)
y_train, y_valid = np.squeeze(y_train.values), np.squeeze(y_valid.values)
else:
raise ValueError('LightGBM doesn\'t support multi-label training')
raise ValueError("LightGBM doesn't support multi-label training")
w_train_weight = None if w_train is None else w_train.values
w_valid_weight = None if w_valid is None else w_valid.values
dtrain = lgb.Dataset(x_train.values, label=y_train)
dvalid = lgb.Dataset(x_valid.values, label=y_valid)
dtrain = lgb.Dataset(x_train.values, label=y_train_1d, weight=w_train_weight)
dvalid = lgb.Dataset(x_valid.values, label=y_valid_1d, weight=w_valid_weight)
self._model = lgb.train(
self._params,
dtrain,
# fit the model
self.model = lgb.train(
self.params,
dtrain,
num_boost_round=num_boost_round,
valid_sets=[dtrain, dvalid],
valid_names=['train', 'valid'],
valid_names=["train", "valid"],
early_stopping_rounds=early_stopping_rounds,
verbose_eval=verbose_eval,
evals_result=evals_result,
**kwargs
)
- Override the `predict` method
- The parameters include the test features.
- The parameters must include training feature `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 dnn to predict the label(such as `preds`) of test data `x_test` and return it.
- 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.
.. code-block:: Python
def predict(self, x_test:pd.DataFrame, **kwargs)-> numpy.ndarray:
if self._model is None:
raise ValueError('model is not fitted yet!')
return self._model.predict(x_test.values)
def predict(self, dataset: DatasetH, **kwargs)-> pandas.Series:
if self.model is None:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature", data_key=DataHandlerLP.DK_I)
return pd.Series(self.model.predict(x_test.values), index=x_test.index)
- Override the `save` method & `load` method
- The `save` method parameter includes the a `filename` that represents an absolute path, user need to save model into the path.
- The `load` method parameter includes the a `buffer` read from the `filename` passed in the `save` method, users need to load model from the `buffer`.
- Code Example:
- Override the `finetune` method
- The parameters must include training feature `dataset`.
- Code Example: In the following example, users will use `LightGBM` as the model and finetune it.
.. code-block:: Python
def save(self, filename):
if self._model is None:
raise ValueError('model is not fitted yet!')
self._model.save_model(filename)
def load(self, buffer):
self._model = lgb.Booster(params={'model_str': buffer.decode('utf-8')})
.. Without tuner, this part will not be used
.. - Override the `score` method(This step is optional)
.. - The parameters include the test features and test labels.
.. - Return the evaluation score of the model. It's recommended to adopt the loss between labels and `prediction score`.
.. - Code Example: In the following example, users need to calculate the weighted loss with test data `x_test`, test label `y_test` and the weight `w_test`.
.. .. code-block:: Python
..
.. def score(self, x_test:pd.Dataframe, y_test:pd.Dataframe, w_test:pd.DataFrame = None) -> float:
.. # Remove rows from x, y and w, which contain Nan in any columns in y_test.
.. x_test, y_test, w_test = drop_nan_by_y_index(x_test, y_test, w_test)
.. preds = self.predict(x_test)
.. w_test_weight = None if w_test is None else w_test.values
.. scorer = mean_squared_error if self.loss_type == 'mse' else roc_auc_score
.. return scorer(y_test.values, preds, sample_weight=w_test_weight)
def finetune(self, dataset: DatasetH, num_boost_round=10, verbose_eval=20):
dtrain, _ = self._prepare_data(dataset)
self.model = lgb.train(
self.params,
dtrain,
num_boost_round=num_boost_round,
init_model=self.model,
valid_sets=[dtrain],
valid_names=["train"],
verbose_eval=verbose_eval,
)
Configuration File
=======================
The configuration file is described in detail in the `estimator <../component/estimator.html#complete-example>`_ document. In order to integrate the custom model into ``Qlib``, users need to modify the "model" field in the configuration file.
The configuration file is described in detail in the `Workflow <../component/workflow.html#complete-example>`_ document. In order to integrate the custom model into ``Qlib``, users need to modify the "model" field in the configuration file.
- Example: The following example describes the `model` field of configuration file about the custom lightgbm model mentioned above, where `module_path` is the module path, `class` is the class name, and `args` is the hyperparameter passed into the __init__ method. All parameters in the field is passed to `self._params` by `\*\*kwargs` in `__init__` except `loss = mse`.
@@ -124,20 +121,20 @@ The configuration file is described in detail in the `estimator <../component/es
num_leaves: 210
num_threads: 20
Users could find configuration file of the baseline of the ``Model`` in ``qlib/examples/estimator/estimator_config.yaml`` and ``qlib/examples/estimator/estimator_config_dnn.yaml``
Users could find configuration file of the baselines of the ``Model`` in ``examples/benchmarks``. All the configurations of different models are listed under the corresponding model folder.
Model Testing
=====================
Assuming that the configuration file is ``examples/estimator/estimator_config.yaml``, users can run the following command to test the custom model:
Assuming that the configuration file is ``examples/benchmarks/LightGBM/workflow_config_lightgbm.yaml``, users can run the following command to test the custom model:
.. code-block:: bash
cd examples # Avoid running program under the directory contains `qlib`
estimator -c estimator/estimator_config.yaml
qrun benchmarks/LightGBM/workflow_config_lightgbm.yaml
.. note:: ``estimator`` is a built-in command of ``Qlib``.
.. note:: ``qrun`` is a built-in command of ``Qlib``.
Also, ``Model`` can also be tested as a single module. An example has been given in ``examples/train_backtest_analyze.ipynb``.
Also, ``Model`` can also be tested as a single module. An example has been given in ``examples/workflow_by_code.ipynb``.
Reference

4
examples/benchmarks/HATS/README.md
View File

@@ -1,11 +1,11 @@
##Requirement
## Requirement
* pandas==1.1.2
* numpy==1.17.4
* scikit_learn==0.23.2
* torch==1.7.0
##HATS
## HATS
* HATS is a a hierarchical attention network for stock prediction which uses relational data for stock market prediction. HATS selectively aggregates information
on different relation types and adds the information to the representations of each company. HATS is used as a relational modeling module with initialized node representations.Furthermore, HATS

2
examples/benchmarks/TFT/README.md
View File

@@ -5,7 +5,7 @@
**GitHub**: https://github.com/google-research/google-research/tree/master/tft
## Run the Workflow
Users can follow the ``workflow_by_code_tft.py`` to run the benchmark.
Users can follow the ``workflow_by_code_tft.py`` to run the benchmark. Please be **aware** that this script can only support Python 3.5 - 3.8.
### Notes
1. The model must run in GPU, or an error will be raised.

2
examples/benchmarks/TabNet/workflow_config_tabnet.yaml
View File

@@ -44,7 +44,7 @@ task:
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha158
class: ALPHA360_Denoise
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:

84
qlib/contrib/evaluate.py
View File

@@ -26,9 +26,9 @@ def risk_analysis(r, N=252):
Parameters
----------
r : pandas.Series
daily return series
daily return series.
N: int
scaler for annualizing information_ratio (day: 250, week: 50, month: 12)
scaler for annualizing information_ratio (day: 250, week: 50, month: 12).
"""
mean = r.mean()
std = r.std(ddof=1)
@@ -61,7 +61,7 @@ def get_strategy(
----------
strategy : Strategy()
strategy used in backtest
strategy used in backtest.
topk : int (Default value: 50)
top-N stocks to buy.
margin : int or float(Default value: 0.5)
@@ -73,14 +73,14 @@ def get_strategy(
sell_limit = pred_in_a_day.count() * margin
buffer margin, in single score_mode, continue holding stock if it is in nlargest(sell_limit)
sell_limit should be no less than topk
buffer margin, in single score_mode, continue holding stock if it is in nlargest(sell_limit).
sell_limit should be no less than topk.
n_drop : int
number of stocks to be replaced in each trading date
number of stocks to be replaced in each trading date.
risk_degree: float
0-1, 0.95 for example, use 95% money to trade
0-1, 0.95 for example, use 95% money to trade.
str_type: 'amount', 'weight' or 'dropout'
strategy type: TopkAmountStrategy ,TopkWeightStrategy or TopkDropoutStrategy
strategy type: TopkAmountStrategy ,TopkWeightStrategy or TopkDropoutStrategy.
Returns
-------
@@ -126,21 +126,21 @@ def get_exchange(
----------
# exchange related arguments
exchange: Exchange()
exchange: Exchange().
subscribe_fields: list
subscribe fields
subscribe fields.
open_cost : float
open transaction cost
open transaction cost.
close_cost : float
close transaction cost
close transaction cost.
min_cost : float
min transaction cost
min transaction cost.
trade_unit : int
100 for China A
100 for China A.
deal_price: str
dealing price type: 'close', 'open', 'vwap'
dealing price type: 'close', 'open', 'vwap'.
limit_threshold : float
limit move 0.1 (10%) for example, long and short with same limit
limit move 0.1 (10%) for example, long and short with same limit.
extract_codes: bool
will we pass the codes extracted from the pred to the exchange.
NOTE: This will be faster with offline qlib.
@@ -193,20 +193,20 @@ def backtest(pred, account=1e9, shift=1, benchmark="SH000905", verbose=True, **k
- **backtest workflow related or commmon arguments**
pred : pandas.DataFrame
predict should has <datetime, instrument> index and one `score` column
predict should has <datetime, instrument> index and one `score` column.
account : float
init account value
init account value.
shift : int
whether to shift prediction by one day
whether to shift prediction by one day.
benchmark : str
benchmark code, default is SH000905 CSI 500
benchmark code, default is SH000905 CSI 500.
verbose : bool
whether to print log
whether to print log.
- **strategy related arguments**
strategy : Strategy()
strategy used in backtest
strategy used in backtest.
topk : int (Default value: 50)
top-N stocks to buy.
margin : int or float(Default value: 0.5)
@@ -218,33 +218,33 @@ def backtest(pred, account=1e9, shift=1, benchmark="SH000905", verbose=True, **k
sell_limit = pred_in_a_day.count() * margin
buffer margin, in single score_mode, continue holding stock if it is in nlargest(sell_limit)
sell_limit should be no less than topk
buffer margin, in single score_mode, continue holding stock if it is in nlargest(sell_limit).
sell_limit should be no less than topk.
n_drop : int
number of stocks to be replaced in each trading date
number of stocks to be replaced in each trading date.
risk_degree: float
0-1, 0.95 for example, use 95% money to trade
0-1, 0.95 for example, use 95% money to trade.
str_type: 'amount', 'weight' or 'dropout'
strategy type: TopkAmountStrategy ,TopkWeightStrategy or TopkDropoutStrategy
strategy type: TopkAmountStrategy ,TopkWeightStrategy or TopkDropoutStrategy.
- **exchange related arguments**
exchange: Exchange()
pass the exchange for speeding up.
subscribe_fields: list
subscribe fields
subscribe fields.
open_cost : float
open transaction cost. The default value is 0.002(0.2%).
close_cost : float
close transaction cost. The default value is 0.002(0.2%).
min_cost : float
min transaction cost
min transaction cost.
trade_unit : int
100 for China A
100 for China A.
deal_price: str
dealing price type: 'close', 'open', 'vwap'
dealing price type: 'close', 'open', 'vwap'.
limit_threshold : float
limit move 0.1 (10%) for example, long and short with same limit
limit move 0.1 (10%) for example, long and short with same limit.
extract_codes: bool
will we pass the codes extracted from the pred to the exchange.
@@ -291,17 +291,17 @@ def long_short_backtest(
"""
A backtest for long-short strategy
:param pred: The trading signal produced on day `T`
:param topk: The short topk securities and long topk securities
:param deal_price: The price to deal the trading
:param pred: The trading signal produced on day `T`.
:param topk: The short topk securities and long topk securities.
:param deal_price: The price to deal the trading.
:param shift: Whether to shift prediction by one day. The trading day will be T+1 if shift==1.
:param open_cost: open transaction cost
:param close_cost: close transaction cost
:param trade_unit: 100 for China A
:param limit_threshold: limit move 0.1 (10%) for example, long and short with same limit
:param min_cost: min transaction cost
:param subscribe_fields: subscribe fields
:param extract_codes: bool
:param open_cost: open transaction cost.
:param close_cost: close transaction cost.
:param trade_unit: 100 for China A.
:param limit_threshold: limit move 0.1 (10%) for example, long and short with same limit.
:param min_cost: min transaction cost.
:param subscribe_fields: subscribe fields.
:param extract_codes: bool.
will we pass the codes extracted from the pred to the exchange.
NOTE: This will be faster with offline qlib.
:return: The result of backtest, it is represented by a dict.

16
qlib/contrib/report/analysis_model/analysis_model_performance.py
View File

@@ -252,7 +252,7 @@ def model_performance_graph(
"""Model performance
:param pred_label: index is **pd.MultiIndex**, index name is **[instrument, datetime]**; columns names is **[score,
label]**. It is usually same as the label of model training(e.g. "Ref($close, -2)/Ref($close, -1) - 1")
label]**. It is usually same as the label of model training(e.g. "Ref($close, -2)/Ref($close, -1) - 1").
.. code-block:: python
@@ -266,13 +266,13 @@ def model_performance_graph(
:param lag: `pred.groupby(level='instrument')['score'].shift(lag)`. It will be only used in the auto-correlation computing.
:param N: group number, default 5
:param reverse: if `True`, `pred['score'] *= -1`
:param rank: if **True**, calculate rank ic
:param graph_names: graph names; default ['cumulative_return', 'pred_ic', 'pred_autocorr', 'pred_turnover']
:param show_notebook: whether to display graphics in notebook, the default is `True`
:param show_nature_day: whether to display the abscissa of non-trading day
:return: if show_notebook is True, display in notebook; else return `plotly.graph_objs.Figure` list
:param N: group number, default 5.
:param reverse: if `True`, `pred['score'] *= -1`.
:param rank: if **True**, calculate rank ic.
:param graph_names: graph names; default ['cumulative_return', 'pred_ic', 'pred_autocorr', 'pred_turnover'].
:param show_notebook: whether to display graphics in notebook, the default is `True`.
:param show_nature_day: whether to display the abscissa of non-trading day.
:return: if show_notebook is True, display in notebook; else return `plotly.graph_objs.Figure` list.
"""
figure_list = []
for graph_name in graph_names:

6
qlib/contrib/report/analysis_position/cumulative_return.py
View File

@@ -218,10 +218,10 @@ def cumulative_return_graph(
Graph desc:
- Axis X: Trading day
- Axis X: Trading day.
- Axis Y:
- Above axis Y: `(((Ref($close, -1)/$close - 1) * weight).sum() / weight.sum()).cumsum()`
- Below axis Y: Daily weight sum
- Above axis Y: `(((Ref($close, -1)/$close - 1) * weight).sum() / weight.sum()).cumsum()`.
- Below axis Y: Daily weight sum.
- In the **sell** graph, `y < 0` stands for profit; in other cases, `y > 0` stands for profit.
- In the **buy_minus_sell** graph, the **y** value of the **weight** graph at the bottom is `buy_weight + sell_weight`.
- In each graph, the **red line** in the histogram on the right represents the average.

6
qlib/contrib/report/analysis_position/rank_label.py
View File

@@ -97,9 +97,9 @@ def rank_label_graph(
qcr.rank_label_graph(positions, features_df, pred_df_dates.min(), pred_df_dates.max())
:param position: position data; **qlib.contrib.backtest.backtest.backtest** result
:param position: position data; **qlib.contrib.backtest.backtest.backtest** result.
:param label_data: **D.features** result; index is **pd.MultiIndex**, index name is **[instrument, datetime]**; columns names is **[label]**.
**The label T is the change from T to T+1**, it is recommended to use ``close``, example: `D.features(D.instruments('csi500'), ['Ref($close, -1)/$close-1'])`
**The label T is the change from T to T+1**, it is recommended to use ``close``, example: `D.features(D.instruments('csi500'), ['Ref($close, -1)/$close-1'])`.
.. code-block:: python
@@ -115,7 +115,7 @@ def rank_label_graph(
:param start_date: start date
:param end_date: end_date
:param show_notebook: **True** or **False**. If True, show graph in notebook, else return figures
:param show_notebook: **True** or **False**. If True, show graph in notebook, else return figures.
:return:
"""
position = copy.deepcopy(position)

6
qlib/contrib/report/analysis_position/report.py
View File

@@ -186,7 +186,7 @@ def report_graph(report_df: pd.DataFrame, show_notebook: bool = True) -> [list,
qcr.report_graph(report_normal_df)
:param report_df: **df.index.name** must be **date**, **df.columns** must contain **return**, **turnover**, **cost**, **bench**
:param report_df: **df.index.name** must be **date**, **df.columns** must contain **return**, **turnover**, **cost**, **bench**.
.. code-block:: python
@@ -200,8 +200,8 @@ def report_graph(report_df: pd.DataFrame, show_notebook: bool = True) -> [list,
2017-01-10 -0.000416 0.000440 -0.003350 0.208396
:param show_notebook: whether to display graphics in notebook, the default is **True**
:return: if show_notebook is True, display in notebook; else return **plotly.graph_objs.Figure** list
:param show_notebook: whether to display graphics in notebook, the default is **True**.
:return: if show_notebook is True, display in notebook; else return **plotly.graph_objs.Figure** list.
"""
report_df = report_df.copy()
fig_list = _report_figure(report_df)

6
qlib/contrib/report/analysis_position/risk_analysis.py
View File

@@ -218,7 +218,7 @@ def risk_analysis_graph(
max_drawdown -0.088263
:param report_normal_df: **df.index.name** must be **date**, df.columns must contain **return**, **turnover**, **cost**, **bench**
:param report_normal_df: **df.index.name** must be **date**, df.columns must contain **return**, **turnover**, **cost**, **bench**.
.. code-block:: python
@@ -232,7 +232,7 @@ def risk_analysis_graph(
2017-01-10 -0.000416 0.000440 -0.003350 0.208396
:param report_long_short_df: **df.index.name** must be **date**, df.columns contain **long**, **short**, **long_short**
:param report_long_short_df: **df.index.name** must be **date**, df.columns contain **long**, **short**, **long_short**.
.. code-block:: python
@@ -246,7 +246,7 @@ def risk_analysis_graph(
2017-01-10 0.000824 -0.001944 -0.001120
:param show_notebook: Whether to display graphics in a notebook, default **True**
:param show_notebook: Whether to display graphics in a notebook, default **True**.
If True, show graph in notebook
If False, return graph figure
:return:

6
qlib/contrib/report/analysis_position/score_ic.py
View File

@@ -36,7 +36,7 @@ def score_ic_graph(pred_label: pd.DataFrame, show_notebook: bool = True) -> [lis
analysis_position.score_ic_graph(pred_label)
:param pred_label: index is **pd.MultiIndex**, index name is **[instrument, datetime]**; columns names is **[score, label]**
:param pred_label: index is **pd.MultiIndex**, index name is **[instrument, datetime]**; columns names is **[score, label]**.
.. code-block:: python
@@ -49,8 +49,8 @@ def score_ic_graph(pred_label: pd.DataFrame, show_notebook: bool = True) -> [lis
2017-12-15 -0.102778 -0.102778
:param show_notebook: whether to display graphics in notebook, the default is **True**
:return: if show_notebook is True, display in notebook; else return **plotly.graph_objs.Figure** list
:param show_notebook: whether to display graphics in notebook, the default is **True**.
:return: if show_notebook is True, display in notebook; else return **plotly.graph_objs.Figure** list.
"""
_ic_df = _get_score_ic(pred_label)
# FIXME: support HIGH-FREQ

70
qlib/contrib/strategy/strategy.py
View File

@@ -31,16 +31,16 @@ class BaseStrategy:
Parameters
-----------
score_series : pd.Seires
stock_id , score
stock_id , score.
current : Position()
current state of position
DO NOT directly change the state of current
current state of position.
DO NOT directly change the state of current.
trade_exchange : Exchange()
trade exchange
trade exchange.
pred_date : pd.Timestamp
predict date
predict date.
trade_date : pd.Timestamp
trade date
trade date.
"""
pass
@@ -49,11 +49,11 @@ class BaseStrategy:
Parameters
-----------
score_series : pd.Series
stock_id , score
stock_id , score.
pred_date : pd.Timestamp
oredict date
oredict date.
trade_date : pd.Timestamp
trade date
trade date.
"""
pass
@@ -67,7 +67,7 @@ class BaseStrategy:
"""
This method only be used in 'online' module, it will generate the *args to initial the strategy.
:param
mode : model used in 'online' module
mode : model used in 'online' module.
"""
return {}
@@ -82,7 +82,7 @@ class StrategyWrapper:
def __init__(self, inner_strategy):
"""__init__
:param inner_strategy: set the inner strategy
:param inner_strategy: set the inner strategy.
"""
self.inner_strategy = inner_strategy
@@ -99,9 +99,9 @@ class AdjustTimer:
Responsible for timing of position adjusting
This is designed as multiple inheritance mechanism due to:
- the is_adjust may need access to the internel state of a strategy
- the is_adjust may need access to the internel state of a strategy.
- it can be reguard as a enhancement to the existing strategy
- it can be reguard as a enhancement to the existing strategy.
"""
# adjust position in each trade date
@@ -146,12 +146,12 @@ class WeightStrategyBase(BaseStrategy, AdjustTimer):
Parameters
-----------
score : pd.Series
pred score for this trade date, index is stock_id, contain 'score' column
pred score for this trade date, index is stock_id, contain 'score' column.
current : Position()
current position
current position.
trade_exchange : Exchange()
trade_date : pd.Timestamp
trade date
trade date.
"""
raise NotImplementedError()
@@ -160,13 +160,13 @@ class WeightStrategyBase(BaseStrategy, AdjustTimer):
Parameters
-----------
score_series : pd.Seires
stock_id , score
stock_id , score.
current : Position()
current of account
current of account.
trade_exchange : Exchange()
exchange
exchange.
trade_date : pd.Timestamp
date
date.
"""
# judge if to adjust
if not self.is_adjust(trade_date):
@@ -206,26 +206,26 @@ class TopkDropoutStrategy(BaseStrategy, ListAdjustTimer):
Parameters
-----------
topk : int
The number of stocks in the portfolio
the number of stocks in the portfolio.
n_drop : int
number of stocks to be replaced in each trading date
number of stocks to be replaced in each trading date.
method_sell : str
dropout method_sell, random/bottom
dropout method_sell, random/bottom.
method_buy : str
dropout method_buy, random/top
dropout method_buy, random/top.
risk_degree : float
position percentage of total value
position percentage of total value.
thresh : int
minimun holding days since last buy singal of the stock
minimun holding days since last buy singal of the stock.
hold_thresh : int
minimum holding days
before sell stock , will check current.get_stock_count(order.stock_id) >= self.thresh
before sell stock , will check current.get_stock_count(order.stock_id) >= self.thresh.
only_tradable : bool
will the strategy only consider the tradable stock when buying and selling.
if only_tradable:
strategy will make buy sell decision without checking the tradable state of the stock
strategy will make buy sell decision without checking the tradable state of the stock.
else:
strategy will make decision with the tradable state of the stock info and avoid buy and sell them
strategy will make decision with the tradable state of the stock info and avoid buy and sell them.
"""
super(TopkDropoutStrategy, self).__init__()
ListAdjustTimer.__init__(self, kwargs.get("adjust_dates", None))
@@ -245,7 +245,7 @@ class TopkDropoutStrategy(BaseStrategy, ListAdjustTimer):
def get_risk_degree(self, date):
"""get_risk_degree
Return the proportion of your total value you will used in investment.
Dynamically risk_degree will result in Market timing
Dynamically risk_degree will result in Market timing.
"""
# It will use 95% amoutn of your total value by default
return self.risk_degree
@@ -257,15 +257,15 @@ class TopkDropoutStrategy(BaseStrategy, ListAdjustTimer):
Parameters
-----------
score_series : pd.Series
stock_id , score
stock_id , score.
current : Position()
current of account
current of account.
trade_exchange : Exchange()
exchange
exchange.
pred_date : pd.Timestamp
predict date
predict date.
trade_date : pd.Timestamp
trade date
trade date.
"""
if not self.is_adjust(trade_date):
return []

8
qlib/data/base.py
View File

@@ -129,13 +129,13 @@ class Expression(abc.ABC):
Parameters
----------
instrument : str
instrument code
instrument code.
start_index : str
feature start index [in calendar]
feature start index [in calendar].
end_index : str
feature end index [in calendar]
feature end index [in calendar].
freq : str
feature frequency
feature frequency.
Returns
----------

40
qlib/data/cache.py
View File

@@ -76,8 +76,8 @@ class MemCache(object):
Parameters
----------
mem_cache_size_limit: cache max size
limit_type: length or sizeof; length(call fun: len), size(call fun: sys.getsizeof)
mem_cache_size_limit: cache max size.
limit_type: length or sizeof; length(call fun: len), size(call fun: sys.getsizeof).
"""
if limit_type not in ["length", "sizeof"]:
raise ValueError(f"limit_type must be length or sizeof, your limit_type is {limit_type}")
@@ -118,9 +118,9 @@ class MemCacheExpire:
def set_cache(mem_cache, key, value):
"""set cache
:param mem_cache: MemCache attribute('c'/'i'/'f')
:param key: cache key
:param value: cache value
:param mem_cache: MemCache attribute('c'/'i'/'f').
:param key: cache key.
:param value: cache value.
"""
mem_cache[key] = value, time.time()
@@ -128,9 +128,9 @@ class MemCacheExpire:
def get_cache(mem_cache, key):
"""get mem cache
:param mem_cache: MemCache attribute('c'/'i'/'f')
:param key: cache key
:return: cache value; if cache not exist, return None
:param mem_cache: MemCache attribute('c'/'i'/'f').
:param key: cache key.
:return: cache value; if cache not exist, return None.
"""
value = None
expire = False
@@ -275,12 +275,12 @@ class ExpressionCache(BaseProviderCache):
Parameters
----------
cache_uri : str
the complete uri of expression cache file (include dir path)
the complete uri of expression cache file (include dir path).
Returns
-------
int
0(successful update)/ 1(no need to update)/ 2(update failure)
0(successful update)/ 1(no need to update)/ 2(update failure).
"""
raise NotImplementedError("Implement this method if you want to make expression cache up to date")
@@ -348,7 +348,7 @@ class DatasetCache(BaseProviderCache):
Parameters
----------
cache_uri : str
the complete uri of dataset cache file (include dir path)
the complete uri of dataset cache file (include dir path).
Returns
-------
@@ -361,9 +361,9 @@ class DatasetCache(BaseProviderCache):
def cache_to_origin_data(data, fields):
"""cache data to origin data
:param data: pd.DataFrame, cache data
:param fields: feature fields
:return: pd.DataFrame
:param data: pd.DataFrame, cache data.
:param fields: feature fields.
:return: pd.DataFrame.
"""
not_space_fields = remove_fields_space(fields)
data = data.loc[:, not_space_fields]
@@ -583,7 +583,7 @@ class DiskDatasetCache(DatasetCache):
:param cache_path:
:param start_time:
:param end_time:
:param fields: The fields order of the dataset cache is sorted. So rearrange the columns to make it consistent
:param fields: The fields order of the dataset cache is sorted. So rearrange the columns to make it consistent.
:return:
"""
@@ -771,12 +771,12 @@ class DiskDatasetCache(DatasetCache):
- This is a hdf file sorted by datetime
:param cache_path: The path to store the cache
:param instruments: The instruments to store the cache
:param fields: The fields to store the cache
:param freq: The freq to store the cache
:param cache_path: The path to store the cache.
:param instruments: The instruments to store the cache.
:param fields: The fields to store the cache.
:param freq: The freq to store the cache.
:return type pd.DataFrame; The fields of the returned DataFrame are consistent with the parameters of the function
:return type pd.DataFrame; The fields of the returned DataFrame are consistent with the parameters of the function.
"""
# get calendar
from .data import Cal

8
qlib/data/client.py
View File

@@ -51,13 +51,13 @@ class Client(object):
Parameters
----------
request_type : str
type of proposed request, 'calendar'/'instrument'/'feature'
type of proposed request, 'calendar'/'instrument'/'feature'.
request_content : dict
records the information of the request
records the information of the request.
msg_proc_func : func
the function to process the message when receiving response, should have arg `*args`
the function to process the message when receiving response, should have arg `*args`.
msg_queue: Queue
The queue to pass the messsage after callback
The queue to pass the messsage after callback.
"""
head_info = {"version": qlib.__version__}

92
qlib/data/data.py
View File

@@ -41,13 +41,13 @@ class CalendarProvider(abc.ABC):
Parameters
----------
start_time : str
start of the time range
start of the time range.
end_time : str
end of the time range
end of the time range.
freq : str
time frequency, available: year/quarter/month/week/day
time frequency, available: year/quarter/month/week/day.
future : bool
whether including future trading day
whether including future trading day.
Returns
----------
@@ -62,24 +62,24 @@ class CalendarProvider(abc.ABC):
Parameters
----------
start_time : str
start of the time range
start of the time range.
end_time : str
end of the time range
end of the time range.
freq : str
time frequency, available: year/quarter/month/week/day
time frequency, available: year/quarter/month/week/day.
future : bool
whether including future trading day
whether including future trading day.
Returns
-------
pd.Timestamp
the real start time
the real start time.
pd.Timestamp
the real end time
the real end time.
int
the index of start time
the index of start time.
int
the index of end time
the index of end time.
"""
start_time = pd.Timestamp(start_time)
end_time = pd.Timestamp(end_time)
@@ -103,16 +103,16 @@ class CalendarProvider(abc.ABC):
Parameters
----------
freq : str
frequency of read calendar file
frequency of read calendar file.
future : bool
whether including future trading day
whether including future trading day.
Returns
-------
list
list of timestamps
list of timestamps.
dict
dict composed by timestamp as key and index as value for fast search
dict composed by timestamp as key and index as value for fast search.
"""
flag = f"{freq}_future_{future}"
if flag in H["c"]:
@@ -141,14 +141,14 @@ class InstrumentProvider(abc.ABC):
Parameters
----------
market : str
market/industry/index shortname, e.g. all/sse/szse/sse50/csi300/csi500
market/industry/index shortname, e.g. all/sse/szse/sse50/csi300/csi500.
filter_pipe : list
the list of dynamic filters
the list of dynamic filters.
Returns
----------
dict
dict of stockpool config
dict of stockpool config.
{`market`=>base market name, `filter_pipe`=>list of filters}
example :
@@ -182,13 +182,13 @@ class InstrumentProvider(abc.ABC):
Parameters
----------
instruments : dict
stockpool config
stockpool config.
start_time : str
start of the time range
start of the time range.
end_time : str
end of the time range
end of the time range.
as_list : bool
return instruments as list or dict
return instruments as list or dict.
Returns
-------
@@ -243,15 +243,15 @@ class FeatureProvider(abc.ABC):
Parameters
----------
instrument : str
a certain instrument
a certain instrument.
field : str
a certain field of feature
a certain field of feature.
start_time : str
start of the time range
start of the time range.
end_time : str
end of the time range
end of the time range.
freq : str
time frequency, available: year/quarter/month/week/day
time frequency, available: year/quarter/month/week/day.
Returns
-------
@@ -294,15 +294,15 @@ class ExpressionProvider(abc.ABC):
Parameters
----------
instrument : str
a certain instrument
a certain instrument.
field : str
a certain field of feature
a certain field of feature.
start_time : str
start of the time range
start of the time range.
end_time : str
end of the time range
end of the time range.
freq : str
time frequency, available: year/quarter/month/week/day
time frequency, available: year/quarter/month/week/day.
Returns
-------
@@ -325,20 +325,20 @@ class DatasetProvider(abc.ABC):
Parameters
----------
instruments : list or dict
list/dict of instruments or dict of stockpool config
list/dict of instruments or dict of stockpool config.
fields : list
list of feature instances
list of feature instances.
start_time : str
start of the time range
start of the time range.
end_time : str
end of the time range
end of the time range.
freq : str
time frequency
time frequency.
Returns
----------
pd.DataFrame
a pandas dataframe with <instrument, datetime> index
a pandas dataframe with <instrument, datetime> index.
"""
raise NotImplementedError("Subclass of DatasetProvider must implement `Dataset` method")
@@ -357,17 +357,17 @@ class DatasetProvider(abc.ABC):
Parameters
----------
instruments : list or dict
list/dict of instruments or dict of stockpool config
list/dict of instruments or dict of stockpool config.
fields : list
list of feature instances
list of feature instances.
start_time : str
start of the time range
start of the time range.
end_time : str
end of the time range
end of the time range.
freq : str
time frequency
time frequency.
disk_cache : int
whether to skip(0)/use(1)/replace(2) disk_cache
whether to skip(0)/use(1)/replace(2) disk_cache.
"""
return DiskDatasetCache._uri(instruments, fields, start_time, end_time, freq, disk_cache)
@@ -526,7 +526,7 @@ class LocalCalendarProvider(CalendarProvider):
Parameters
----------
freq : str
frequency of read calendar file
frequency of read calendar file.
Returns
----------

30
qlib/data/dataset/__init__.py
View File

@@ -17,7 +17,7 @@ class Dataset(Serializable):
init is designed to finish following steps:
- setup data
- The data related attributes' names should start with '_' so that it will not be saved on disk when serializing
- The data related attributes' names should start with '_' so that it will not be saved on disk when serializing.
- initialize the state of the dataset(info to prepare the data)
- The name of essential state for preparing data should not start with '_' so that it could be serialized on disk when serializing.
@@ -29,17 +29,17 @@ class Dataset(Serializable):
def setup_data(self, *args, **kwargs):
"""
setup the data
Setup the data.
We split the setup_data function for following situation:
- User have a Dataset object with learned status on disk
- User have a Dataset object with learned status on disk.
- User load the Dataset object from the disk(Note the init function is skiped)
- User load the Dataset object from the disk(Note the init function is skiped).
- User call `setup_data` to load new data
- User call `setup_data` to load new data.
- User prepare data for model based on previous status
- User prepare data for model based on previous status.
"""
pass
@@ -66,9 +66,10 @@ class DatasetH(Dataset):
User should try to put the data preprocessing functions into handler.
Only following data processing functions should be placed in Dataset:
- The processing is related to specific model.
- The processing is related to data split
- The processing is related to data split.
"""
def __init__(self, handler: Union[dict, DataHandler], segments: list):
@@ -76,15 +77,15 @@ class DatasetH(Dataset):
Parameters
----------
handler : Union[dict, DataHandler]
handler will be passed into setup_data
handler will be passed into setup_data.
segments : list
handler will be passed into setup_data
handler will be passed into setup_data.
"""
super().__init__(handler, segments)
def setup_data(self, handler: Union[dict, DataHandler], segments: list):
"""
setup the underlying data
Setup the underlying data.
Parameters
----------
@@ -121,7 +122,7 @@ class DatasetH(Dataset):
**kwargs,
) -> Union[List[pd.DataFrame], pd.DataFrame]:
"""
prepare the data for learning and inference
Prepare the data for learning and inference.
Parameters
----------
@@ -132,11 +133,12 @@ class DatasetH(Dataset):
- 'train'
- ['train', 'valid']
col_set : str
The col_set will be passed to self._handler when fetching data
data_key: str
The col_set will be passed to self._handler when fetching data.
data_key : str
The data to fetch: DK_*
Default is DK_I, which indicate fetching data for **inference**
Default is DK_I, which indicate fetching data for **inference**.
Returns
-------

38
qlib/data/dataset/handler.py
View File

@@ -29,7 +29,7 @@ class DataHandler(Serializable):
"""
The steps to using a handler
1. initialized data handler (call by `init`).
2. use the data
2. use the data.
The data handler try to maintain a handler with 2 level.
@@ -65,17 +65,17 @@ class DataHandler(Serializable):
Parameters
----------
instruments :
The stock list to retrive
The stock list to retrive.
start_time :
start_time of the original data
start_time of the original data.
end_time :
end_time of the original data
end_time of the original data.
data_loader : Tuple[dict, str, DataLoader]
data loader to load the data
data loader to load the data.
init_data :
intialize the original data in the constructor
intialize the original data in the constructor.
fetch_orig : bool
Return the original data instead of copy if possible
Return the original data instead of copy if possible.
"""
# Set logger
self.logger = get_module_logger("DataHandler")
@@ -219,9 +219,9 @@ class DataHandler(Serializable):
get a iterator of sliced data with given periods
Args:
periods (int): number of periods
min_periods (int): minimum periods for sliced dataframe
kwargs (dict): will be passed to `self.fetch`
periods (int): number of periods.
min_periods (int): minimum periods for sliced dataframe.
kwargs (dict): will be passed to `self.fetch`.
"""
trading_dates = self._data.index.unique(level="datetime")
if min_periods is None:
@@ -377,7 +377,7 @@ class DataHandlerLP(DataHandler):
Parameters
----------
init_type : str
The type `IT_*` listed above
The type `IT_*` listed above.
enable_cache : bool
default value is false:
@@ -419,13 +419,13 @@ class DataHandlerLP(DataHandler):
Parameters
----------
selector : Union[pd.Timestamp, slice, str]
describe how to select data by index
describe how to select data by index.
level : Union[str, int]
which index level to select the data
which index level to select the data.
col_set : str
select a set of meaningful columns.(e.g. features, columns)
data_key: str
The data to fetch: DK_*
select a set of meaningful columns.(e.g. features, columns).
data_key : str
the data to fetch: DK_*.
Returns
-------
@@ -443,9 +443,9 @@ class DataHandlerLP(DataHandler):
Parameters
----------
col_set : str
select a set of meaningful columns.(e.g. features, columns)
data_key: str
The data to fetch: DK_*
select a set of meaningful columns.(e.g. features, columns).
data_key : str
the data to fetch: DK_*.
Returns
-------

8
qlib/data/dataset/loader.py
View File

@@ -100,16 +100,16 @@ class DLWParser(DataLoader):
Parameters
----------
instruments :
the instruments
the instruments.
exprs : list
The expressions to describe the content of the data
the expressions to describe the content of the data.
names : list
The name of the data
the name of the data.
Returns
-------
pd.DataFrame:
the queried dataframe
the queried dataframe.
"""
pass

6
qlib/data/dataset/processor.py
View File

@@ -21,7 +21,7 @@ def get_group_columns(df: pd.DataFrame, group: str):
Parameters
----------
df : pd.DataFrame
with multi of columns
with multi of columns.
group : str
the name of the feature group, i.e. the first level value of the group index.
"""
@@ -56,7 +56,7 @@ class Processor(Serializable):
Parameters
----------
df : pd.DataFrame
The raw_df of handler or result from previous processor
The raw_df of handler or result from previous processor.
"""
pass
@@ -68,7 +68,7 @@ class Processor(Serializable):
Returns
-------
bool:
if it is usable for infenrece
if it is usable for infenrece.
"""
return True

60
qlib/data/filter.py
View File

@@ -32,7 +32,7 @@ class BaseDFilter(abc.ABC):
Parameters
----------
config : dict
dict of config parameters
dict of config parameters.
"""
raise NotImplementedError("Subclass of BaseDFilter must reimplement `from_config` method")
@@ -43,7 +43,7 @@ class BaseDFilter(abc.ABC):
Returns
----------
dict
return the dict of config parameters
return the dict of config parameters.
"""
raise NotImplementedError("Subclass of BaseDFilter must reimplement `to_config` method")
@@ -69,9 +69,9 @@ class SeriesDFilter(BaseDFilter):
Parameters
----------
fstart_time: str
the time for the filter rule to start filter the instruments
the time for the filter rule to start filter the instruments.
fend_time: str
the time for the filter rule to stop filter the instruments
the time for the filter rule to stop filter the instruments.
"""
super(SeriesDFilter, self).__init__()
self.filter_start_time = pd.Timestamp(fstart_time) if fstart_time else None
@@ -83,12 +83,12 @@ class SeriesDFilter(BaseDFilter):
Parameters
----------
instruments: dict
the dict of instruments in the form {instrument_name => list of timestamp tuple}
the dict of instruments in the form {instrument_name => list of timestamp tuple}.
Returns
----------
pd.Timestamp, pd.Timestamp
the lower time bound and upper time bound of all the instruments
the lower time bound and upper time bound of all the instruments.
"""
trange = Cal.calendar(freq=self.filter_freq)
ubound, lbound = trange[0], trange[-1]
@@ -105,14 +105,14 @@ class SeriesDFilter(BaseDFilter):
Parameters
----------
time_range : D.calendar
the time range of the instruments
the time range of the instruments.
target_timestamp : list
the list of tuple (timestamp, timestamp)
the list of tuple (timestamp, timestamp).
Returns
----------
pd.Series
the series of bool value for an instrument
the series of bool value for an instrument.
"""
# Construct a whole dict of {date => bool}
timestamp_series = {timestamp: False for timestamp in time_range}
@@ -124,19 +124,19 @@ class SeriesDFilter(BaseDFilter):
return timestamp_series
def _filterSeries(self, timestamp_series, filter_series):
"""Filter the timestamp series with filter series by using element-wise AND operation of the two series
"""Filter the timestamp series with filter series by using element-wise AND operation of the two series.
Parameters
----------
timestamp_series : pd.Series
the series of bool value indicating existing time
the series of bool value indicating existing time.
filter_series : pd.Series
the series of bool value indicating filter feature
the series of bool value indicating filter feature.
Returns
----------
pd.Series
the series of bool value indicating whether the date satisfies the filter condition and exists in target timestamp
the series of bool value indicating whether the date satisfies the filter condition and exists in target timestamp.
"""
fstart, fend = list(filter_series.keys())[0], list(filter_series.keys())[-1]
filter_series = filter_series.astype("bool") # Make sure the filter_series is boolean
@@ -144,17 +144,17 @@ class SeriesDFilter(BaseDFilter):
return timestamp_series
def _toTimestamp(self, timestamp_series):
"""Convert the timestamp series to a list of tuple (timestamp, timestamp) indicating a continuous range of TRUE
"""Convert the timestamp series to a list of tuple (timestamp, timestamp) indicating a continuous range of TRUE.
Parameters
----------
timestamp_series: pd.Series
the series of bool value after being filtered
the series of bool value after being filtered.
Returns
----------
list
the list of tuple (timestamp, timestamp)
the list of tuple (timestamp, timestamp).
"""
# sort the timestamp_series according to the timestamps
timestamp_series.sort_index()
@@ -194,18 +194,18 @@ class SeriesDFilter(BaseDFilter):
Parameters
----------
instruments : dict
the dict of instruments to be filtered
the dict of instruments to be filtered.
fstart : pd.Timestamp
start time of filter
start time of filter.
fend : pd.Timestamp
end time of filter
end time of filter.
.. note:: fstart/fend indicates the intersection of instruments start/end time and filter start/end time
.. note:: fstart/fend indicates the intersection of instruments start/end time and filter start/end time.
Returns
----------
pd.Dataframe
a series of {pd.Timestamp => bool}
a series of {pd.Timestamp => bool}.
"""
raise NotImplementedError("Subclass of SeriesDFilter must reimplement `getFilterSeries` method")
@@ -215,16 +215,16 @@ class SeriesDFilter(BaseDFilter):
Parameters
----------
instruments: dict
input instruments to be filtered
input instruments to be filtered.
start_time: str
start of the time range
start of the time range.
end_time: str
end of the time range
end of the time range.
Returns
----------
dict
filtered instruments, same structure as input instruments
filtered instruments, same structure as input instruments.
"""
lbound, ubound = self._getTimeBound(instruments)
start_time = pd.Timestamp(start_time or lbound)
@@ -272,7 +272,7 @@ class NameDFilter(SeriesDFilter):
params:
------
name_rule_re: str
regular expression for the name rule
regular expression for the name rule.
"""
super(NameDFilter, self).__init__(fstart_time, fend_time)
self.name_rule_re = name_rule_re
@@ -325,13 +325,13 @@ class ExpressionDFilter(SeriesDFilter):
params:
------
fstart_time: str
filter the feature starting from this time
filter the feature starting from this time.
fend_time: str
filter the feature ending by this time
filter the feature ending by this time.
rule_expression: str
an input expression for the rule
an input expression for the rule.
keep: bool
whether to keep the instruments of which features don't exist in the filter time span
whether to keep the instruments of which features don't exist in the filter time span.
"""
super(ExpressionDFilter, self).__init__(fstart_time, fend_time)
self.rule_expression = rule_expression

6
qlib/model/base.py
View File

@@ -33,7 +33,7 @@ class Model(BaseModel):
Parameters
----------
dataset : Dataset
dataset will generate the processed data from model training
dataset will generate the processed data from model training.
"""
raise NotImplementedError()
@@ -44,7 +44,7 @@ class Model(BaseModel):
Parameters
----------
dataset : Dataset
dataset will generate the processed dataset from model training
dataset will generate the processed dataset from model training.
"""
raise NotImplementedError()
@@ -59,6 +59,6 @@ class ModelFT(Model):
Parameters
----------
dataset : Dataset
dataset will generate the processed dataset from model training
dataset will generate the processed dataset from model training.
"""
raise NotImplementedError()

44
qlib/model/riskmodel.py
View File

@@ -23,9 +23,9 @@ class RiskModel(BaseModel):
def __init__(self, nan_option: str = "ignore", assume_centered: bool = False, scale_return: bool = True):
"""
Args:
nan_option (str): nan handling option (`ignore`/`mask`/`fill`)
assume_centered (bool): whether the data is assumed to be centered
scale_return (bool): whether scale returns as percentage
nan_option (str): nan handling option (`ignore`/`mask`/`fill`).
assume_centered (bool): whether the data is assumed to be centered.
scale_return (bool): whether scale returns as percentage.
"""
# nan
assert nan_option in [
@@ -45,11 +45,11 @@ class RiskModel(BaseModel):
Args:
X (pd.Series, pd.DataFrame or np.ndarray): data from which to estimate the covariance,
with variables as columns and observations as rows.
return_corr (bool): whether return the correlation matrix
is_price (bool): whether `X` contains price (if not assume stock returns)
return_corr (bool): whether return the correlation matrix.
is_price (bool): whether `X` contains price (if not assume stock returns).
Returns:
pd.DataFrame or np.ndarray: estimated covariance (or correlation)
pd.DataFrame or np.ndarray: estimated covariance (or correlation).
"""
# transform input into 2D array
if not isinstance(X, (pd.Series, pd.DataFrame)):
@@ -101,10 +101,10 @@ class RiskModel(BaseModel):
By default, this method implements the empirical covariance estimation.
Args:
X (np.ndarray): data matrix containing multiple variables (columns) and observations (rows)
X (np.ndarray): data matrix containing multiple variables (columns) and observations (rows).
Returns:
np.ndarray: covariance matrix
np.ndarray: covariance matrix.
"""
xTx = np.asarray(X.T.dot(X))
N = len(X)
@@ -117,7 +117,7 @@ class RiskModel(BaseModel):
"""handle nan and centerize data
Note:
if `nan_option='mask'` then the returned array will be `np.ma.MaskedArray`
if `nan_option='mask'` then the returned array will be `np.ma.MaskedArray`.
"""
# handle nan
if self.nan_option == self.FILL_NAN:
@@ -139,15 +139,15 @@ class ShrinkCovEstimator(RiskModel):
where `alpha` is the shrink parameter and `F` is the shrinking target.
The following shrinking parameters (`alpha`) are supported:
- `lw` [1][2][3]: use Ledoit-Wolf shrinking parameter
- `oas` [4]: use Oracle Approximating Shrinkage shrinking parameter
- float: directly specify the shrink parameter, should be between [0, 1]
- `lw` [1][2][3]: use Ledoit-Wolf shrinking parameter.
- `oas` [4]: use Oracle Approximating Shrinkage shrinking parameter.
- float: directly specify the shrink parameter, should be between [0, 1].
The following shrinking targets (`F`) are supported:
- `const_var` [1][4][5]: assume stocks have the same constant variance and zero correlation
- `const_corr` [2][6]: assume stocks have different variance but equal correlation
- `single_factor` [3][7]: assume single factor model as the shrinking target
- np.ndarray: provide the shrinking targets directly
- `const_var` [1][4][5]: assume stocks have the same constant variance and zero correlation.
- `const_corr` [2][6]: assume stocks have different variance but equal correlation.
- `single_factor` [3][7]: assume single factor model as the shrinking target.
- np.ndarray: provide the shrinking targets directly.
Note:
- The optimal shrinking parameter depends on the selection of the shrinking target.
@@ -402,13 +402,13 @@ class POETCovEstimator(RiskModel):
def __init__(self, num_factors: int = 0, thresh: float = 1.0, thresh_method: str = "soft", **kwargs):
"""
Args:
num_factors (int): number of factors (if set to zero, no factor model will be used)
thresh (float): the positive constant for thresholding
num_factors (int): number of factors (if set to zero, no factor model will be used).
thresh (float): the positive constant for thresholding.
thresh_method (str): thresholding method, which can be
- 'soft': soft thresholding
- 'hard': hard thresholding
- 'scad': scad thresholding
kwargs: see `RiskModel` for more information
- 'soft': soft thresholding.
- 'hard': hard thresholding.
- 'scad': scad thresholding.
kwargs: see `RiskModel` for more information.
"""
super().__init__(**kwargs)