`Qlib` Documentation¶

Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.

Document Structure¶

`Qlib`: Quantitative Platform¶

Introduction¶

Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.

With Qlib, users can easily try their ideas to create better Quant investment strategies.

Framework¶

At the module level, Qlib is a platform that consists of above components. The components are designed as loose-coupled modules and each component could be used stand-alone.

Name	Description
Infrastructure layer	Infrastructure layer provides underlying support for Quant research. DataServer provides high-performance infrastructure for users to manage and retrieve raw data. Trainer provides flexible interface to control the training process of models which enable algorithms controlling the training process.
Workflow layer	Workflow layer covers the whole workflow of quantitative investment. Information Extractor extracts data for models. Forecast Model focuses on producing all kinds of forecast signals (e.g. alpha, risk) for other modules. With these signals Decision Generator will generate the target trading decisions(i.e. portfolio, orders) to be executed by Execution Env (i.e. the trading market). There may be multiple levels of Trading Agent and Execution Env (e.g. an order executor trading agent and intraday order execution environment could behave like an interday trading environment and nested in daily portfolio management trading agent and interday trading environment )
Interface layer	Interface layer tries to present a user-friendly interface for the underlying system. Analyser module will provide users detailed analysis reports of forecasting signals, portfolios and execution results

The modules with hand-drawn style are under development and will be released in the future.
The modules with dashed borders are highly user-customizable and extendible.

Quick Start¶

Introduction¶

This Quick Start guide tries to demonstrate

It’s very easy to build a complete Quant research workflow and try users’ ideas with Qlib.
Though with public data and simple models, machine learning technologies work very well in practical Quant investment.

Installation¶

Users can easily intsall Qlib according to the following steps:

Before installing Qlib from source, users need to install some dependencies:
Clone the repository and install Qlib

To kown more about installation, please refer to Qlib Installation.

Prepare Data¶

Load and prepare data by running the following code:

This dataset is created by public data collected by crawler scripts in scripts/data_collector/, which have been released in the same repository. Users could create the same dataset with it.

To kown more about prepare data, please refer to Data Preparation.

Auto Quant Research Workflow¶

Qlib provides a tool named qrun to run the whole workflow automatically (including building dataset, training models, backtest and evaluation). Users can start an auto quant research workflow and have a graphical reports analysis according to the following steps:

Quant Research Workflow:

Run qrun with a config file of the LightGBM model workflow_config_lightgbm.yaml as following.

Workflow result

The result of qrun is as follows, which is also the typical result of Forecast model(alpha). Please refer to Intraday Trading. for more details about the result.

                                                  risk
excess_return_without_cost mean               0.000605
                           std                0.005481
                           annualized_return  0.152373
                           information_ratio  1.751319
                           max_drawdown      -0.059055
excess_return_with_cost    mean               0.000410
                           std                0.005478
                           annualized_return  0.103265
                           information_ratio  1.187411
                           max_drawdown      -0.075024

To know more about workflow and qrun, please refer to Workflow: Workflow Management.

Graphical Reports Analysis:
- Run examples/workflow_by_code.ipynb with jupyter notebook
  
  Users can have portfolio analysis or prediction score (model prediction) analysis by run examples/workflow_by_code.ipynb.
- Graphical Reports
  
  Users can get graphical reports about the analysis, please refer to Analysis: Evaluation & Results Analysis for more details.

Custom Model Integration¶

Qlib provides a batch of models (such as lightGBM and MLP models) as examples of Forecast Model. In addition to the default model, users can integrate their own custom models into Qlib. If users are interested in the custom model, please refer to Custom Model Integration.

Installation¶

`Qlib` Installation¶

Note

Qlib supports both Windows and Linux. It’s recommended to use Qlib in Linux. Qlib supports Python3, which is up to Python3.8.

Users can easily install Qlib by pip according to the following command:

pip install pyqlib

Also, Users can install Qlib by the source code according to the following steps:

Enter the root directory of Qlib, in which the file setup.py exists.

Then, please execute the following command to install the environment dependencies and install Qlib:

$ pip install numpy
$ pip install --upgrade cython
$ git clone https://github.com/microsoft/qlib.git && cd qlib
$ python setup.py install

Note

It’s recommended to use anaconda/miniconda to setup the environment. Qlib needs lightgbm and pytorch packages, use pip to install them.

Use the following code to make sure the installation successful:

>>> import qlib
>>> qlib.__version__
<LATEST VERSION>

Qlib Initialization¶

Initialization¶

Please follow the steps below to initialize Qlib.

Download and prepare the Data: execute the following command to download stock data. Please pay attention that the data is collected from Yahoo Finance and the data might not be perfect. We recommend users to prepare their own data if they have high-quality datasets. Please refer to Data for more information about customized dataset.

python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn

Please refer to Data Preparation for more information about get_data.py,

Initialize Qlib before calling other APIs: run following code in python.

import qlib
# region in [REG_CN, REG_US]
from qlib.config import REG_CN
provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
qlib.init(provider_uri=provider_uri, region=REG_CN)

Note

Do not import qlib package in the repository directory of Qlib, otherwise, errors may occur.

Parameters¶

Besides provider_uri and region, qlib.init has other parameters. The following are several important parameters of qlib.init:

provider_uri

Type: str. The URI of the Qlib data. For example, it could be the location where the data loaded by get_data.py are stored.
region

Type: str, optional parameter(default: qlib.config.REG_CN).

Currently: qlib.config.REG_US (‘us’) and qlib.config.REG_CN (‘cn’) is supported. Different value of region will result in different stock market mode. - qlib.config.REG_US: US stock market. - qlib.config.REG_CN: China stock market.

Different modes will result in different trading limitations and costs. The region is just shortcuts for defining a batch of configurations. Users can set the key configurations manually if the existing region setting can’t meet their requirements.
redis_host

Type: str, optional parameter(default: “127.0.0.1”), host of redis

The lock and cache mechanism relies on redis.
redis_port

Type: int, optional parameter(default: 6379), port of redis

Note

The value of region should be aligned with the data stored in provider_uri. Currently, scripts/get_data.py only provides China stock market data. If users want to use the US stock market data, they should prepare their own US-stock data in provider_uri and switch to US-stock mode.

Note

If Qlib fails to connect redis via redis_host and redis_port, cache mechanism will not be used! Please refer to Cache for details.

exp_manager

Type: dict, optional parameter, the setting of experiment manager to be used in qlib. Users can specify an experiment manager class, as well as the tracking URI for all the experiments. However, please be aware that we only support input of a dictionary in the following style for exp_manager. For more information about exp_manager, users can refer to Recorder: Experiment Management.

# For example, if you want to set your tracking_uri to a <specific folder>, you can initialize qlib below
qlib.init(provider_uri=provider_uri, region=REG_CN, exp_manager= {
    "class": "MLflowExpManager",
    "module_path": "qlib.workflow.expm",
    "kwargs": {
        "uri": "python_execution_path/mlruns",
        "default_exp_name": "Experiment",
    }
})

mongo
Type: dict, optional parameter, the setting of MongoDB which will be used in some features such as Task Management, with high performance and clustered processing. Users need to follow the steps in installation to install MongoDB firstly and then access it via a URI. Users can access mongodb with credential by setting “task_url” to a string like “mongodb://%s:%s@%s” % (user, pwd, host + “:” + port).
# For example, you can initialize qlib below qlib.init(provider_uri=provider_uri, region=REG_CN, mongo={ "task_url": "mongodb://localhost:27017/", # your mongo url "task_db_name": "rolling_db", # the database name of Task Management })

Data Retrieval¶

Introduction¶

Users can get stock data with Qlib. The following examples demonstrate the basic user interface.

Examples¶

QLib Initialization:

Note

In order to get the data, users need to initialize Qlib with qlib.init first. Please refer to initialization.

If users followed steps in initialization and downloaded the data, they should use the following code to initialize qlib

>> import qlib
>> qlib.init(provider_uri='~/.qlib/qlib_data/cn_data')

Load trading calendar with given time range and frequency:

>> from qlib.data import D
>> D.calendar(start_time='2010-01-01', end_time='2017-12-31', freq='day')[:2]
[Timestamp('2010-01-04 00:00:00'), Timestamp('2010-01-05 00:00:00')]

Parse a given market name into a stock pool config:

>> from qlib.data import D
>> D.instruments(market='all')
{'market': 'all', 'filter_pipe': []}

Load instruments of certain stock pool in the given time range:

>> from qlib.data import D
>> instruments = D.instruments(market='csi300')
>> D.list_instruments(instruments=instruments, start_time='2010-01-01', end_time='2017-12-31', as_list=True)[:6]
['SH600036', 'SH600110', 'SH600087', 'SH600900', 'SH600089', 'SZ000912']

Load dynamic instruments from a base market according to a name filter

>> from qlib.data import D
>> from qlib.data.filter import NameDFilter
>> nameDFilter = NameDFilter(name_rule_re='SH[0-9]{4}55')
>> instruments = D.instruments(market='csi300', filter_pipe=[nameDFilter])
>> D.list_instruments(instruments=instruments, start_time='2015-01-01', end_time='2016-02-15', as_list=True)
['SH600655', 'SH601555']

Load dynamic instruments from a base market according to an expression filter

>> from qlib.data import D
>> from qlib.data.filter import ExpressionDFilter
>> expressionDFilter = ExpressionDFilter(rule_expression='$close>2000')
>> instruments = D.instruments(market='csi300', filter_pipe=[expressionDFilter])
>> D.list_instruments(instruments=instruments, start_time='2015-01-01', end_time='2016-02-15', as_list=True)
['SZ000651', 'SZ000002', 'SH600655', 'SH600570']

For more details about filter, please refer Filter API.

Load features of certain instruments in a given time range:

>> from qlib.data import D
>> instruments = ['SH600000']
>> fields = ['$close', '$volume', 'Ref($close, 1)', 'Mean($close, 3)', '$high-$low']
>> D.features(instruments, fields, start_time='2010-01-01', end_time='2017-12-31', freq='day').head()

                           $close     $volume  Ref($close, 1)  Mean($close, 3)  $high-$low
   instrument  datetime
   SH600000    2010-01-04  86.778313  16162960.0       88.825928        88.061483    2.907631
               2010-01-05  87.433578  28117442.0       86.778313        87.679273    3.235252
               2010-01-06  85.713585  23632884.0       87.433578        86.641825    1.720009
               2010-01-07  83.788803  20813402.0       85.713585        85.645322    3.030487
               2010-01-08  84.730675  16044853.0       83.788803        84.744354    2.047623

Load features of certain stock pool in a given time range:

Note

With cache enabled, the qlib data server will cache data all the time for the requested stock pool and fields, it may take longer to process the request for the first time than that without cache. But after the first time, requests with the same stock pool and fields will hit the cache and be processed faster even the requested time period changes.

>> from qlib.data import D
>> from qlib.data.filter import NameDFilter, ExpressionDFilter
>> nameDFilter = NameDFilter(name_rule_re='SH[0-9]{4}55')
>> expressionDFilter = ExpressionDFilter(rule_expression='$close>Ref($close,1)')
>> instruments = D.instruments(market='csi300', filter_pipe=[nameDFilter, expressionDFilter])
>> fields = ['$close', '$volume', 'Ref($close, 1)', 'Mean($close, 3)', '$high-$low']
>> D.features(instruments, fields, start_time='2010-01-01', end_time='2017-12-31', freq='day').head()

                              $close        $volume  Ref($close, 1)  Mean($close, 3)  $high-$low
   instrument  datetime
   SH600655    2010-01-04  2699.567383  158193.328125     2619.070312      2626.097738  124.580566
               2010-01-08  2612.359619   77501.406250     2584.567627      2623.220133   83.373047
               2010-01-11  2712.982422  160852.390625     2612.359619      2636.636556  146.621582
               2010-01-12  2788.688232  164587.937500     2712.982422      2704.676758  128.413818
               2010-01-13  2790.604004  145460.453125     2788.688232      2764.091553  128.413818

For more details about features, please refer Feature API.

Note

When calling D.features() at the client, use parameter disk_cache=0 to skip dataset cache, use disk_cache=1 to generate and use dataset cache. In addition, when calling at the server, users can use disk_cache=2 to update the dataset cache.

API¶

To know more about how to use the Data, go to API Reference: Data API

Custom Model Integration¶

Introduction¶

Qlib’s Model Zoo includes models such as LightGBM, MLP, LSTM, etc.. These models are examples of Forecast 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.

Define a custom model class, which should be a subclass of the qlib.model.base.Model.
Write a configuration file that describes the path and parameters of the custom model.
Test the custom model.

Custom Model Class¶

The Custom models need to inherit qlib.model.base.Model and override the methods in it.

Override the __init__ method
- Qlib passes the initialized parameters to the __init__ method.
- The hyperparameters of model in the configuration must be consistent with those defined in the __init__ method.
- Code Example: In the following example, the hyperparameters of model in the configuration file should contain parameters such as loss:mse.
def __init__(self, loss='mse', **kwargs): if loss not in {'mse', 'binary'}: raise NotImplementedError self._scorer = mean_squared_error if loss == 'mse' else roc_auc_score self._params.update(objective=loss, **kwargs) self._model = None

Override the fit method

Qlib calls the fit method to train the model.
The parameters must include training feature dataset, which is designed in the interface.
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.

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, y_valid = np.squeeze(y_train.values), np.squeeze(y_valid.values)
    else:
        raise ValueError("LightGBM doesn't support multi-label training")

    dtrain = lgb.Dataset(x_train.values, label=y_train)
    dvalid = lgb.Dataset(x_valid.values, label=y_valid)

    # fit the model
    self.model = lgb.train(
        self.params,
        dtrain,
        num_boost_round=num_boost_round,
        valid_sets=[dtrain, dvalid],
        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 must include the parameter dataset, which will be userd to get the test dataset.
Return the prediction score.
Please refer to Model API 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.

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 finetune method (Optional)

This method is optional to the users. When users want to use this method on their own models, they should inherit the ModelFT base class, which includes the interface of finetune.
The parameters must include the parameter dataset.
Code Example: In the following example, users will use LightGBM as the model and finetune it.

def finetune(self, dataset: DatasetH, num_boost_round=10, verbose_eval=20):
    # Based on existing model and finetune by train more rounds
    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 Workflow document. In order to integrate the custom model into Qlib, users need to modify the “model” field in the configuration file. The configuration describes which models to use and how we can initialize it.

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.

model:
    class: LGBModel
    module_path: qlib.contrib.model.gbdt
    args:
        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

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/benchmarks/LightGBM/workflow_config_lightgbm.yaml, users can run the following command to test the custom model:

cd examples  # Avoid running program under the directory contains `qlib`
qrun benchmarks/LightGBM/workflow_config_lightgbm.yaml

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/workflow_by_code.ipynb.

Reference¶

To know more about Forecast Model, please refer to Forecast Model: Model Training & Prediction and Model API.

Workflow: Workflow Management¶

Introduction¶

The components in Qlib Framework are designed in a loosely-coupled way. Users could build their own Quant research workflow with these components like Example.

Besides, Qlib provides more user-friendly interfaces named qrun to automatically run the whole workflow defined by configuration. Running the whole workflow is called an execution. With qrun, user can easily start an execution, which includes the following steps:

Data
- Loading
- Processing
- Slicing
Model
- Training and inference
- Saving & loading
Evaluation
- Forecast signal analysis
- Backtest

For each execution, Qlib has a complete system to tracking all the information as well as artifacts generated during training, inference and evaluation phase. For more information about how Qlib handles this, please refer to the related document: Recorder: Experiment Management.

Complete Example¶

Before getting into details, here is a complete example of qrun, which defines the workflow in typical Quant research. Below is a typical config file of qrun.

qlib_init:
    provider_uri: "~/.qlib/qlib_data/cn_data"
    region: cn
market: &market csi300
benchmark: &benchmark SH000300
data_handler_config: &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
port_analysis_config: &port_analysis_config
    strategy:
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy.strategy
        kwargs:
            topk: 50
            n_drop: 5
            signal:
                - <MODEL>
                - <DATASET>
    backtest:
        limit_threshold: 0.095
        account: 100000000
        benchmark: *benchmark
        deal_price: close
        open_cost: 0.0005
        close_cost: 0.0015
        min_cost: 5
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]
    record:
        - class: SignalRecord
          module_path: qlib.workflow.record_temp
          kwargs: {}
        - class: PortAnaRecord
          module_path: qlib.workflow.record_temp
          kwargs:
              config: *port_analysis_config

After saving the config into configuration.yaml, users could start the workflow and test their ideas with a single command below.

qrun configuration.yaml

If users want to use qrun under debug mode, please use the following command:

python -m pdb qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml

Note

qrun will be placed in your $PATH directory when installing Qlib.

Note

The symbol & in yaml file stands for an anchor of a field, which is useful when another fields include this parameter as part of the value. Taking the configuration file above as an example, users can directly change the value of market and benchmark without traversing the entire configuration file.

Configuration File¶

Let’s get into details of qrun in this section.

Before using qrun, users need to prepare a configuration file. The following content shows how to prepare each part of the configuration file.

Qlib Init Section¶

At first, the configuration file needs to contain several basic parameters which will be used for qlib initialization.

provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn

The meaning of each field is as follows:

provider_uri

Type: str. The URI of the Qlib data. For example, it could be the location where the data loaded by get_data.py are stored.
region
- If region == “us”, Qlib will be initialized in US-stock mode.
- If region == “cn”, Qlib will be initialized in China-stock mode.
Note

The value of region should be aligned with the data stored in provider_uri.

Task Section¶

The task field in the configuration corresponds to a task, which contains the parameters of three different subsections: Model, Dataset and Record.

Model Section¶

In the task field, the model section describes the parameters of the model to be used for training and inference. For more information about the base Model class, please refer to Qlib Model.

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

The meaning of each field is as follows:

class

Type: str. The name for the model class.
module_path

Type: str. The path for the model in qlib.
kwargs

The keywords arguments for the model. Please refer to the specific model implementation for more information: models.

Note

Qlib provides a util named: init_instance_by_config to initialize any class inside Qlib with the configuration includes the fields: class, module_path and kwargs.

Dataset Section¶

The dataset field describes the parameters for the Dataset module in Qlib as well those for the module DataHandler. For more information about the Dataset module, please refer to Qlib Model.

The keywords arguments configuration of the DataHandler is as follows:

data_handler_config: &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

Users can refer to the document of DataHandler for more information about the meaning of each field in the configuration.

Here is the configuration for the Dataset module which will take care of data preprossing and slicing during the training and testing phase.

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]

Record Section¶

The record field is about the parameters the Record module in Qlib. Record is responsible for tracking training process and results such as information Coefficient (IC) and backtest in a standard format.

The following script is the configuration of backtest and the strategy used in backtest:

port_analysis_config: &port_analysis_config
    strategy:
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy.strategy
        kwargs:
            topk: 50
            n_drop: 5
            signal:
                - <MODEL>
                - <DATASET>
    backtest:
        limit_threshold: 0.095
        account: 100000000
        benchmark: *benchmark
        deal_price: close
        open_cost: 0.0005
        close_cost: 0.0015
        min_cost: 5

For more information about the meaning of each field in configuration of strategy and backtest, users can look up the documents: Strategy and Backtest.

Here is the configuration details of different Record Template such as SignalRecord and PortAnaRecord:

record:
    - class: SignalRecord
      module_path: qlib.workflow.record_temp
      kwargs: {}
    - class: PortAnaRecord
      module_path: qlib.workflow.record_temp
      kwargs:
        config: *port_analysis_config

For more information about the Record module in Qlib, user can refer to the related document: Record.

Data Layer: Data Framework & Usage¶

Introduction¶

Data Layer provides user-friendly APIs to manage and retrieve data. It provides high-performance data infrastructure.

It is designed for quantitative investment. For example, users could build formulaic alphas with Data Layer easily. Please refer to Building Formulaic Alphas for more details.

The introduction of Data Layer includes the following parts.

Data Preparation
Data API
Data Loader
Data Handler
Dataset
Cache
Data and Cache File Structure

Data Preparation¶

Qlib Format Data¶

We’ve specially designed a data structure to manage financial data, please refer to the File storage design section in Qlib paper for detailed information. Such data will be stored with filename suffix .bin (We’ll call them .bin file, .bin format, or qlib format). .bin file is designed for scientific computing on finance data.

Qlib provides two different off-the-shelf datasets, which can be accessed through this link:

Dataset	US Market	China Market
Alpha360	√	√
Alpha158	√	√

Also, Qlib provides a high-frequency dataset. Users can run a high-frequency dataset example through this link.

Qlib Format Dataset¶

Qlib has provided an off-the-shelf dataset in .bin format, users could use the script scripts/get_data.py to download the China-Stock dataset as follows.

# download 1d
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn

# download 1min
python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_cn_1min --region cn --interval 1min

In addition to China-Stock data, Qlib also includes a US-Stock dataset, which can be downloaded with the following command:

python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/us_data --region us

After running the above command, users can find china-stock and us-stock data in Qlib format in the ~/.qlib/qlib_data/cn_data directory and ~/.qlib/qlib_data/us_data directory respectively.

Qlib also provides the scripts in scripts/data_collector to help users crawl the latest data on the Internet and convert it to qlib format.

When Qlib is initialized with this dataset, users could build and evaluate their own models with it. Please refer to Initialization for more details.

Automatic update of daily frequency data¶

It is recommended that users update the data manually once (–trading_date 2021-05-25) and then set it to update automatically.

For more information refer to: yahoo collector
Automatic update of data to the “qlib” directory each trading day(Linux)
use crontab: crontab -e
set up timed tasks:
* * * * 1-5 python <script path> update_data_to_bin --qlib_data_1d_dir <user data dir>
script path: scripts/data_collector/yahoo/collector.py
Manual update of data
python scripts/data_collector/yahoo/collector.py update_data_to_bin --qlib_data_1d_dir <user data dir> --trading_date <start date> --end_date <end date>
trading_date: start of trading day

end_date: end of trading day(not included)

Converting CSV Format into Qlib Format¶

Qlib has provided the script scripts/dump_bin.py to convert any data in CSV format into .bin files (Qlib format) as long as they are in the correct format.

Besides downloading the prepared demo data, users could download demo data directly from the Collector as follows for reference to the CSV format. Here are some example:

for daily data:

python scripts/get_data.py csv_data_cn --target_dir ~/.qlib/csv_data/cn_data

for 1min data:

python scripts/data_collector/yahoo/collector.py download_data --source_dir ~/.qlib/stock_data/source/cn_1min --region CN --start 2021-05-20 --end 2021-05-23 --delay 0.1 --interval 1min --limit_nums 10

Users can also provide their own data in CSV format. However, the CSV data must satisfies following criterions:

CSV file is named after a specific stock or the CSV file includes a column of the stock name
- Name the CSV file after a stock: SH600000.csv, AAPL.csv (not case sensitive).
- CSV file includes a column of the stock name. User must specify the column name when dumping the data. Here is an example:
  python scripts/dump_bin.py dump_all ... --symbol_field_name symbol
  
  where the data are in the following format:
CSV file must includes a column for the date, and when dumping the data, user must specify the date column name. Here is an example:
python scripts/dump_bin.py dump_all ... --date_field_name date
where the data are in the following format:

Supposed that users prepare their CSV format data in the directory ~/.qlib/csv_data/my_data, they can run the following command to start the conversion.

python scripts/dump_bin.py dump_all --csv_path  ~/.qlib/csv_data/my_data --qlib_dir ~/.qlib/qlib_data/my_data --include_fields open,close,high,low,volume,factor

For other supported parameters when dumping the data into .bin file, users can refer to the information by running the following commands:

python dump_bin.py dump_all --help

After conversion, users can find their Qlib format data in the directory ~/.qlib/qlib_data/my_data.

Note

The arguments of –include_fields should correspond with the column names of CSV files. The columns names of dataset provided by Qlib should include open, close, high, low, volume and factor at least.

open

The adjusted opening price
close

The adjusted closing price
high

The adjusted highest price
low

The adjusted lowest price
volume

The adjusted trading volume
factor

The Restoration factor. Normally, factor = adjusted_price / original_price, adjusted price reference: split adjusted

In the convention of Qlib data processing, open, close, high, low, volume, money and factor will be set to NaN if the stock is suspended. If you want to use your own alpha-factor which can’t be calculate by OCHLV, like PE, EPS and so on, you could add it to the CSV files with OHCLV together and then dump it to the Qlib format data.

Stock Pool (Market)¶

Qlib defines stock pool as stock list and their date ranges. Predefined stock pools (e.g. csi300) may be imported as follows.

python collector.py --index_name CSI300 --qlib_dir <user qlib data dir> --method parse_instruments

Multiple Stock Modes¶

Qlib now provides two different stock modes for users: China-Stock Mode & US-Stock Mode. Here are some different settings of these two modes:

Region	Trade Unit	Limit Threshold
China	100	0.099
US	1	None

The trade unit defines the unit number of stocks can be used in a trade, and the limit threshold defines the bound set to the percentage of ups and downs of a stock.

If users use Qlib in china-stock mode, china-stock data is required. Users can use Qlib in china-stock mode according to the following steps:
- Download china-stock in qlib format, please refer to section Qlib Format Dataset.
- Initialize Qlib in china-stock mode
  
  Supposed that users download their Qlib format data in the directory ~/.qlib/qlib_data/cn_data. Users only need to initialize Qlib as follows.
  
  from qlib.config import REG_CN qlib.init(provider_uri='~/.qlib/qlib_data/cn_data', region=REG_CN)
If users use Qlib in US-stock mode, US-stock data is required. Qlib also provides a script to download US-stock data. Users can use Qlib in US-stock mode according to the following steps:
- Download us-stock in qlib format, please refer to section Qlib Format Dataset.
- Initialize Qlib in US-stock mode
  
  Supposed that users prepare their Qlib format data in the directory ~/.qlib/qlib_data/us_data. Users only need to initialize Qlib as follows.
  
  from qlib.config import REG_US qlib.init(provider_uri='~/.qlib/qlib_data/us_data', region=REG_US)

Note

PRs for new data source are highly welcome! Users could commit the code to crawl data as a PR like the examples here. And then we will use the code to create data cache on our server which other users could use directly.

Data API¶

Data Retrieval¶

Users can use APIs in qlib.data to retrieve data, please refer to Data Retrieval.

Feature¶

Qlib provides Feature and ExpressionOps to fetch the features according to users’ needs.

Feature

Load data from the data provider. User can get the features like $high, $low, $open, $close, .etc, which should correspond with the arguments of –include_fields, please refer to section Converting CSV Format into Qlib Format.
ExpressionOps

ExpressionOps will use operator for feature construction. To know more about Operator, please refer to Operator API. Also, Qlib supports users to define their own custom Operator, an example has been given in tests/test_register_ops.py.

To know more about Feature, please refer to Feature API.

Filter¶

Qlib provides NameDFilter and ExpressionDFilter to filter the instruments according to users’ needs.

NameDFilter

Name dynamic instrument filter. Filter the instruments based on a regulated name format. A name rule regular expression is required.
ExpressionDFilter
Expression dynamic instrument filter. Filter the instruments based on a certain expression. An expression rule indicating a certain feature field is required.
- basic features filter: rule_expression = ‘$close/$open>5’
- cross-sectional features filter : rule_expression = ‘$rank($close)<10’
- time-sequence features filter: rule_expression = ‘$Ref($close, 3)>100’

Here is a simple example showing how to use filter in a basic Qlib workflow configuration file:

filter: &filter
    filter_type: ExpressionDFilter
    rule_expression: "Ref($close, -2) / Ref($close, -1) > 1"
    filter_start_time: 2010-01-01
    filter_end_time: 2010-01-07
    keep: False

data_handler_config: &data_handler_config
    start_time: 2010-01-01
    end_time: 2021-01-22
    fit_start_time: 2010-01-01
    fit_end_time: 2015-12-31
    instruments: *market
    filter_pipe: [*filter]

To know more about Filter, please refer to Filter API.

Reference¶

To know more about Data API, please refer to Data API.

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.

QlibDataLoader¶

The QlibDataLoader class in Qlib is such an interface that allows users to load raw data from the Qlib data source.

StaticDataLoader¶

The StaticDataLoader class in Qlib is such an interface that allows users to load raw data from file or as provided.

Interface¶

Here are some interfaces of the QlibDataLoader class:

class qlib.data.dataset.loader.DataLoader¶

DataLoader is designed for loading raw data from original data source.

load(instruments, start_time=None, end_time=None) → pandas.core.frame.DataFrame¶

load the data as pd.DataFrame.

Example of the data (The multi-index of the columns is optional.):

                        feature                                                             label
                        $close     $volume     Ref($close, 1)  Mean($close, 3)  $high-$low  LABEL0
datetime    instrument
2010-01-04  SH600000    81.807068  17145150.0       83.737389        83.016739    2.741058  0.0032
            SH600004    13.313329  11800983.0       13.313329        13.317701    0.183632  0.0042
            SH600005    37.796539  12231662.0       38.258602        37.919757    0.970325  0.0289

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:	data load from the under layer source
Return type:	pd.DataFrame

API¶

To know more about Data Loader, please refer to Data Loader API.

Data Handler¶

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 for more details.

DataHandlerLP¶

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.

In order to achieve so, Qlib provides a base class qlib.data.dataset.DataHandlerLP. The core idea of this class is that: we will have some leanable Processors which can learn the parameters of data processing(e.g., parameters for zscore normalization). When new data comes in, these trained Processors can then process the new data and thus processing real-time data in an efficient way becomes possible. More information about Processors will be listed in the next subsection.

Interface¶

Here are some important interfaces that DataHandlerLP provides:

class qlib.data.dataset.handler.DataHandlerLP(instruments=None, start_time=None, end_time=None, data_loader: Union[dict, str, qlib.data.dataset.loader.DataLoader] = None, infer_processors: List[T] = [], learn_processors: List[T] = [], shared_processors: List[T] = [], process_type='append', drop_raw=False, **kwargs)¶

DataHandler with (L)earnable (P)rocessor

Tips to improving the performance of data handler - To reduce the memory cost

drop_raw=True: this will modify the data inplace on raw data;

__init__(instruments=None, start_time=None, end_time=None, data_loader: Union[dict, str, qlib.data.dataset.loader.DataLoader] = None, infer_processors: List[T] = [], learn_processors: List[T] = [], shared_processors: List[T] = [], process_type='append', drop_raw=False, **kwargs)¶

Parameters:

infer_processors (list) –
- list of <description info> of processors to generate data for inference
- example of <description info>:
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 )
drop_raw (bool) – Whether to drop the raw data

fit()¶: fit data without processing the data

fit_process_data()¶

fit and process data

The input of the fit will be the output of the previous processor

process_data(with_fit: bool = False)¶

process_data data. Fun processor.fit if necessary

Notation: (data) [processor]

# data processing flow of self.process_type == DataHandlerLP.PTYPE_I (self._data)-[shared_processors]-(_shared_df)-[learn_processors]-(_learn_df)

-[infer_processors]-(_infer_df)

# data processing flow of self.process_type == DataHandlerLP.PTYPE_A (self._data)-[shared_processors]-(_shared_df)-[infer_processors]-(_infer_df)-[learn_processors]-(_learn_df)

Parameters:	with_fit (bool) – The input of the fit will be the output of the previous processor

config(processor_kwargs: dict = None, **kwargs)¶

configuration of data. # what data to be loaded from data source

This method will be used when loading pickled handler from dataset. The data will be initialized with different time range.

setup_data(init_type: str = 'fit_seq', **kwargs)¶

Set up the data in case of running initialization for multiple time

Parameters:	init_type (str) – The type IT_* listed above. enable_cache (bool) – default value is false: if enable_cache == True: the processed data will be saved on disk, and handler will load the cached data from the disk directly when we call init next time

fetch(selector: Union[pandas._libs.tslibs.timestamps.Timestamp, slice, str] = slice(None, None, None), level: Union[str, int] = 'datetime', col_set='__all', data_key: str = 'infer', proc_func: Callable = None) → pandas.core.frame.DataFrame¶

fetch 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_. proc_func* (Callable) – please refer to the doc of DataHandler.fetch
Returns:
Return type:	pd.DataFrame

get_cols(col_set='__all', data_key: str = 'infer') → list¶

get 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:	list of column names
Return type:	list

classmethod cast(handler: qlib.data.dataset.handler.DataHandlerLP) → qlib.data.dataset.handler.DataHandlerLP¶

Motivation - A user create a datahandler in his customized package. Then he want to share the processed handler to other users without introduce the package dependency and complicated data processing logic. - This class make it possible by casting the class to DataHandlerLP and only keep the processed data

Parameters:	handler (DataHandlerLP) – A subclass of DataHandlerLP
Returns:	the converted processed data
Return type:	DataHandlerLP

If users want to load features and labels by config, users can define a new handler and call the static method parse_config_to_fields of qlib.contrib.data.handler.Alpha158.

Also, users can pass qlib.contrib.data.processor.ConfigSectionProcessor that provides some preprocess methods for features defined by config into the new handler.

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.
RobustZScoreNorm: processor that applies robust z-score normalization.
CSZScoreNorm: processor that applies cross sectional z-score normalization.
CSRankNorm: processor that applies cross sectional rank normalization.
CSZFillna: processor that fills N/A values in a cross sectional way by the mean of the column.

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).

To know more about Processor, please refer to Processor API.

Example¶

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 qrun, please refer to Workflow: Workflow Management

Qlib provides implemented data handler Alpha158. The following example shows how to run Alpha158 as a single module.

Note

Users need to initialize Qlib with qlib.init first, please refer to initialization.

import qlib
from qlib.contrib.data.handler import Alpha158

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",
}

if __name__ == "__main__":
    qlib.init()
    h = Alpha158(**data_handler_config)

    # get all the columns of the data
    print(h.get_cols())

    # fetch all the labels
    print(h.fetch(col_set="label"))

    # fetch all the features
    print(h.fetch(col_set="feature"))

Note

In the Alpha158, Qlib uses the label Ref($close, -2)/Ref($close, -1) - 1 that means the change from T+1 to T+2, rather than Ref($close, -1)/$close - 1, of which the reason is that when getting the T day close price of a china stock, the stock can be bought on T+1 day and sold on T+2 day.

API¶

To know more about Data Handler, please refer to Data Handler API.

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 flexibility 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 MLP will break down on such data.

If user’s model need process its data in a different way, user could implement his own Dataset class. If the model’s data processing is not special, DatasetH can be used directly.

The DatasetH class is the dataset with Data Handler. Here is the most important interface of the class:

class qlib.data.dataset.__init__.DatasetH(handler: Union[Dict[KT, VT], qlib.data.dataset.handler.DataHandler], segments: Dict[str, Tuple], **kwargs)¶

Dataset with Data(H)andler

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.

__init__(handler: Union[Dict[KT, VT], qlib.data.dataset.handler.DataHandler], segments: Dict[str, Tuple], **kwargs)¶

Setup the underlying data.

Parameters:	handler (Union[dict, DataHandler]) – handler could be: instance of DataHandler config of DataHandler. Please refer to DataHandler segments (dict) – Describe the options to segment the data. Here are some examples:

config(handler_kwargs: dict = None, **kwargs)¶

Initialize the DatasetH

Parameters:	handler_kwargs (dict) – Config of DataHandler, which could include the following arguments: arguments of DataHandler.conf_data, such as ‘instruments’, ‘start_time’ and ‘end_time’. kwargs (dict) – Config of DatasetH, such as segments : dict Config of segments which is same as ‘segments’ in self.__init__

setup_data(handler_kwargs: dict = None, **kwargs)¶

Setup the Data

Parameters:

handler_kwargs (dict) –

init arguments of DataHandler, which could include the following arguments:

init_type : Init Type of Handler
enable_cache : whether to enable cache

prepare(segments: Union[List[str], Tuple[str], str, slice], col_set='__all', data_key='infer', **kwargs) → Union[List[pandas.core.frame.DataFrame], pandas.core.frame.DataFrame]¶

Prepare the data for learning and inference.

Parameters:	segments (Union[List[Text], Tuple[Text], Text, 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. kwargs – The parameters that kwargs may contain: flt_col : str It only exists in TSDatasetH, can be used to add a column of data(True or False) to filter data. This parameter is only supported when it is an instance of TSDatasetH.
Returns:
Return type:	Union[List[pd.DataFrame], pd.DataFrame]
Raises:	NotImplementedError:

API¶

To know more about Dataset, please refer to Dataset API.

Cache¶

Cache is an optional module that helps accelerate providing data by saving some frequently-used data as cache file. Qlib provides a Memcache class to cache the most-frequently-used data in memory, an inheritable ExpressionCache class, and an inheritable DatasetCache class.

Global Memory Cache¶

Memcache is a global memory cache mechanism that composes of three MemCacheUnit instances to cache Calendar, Instruments, and Features. The MemCache is defined globally in cache.py as H. Users can use H[‘c’], H[‘i’], H[‘f’] to get/set memcache.

class qlib.data.cache.MemCacheUnit(*args, **kwargs)¶

Memory Cache Unit.

__init__(*args, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

limited¶: whether memory cache is limited

class qlib.data.cache.MemCache(mem_cache_size_limit=None, limit_type='length')¶

Memory cache.

__init__(mem_cache_size_limit=None, limit_type='length')¶

Parameters:	mem_cache_size_limit (cache max size.) – limit_type (length or sizeof; length(call fun: len), size(call fun: sys.getsizeof)) –

ExpressionCache¶

ExpressionCache is a cache mechanism that saves expressions such as Mean($close, 5). Users can inherit this base class to define their own cache mechanism that saves expressions according to the following steps.

Override self._uri method to define how the cache file path is generated
Override self._expression method to define what data will be cached and how to cache it.

The following shows the details about the interfaces:

class qlib.data.cache.ExpressionCache(provider)¶

Expression cache mechanism base class.

This class is used to wrap expression provider with self-defined expression cache mechanism.

Note

Override the _uri and _expression method to create your own expression cache mechanism.

expression(instrument, field, start_time, end_time, freq)¶: Get expression data.

Note

Same interface as expression method in expression provider

update(cache_uri: Union[str, pathlib.Path], freq: str = 'day')¶

Update expression cache to latest calendar.

Overide this method to define how to update expression cache corresponding to users’ own cache mechanism.

Parameters:	cache_uri (str or Path) – the complete uri of expression cache file (include dir path). freq (str) –
Returns:	0(successful update)/ 1(no need to update)/ 2(update failure).
Return type:	int

Qlib has currently provided implemented disk cache DiskExpressionCache which inherits from ExpressionCache . The expressions data will be stored in the disk.

DatasetCache¶

DatasetCache is a cache mechanism that saves datasets. A certain dataset is regulated by a stock pool configuration (or a series of instruments, though not recommended), a list of expressions or static feature fields, the start time, and end time for the collected features and the frequency. Users can inherit this base class to define their own cache mechanism that saves datasets according to the following steps.

Override self._uri method to define how their cache file path is generated
Override self._expression method to define what data will be cached and how to cache it.

The following shows the details about the interfaces:

class qlib.data.cache.DatasetCache(provider)¶

Dataset cache mechanism base class.

This class is used to wrap dataset provider with self-defined dataset cache mechanism.

Note

Override the _uri and _dataset method to create your own dataset cache mechanism.

dataset(instruments, fields, start_time=None, end_time=None, freq='day', disk_cache=1, inst_processors=[])¶: Get feature dataset.

Note

Same interface as dataset method in dataset provider

Note

The server use redis_lock to make sure read-write conflicts will not be triggered

but client readers are not considered.

update(cache_uri: Union[str, pathlib.Path], freq: str = 'day')¶

Update dataset cache to latest calendar.

Overide this method to define how to update dataset cache corresponding to users’ own cache mechanism.

Parameters:	cache_uri (str or Path) – the complete uri of dataset cache file (include dir path). freq (str) –
Returns:	0(successful update)/ 1(no need to update)/ 2(update failure)
Return type:	int

static cache_to_origin_data(data, fields)¶

cache data to origin data

Parameters:	data – pd.DataFrame, cache data. fields – feature fields.
Returns:	pd.DataFrame.

static normalize_uri_args(instruments, fields, freq)¶: normalize uri args

Qlib has currently provided implemented disk cache DiskDatasetCache which inherits from DatasetCache . The datasets’ data will be stored in the disk.

Data and Cache File Structure¶

We’ve specially designed a file structure to manage data and cache, please refer to the File storage design section in Qlib paper for detailed information. The file structure of data and cache is listed as follows.

- data/
    [raw data] updated by data providers
    - calendars/
        - day.txt
    - instruments/
        - all.txt
        - csi500.txt
        - ...
    - features/
        - sh600000/
            - open.day.bin
            - close.day.bin
            - ...
        - ...
    [cached data] updated when raw data is updated
    - calculated features/
        - sh600000/
            - [hash(instrtument, field_expression, freq)]
                - all-time expression -cache data file
                - .meta : an assorted meta file recording the instrument name, field name, freq, and visit times
        - ...
    - cache/
        - [hash(stockpool_config, field_expression_list, freq)]
            - all-time Dataset-cache data file
            - .meta : an assorted meta file recording the stockpool config, field names and visit times
            - .index : an assorted index file recording the line index of all calendars
        - ...

Forecast Model: Model Training & Prediction¶

Introduction¶

Forecast Model is designed to make the prediction score about stocks. Users can use the Forecast Model in an automatic workflow by qrun, please refer to Workflow: Workflow Management.

Because the components in Qlib are designed in a loosely-coupled way, Forecast Model can be used as an independent module also.

Base Class & Interface¶

Qlib provides a base class qlib.model.base.Model from which all models should inherit.

The base class provides the following interfaces:

class qlib.model.base.Model¶

Learnable Models

fit(dataset: qlib.data.dataset.Dataset)¶

Learn model from the base model

Note

The attribute names of learned model should not start with ‘_’. So that the model could be dumped to disk.

The following code example shows how to retrieve x_train, y_train and w_train from the dataset:

# 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"]

# 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)

Parameters:	dataset (Dataset) – dataset will generate the processed data from model training.

predict(dataset: qlib.data.dataset.Dataset, segment: Union[str, slice] = 'test') → object¶

give prediction given Dataset

Parameters:	dataset (Dataset) – dataset will generate the processed dataset from model training. segment (Text or slice) – dataset will use this segment to prepare data. (default=test)
Returns:
Return type:	Prediction results with certain type such as pandas.Series.

Qlib also provides a base class qlib.model.base.ModelFT, which includes the method for finetuning the model.

For other interfaces such as finetune, please refer to Model API.

Example¶

Qlib’s Model Zoo includes models such as LightGBM, MLP, LSTM, etc.. These models are treated as the baselines of Forecast Model. The following steps show how to run`` LightGBM`` as an independent module.

Initialize Qlib with qlib.init first, please refer to Initialization.

Run the following code to get the prediction score pred_score

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

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,
}

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"])

# 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()

Note

Alpha158 is the data handler provided by Qlib, please refer to Data Handler. SignalRecord is the Record Template in Qlib, please refer to Workflow.

Also, the above example has been given in examples/train_backtest_analyze.ipynb.

Custom Model¶

Qlib supports custom models. If users are interested in customizing their own models and integrating the models into Qlib, please refer to Custom Model Integration.

API¶

Please refer to Model API.

Portfolio Strategy: Portfolio Management¶

Introduction¶

Portfolio Strategy is designed to adopt different portfolio strategies, which means that users can adopt different algorithms to generate investment portfolios based on the prediction scores of the Forecast Model. Users can use the Portfolio Strategy in an automatic workflow by Workflow module, please refer to Workflow: Workflow Management.

Because the components in Qlib are designed in a loosely-coupled way, Portfolio Strategy can be used as an independent module also.

Qlib provides several implemented portfolio strategies. Also, Qlib supports custom strategy, users can customize strategies according to their own requirements.

After users specifying the models(forecasting signals) and strategies, running backtest will help users to check the performance of a custom model(forecasting signals)/strategy.

Base Class & Interface¶

BaseStrategy¶

Qlib provides a base class qlib.contrib.strategy.BaseStrategy. All strategy classes need to inherit the base class and implement its interface.

get_risk_degree

Return the proportion of your total value you will use in investment. Dynamically risk_degree will result in Market timing.
generate_order_list

Return the order list.

Users can inherit BaseStrategy to customize their strategy class.

WeightStrategyBase¶

Qlib also provides a class qlib.contrib.strategy.WeightStrategyBase that is a subclass of BaseStrategy.

WeightStrategyBase only focuses on the target positions, and automatically generates an order list based on positions. It provides the generate_target_weight_position interface.

generate_target_weight_position
- According to the current position and trading date to generate the target position. The cash is not considered in the output weight distribution.
- Return the target position.
Note

Here the target position means the target percentage of total assets.

WeightStrategyBase implements the interface generate_order_list, whose processions is as follows.

Call generate_target_weight_position method to generate the target position.
Generate the target amount of stocks from the target position.
Generate the order list from the target amount

Users can inherit WeightStrategyBase and implement the interface generate_target_weight_position to customize their strategy class, which only focuses on the target positions.

Implemented Strategy¶

Qlib provides a implemented strategy classes named TopkDropoutStrategy.

TopkDropoutStrategy¶

TopkDropoutStrategy is a subclass of BaseStrategy and implement the interface generate_order_list whose process is as follows.

Adopt the Topk-Drop algorithm to calculate the target amount of each stock
Note

Topk-Drop algorithm：
- Topk: The number of stocks held
- Drop: The number of stocks sold on each trading day
Currently, the number of held stocks is Topk. On each trading day, the Drop number of held stocks with the worst prediction score will be sold, and the same number of unheld stocks with the best prediction score will be bought.

TopkDrop algorithm sells Drop stocks every trading day, which guarantees a fixed turnover rate.
Generate the order list from the target amount

Usage & Example¶

First, user can create a model to get trading signals(the variable name is pred_score in following cases).

Prediction Score¶

The prediction score is a pandas DataFrame. Its index is <datetime(pd.Timestamp), instrument(str)> and it must contains a score column.

A prediction sample is shown as follows.

  datetime instrument     score
2019-01-04   SH600000 -0.505488
2019-01-04   SZ002531 -0.320391
2019-01-04   SZ000999  0.583808
2019-01-04   SZ300569  0.819628
2019-01-04   SZ001696 -0.137140
             ...            ...
2019-04-30   SZ000996 -1.027618
2019-04-30   SH603127  0.225677
2019-04-30   SH603126  0.462443
2019-04-30   SH603133 -0.302460
2019-04-30   SZ300760 -0.126383

Forecast Model module can make predictions, please refer to Forecast Model: Model Training & Prediction.

Running backtest¶

In most cases, users could backtest their portfolio management strategy with backtest_daily.

from pprint import pprint

import qlib
import pandas as pd
from qlib.utils.time import Freq
from qlib.utils import flatten_dict
from qlib.contrib.evaluate import backtest_daily
from qlib.contrib.evaluate import risk_analysis
from qlib.contrib.strategy import TopkDropoutStrategy

# init qlib
qlib.init(provider_uri=<qlib data dir>)

CSI300_BENCH = "SH000300"
STRATEGY_CONFIG = {
    "topk": 50,
    "n_drop": 5,
    # pred_score, pd.Series
    "signal": pred_score,
}


strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
report_normal, positions_normal = backtest_daily(
    start_time="2017-01-01", end_time="2020-08-01", strategy=strategy_obj
)
analysis = dict()
analysis["excess_return_without_cost"] = risk_analysis(
    report_normal["return"] - report_normal["bench"], freq=analysis_freq
)
analysis["excess_return_with_cost"] = risk_analysis(
    report_normal["return"] - report_normal["bench"] - report_normal["cost"], freq=analysis_freq
)

analysis_df = pd.concat(analysis)  # type: pd.DataFrame
pprint(analysis_df)

If users would like to control their strategies in a more detailed(e.g. users have a more advanced version of executor), user could follow this example.

from pprint import pprint

import qlib
import pandas as pd
from qlib.utils.time import Freq
from qlib.utils import flatten_dict
from qlib.backtest import backtest, executor
from qlib.contrib.evaluate import risk_analysis
from qlib.contrib.strategy import TopkDropoutStrategy

# init qlib
qlib.init(provider_uri=<qlib data dir>)

CSI300_BENCH = "SH000300"
FREQ = "day"
STRATEGY_CONFIG = {
    "topk": 50,
    "n_drop": 5,
    # pred_score, pd.Series
    "signal": pred_score,
}

EXECUTOR_CONFIG = {
    "time_per_step": "day",
    "generate_portfolio_metrics": True,
}

backtest_config = {
    "start_time": "2017-01-01",
    "end_time": "2020-08-01",
    "account": 100000000,
    "benchmark": CSI300_BENCH,
    "exchange_kwargs": {
        "freq": FREQ,
        "limit_threshold": 0.095,
        "deal_price": "close",
        "open_cost": 0.0005,
        "close_cost": 0.0015,
        "min_cost": 5,
    },
}

# strategy object
strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
# executor object
executor_obj = executor.SimulatorExecutor(**EXECUTOR_CONFIG)
# backtest
portfolio_metric_dict, indicator_dict = backtest(executor=executor_obj, strategy=strategy_obj, **backtest_config)
analysis_freq = "{0}{1}".format(*Freq.parse(FREQ))
# backtest info
report_normal, positions_normal = portfolio_metric_dict.get(analysis_freq)

# analysis
analysis = dict()
analysis["excess_return_without_cost"] = risk_analysis(
    report_normal["return"] - report_normal["bench"], freq=analysis_freq
)
analysis["excess_return_with_cost"] = risk_analysis(
    report_normal["return"] - report_normal["bench"] - report_normal["cost"], freq=analysis_freq
)

analysis_df = pd.concat(analysis)  # type: pd.DataFrame
# log metrics
analysis_dict = flatten_dict(analysis_df["risk"].unstack().T.to_dict())
# print out results
pprint(f"The following are analysis results of benchmark return({analysis_freq}).")
pprint(risk_analysis(report_normal["bench"], freq=analysis_freq))
pprint(f"The following are analysis results of the excess return without cost({analysis_freq}).")
pprint(analysis["excess_return_without_cost"])
pprint(f"The following are analysis results of the excess return with cost({analysis_freq}).")
pprint(analysis["excess_return_with_cost"])

Result¶

The backtest results are in the following form:

                                                  risk
excess_return_without_cost mean               0.000605
                           std                0.005481
                           annualized_return  0.152373
                           information_ratio  1.751319
                           max_drawdown      -0.059055
excess_return_with_cost    mean               0.000410
                           std                0.005478
                           annualized_return  0.103265
                           information_ratio  1.187411
                           max_drawdown      -0.075024

excess_return_without_cost
- mean
  
  Mean value of the CAR (cumulative abnormal return) without cost
- std
  
  The Standard Deviation of CAR (cumulative abnormal return) without cost.
- annualized_return
  
  The Annualized Rate of CAR (cumulative abnormal return) without cost.
- information_ratio
  
  The Information Ratio without cost. please refer to Information Ratio – IR.
- max_drawdown
  
  The Maximum Drawdown of CAR (cumulative abnormal return) without cost, please refer to Maximum Drawdown (MDD).
excess_return_with_cost
- mean
  
  Mean value of the CAR (cumulative abnormal return) series with cost
- std
  
  The Standard Deviation of CAR (cumulative abnormal return) series with cost.
- annualized_return
  
  The Annualized Rate of CAR (cumulative abnormal return) with cost.
- information_ratio
  
  The Information Ratio with cost. please refer to Information Ratio – IR.
- max_drawdown
  
  The Maximum Drawdown of CAR (cumulative abnormal return) with cost, please refer to Maximum Drawdown (MDD).

Reference¶

To know more about the prediction score pred_score output by Forecast Model, please refer to Forecast Model: Model Training & Prediction.

Design of Nested Decision Execution Framework for High-Frequency Trading¶

Introduction¶

Daily trading (e.g. portfolio management) and intraday trading (e.g. orders execution) are two hot topics in Quant investment and usually studied separately.

To get the join trading performance of daily and intraday trading, they must interact with each other and run backtest jointly. In order to support the joint backtest strategies in multiple levels, a corresponding framework is required. None of the publicly available high-frequency trading frameworks considers multi-level joint trading, which make the backtesting aforementioned inaccurate.

Besides backtesting, the optimization of strategies from different levels is not standalone and can be affected by each other. For example, the best portfolio management strategy may change with the performance of order executions(e.g. a portfolio with higher turnover may becomes a better choice when we imporve the order execution strategies). To achieve the overall good performance , it is necessary to consider the interaction of strategies in different level.

Therefore, building a new framework for trading in multiple levels becomes necessary to solve the various problems mentioned above, for which we designed a nested decision execution framework that consider the interaction of strategies.

The design of the framework is shown in the yellow part in the middle of the figure above. Each level consists of Trading Agent and Execution Env. Trading Agent has its own data processing module (Information Extractor), forecasting module (Forecast Model) and decision generator (Decision Generator). The trading algorithm generates the decisions by the Decision Generator based on the forecast signals output by the Forecast Module, and the decisions generated by the trading algorithm are passed to the Execution Env, which returns the execution results.

The frequency of trading algorithm, decision content and execution environment can be customized by users (e.g. intraday trading, daily-frequency trading, weekly-frequency trading), and the execution environment can be nested with finer-grained trading algorithm and execution environment inside (i.e. sub-workflow in the figure, e.g. daily-frequency orders can be turned into finer-grained decisions by splitting orders within the day). The flexibility of nested decision execution framework makes it easy for users to explore the effects of combining different levels of trading strategies and break down the optimization barriers between different levels of trading algorithm.

Example¶

An example of nested decision execution framework for high-frequency can be found here.

Qlib Recorder: Experiment Management¶

Introduction¶

Qlib contains an experiment management system named QlibRecorder, which is designed to help users handle experiment and analyse results in an efficient way.

There are three components of the system:

ExperimentManager

a class that manages experiments.
Experiment

a class of experiment, and each instance of it is responsible for a single experiment.
Recorder

a class of recorder, and each instance of it is responsible for a single run.

Here is a general view of the structure of the system:

This experiment management system defines a set of interface and provided a concrete implementation MLflowExpManager, which is based on the machine learning platform: MLFlow (link).

If users set the implementation of ExpManager to be MLflowExpManager, they can use the command mlflow ui to visualize and check the experiment results. For more information, pleaes refer to the related documents here.

Qlib Recorder¶

QlibRecorder provides a high level API for users to use the experiment management system. The interfaces are wrapped in the variable R in Qlib, and users can directly use R to interact with the system. The following command shows how to import R in Python:

from qlib.workflow import R

QlibRecorder includes several common API for managing experiments and recorders within a workflow. For more available APIs, please refer to the following section about Experiment Manager, Experiment and Recorder.

Here are the available interfaces of QlibRecorder:

class qlib.workflow.__init__.QlibRecorder(exp_manager)¶

A global system that helps to manage the experiments.

__init__(exp_manager)¶: Initialize self. See help(type(self)) for accurate signature.

start(*, experiment_id: Optional[str] = None, experiment_name: Optional[str] = None, recorder_id: Optional[str] = None, recorder_name: Optional[str] = None, uri: Optional[str] = None, resume: bool = False)¶

Method to start an experiment. This method can only be called within a Python’s with statement. Here is the example code:

# start new experiment and recorder
with R.start(experiment_name='test', recorder_name='recorder_1'):
    model.fit(dataset)
    R.log...
    ... # further operations

# resume previous experiment and recorder
with R.start(experiment_name='test', recorder_name='recorder_1', resume=True): # if users want to resume recorder, they have to specify the exact same name for experiment and recorder.
    ... # further operations

Parameters:

experiment_id (str) – id of the experiment one wants to start.
experiment_name (str) – name of the experiment one wants to start.
recorder_id (str) – id of the recorder under the experiment one wants to start.
recorder_name (str) – name of the recorder under the experiment one wants to start.
uri (str) – The tracking uri of the experiment, where all the artifacts/metrics etc. will be stored. The default uri is set in the qlib.config. Note that this uri argument will not change the one defined in the config file. Therefore, the next time when users call this function in the same experiment, they have to also specify this argument with the same value. Otherwise, inconsistent uri may occur.
resume (bool) – whether to resume the specific recorder with given name under the given experiment.

start_exp(*, experiment_id=None, experiment_name=None, recorder_id=None, recorder_name=None, uri=None, resume=False)¶

Lower level method for starting an experiment. When use this method, one should end the experiment manually and the status of the recorder may not be handled properly. Here is the example code:

R.start_exp(experiment_name='test', recorder_name='recorder_1')
... # further operations
R.end_exp('FINISHED') or R.end_exp(Recorder.STATUS_S)

Parameters:	experiment_id (str) – id of the experiment one wants to start. experiment_name (str) – the name of the experiment to be started recorder_id (str) – id of the recorder under the experiment one wants to start. recorder_name (str) – name of the recorder under the experiment one wants to start. uri (str) – the tracking uri of the experiment, where all the artifacts/metrics etc. will be stored. The default uri are set in the qlib.config. resume (bool) – whether to resume the specific recorder with given name under the given experiment.
Returns:
Return type:	An experiment instance being started.

end_exp(recorder_status='FINISHED')¶

Method for ending an experiment manually. It will end the current active experiment, as well as its active recorder with the specified status type. Here is the example code of the method:

R.start_exp(experiment_name='test')
... # further operations
R.end_exp('FINISHED') or R.end_exp(Recorder.STATUS_S)

Parameters:	status (str) – The status of a recorder, which can be SCHEDULED, RUNNING, FINISHED, FAILED.

search_records(experiment_ids, **kwargs)¶

Get a pandas DataFrame of records that fit the search criteria.

The arguments of this function are not set to be rigid, and they will be different with different implementation of ExpManager in Qlib. Qlib now provides an implementation of ExpManager with mlflow, and here is the example code of the this method with the MLflowExpManager:

R.log_metrics(m=2.50, step=0)
records = R.search_runs([experiment_id], order_by=["metrics.m DESC"])

Parameters:

experiment_ids (list) – list of experiment IDs.
filter_string (str) – filter query string, defaults to searching all runs.
run_view_type (int) – one of enum values ACTIVE_ONLY, DELETED_ONLY, or ALL (e.g. in mlflow.entities.ViewType).
max_results (int) – the maximum number of runs to put in the dataframe.
order_by (list) – list of columns to order by (e.g., “metrics.rmse”).

Returns:

A pandas.DataFrame of records, where each metric, parameter, and tag
are expanded into their own columns named metrics., params.*, and tags.**
respectively. For records that don’t have a particular metric, parameter, or tag, their
value will be (NumPy) Nan, None, or None respectively.

list_experiments()¶

Method for listing all the existing experiments (except for those being deleted.)

exps = R.list_experiments()

Returns:
Return type:	A dictionary (name -> experiment) of experiments information that being stored.

list_recorders(experiment_id=None, experiment_name=None)¶

Method for listing all the recorders of experiment with given id or name.

If user doesn’t provide the id or name of the experiment, this method will try to retrieve the default experiment and list all the recorders of the default experiment. If the default experiment doesn’t exist, the method will first create the default experiment, and then create a new recorder under it. (More information about the default experiment can be found here).

Here is the example code:

recorders = R.list_recorders(experiment_name='test')

Parameters:	experiment_id (str) – id of the experiment. experiment_name (str) – name of the experiment.
Returns:
Return type:	A dictionary (id -> recorder) of recorder information that being stored.

get_exp(*, experiment_id=None, experiment_name=None, create: bool = True) → qlib.workflow.exp.Experiment¶

Method for retrieving an experiment with given id or name. Once the create argument is set to True, if no valid experiment is found, this method will create one for you. Otherwise, it will only retrieve a specific experiment or raise an Error.

If ‘create’ is True:
- If active experiment exists:
  no id or name specified, return the active experiment.
  
  if id or name is specified, return the specified experiment. If no such exp found, create a new experiment with given id or name.
- If active experiment not exists:
  no id or name specified, create a default experiment, and the experiment is set to be active.
  
  if id or name is specified, return the specified experiment. If no such exp found, create a new experiment with given name or the default experiment.
Else If ‘create’ is False:
- If ``active experiment` exists:
  no id or name specified, return the active experiment.
  
  if id or name is specified, return the specified experiment. If no such exp found, raise Error.
- If active experiment not exists:
  no id or name specified. If the default experiment exists, return it, otherwise, raise Error.
  
  if id or name is specified, return the specified experiment. If no such exp found, raise Error.

Here are some use cases:

# Case 1
with R.start('test'):
    exp = R.get_exp()
    recorders = exp.list_recorders()

# Case 2
with R.start('test'):
    exp = R.get_exp(experiment_name='test1')

# Case 3
exp = R.get_exp() -> a default experiment.

# Case 4
exp = R.get_exp(experiment_name='test')

# Case 5
exp = R.get_exp(create=False) -> the default experiment if exists.

Parameters:	experiment_id (str) – id of the experiment. experiment_name (str) – name of the experiment. create (boolean) – an argument determines whether the method will automatically create a new experiment according to user’s specification if the experiment hasn’t been created before.
Returns:
Return type:	An experiment instance with given id or name.

delete_exp(experiment_id=None, experiment_name=None)¶

Method for deleting the experiment with given id or name. At least one of id or name must be given, otherwise, error will occur.

Here is the example code:

R.delete_exp(experiment_name='test')

Parameters:	experiment_id (str) – id of the experiment. experiment_name (str) – name of the experiment.

get_uri()¶

Method for retrieving the uri of current experiment manager.

Here is the example code:

uri = R.get_uri()

Returns:
Return type:	The uri of current experiment manager.

set_uri(uri: Optional[str])¶: Method to reset the current uri of current experiment manager.

uri_context(uri: str)¶

Temporarily set the exp_manager’s uri to uri

NOTE: - Please refer to the NOTE in the set_uri

Parameters:	uri (Text) – the temporal uri

get_recorder(*, recorder_id=None, recorder_name=None, experiment_id=None, experiment_name=None) → qlib.workflow.recorder.Recorder¶

Method for retrieving a recorder.

If active recorder exists:
- no id or name specified, return the active recorder.
- if id or name is specified, return the specified recorder.
If active recorder not exists:
- no id or name specified, raise Error.
- if id or name is specified, and the corresponding experiment_name must be given, return the specified recorder. Otherwise, raise Error.

The recorder can be used for further process such as save_object, load_object, log_params, log_metrics, etc.

Here are some use cases:

# Case 1
with R.start(experiment_name='test'):
    recorder = R.get_recorder()

# Case 2
with R.start(experiment_name='test'):
    recorder = R.get_recorder(recorder_id='2e7a4efd66574fa49039e00ffaefa99d')

# Case 3
recorder = R.get_recorder() -> Error

# Case 4
recorder = R.get_recorder(recorder_id='2e7a4efd66574fa49039e00ffaefa99d') -> Error

# Case 5
recorder = R.get_recorder(recorder_id='2e7a4efd66574fa49039e00ffaefa99d', experiment_name='test')

Parameters:	recorder_id (str) – id of the recorder. recorder_name (str) – name of the recorder. experiment_name (str) – name of the experiment.
Returns:
Return type:	A recorder instance.

delete_recorder(recorder_id=None, recorder_name=None)¶

Method for deleting the recorders with given id or name. At least one of id or name must be given, otherwise, error will occur.

Here is the example code:

R.delete_recorder(recorder_id='2e7a4efd66574fa49039e00ffaefa99d')

Parameters:	recorder_id (str) – id of the experiment. recorder_name (str) – name of the experiment.

save_objects(local_path=None, artifact_path=None, **kwargs)¶

Method for saving objects as artifacts in the experiment to the uri. It supports either saving from a local file/directory, or directly saving objects. User can use valid python’s keywords arguments to specify the object to be saved as well as its name (name: value).

In summary, this API is designs for saving objects to the experiments management backend path, 1. Qlib provide two methods to specify objects - Passing in the object directly by passing with **kwargs (e.g. R.save_objects(trained_model=model)) - Passing in the local path to the object, i.e. local_path parameter. 2. artifact_path represents the the experiments management backend path

If active recorder exists: it will save the objects through the active recorder.
If active recorder not exists: the system will create a default experiment, and a new recorder and save objects under it.

Note

If one wants to save objects with a specific recorder. It is recommended to first get the specific recorder through get_recorder API and use the recorder the save objects. The supported arguments are the same as this method.

Here are some use cases:

# Case 1
with R.start(experiment_name='test'):
    pred = model.predict(dataset)
    R.save_objects(**{"pred.pkl": pred}, artifact_path='prediction')
    rid = R.get_recorder().id
...
R.get_recorder(recorder_id=rid).load_object("prediction/pred.pkl")  #  after saving objects, you can load the previous object with this api

# Case 2
with R.start(experiment_name='test'):
    R.save_objects(local_path='results/pred.pkl', artifact_path="prediction")
    rid = R.get_recorder().id
...
R.get_recorder(recorder_id=rid).load_object("prediction/pred.pkl")  #  after saving objects, you can load the previous object with this api

Parameters:	local_path (str) – if provided, them save the file or directory to the artifact URI. artifact_path (str) – the relative path for the artifact to be stored in the URI. *kwargs (Dict[Text, Any*]) – the object to be saved. For example, {“pred.pkl”: pred}

load_object(name: str)¶: Method for loading an object from artifacts in the experiment in the uri.

log_params(**kwargs)¶

Method for logging parameters during an experiment. In addition to using R, one can also log to a specific recorder after getting it with get_recorder API.

If active recorder exists: it will log parameters through the active recorder.
If active recorder not exists: the system will create a default experiment as well as a new recorder, and log parameters under it.

Here are some use cases:

# Case 1
with R.start('test'):
    R.log_params(learning_rate=0.01)

# Case 2
R.log_params(learning_rate=0.01)

Parameters:	argument (keyword) – name1=value1, name2=value2, …

log_metrics(step=None, **kwargs)¶

Method for logging metrics during an experiment. In addition to using R, one can also log to a specific recorder after getting it with get_recorder API.

If active recorder exists: it will log metrics through the active recorder.
If active recorder not exists: the system will create a default experiment as well as a new recorder, and log metrics under it.

Here are some use cases:

# Case 1
with R.start('test'):
    R.log_metrics(train_loss=0.33, step=1)

# Case 2
R.log_metrics(train_loss=0.33, step=1)

Parameters:	argument (keyword) – name1=value1, name2=value2, …

set_tags(**kwargs)¶

Method for setting tags for a recorder. In addition to using R, one can also set the tag to a specific recorder after getting it with get_recorder API.

If active recorder exists: it will set tags through the active recorder.
If active recorder not exists: the system will create a default experiment as well as a new recorder, and set the tags under it.

Here are some use cases:

# Case 1
with R.start('test'):
    R.set_tags(release_version="2.2.0")

# Case 2
R.set_tags(release_version="2.2.0")

Parameters:	argument (keyword) – name1=value1, name2=value2, …

Experiment Manager¶

The ExpManager module in Qlib is responsible for managing different experiments. Most of the APIs of ExpManager are similar to QlibRecorder, and the most important API will be the get_exp method. User can directly refer to the documents above for some detailed information about how to use the get_exp method.

class qlib.workflow.expm.ExpManager(uri: str, default_exp_name: Optional[str])¶

This is the ExpManager class for managing experiments. The API is designed similar to mlflow. (The link: https://mlflow.org/docs/latest/python_api/mlflow.html)

__init__(uri: str, default_exp_name: Optional[str])¶: Initialize self. See help(type(self)) for accurate signature.

start_exp(*, experiment_id: Optional[str] = None, experiment_name: Optional[str] = None, recorder_id: Optional[str] = None, recorder_name: Optional[str] = None, uri: Optional[str] = None, resume: bool = False, **kwargs)¶

Start an experiment. This method includes first get_or_create an experiment, and then set it to be active.

Parameters:	experiment_id (str) – id of the active experiment. experiment_name (str) – name of the active experiment. recorder_id (str) – id of the recorder to be started. recorder_name (str) – name of the recorder to be started. uri (str) – the current tracking URI. resume (boolean) – whether to resume the experiment and recorder.
Returns:
Return type:	An active experiment.

end_exp(recorder_status: str = 'SCHEDULED', **kwargs)¶

End an active experiment.

Parameters:	experiment_name (str) – name of the active experiment. recorder_status (str) – the status of the active recorder of the experiment.

create_exp(experiment_name: Optional[str] = None)¶

Create an experiment.

Parameters:	experiment_name (str) – the experiment name, which must be unique.
Returns:	An experiment object. Raise —– ExpAlreadyExistError

search_records(experiment_ids=None, **kwargs)¶

Get a pandas DataFrame of records that fit the search criteria of the experiment. Inputs are the search critera user want to apply.

Returns:	A pandas.DataFrame of records, where each metric, parameter, and tag are expanded into their own columns named metrics., params., and tags.* respectively. For records that don’t have a particular metric, parameter, or tag, their value will be (NumPy) Nan, None, or None respectively.

get_exp(*, experiment_id=None, experiment_name=None, create: bool = True, start: bool = False)¶

Retrieve an experiment. This method includes getting an active experiment, and get_or_create a specific experiment.

When user specify experiment id and name, the method will try to return the specific experiment. When user does not provide recorder id or name, the method will try to return the current active experiment. The create argument determines whether the method will automatically create a new experiment according to user’s specification if the experiment hasn’t been created before.

If create is True:
- If active experiment exists:
  no id or name specified, return the active experiment.
  
  if id or name is specified, return the specified experiment. If no such exp found, create a new experiment with given id or name. If start is set to be True, the experiment is set to be active.
- If active experiment not exists:
  no id or name specified, create a default experiment.
  
  if id or name is specified, return the specified experiment. If no such exp found, create a new experiment with given id or name. If start is set to be True, the experiment is set to be active.
Else If create is False:
- If active experiment exists:
  no id or name specified, return the active experiment.
  
  if id or name is specified, return the specified experiment. If no such exp found, raise Error.
- If active experiment not exists:
  no id or name specified. If the default experiment exists, return it, otherwise, raise Error.
  
  if id or name is specified, return the specified experiment. If no such exp found, raise Error.

Parameters:	experiment_id (str) – id of the experiment to return. experiment_name (str) – name of the experiment to return. create (boolean) – create the experiment it if hasn’t been created before. start (boolean) – start the new experiment if one is created.
Returns:
Return type:	An experiment object.

delete_exp(experiment_id=None, experiment_name=None)¶

Delete an experiment.

Parameters:	experiment_id (str) – the experiment id. experiment_name (str) – the experiment name.

default_uri¶: Get the default tracking URI from qlib.config.C

uri¶

Get the default tracking URI or current URI.

Returns:
Return type:	The tracking URI string.

set_uri(uri: Optional[str] = None)¶

Set the current tracking URI and the corresponding variables.

Parameters:	uri (str) –

list_experiments()¶

List all the existing experiments.

Returns:
Return type:	A dictionary (name -> experiment) of experiments information that being stored.

For other interfaces such as create_exp, delete_exp, please refer to Experiment Manager API.

Experiment¶

The Experiment class is solely responsible for a single experiment, and it will handle any operations that are related to an experiment. Basic methods such as start, end an experiment are included. Besides, methods related to recorders are also available: such methods include get_recorder and list_recorders.

class qlib.workflow.exp.Experiment(id, name)¶

This is the Experiment class for each experiment being run. The API is designed similar to mlflow. (The link: https://mlflow.org/docs/latest/python_api/mlflow.html)

__init__(id, name)¶: Initialize self. See help(type(self)) for accurate signature.

start(*, recorder_id=None, recorder_name=None, resume=False)¶

Start the experiment and set it to be active. This method will also start a new recorder.

Parameters:	recorder_id (str) – the id of the recorder to be created. recorder_name (str) – the name of the recorder to be created. resume (bool) – whether to resume the first recorder
Returns:
Return type:	An active recorder.

end(recorder_status='SCHEDULED')¶

End the experiment.

Parameters:	recorder_status (str) – the status the recorder to be set with when ending (SCHEDULED, RUNNING, FINISHED, FAILED).

create_recorder(recorder_name=None)¶

Create a recorder for each experiment.

Parameters:	recorder_name (str) – the name of the recorder to be created.
Returns:
Return type:	A recorder object.

search_records(**kwargs)¶

Get a pandas DataFrame of records that fit the search criteria of the experiment. Inputs are the search critera user want to apply.

Returns:	A pandas.DataFrame of records, where each metric, parameter, and tag are expanded into their own columns named metrics., params., and tags.* respectively. For records that don’t have a particular metric, parameter, or tag, their value will be (NumPy) Nan, None, or None respectively.

delete_recorder(recorder_id)¶

Create a recorder for each experiment.

Parameters:	recorder_id (str) – the id of the recorder to be deleted.

get_recorder(recorder_id=None, recorder_name=None, create: bool = True, start: bool = False)¶

Retrieve a Recorder for user. When user specify recorder id and name, the method will try to return the specific recorder. When user does not provide recorder id or name, the method will try to return the current active recorder. The create argument determines whether the method will automatically create a new recorder according to user’s specification if the recorder hasn’t been created before.

If create is True:
- If active recorder exists:
  no id or name specified, return the active recorder.
  
  if id or name is specified, return the specified recorder. If no such exp found, create a new recorder with given id or name. If start is set to be True, the recorder is set to be active.
- If active recorder not exists:
  no id or name specified, create a new recorder.
  
  if id or name is specified, return the specified experiment. If no such exp found, create a new recorder with given id or name. If start is set to be True, the recorder is set to be active.
Else If create is False:
- If active recorder exists:
  no id or name specified, return the active recorder.
  
  if id or name is specified, return the specified recorder. If no such exp found, raise Error.
- If active recorder not exists:
  no id or name specified, raise Error.
  
  if id or name is specified, return the specified recorder. If no such exp found, raise Error.

Parameters:	recorder_id (str) – the id of the recorder to be deleted. recorder_name (str) – the name of the recorder to be deleted. create (boolean) – create the recorder if it hasn’t been created before. start (boolean) – start the new recorder if one is created.
Returns:
Return type:	A recorder object.

list_recorders(**flt_kwargs)¶

List all the existing recorders of this experiment. Please first get the experiment instance before calling this method. If user want to use the method R.list_recorders(), please refer to the related API document in QlibRecorder.

flt_kwargs : dict: filter recorders by conditions e.g. list_recorders(status=Recorder.STATUS_FI)

Returns:
Return type:	A dictionary (id -> recorder) of recorder information that being stored.

For other interfaces such as search_records, delete_recorder, please refer to Experiment API.

Qlib also provides a default Experiment, which will be created and used under certain situations when users use the APIs such as log_metrics or get_exp. If the default Experiment is used, there will be related logged information when running Qlib. Users are able to change the name of the default Experiment in the config file of Qlib or during Qlib’s initialization, which is set to be ‘Experiment’.

Recorder¶

The Recorder class is responsible for a single recorder. It will handle some detailed operations such as log_metrics, log_params of a single run. It is designed to help user to easily track results and things being generated during a run.

Here are some important APIs that are not included in the QlibRecorder:

class qlib.workflow.recorder.Recorder(experiment_id, name)¶

This is the Recorder class for logging the experiments. The API is designed similar to mlflow. (The link: https://mlflow.org/docs/latest/python_api/mlflow.html)

The status of the recorder can be SCHEDULED, RUNNING, FINISHED, FAILED.

__init__(experiment_id, name)¶: Initialize self. See help(type(self)) for accurate signature.

save_objects(local_path=None, artifact_path=None, **kwargs)¶

Save objects such as prediction file or model checkpoints to the artifact URI. User can save object through keywords arguments (name:value).

Please refer to the docs of qlib.workflow:R.save_objects

Parameters:	local_path (str) – if provided, them save the file or directory to the artifact URI. artifact_path=None (str) – the relative path for the artifact to be stored in the URI.

load_object(name)¶

Load objects such as prediction file or model checkpoints.

Parameters:	name (str) – name of the file to be loaded.
Returns:
Return type:	The saved object.

start_run()¶

Start running or resuming the Recorder. The return value can be used as a context manager within a with block; otherwise, you must call end_run() to terminate the current run. (See ActiveRun class in mlflow)

Returns:
Return type:	An active running object (e.g. mlflow.ActiveRun object)

end_run()¶: End an active Recorder.

log_params(**kwargs)¶

Log a batch of params for the current run.

Parameters:	arguments (keyword) – key, value pair to be logged as parameters.

log_metrics(step=None, **kwargs)¶

Log multiple metrics for the current run.

Parameters:	arguments (keyword) – key, value pair to be logged as metrics.

set_tags(**kwargs)¶

Log a batch of tags for the current run.

Parameters:	arguments (keyword) – key, value pair to be logged as tags.

delete_tags(*keys)¶

Delete some tags from a run.

Parameters:	keys (series of strs of the keys) – all the name of the tag to be deleted.

list_artifacts(artifact_path: str = None)¶

List all the artifacts of a recorder.

Parameters:	artifact_path (str) – the relative path for the artifact to be stored in the URI.
Returns:
Return type:	A list of artifacts information (name, path, etc.) that being stored.

list_metrics()¶

List all the metrics of a recorder.

Returns:
Return type:	A dictionary of metrics that being stored.

list_params()¶

List all the params of a recorder.

Returns:
Return type:	A dictionary of params that being stored.

list_tags()¶

List all the tags of a recorder.

Returns:
Return type:	A dictionary of tags that being stored.

For other interfaces such as save_objects, load_object, please refer to Recorder API.

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 prediction results of the model.
SigAnaRecord: This class generates the IC, ICIR, Rank IC and Rank ICIR of the model.

Here is a simple example of what is done in SigAnaRecord, which users can refer to if they want to calculate IC, Rank IC, Long-Short Return with their own prediction and label.

from qlib.contrib.eva.alpha import calc_ic, calc_long_short_return

ic, ric = calc_ic(pred.iloc[:, 0], label.iloc[:, 0])
long_short_r, long_avg_r = calc_long_short_return(pred.iloc[:, 0], label.iloc[:, 0])

PortAnaRecord: This class generates the results of backtest. The detailed information about backtest as well as the available strategy, users can refer to Strategy and Backtest.

Here is a simple exampke of what is done in PortAnaRecord, which users can refer to if they want to do backtest based on their own prediction and label.

from qlib.contrib.strategy.strategy import TopkDropoutStrategy
from qlib.contrib.evaluate import (
    backtest as normal_backtest,
    risk_analysis,
)

# backtest
STRATEGY_CONFIG = {
    "topk": 50,
    "n_drop": 5,
}
BACKTEST_CONFIG = {
    "limit_threshold": 0.095,
    "account": 100000000,
    "benchmark": BENCHMARK,
    "deal_price": "close",
    "open_cost": 0.0005,
    "close_cost": 0.0015,
    "min_cost": 5,
}

strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
report_normal, positions_normal = normal_backtest(pred_score, strategy=strategy, **BACKTEST_CONFIG)

# analysis
analysis = dict()
analysis["excess_return_without_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"])
analysis["excess_return_with_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"] - report_normal["cost"])
analysis_df = pd.concat(analysis)  # type: pd.DataFrame
print(analysis_df)

For more information about the APIs, please refer to Record Template API.

Analysis: Evaluation & Results Analysis¶

Introduction¶

Analysis is designed to show the graphical reports of Intraday Trading , which helps users to evaluate and analyse investment portfolios visually. The following are some graphics to view:

analysis_position
- report_graph
- score_ic_graph
- cumulative_return_graph
- risk_analysis_graph
- rank_label_graph
analysis_model
- model_performance_graph

Graphical Reports¶

Users can run the following code to get all supported reports.

>> import qlib.contrib.report as qcr
>> print(qcr.GRAPH_NAME_LIST)
['analysis_position.report_graph', 'analysis_position.score_ic_graph', 'analysis_position.cumulative_return_graph', 'analysis_position.risk_analysis_graph', 'analysis_position.rank_label_graph', 'analysis_model.model_performance_graph']

Note

For more details, please refer to the function document: similar to help(qcr.analysis_position.report_graph)

Usage & Example¶

Usage of analysis_position.report¶

API¶

qlib.contrib.report.analysis_position.report.report_graph(report_df: pandas.core.frame.DataFrame, show_notebook: bool = True) → [<class 'list'>, <class 'tuple'>]¶

display backtest report

Example:

import qlib
import pandas as pd
from qlib.utils.time import Freq
from qlib.utils import flatten_dict
from qlib.backtest import backtest, executor
from qlib.contrib.evaluate import risk_analysis
from qlib.contrib.strategy import TopkDropoutStrategy

# init qlib
qlib.init(provider_uri=<qlib data dir>)

CSI300_BENCH = "SH000300"
FREQ = "day"
STRATEGY_CONFIG = {
    "topk": 50,
    "n_drop": 5,
    # pred_score, pd.Series
    "signal": pred_score,
}

EXECUTOR_CONFIG = {
    "time_per_step": "day",
    "generate_portfolio_metrics": True,
}

backtest_config = {
    "start_time": "2017-01-01",
    "end_time": "2020-08-01",
    "account": 100000000,
    "benchmark": CSI300_BENCH,
    "exchange_kwargs": {
        "freq": FREQ,
        "limit_threshold": 0.095,
        "deal_price": "close",
        "open_cost": 0.0005,
        "close_cost": 0.0015,
        "min_cost": 5,
    },
}

# strategy object
strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
# executor object
executor_obj = executor.SimulatorExecutor(**EXECUTOR_CONFIG)
# backtest
portfolio_metric_dict, indicator_dict = backtest(executor=executor_obj, strategy=strategy_obj, **backtest_config)
analysis_freq = "{0}{1}".format(*Freq.parse(FREQ))
# backtest info
report_normal_df, positions_normal = portfolio_metric_dict.get(analysis_freq)

qcr.analysis_position.report_graph(report_normal_df)

Parameters:

report_df –

df.index.name must be date, df.columns must contain return, turnover, cost, bench.

            return      cost        bench       turnover
date
2017-01-04  0.003421    0.000864    0.011693    0.576325
2017-01-05  0.000508    0.000447    0.000721    0.227882
2017-01-06  -0.003321   0.000212    -0.004322   0.102765
2017-01-09  0.006753    0.000212    0.006874    0.105864
2017-01-10  -0.000416   0.000440    -0.003350   0.208396

show_notebook – whether to display graphics in notebook, the default is True.

Returns:

if show_notebook is True, display in notebook; else return plotly.graph_objs.Figure list.

Graphical Result¶

Note

Axis X: Trading day
Axis Y:
- cum bench
  
  Cumulative returns series of benchmark
- cum return wo cost
  
  Cumulative returns series of portfolio without cost
- cum return w cost
  
  Cumulative returns series of portfolio with cost
- return wo mdd
  
  Maximum drawdown series of cumulative return without cost
- return w cost mdd:
  
  Maximum drawdown series of cumulative return with cost
- cum ex return wo cost
  
  The CAR (cumulative abnormal return) series of the portfolio compared to the benchmark without cost.
- cum ex return w cost
  
  The CAR (cumulative abnormal return) series of the portfolio compared to the benchmark with cost.
- turnover
  
  Turnover rate series
- cum ex return wo cost mdd
  
  Drawdown series of CAR (cumulative abnormal return) without cost
- cum ex return w cost mdd
  
  Drawdown series of CAR (cumulative abnormal return) with cost
The shaded part above: Maximum drawdown corresponding to cum return wo cost
The shaded part below: Maximum drawdown corresponding to cum ex return wo cost

Usage of analysis_position.score_ic¶

API¶

qlib.contrib.report.analysis_position.score_ic.score_ic_graph(pred_label: pandas.core.frame.DataFrame, show_notebook: bool = True) → [<class 'list'>, <class 'tuple'>]¶

score IC

Example:

from qlib.data import D
from qlib.contrib.report import analysis_position
pred_df_dates = pred_df.index.get_level_values(level='datetime')
features_df = D.features(D.instruments('csi500'), ['Ref($close, -2)/Ref($close, -1)-1'], pred_df_dates.min(), pred_df_dates.max())
features_df.columns = ['label']
pred_label = pd.concat([features_df, pred], axis=1, sort=True).reindex(features_df.index)
analysis_position.score_ic_graph(pred_label)

Parameters:

pred_label –

index is pd.MultiIndex, index name is [instrument, datetime]; columns names is [score, label].

instrument  datetime        score         label
SH600004  2017-12-11     -0.013502       -0.013502
            2017-12-12   -0.072367       -0.072367
            2017-12-13   -0.068605       -0.068605
            2017-12-14    0.012440        0.012440
            2017-12-15   -0.102778       -0.102778

show_notebook – whether to display graphics in notebook, the default is True.

Returns:

if show_notebook is True, display in notebook; else return plotly.graph_objs.Figure list.

Graphical Result¶

Note

Axis X: Trading day
Axis Y:
- ic
  
  The Pearson correlation coefficient series between label and prediction score. In the above example, the label is formulated as Ref($close, -1)/$close - 1. Please refer to Data Feature for more details.
- rank_ic
  
  The Spearman’s rank correlation coefficient series between label and prediction score.

Usage of analysis_position.risk_analysis¶

API¶

qlib.contrib.report.analysis_position.risk_analysis.risk_analysis_graph(analysis_df: pandas.core.frame.DataFrame = None, report_normal_df: pandas.core.frame.DataFrame = None, report_long_short_df: pandas.core.frame.DataFrame = None, show_notebook: bool = True) → Iterable[plotly.graph_objs._figure.Figure]¶

Generate analysis graph and monthly analysis

Example:

import qlib
import pandas as pd
from qlib.utils.time import Freq
from qlib.utils import flatten_dict
from qlib.backtest import backtest, executor
from qlib.contrib.evaluate import risk_analysis
from qlib.contrib.strategy import TopkDropoutStrategy

# init qlib
qlib.init(provider_uri=<qlib data dir>)

CSI300_BENCH = "SH000300"
FREQ = "day"
STRATEGY_CONFIG = {
    "topk": 50,
    "n_drop": 5,
    # pred_score, pd.Series
    "signal": pred_score,
}

EXECUTOR_CONFIG = {
    "time_per_step": "day",
    "generate_portfolio_metrics": True,
}

backtest_config = {
    "start_time": "2017-01-01",
    "end_time": "2020-08-01",
    "account": 100000000,
    "benchmark": CSI300_BENCH,
    "exchange_kwargs": {
        "freq": FREQ,
        "limit_threshold": 0.095,
        "deal_price": "close",
        "open_cost": 0.0005,
        "close_cost": 0.0015,
        "min_cost": 5,
    },
}

# strategy object
strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
# executor object
executor_obj = executor.SimulatorExecutor(**EXECUTOR_CONFIG)
# backtest
portfolio_metric_dict, indicator_dict = backtest(executor=executor_obj, strategy=strategy_obj, **backtest_config)
analysis_freq = "{0}{1}".format(*Freq.parse(FREQ))
# backtest info
report_normal_df, positions_normal = portfolio_metric_dict.get(analysis_freq)
analysis = dict()
analysis["excess_return_without_cost"] = risk_analysis(
    report_normal_df["return"] - report_normal_df["bench"], freq=analysis_freq
)
analysis["excess_return_with_cost"] = risk_analysis(
    report_normal_df["return"] - report_normal_df["bench"] - report_normal_df["cost"], freq=analysis_freq
)

analysis_df = pd.concat(analysis)  # type: pd.DataFrame
analysis_position.risk_analysis_graph(analysis_df, report_normal_df)

Parameters:

analysis_df –

analysis data, index is pd.MultiIndex; columns names is [risk].

                                                  risk
excess_return_without_cost mean               0.000692
                           std                0.005374
                           annualized_return  0.174495
                           information_ratio  2.045576
                           max_drawdown      -0.079103
excess_return_with_cost    mean               0.000499
                           std                0.005372
                           annualized_return  0.125625
                           information_ratio  1.473152
                           max_drawdown      -0.088263

report_normal_df –

df.index.name must be date, df.columns must contain return, turnover, cost, bench.

            return      cost        bench       turnover
date
2017-01-04  0.003421    0.000864    0.011693    0.576325
2017-01-05  0.000508    0.000447    0.000721    0.227882
2017-01-06  -0.003321   0.000212    -0.004322   0.102765
2017-01-09  0.006753    0.000212    0.006874    0.105864
2017-01-10  -0.000416   0.000440    -0.003350   0.208396

report_long_short_df –

df.index.name must be date, df.columns contain long, short, long_short.

            long        short       long_short
date
2017-01-04  -0.001360   0.001394    0.000034
2017-01-05  0.002456    0.000058    0.002514
2017-01-06  0.000120    0.002739    0.002859
2017-01-09  0.001436    0.001838    0.003273
2017-01-10  0.000824    -0.001944   -0.001120

show_notebook – Whether to display graphics in a notebook, default True. If True, show graph in notebook If False, return graph figure

Returns:

Graphical Result¶

Note

general graphics
- std
  
  excess_return_without_cost
  
  The Standard Deviation of CAR (cumulative abnormal return) without cost.
  
  excess_return_with_cost
  
  The Standard Deviation of CAR (cumulative abnormal return) with cost.
- annualized_return
  
  excess_return_without_cost
  
  The Annualized Rate of CAR (cumulative abnormal return) without cost.
  
  excess_return_with_cost
  
  The Annualized Rate of CAR (cumulative abnormal return) with cost.
- information_ratio
  
  excess_return_without_cost
  
  The Information Ratio without cost.
  
  excess_return_with_cost
  
  The Information Ratio with cost.
  
  To know more about Information Ratio, please refer to Information Ratio – IR.
- max_drawdown
  
  excess_return_without_cost
  
  The Maximum Drawdown of CAR (cumulative abnormal return) without cost.
  
  excess_return_with_cost
  
  The Maximum Drawdown of CAR (cumulative abnormal return) with cost.

Note

annualized_return/max_drawdown/information_ratio/std graphics
- Axis X: Trading days grouped by month
- Axis Y:
  
  annualized_return graphics
  
  excess_return_without_cost_annualized_return
  
  The Annualized Rate series of monthly CAR (cumulative abnormal return) without cost.
  
  excess_return_with_cost_annualized_return
  
  The Annualized Rate series of monthly CAR (cumulative abnormal return) with cost.
  
  max_drawdown graphics
  
  excess_return_without_cost_max_drawdown
  
  The Maximum Drawdown series of monthly CAR (cumulative abnormal return) without cost.
  
  excess_return_with_cost_max_drawdown
  
  The Maximum Drawdown series of monthly CAR (cumulative abnormal return) with cost.
  
  information_ratio graphics
  
  excess_return_without_cost_information_ratio
  
  The Information Ratio series of monthly CAR (cumulative abnormal return) without cost.
  
  excess_return_with_cost_information_ratio
  
  The Information Ratio series of monthly CAR (cumulative abnormal return) with cost.
  
  std graphics
  
  excess_return_without_cost_max_drawdown
  
  The Standard Deviation series of monthly CAR (cumulative abnormal return) without cost.
  
  excess_return_with_cost_max_drawdown
  
  The Standard Deviation series of monthly CAR (cumulative abnormal return) with cost.

_images/risk_analysis_annualized_return.png

_images/risk_analysis_information_ratio.png

Usage of analysis_model.analysis_model_performance¶

API¶

qlib.contrib.report.analysis_model.analysis_model_performance.ic_figure(ic_df: pandas.core.frame.DataFrame, show_nature_day=True, **kwargs) → plotly.graph_objs._figure.Figure¶

IC figure

Parameters:	ic_df – ic DataFrame show_nature_day – whether to display the abscissa of non-trading day
Returns:	plotly.graph_objs.Figure

qlib.contrib.report.analysis_model.analysis_model_performance.model_performance_graph(pred_label: pandas.core.frame.DataFrame, lag: int = 1, N: int = 5, reverse=False, rank=False, graph_names: list = ['group_return', 'pred_ic', 'pred_autocorr'], show_notebook: bool = True, show_nature_day=True) → [<class 'list'>, <class 'tuple'>]¶

Model performance

Parameters:	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”).

instrument  datetime        score       label
SH600004    2017-12-11  -0.013502       -0.013502
                2017-12-12  -0.072367       -0.072367
                2017-12-13  -0.068605       -0.068605
                2017-12-14  0.012440        0.012440
                2017-12-15  -0.102778       -0.102778

Parameters:

lag – pred.groupby(level=’instrument’)[‘score’].shift(lag). It will be only used in the auto-correlation computing.
N – group number, default 5.
reverse – if True, pred[‘score’] *= -1.
rank – if True, calculate rank ic.
graph_names – graph names; default [‘cumulative_return’, ‘pred_ic’, ‘pred_autocorr’, ‘pred_turnover’].
show_notebook – whether to display graphics in notebook, the default is True.
show_nature_day – whether to display the abscissa of non-trading day.

Returns:

if show_notebook is True, display in notebook; else return plotly.graph_objs.Figure list.

Graphical Results¶

Note

cumulative return graphics
- Group1:
  
  The Cumulative Return series of stocks group with (ranking ratio of label <= 20%)
- Group2:
  
  The Cumulative Return series of stocks group with (20% < ranking ratio of label <= 40%)
- Group3:
  
  The Cumulative Return series of stocks group with (40% < ranking ratio of label <= 60%)
- Group4:
  
  The Cumulative Return series of stocks group with (60% < ranking ratio of label <= 80%)
- Group5:
  
  The Cumulative Return series of stocks group with (80% < ranking ratio of label)
- long-short:
  
  The Difference series between Cumulative Return of Group1 and of Group5
- long-average
  
  The Difference series between Cumulative Return of Group1 and average Cumulative Return for all stocks.
The ranking ratio can be formulated as follows.

\[ranking\ ratio = \frac{Ascending\ Ranking\ of\ label}{Number\ of\ Stocks\ in\ the\ Portfolio}\]

_images/analysis_model_cumulative_return.png

Note

long-short/long-average

The distribution of long-short/long-average returns on each trading day

Note

Information Coefficient
- The Pearson correlation coefficient series between labels and prediction scores of stocks in portfolio.
- The graphics reports can be used to evaluate the prediction scores.

Note

Monthly IC

Monthly average of the Information Coefficient

Note

IC

The distribution of the Information Coefficient on each trading day.
IC Normal Dist. Q-Q

The Quantile-Quantile Plot is used for the normal distribution of Information Coefficient on each trading day.

Note

Auto Correlation
- The Pearson correlation coefficient series between the latest prediction scores and the prediction scores lag days ago of stocks in portfolio on each trading day.
- The graphics reports can be used to estimate the turnover rate.

_images/analysis_model_auto_correlation.png

Online Serving¶

Introduction¶

In addition to backtesting, one way to test a model is effective is to make predictions in real market conditions or even do real trading based on those predictions. Online Serving is a set of modules for online models using the latest data, which including Online Manager, Online Strategy, Online Tool, Updater.

Here are several examples for reference, which demonstrate different features of Online Serving. If you have many models or task needs to be managed, please consider Task Management. The examples are based on some components in Task Management such as TrainerRM or Collector.

NOTE: User should keep his data source updated to support online serving. For example, Qlib provides a batch of scripts to help users update Yahoo daily data.

Online Manager¶

OnlineManager can manage a set of Online Strategy and run them dynamically.

With the change of time, the decisive models will be also changed. In this module, we call those contributing models online models. In every routine(such as every day or every minute), the online models may be changed and the prediction of them needs to be updated. So this module provides a series of methods to control this process.

This module also provides a method to simulate Online Strategy in history. Which means you can verify your strategy or find a better one.

There are 4 total situations for using different trainers in different situations:

Situations	Description
Online + Trainer	When you want to do a REAL routine, the Trainer will help you train the models. It will train models task by task and strategy by strategy.
Online + DelayTrainer	DelayTrainer will skip concrete training until all tasks have been prepared by different strategies. It makes users can parallelly train all tasks at the end of routine or first_train. Otherwise, these functions will get stuck when each strategy prepare tasks.
Simulation + Trainer	It will behave in the same way as Online + Trainer. The only difference is that it is for simulation/backtesting instead of online trading
Simulation + DelayTrainer	When your models don’t have any temporal dependence, you can use DelayTrainer for the ability to multitasking. It means all tasks in all routines can be REAL trained at the end of simulating. The signals will be prepared well at different time segments (based on whether or not any new model is online).

Here is some pseudo code the demonstrate the workflow of each situation

For simplicity

Only one strategy is used in the strategy
update_online_pred is only called in the online mode and is ignored

Online + Trainer

tasks = first_train()
models = trainer.train(tasks)
trainer.end_train(models)
for day in online_trading_days:
    # OnlineManager.routine
    models = trainer.train(strategy.prepare_tasks())  # for each strategy
    strategy.prepare_online_models(models)  # for each strategy

    trainer.end_train(models)
    prepare_signals()  # prepare trading signals daily

Online + DelayTrainer: the workflow is the same as Online + Trainer.

Simulation + DelayTrainer

# simulate
tasks = first_train()
models = trainer.train(tasks)
for day in historical_calendars:
    # OnlineManager.routine
    models = trainer.train(strategy.prepare_tasks())  # for each strategy
    strategy.prepare_online_models(models)  # for each strategy
# delay_prepare()
# FIXME: Currently the delay_prepare is not implemented in a proper way.
trainer.end_train(<for all previous models>)
prepare_signals()

# Can we simplify current workflow? - Can reduce the number of state of tasks?

For each task, we have three phases (i.e. task, partly trained task, final trained task)

class qlib.workflow.online.manager.OnlineManager(strategies: Union[qlib.workflow.online.strategy.OnlineStrategy, List[qlib.workflow.online.strategy.OnlineStrategy]], trainer: qlib.model.trainer.Trainer = None, begin_time: Union[str, pandas._libs.tslibs.timestamps.Timestamp] = None, freq='day')¶

OnlineManager can manage online models with Online Strategy. It also provides a history recording of which models are online at what time.

__init__(strategies: Union[qlib.workflow.online.strategy.OnlineStrategy, List[qlib.workflow.online.strategy.OnlineStrategy]], trainer: qlib.model.trainer.Trainer = None, begin_time: Union[str, pandas._libs.tslibs.timestamps.Timestamp] = None, freq='day')¶

Init OnlineManager. One OnlineManager must have at least one OnlineStrategy.

Parameters:

strategies (Union[OnlineStrategy, List[OnlineStrategy]]) – an instance of OnlineStrategy or a list of OnlineStrategy
begin_time (Union[str,pd.Timestamp], optional) – the OnlineManager will begin at this time. Defaults to None for using the latest date.
trainer (Trainer) – the trainer to train task. None for using TrainerR.
freq (str, optional) – data frequency. Defaults to “day”.

first_train(strategies: List[qlib.workflow.online.strategy.OnlineStrategy] = None, model_kwargs: dict = {})¶

Get tasks from every strategy’s first_tasks method and train them. If using DelayTrainer, it can finish training all together after every strategy’s first_tasks.

Parameters:	strategies (List[OnlineStrategy]) – the strategies list (need this param when adding strategies). None for use default strategies. model_kwargs (dict) – the params for prepare_online_models

routine(cur_time: Union[str, pandas._libs.tslibs.timestamps.Timestamp] = None, task_kwargs: dict = {}, model_kwargs: dict = {}, signal_kwargs: dict = {})¶

Typical update process for every strategy and record the online history.

The typical update process after a routine, such as day by day or month by month. The process is: Update predictions -> Prepare tasks -> Prepare online models -> Prepare signals.

If using DelayTrainer, it can finish training all together after every strategy’s prepare_tasks.

Parameters:	cur_time (Union[str,pd.Timestamp], optional) – run routine method in this time. Defaults to None. task_kwargs (dict) – the params for prepare_tasks model_kwargs (dict) – the params for prepare_online_models signal_kwargs (dict) – the params for prepare_signals

get_collector(**kwargs) → qlib.workflow.task.collect.MergeCollector¶

Get the instance of Collector to collect results from every strategy. This collector can be a basis as the signals preparation.

Parameters:	**kwargs – the params for get_collector.
Returns:	the collector to merge other collectors.
Return type:	MergeCollector

add_strategy(strategies: Union[qlib.workflow.online.strategy.OnlineStrategy, List[qlib.workflow.online.strategy.OnlineStrategy]])¶

Add some new strategies to OnlineManager.

Parameters:	strategy (Union[OnlineStrategy, List[OnlineStrategy]]) – a list of OnlineStrategy

prepare_signals(prepare_func: Callable = <qlib.model.ens.ensemble.AverageEnsemble object>, over_write=False)¶

After preparing the data of the last routine (a box in box-plot) which means the end of the routine, we can prepare trading signals for the next routine.

NOTE: Given a set prediction, all signals before these prediction end times will be prepared well.

Even if the latest signal already exists, the latest calculation result will be overwritten.

Note

Given a prediction of a certain time, all signals before this time will be prepared well.

Parameters:	prepare_func (Callable, optional) – Get signals from a dict after collecting. Defaults to AverageEnsemble(), the results collected by MergeCollector must be {xxx:pred}. over_write (bool, optional) – If True, the new signals will overwrite. If False, the new signals will append to the end of signals. Defaults to False.
Returns:	the signals.
Return type:	pd.DataFrame

get_signals() → Union[pandas.core.series.Series, pandas.core.frame.DataFrame]¶

Get prepared online signals.

Returns:	pd.Series for only one signals every datetime. pd.DataFrame for multiple signals, for example, buy and sell operations use different trading signals.
Return type:	Union[pd.Series, pd.DataFrame]

simulate(end_time=None, frequency='day', task_kwargs={}, model_kwargs={}, signal_kwargs={}) → Union[pandas.core.series.Series, pandas.core.frame.DataFrame]¶

Starting from the current time, this method will simulate every routine in OnlineManager until the end time.

Considering the parallel training, the models and signals can be prepared after all routine simulating.

The delay training way can be DelayTrainer and the delay preparing signals way can be delay_prepare.

Parameters:	end_time – the time the simulation will end frequency – the calendar frequency task_kwargs (dict) – the params for prepare_tasks model_kwargs (dict) – the params for prepare_online_models signal_kwargs (dict) – the params for prepare_signals
Returns:	pd.Series for only one signals every datetime. pd.DataFrame for multiple signals, for example, buy and sell operations use different trading signals.
Return type:	Union[pd.Series, pd.DataFrame]

delay_prepare(model_kwargs={}, signal_kwargs={})¶

Prepare all models and signals if something is waiting for preparation.

Parameters:	model_kwargs – the params for end_train signal_kwargs – the params for prepare_signals

Online Strategy¶

OnlineStrategy module is an element of online serving.

class qlib.workflow.online.strategy.OnlineStrategy(name_id: str)¶

OnlineStrategy is working with Online Manager, responding to how the tasks are generated, the models are updated and signals are prepared.

__init__(name_id: str)¶

Init OnlineStrategy. This module MUST use Trainer to finishing model training.

Parameters:	name_id (str) – a unique name or id. trainer (Trainer, optional) – a instance of Trainer. Defaults to None.

prepare_tasks(cur_time, **kwargs) → List[dict]¶

After the end of a routine, check whether we need to prepare and train some new tasks based on cur_time (None for latest).. Return the new tasks waiting for training.

You can find the last online models by OnlineTool.online_models.

prepare_online_models(trained_models, cur_time=None) → List[object]¶

Select some models from trained models and set them to online models. This is a typical implementation to online all trained models, you can override it to implement the complex method. You can find the last online models by OnlineTool.online_models if you still need them.

NOTE: Reset all online models to trained models. If there are no trained models, then do nothing.

NOTE:: Current implementation is very naive. Here is a more complex situation which is more closer to the practical scenarios. 1. Train new models at the day before test_start (at time stamp T) 2. Switch models at the test_start (at time timestamp T + 1 typically)

Parameters:	models (list) – a list of models. cur_time (pd.Dataframe) – current time from OnlineManger. None for the latest.
Returns:	a list of online models.
Return type:	List[object]

first_tasks() → List[dict]¶: Generate a series of tasks firstly and return them.

get_collector() → qlib.workflow.task.collect.Collector¶

Get the instance of Collector to collect different results of this strategy.

For example:

collect predictions in Recorder
collect signals in a txt file

Returns:	Collector

class qlib.workflow.online.strategy.RollingStrategy(name_id: str, task_template: Union[dict, List[dict]], rolling_gen: qlib.workflow.task.gen.RollingGen)¶

This example strategy always uses the latest rolling model sas online models.

__init__(name_id: str, task_template: Union[dict, List[dict]], rolling_gen: qlib.workflow.task.gen.RollingGen)¶

Init RollingStrategy.

Assumption: the str of name_id, the experiment name, and the trainer’s experiment name are the same.

Parameters:	name_id (str) – a unique name or id. Will be also the name of the Experiment. task_template (Union[dict, List[dict]]) – a list of task_template or a single template, which will be used to generate many tasks using rolling_gen. rolling_gen (RollingGen) – an instance of RollingGen

get_collector(process_list=[<qlib.model.ens.group.RollingGroup object>], rec_key_func=None, rec_filter_func=None, artifacts_key=None)¶

Get the instance of Collector to collect results. The returned collector must distinguish results in different models.

Assumption: the models can be distinguished based on the model name and rolling test segments. If you do not want this assumption, please implement your method or use another rec_key_func.

Parameters:	rec_key_func (Callable) – a function to get the key of a recorder. If None, use recorder id. rec_filter_func (Callable, optional) – filter the recorder by return True or False. Defaults to None. artifacts_key (List[str], optional) – the artifacts key you want to get. If None, get all artifacts.

first_tasks() → List[dict]¶

Use rolling_gen to generate different tasks based on task_template.

Returns:	a list of tasks
Return type:	List[dict]

prepare_tasks(cur_time) → List[dict]¶

Prepare new tasks based on cur_time (None for the latest).

You can find the last online models by OnlineToolR.online_models.

Returns:	a list of new tasks.
Return type:	List[dict]

Online Tool¶

OnlineTool is a module to set and unset a series of online models. The online models are some decisive models in some time points, which can be changed with the change of time. This allows us to use efficient submodels as the market-style changing.

class qlib.workflow.online.utils.OnlineTool¶

OnlineTool will manage online models in an experiment that includes the model recorders.

__init__()¶: Init OnlineTool.

set_online_tag(tag, recorder: Union[list, object])¶

Set tag to the model to sign whether online.

Parameters:	tag (str) – the tags in ONLINE_TAG, OFFLINE_TAG recorder (Union[list,object]) – the model’s recorder

get_online_tag(recorder: object) → str¶

Given a model recorder and return its online tag.

Parameters:	recorder (Object) – the model’s recorder
Returns:	the online tag
Return type:	str

reset_online_tag(recorder: Union[list, object])¶

Offline all models and set the recorders to ‘online’.

Parameters:	recorder (Union[list,object]) – the recorder you want to reset to ‘online’.

online_models() → list¶

Get current online models

Returns:	a list of online models.
Return type:	list

update_online_pred(to_date=None)¶

Update the predictions of online models to to_date.

Parameters:	to_date (pd.Timestamp) – the pred before this date will be updated. None for updating to the latest.

class qlib.workflow.online.utils.OnlineToolR(default_exp_name: str = None)¶

The implementation of OnlineTool based on (R)ecorder.

__init__(default_exp_name: str = None)¶

Init OnlineToolR.

Parameters:	default_exp_name (str) – the default experiment name.

set_online_tag(tag, recorder: Union[qlib.workflow.recorder.Recorder, List[T]])¶

Set tag to the model’s recorder to sign whether online.

Parameters:	tag (str) – the tags in ONLINE_TAG, NEXT_ONLINE_TAG, OFFLINE_TAG recorder (Union[Recorder, List]) – a list of Recorder or an instance of Recorder

get_online_tag(recorder: qlib.workflow.recorder.Recorder) → str¶

Given a model recorder and return its online tag.

Parameters:	recorder (Recorder) – an instance of recorder
Returns:	the online tag
Return type:	str

reset_online_tag(recorder: Union[qlib.workflow.recorder.Recorder, List[T]], exp_name: str = None)¶

Offline all models and set the recorders to ‘online’.

Parameters:	recorder (Union[Recorder, List]) – the recorder you want to reset to ‘online’. exp_name (str) – the experiment name. If None, then use default_exp_name.

online_models(exp_name: str = None) → list¶

Get current online models

Parameters:	exp_name (str) – the experiment name. If None, then use default_exp_name.
Returns:	a list of online models.
Return type:	list

update_online_pred(to_date=None, from_date=None, exp_name: str = None)¶

Update the predictions of online models to to_date.

Parameters:	to_date (pd.Timestamp) – the pred before this date will be updated. None for updating to latest time in Calendar. exp_name (str) – the experiment name. If None, then use default_exp_name.

Updater¶

Updater is a module to update artifacts such as predictions when the stock data is updating.

class qlib.workflow.online.update.RMDLoader(rec: qlib.workflow.recorder.Recorder)¶

Recorder Model Dataset Loader

__init__(rec: qlib.workflow.recorder.Recorder)¶: Initialize self. See help(type(self)) for accurate signature.

get_dataset(start_time, end_time, segments=None) → qlib.data.dataset.DatasetH¶

Load, config and setup dataset.

This dataset is for inference.

Parameters:	start_time – the start_time of underlying data end_time – the end_time of underlying data segments – dict the segments config for dataset Due to the time series dataset (TSDatasetH), the test segments maybe different from start_time and end_time
Returns:	the instance of DatasetH
Return type:	DatasetH

class qlib.workflow.online.update.RecordUpdater(record: qlib.workflow.recorder.Recorder, *args, **kwargs)¶

Update a specific recorders

__init__(record: qlib.workflow.recorder.Recorder, *args, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

update(*args, **kwargs)¶: Update info for specific recorder

class qlib.workflow.online.update.DSBasedUpdater(record: qlib.workflow.recorder.Recorder, to_date=None, from_date=None, hist_ref: int = 0, freq='day', fname='pred.pkl')¶

Dataset-Based Updater - Provding updating feature for Updating data based on Qlib Dataset

Assumption - Based on Qlib dataset - The data to be updated is a multi-level index pd.DataFrame. For example label , prediction.

LABEL0

datetime instrument 2021-05-10 SH600000 0.006965

SH600004 0.003407

… … 2021-05-28 SZ300498 0.015748

SZ300676 -0.001321

__init__(record: qlib.workflow.recorder.Recorder, to_date=None, from_date=None, hist_ref: int = 0, freq='day', fname='pred.pkl')¶

Init PredUpdater.

Expected behavior in following cases: - if to_date is greater than the max date in the calendar, the data will be updated to the latest date - if there are data before from_date or after to_date, only the data between from_date and to_date are affected.

Parameters:

record – Recorder
to_date –
update to prediction to the to_date if to_date is None:

data will updated to the latest date.
from_date –
the update will start from from_date if from_date is None:

the updating will occur on the next tick after the latest data in historical data
hist_ref –
int Sometimes, the dataset will have historical depends. Leave the problem to users to set the length of historical dependency

Note

the start_time is not included in the hist_ref

prepare_data() → qlib.data.dataset.DatasetH¶

Load dataset

Separating this function will make it easier to reuse the dataset

Returns:	the instance of DatasetH
Return type:	DatasetH

update(dataset: qlib.data.dataset.DatasetH = None)¶

Update the data in a recorder.

Parameters:	DatasetH – the instance of DatasetH. None for reprepare.

get_update_data(dataset: qlib.data.dataset.Dataset) → pandas.core.frame.DataFrame¶

return the updated data based on the given dataset

The difference between get_update_data and update - update_date only include some data specific feature - update include some general routine steps(e.g. prepare dataset, checking)

class qlib.workflow.online.update.PredUpdater(record: qlib.workflow.recorder.Recorder, to_date=None, from_date=None, hist_ref: int = 0, freq='day', fname='pred.pkl')¶

Update the prediction in the Recorder

get_update_data(dataset: qlib.data.dataset.Dataset) → pandas.core.frame.DataFrame¶

return the updated data based on the given dataset

The difference between get_update_data and update - update_date only include some data specific feature - update include some general routine steps(e.g. prepare dataset, checking)

class qlib.workflow.online.update.LabelUpdater(record: qlib.workflow.recorder.Recorder, to_date=None, **kwargs)¶

Update the label in the recorder

Assumption - The label is generated from record_temp.SignalRecord.

__init__(record: qlib.workflow.recorder.Recorder, to_date=None, **kwargs)¶

Init PredUpdater.

Expected behavior in following cases: - if to_date is greater than the max date in the calendar, the data will be updated to the latest date - if there are data before from_date or after to_date, only the data between from_date and to_date are affected.

Parameters:

record – Recorder
to_date –
update to prediction to the to_date if to_date is None:

data will updated to the latest date.
from_date –
the update will start from from_date if from_date is None:

the updating will occur on the next tick after the latest data in historical data
hist_ref –
int Sometimes, the dataset will have historical depends. Leave the problem to users to set the length of historical dependency

Note

the start_time is not included in the hist_ref

get_update_data(dataset: qlib.data.dataset.Dataset) → pandas.core.frame.DataFrame¶

return the updated data based on the given dataset

The difference between get_update_data and update - update_date only include some data specific feature - update include some general routine steps(e.g. prepare dataset, checking)

Building Formulaic Alphas¶

Introduction¶

In quantitative trading practice, designing novel factors that can explain and predict future asset returns are of vital importance to the profitability of a strategy. Such factors are usually called alpha factors, or alphas in short.

A formulaic alpha, as the name suggests, is a kind of alpha that can be presented as a formula or a mathematical expression.

Building Formulaic Alphas in `Qlib`¶

In Qlib, users can easily build formulaic alphas.

Example¶

MACD, short for moving average convergence/divergence, is a formulaic alpha used in technical analysis of stock prices. It is designed to reveal changes in the strength, direction, momentum, and duration of a trend in a stock’s price.

MACD can be presented as the following formula:

\[MACD = 2\times (DIF-DEA)\]

Note

DIF means Differential value, which is 12-period EMA minus 26-period EMA.

\[DIF = \frac{EMA(CLOSE, 12) - EMA(CLOSE, 26)}{CLOSE}\]

`DEA`means a 9-period EMA of the DIF.

\[DEA = \frac{EMA(DIF, 9)}{CLOSE}\]

Users can use Data Handler to build formulaic alphas MACD in qlib:

Note

Users need to initialize Qlib with qlib.init first. Please refer to initialization.

>> from qlib.data.dataset.loader import QlibDataLoader
>> 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
>> label_names = ['LABEL']
>> data_loader_config = {
..     "feature": (fields, names),
..     "label": (labels, label_names)
.. }
>> data_loader = QlibDataLoader(config=data_loader_config)
>> df = data_loader.load(instruments='csi300', start_time='2010-01-01', end_time='2017-12-31')
>> print(df)
                        feature     label
                           MACD     LABEL
datetime   instrument
2010-01-04 SH600000   -0.011547 -0.019672
           SH600004    0.002745 -0.014721
           SH600006    0.010133  0.002911
           SH600008   -0.001113  0.009818
           SH600009    0.025878 -0.017758
...                         ...       ...
2017-12-29 SZ300124    0.007306 -0.005074
           SZ300136   -0.013492  0.056352
           SZ300144   -0.000966  0.011853
           SZ300251    0.004383  0.021739
           SZ300315   -0.030557  0.012455

Reference¶

To learn more about Data Loader, please refer to Data Loader

To learn more about Data API, please refer to Data API

`Online` & `Offline` mode¶

Introduction¶

Qlib supports Online mode and Offline mode. Only the Offline mode is introduced in this document.

The Online mode is designed to solve the following problems:

Manage the data in a centralized way. Users don’t have to manage data of different versions.
Reduce the amount of cache to be generated.
Make the data can be accessed in a remote way.

Qlib-Server¶

Qlib-Server is the assorted server system for Qlib, which utilizes Qlib for basic calculations and provides extensive server system and cache mechanism. With QLibServer, the data provided for Qlib can be managed in a centralized manner. With Qlib-Server, users can use Qlib in Online mode.

Reference¶

If users are interested in Qlib-Server and Online mode, please refer to Qlib-Server Project and Qlib-Server Document.

Serialization¶

Introduction¶

Qlib supports dumping the state of DataHandler, DataSet, Processor and Model, etc. into a disk and reloading them.

Serializable Class¶

Qlib provides a base class qlib.utils.serial.Serializable, whose state can be dumped into or loaded from disk in pickle format. When users dump the state of a Serializable instance, the attributes of the instance whose name does not start with _ will be saved on the disk. However, users can use config method or override default_dump_all attribute to prevent this feature.

Users can also override pickle_backend attribute to choose a pickle backend. The supported value is “pickle” (default and common) and “dill” (dump more things such as function, more information in here).

Example¶

Qlib’s serializable class includes DataHandler, DataSet, Processor and Model, etc., which are subclass of qlib.utils.serial.Serializable. Specifically, qlib.data.dataset.DatasetH is one of them. Users can serialize DatasetH as follows.

##=============dump dataset=============
dataset.to_pickle(path="dataset.pkl") # dataset is an instance of qlib.data.dataset.DatasetH

##=============reload dataset=============
with open("dataset.pkl", "rb") as file_dataset:
    dataset = pickle.load(file_dataset)

Note

Only state of DatasetH should be saved on the disk, such as some mean and variance used for data normalization, etc.

After reloading the DatasetH, users need to reinitialize it. It means that users can reset some states of DatasetH or QlibDataHandler such as instruments, start_time, end_time and segments, etc., and generate new data according to the states (data is not state and should not be saved on the disk).

A more detailed example is in this link.

API¶

Please refer to Serializable API.

Task Management¶

Introduction¶

The Workflow part introduces how to run research workflow in a loosely-coupled way. But it can only execute one task when you use qrun. To automatically generate and execute different tasks, Task Management provides a whole process including Task Generating, Task Storing, Task Training and Task Collecting. With this module, users can run their task automatically at different periods, in different losses, or even by different models.The processes of task generation, model training and combine and collect data are shown in the following figure.

This whole process can be used in Online Serving.

An example of the entire process is shown here.

Task Generating¶

A task consists of Model, Dataset, Record, or anything added by users. The specific task template can be viewed in Task Section. Even though the task template is fixed, users can customize their TaskGen to generate different task by task template.

Here is the base class of TaskGen:

class qlib.workflow.task.gen.TaskGen¶

The base class for generating different tasks

Example 1:

input: a specific task template and rolling steps

output: rolling version of the tasks

Example 2:

input: a specific task template and losses list

output: a set of tasks with different losses

generate(task: dict) → List[dict]¶

Generate different tasks based on a task template

Parameters:	task (dict) – a task template
Returns:	A list of tasks
Return type:	typing.List[dict]

Qlib provides a class RollingGen to generate a list of task of the dataset in different date segments. This class allows users to verify the effect of data from different periods on the model in one experiment. More information is here.

Task Storing¶

To achieve higher efficiency and the possibility of cluster operation, Task Manager will store all tasks in MongoDB. TaskManager can fetch undone tasks automatically and manage the lifecycle of a set of tasks with error handling. Users MUST finish the configuration of MongoDB when using this module.

Users need to provide the MongoDB URL and database name for using TaskManager in initialization or make a statement like this.

from qlib.config import C
C["mongo"] = {
    "task_url" : "mongodb://localhost:27017/", # your MongoDB url
    "task_db_name" : "rolling_db" # database name
}

class qlib.workflow.task.manage.TaskManager(task_pool: str)¶

Here is what will a task looks like when it created by TaskManager

{
    'def': pickle serialized task definition.  using pickle will make it easier
    'filter': json-like data. This is for filtering the tasks.
    'status': 'waiting' | 'running' | 'done'
    'res': pickle serialized task result,
}

The tasks manager assumes that you will only update the tasks you fetched. The mongo fetch one and update will make it date updating secure.

This class can be used as a tool from commandline. Here are serveral examples. You can view the help of manage module with the following commands: python -m qlib.workflow.task.manage -h # show manual of manage module CLI python -m qlib.workflow.task.manage wait -h # show manual of the wait command of manage

python -m qlib.workflow.task.manage -t <pool_name> wait
python -m qlib.workflow.task.manage -t <pool_name> task_stat

Note

Assumption: the data in MongoDB was encoded and the data out of MongoDB was decoded

Here are four status which are:

STATUS_WAITING: waiting for training

STATUS_RUNNING: training

STATUS_PART_DONE: finished some step and waiting for next step

STATUS_DONE: all work done

__init__(task_pool: str)¶

Init Task Manager, remember to make the statement of MongoDB url and database name firstly. A TaskManager instance serves a specific task pool. The static method of this module serves the whole MongoDB.

Parameters:	task_pool (str) – the name of Collection in MongoDB

static list() → list¶

List the all collection(task_pool) of the db.

Returns:	list

replace_task(task, new_task)¶

Use a new task to replace a old one

Parameters:	task – old task new_task – new task

insert_task(task)¶

Insert a task.

Parameters:	task – the task waiting for insert
Returns:	pymongo.results.InsertOneResult

insert_task_def(task_def)¶

Insert a task to task_pool

Parameters:	task_def (dict) – the task definition
Returns:
Return type:	pymongo.results.InsertOneResult

create_task(task_def_l, dry_run=False, print_nt=False) → List[str]¶

If the tasks in task_def_l are new, then insert new tasks into the task_pool, and record inserted_id. If a task is not new, then just query its _id.

Parameters:	task_def_l (list) – a list of task dry_run (bool) – if insert those new tasks to task pool print_nt (bool) – if print new task
Returns:	a list of the _id of task_def_l
Return type:	List[str]

fetch_task(query={}, status='waiting') → dict¶

Use query to fetch tasks.

Parameters:	query (dict, optional) – query dict. Defaults to {}. status (str, optional) – [description]. Defaults to STATUS_WAITING.
Returns:	a task(document in collection) after decoding
Return type:	dict

safe_fetch_task(query={}, status='waiting')¶

Fetch task from task_pool using query with contextmanager

Parameters:	query (dict) – the dict of query
Returns:	dict
Return type:	a task(document in collection) after decoding

query(query={}, decode=True)¶

Query task in collection. This function may raise exception pymongo.errors.CursorNotFound: cursor id not found if it takes too long to iterate the generator

python -m qlib.workflow.task.manage -t <your task pool> query ‘{“_id”: “615498be837d0053acbc5d58”}’

Parameters:	query (dict) – the dict of query decode (bool) –
Returns:	dict
Return type:	a task(document in collection) after decoding

re_query(_id) → dict¶

Use _id to query task.

Parameters:	_id (str) – _id of a document
Returns:	a task(document in collection) after decoding
Return type:	dict

commit_task_res(task, res, status='done')¶

Commit the result to task[‘res’].

Parameters:	task ([type]) – [description] res (object) – the result you want to save status (str, optional) – STATUS_WAITING, STATUS_RUNNING, STATUS_DONE, STATUS_PART_DONE. Defaults to STATUS_DONE.

return_task(task, status='waiting')¶

Return a task to status. Alway using in error handling.

Parameters:	task ([type]) – [description] status (str, optional) – STATUS_WAITING, STATUS_RUNNING, STATUS_DONE, STATUS_PART_DONE. Defaults to STATUS_WAITING.

remove(query={})¶

Remove the task using query

Parameters:	query (dict) – the dict of query

task_stat(query={}) → dict¶

Count the tasks in every status.

Parameters:	query (dict, optional) – the query dict. Defaults to {}.
Returns:	dict

reset_waiting(query={})¶

Reset all running task into waiting status. Can be used when some running task exit unexpected.

Parameters:	query (dict, optional) – the query dict. Defaults to {}.

prioritize(task, priority: int)¶

Set priority for task

Parameters:	task (dict) – The task query from the database priority (int) – the target priority

wait(query={})¶

When multiprocessing, the main progress may fetch nothing from TaskManager because there are still some running tasks. So main progress should wait until all tasks are trained well by other progress or machines.

Parameters:	query (dict, optional) – the query dict. Defaults to {}.

More information of Task Manager can be found in here.

Task Training¶

After generating and storing those task, it’s time to run the task which is in the WAITING status. Qlib provides a method called run_task to run those task in task pool, however, users can also customize how tasks are executed. An easy way to get the task_func is using qlib.model.trainer.task_train directly. It will run the whole workflow defined by task, which includes Model, Dataset, Record.

qlib.workflow.task.manage.run_task(task_func: Callable, task_pool: str, query: dict = {}, force_release: bool = False, before_status: str = 'waiting', after_status: str = 'done', **kwargs)¶

While the task pool is not empty (has WAITING tasks), use task_func to fetch and run tasks in task_pool

After running this method, here are 4 situations (before_status -> after_status):

STATUS_WAITING -> STATUS_DONE: use task[“def”] as task_func param, it means that the task has not been started

STATUS_WAITING -> STATUS_PART_DONE: use task[“def”] as task_func param

STATUS_PART_DONE -> STATUS_PART_DONE: use task[“res”] as task_func param, it means that the task has been started but not completed

STATUS_PART_DONE -> STATUS_DONE: use task[“res”] as task_func param

Parameters:

task_func (Callable) –

def (task_def, **kwargs) -> <res which will be committed>

the function to run the task
task_pool (str) – the name of the task pool (Collection in MongoDB)
query (dict) – will use this dict to query task_pool when fetching task
force_release (bool) – will the program force to release the resource
before_status (str:) – the tasks in before_status will be fetched and trained. Can be STATUS_WAITING, STATUS_PART_DONE.
after_status (str:) – the tasks after trained will become after_status. Can be STATUS_WAITING, STATUS_PART_DONE.
kwargs – the params for task_func

Meanwhile, Qlib provides a module called Trainer.

class qlib.model.trainer.Trainer¶

The trainer can train a list of models. There are Trainer and DelayTrainer, which can be distinguished by when it will finish real training.

__init__()¶: Initialize self. See help(type(self)) for accurate signature.

train(tasks: list, *args, **kwargs) → list¶

Given a list of task definitions, begin training, and return the models.

For Trainer, it finishes real training in this method. For DelayTrainer, it only does some preparation in this method.

Parameters:	tasks – a list of tasks
Returns:	a list of models
Return type:	list

end_train(models: list, *args, **kwargs) → list¶

Given a list of models, finished something at the end of training if you need. The models may be Recorder, txt file, database, and so on.

For Trainer, it does some finishing touches in this method. For DelayTrainer, it finishes real training in this method.

Parameters:	models – a list of models
Returns:	a list of models
Return type:	list

is_delay() → bool¶

If Trainer will delay finishing end_train.

Returns:	if DelayTrainer
Return type:	bool

has_worker() → bool¶

Some trainer has backend worker to support parallel training This method can tell if the worker is enabled.

Returns:	if the worker is enabled
Return type:	bool

worker()¶

start the worker

Raises:	NotImplementedError: – If the worker is not supported

Trainer will train a list of tasks and return a list of model recorders. Qlib offer two kinds of Trainer, TrainerR is the simplest way and TrainerRM is based on TaskManager to help manager tasks lifecycle automatically. If you do not want to use Task Manager to manage tasks, then use TrainerR to train a list of tasks generated by TaskGen is enough. Here are the details about different Trainer.

Task Collecting¶

Before collecting model training results, you need to use the qlib.init to specify the path of mlruns.

To collect the results of task after training, Qlib provides Collector, Group and Ensemble to collect the results in a readable, expandable and loosely-coupled way.

Collector can collect objects from everywhere and process them such as merging, grouping, averaging and so on. It has 2 step action including collect (collect anything in a dict) and process_collect (process collected dict).

Group also has 2 steps including group (can group a set of object based on group_func and change them to a dict) and reduce (can make a dict become an ensemble based on some rule). For example: {(A,B,C1): object, (A,B,C2): object} —group—> {(A,B): {C1: object, C2: object}} —reduce—> {(A,B): object}

Ensemble can merge the objects in an ensemble. For example: {C1: object, C2: object} —Ensemble—> object. You can set the ensembles you want in the Collector’s process_list. Common ensembles include AverageEnsemble and RollingEnsemble. Average ensemble is used to ensemble the results of different models in the same time period. Rollingensemble is used to ensemble the results of different models in the same time period

So the hierarchy is Collector’s second step corresponds to Group. And Group’s second step correspond to Ensemble.

For more information, please see Collector, Group and Ensemble, or the example.

API Reference¶

Here you can find all Qlib interfaces.

Data¶

Provider¶

class qlib.data.data.CalendarProvider(*args, **kwargs)¶

Calendar provider base class

Provide calendar data.

__init__(*args, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

calendar(start_time=None, end_time=None, freq='day', future=False)¶

Get calendar of certain market in given time range.

Parameters:	start_time (str) – start of the time range. end_time (str) – end of the time range. freq (str) – time frequency, available: year/quarter/month/week/day. future (bool) – whether including future trading day.
Returns:	calendar list
Return type:	list

locate_index(start_time, end_time, freq, future=False)¶

Locate the start time index and end time index in a calendar under certain frequency.

Parameters:

start_time (str) – start of the time range.
end_time (str) – end of the time range.
freq (str) – time frequency, available: year/quarter/month/week/day.
future (bool) – whether including future trading day.

Returns:

pd.Timestamp – the real start time.
pd.Timestamp – the real end time.
int – the index of start time.
int – the index of end time.

load_calendar(freq, future)¶

Load original calendar timestamp from file.

Parameters:	freq (str) – frequency of read calendar file. future (bool) –
Returns:	list of timestamps
Return type:	list

class qlib.data.data.InstrumentProvider(*args, **kwargs)¶

Instrument provider base class

Provide instrument data.

__init__(*args, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

static instruments(market: Union[List[T], str] = 'all', filter_pipe: Optional[List[T]] = None)¶

Get the general config dictionary for a base market adding several dynamic filters.

Parameters:

market (Union[List, str]) –

str:

market/industry/index shortname, e.g. all/sse/szse/sse50/csi300/csi500.

list:

[“ID1”, “ID2”]. A list of stocks
filter_pipe (list) – the list of dynamic filters.

Returns:

dict (if isinstance(market, str)) – dict of stockpool config. {`market`=>base market name, `filter_pipe`=>list of filters}

example :
list (if isinstance(market, list)) – just return the original list directly. NOTE: this will make the instruments compatible with more cases. The user code will be simpler.

list_instruments(instruments, start_time=None, end_time=None, freq='day', as_list=False)¶

List the instruments based on a certain stockpool config.

Parameters:	instruments (dict) – stockpool config. start_time (str) – start of the time range. end_time (str) – end of the time range. as_list (bool) – return instruments as list or dict.
Returns:	instruments list or dictionary with time spans
Return type:	dict or list

class qlib.data.data.FeatureProvider(*args, **kwargs)¶

Feature provider class

Provide feature data.

__init__(*args, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

feature(instrument, field, start_time, end_time, freq)¶

Get feature data.

Parameters:	instrument (str) – a certain instrument. field (str) – a certain field of feature. start_time (str) – start of the time range. end_time (str) – end of the time range. freq (str) – time frequency, available: year/quarter/month/week/day.
Returns:	data of a certain feature
Return type:	pd.Series

class qlib.data.data.ExpressionProvider¶

Expression provider class

Provide Expression data.

__init__()¶: Initialize self. See help(type(self)) for accurate signature.

expression(instrument, field, start_time=None, end_time=None, freq='day')¶

Get Expression data.

Parameters:	instrument (str) – a certain instrument. field (str) – a certain field of feature. start_time (str) – start of the time range. end_time (str) – end of the time range. freq (str) – time frequency, available: year/quarter/month/week/day.
Returns:	data of a certain expression
Return type:	pd.Series

class qlib.data.data.DatasetProvider¶

Dataset provider class

Provide Dataset data.

dataset(instruments, fields, start_time=None, end_time=None, freq='day', inst_processors=[])¶

Get dataset data.

Parameters:	instruments (list or dict) – list/dict of instruments or dict of stockpool config. fields (list) – list of feature instances. start_time (str) – start of the time range. end_time (str) – end of the time range. freq (str) – time frequency. inst_processors (Iterable[Union[dict, InstProcessor]]) – the operations performed on each instrument
Returns:	a pandas dataframe with <instrument, datetime> index.
Return type:	pd.DataFrame

static get_instruments_d(instruments, freq)¶: Parse different types of input instruments to output instruments_d Wrong format of input instruments will lead to exception.

static get_column_names(fields)¶: Get column names from input fields

static dataset_processor(instruments_d, column_names, start_time, end_time, freq, inst_processors=[])¶: Load and process the data, return the data set. - default using multi-kernel method.

static expression_calculator(inst, start_time, end_time, freq, column_names, spans=None, g_config=None, inst_processors=[])¶

Calculate the expressions for one instrument, return a df result. If the expression has been calculated before, load from cache.

return value: A data frame with index ‘datetime’ and other data columns.

class qlib.data.data.LocalCalendarProvider(**kwargs)¶

Local calendar data provider class

Provide calendar data from local data source.

__init__(**kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

load_calendar(freq, future)¶

Load original calendar timestamp from file.

Parameters:	freq (str) – frequency of read calendar file. future (bool) –
Returns:	list of timestamps
Return type:	list

class qlib.data.data.LocalInstrumentProvider(*args, **kwargs)¶

Local instrument data provider class

Provide instrument data from local data source.

list_instruments(instruments, start_time=None, end_time=None, freq='day', as_list=False)¶

List the instruments based on a certain stockpool config.

Parameters:	instruments (dict) – stockpool config. start_time (str) – start of the time range. end_time (str) – end of the time range. as_list (bool) – return instruments as list or dict.
Returns:	instruments list or dictionary with time spans
Return type:	dict or list

class qlib.data.data.LocalFeatureProvider(**kwargs)¶

Local feature data provider class

Provide feature data from local data source.

__init__(**kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

feature(instrument, field, start_index, end_index, freq)¶

Get feature data.

Parameters:	instrument (str) – a certain instrument. field (str) – a certain field of feature. start_time (str) – start of the time range. end_time (str) – end of the time range. freq (str) – time frequency, available: year/quarter/month/week/day.
Returns:	data of a certain feature
Return type:	pd.Series

class qlib.data.data.LocalExpressionProvider¶

Local expression data provider class

Provide expression data from local data source.

expression(instrument, field, start_time=None, end_time=None, freq='day')¶

Get Expression data.

Parameters:	instrument (str) – a certain instrument. field (str) – a certain field of feature. start_time (str) – start of the time range. end_time (str) – end of the time range. freq (str) – time frequency, available: year/quarter/month/week/day.
Returns:	data of a certain expression
Return type:	pd.Series

class qlib.data.data.LocalDatasetProvider¶

Local dataset data provider class

Provide dataset data from local data source.

__init__()¶: Initialize self. See help(type(self)) for accurate signature.

dataset(instruments, fields, start_time=None, end_time=None, freq='day', inst_processors=[])¶

Get dataset data.

Parameters:	instruments (list or dict) – list/dict of instruments or dict of stockpool config. fields (list) – list of feature instances. start_time (str) – start of the time range. end_time (str) – end of the time range. freq (str) – time frequency. inst_processors (Iterable[Union[dict, InstProcessor]]) – the operations performed on each instrument
Returns:	a pandas dataframe with <instrument, datetime> index.
Return type:	pd.DataFrame

static multi_cache_walker(instruments, fields, start_time=None, end_time=None, freq='day')¶: This method is used to prepare the expression cache for the client. Then the client will load the data from expression cache by itself.

static cache_walker(inst, start_time, end_time, freq, column_names)¶: If the expressions of one instrument haven’t been calculated before, calculate it and write it into expression cache.

class qlib.data.data.ClientCalendarProvider¶

Client calendar data provider class

Provide calendar data by requesting data from server as a client.

__init__()¶: Initialize self. See help(type(self)) for accurate signature.

calendar(start_time=None, end_time=None, freq='day', future=False)¶

Get calendar of certain market in given time range.

Parameters:	start_time (str) – start of the time range. end_time (str) – end of the time range. freq (str) – time frequency, available: year/quarter/month/week/day. future (bool) – whether including future trading day.
Returns:	calendar list
Return type:	list

class qlib.data.data.ClientInstrumentProvider¶

Client instrument data provider class

Provide instrument data by requesting data from server as a client.

__init__()¶: Initialize self. See help(type(self)) for accurate signature.

list_instruments(instruments, start_time=None, end_time=None, freq='day', as_list=False)¶

List the instruments based on a certain stockpool config.

Parameters:	instruments (dict) – stockpool config. start_time (str) – start of the time range. end_time (str) – end of the time range. as_list (bool) – return instruments as list or dict.
Returns:	instruments list or dictionary with time spans
Return type:	dict or list

class qlib.data.data.ClientDatasetProvider¶

Client dataset data provider class

Provide dataset data by requesting data from server as a client.

__init__()¶: Initialize self. See help(type(self)) for accurate signature.

dataset(instruments, fields, start_time=None, end_time=None, freq='day', disk_cache=0, return_uri=False, inst_processors=[])¶

Get dataset data.

Parameters:	instruments (list or dict) – list/dict of instruments or dict of stockpool config. fields (list) – list of feature instances. start_time (str) – start of the time range. end_time (str) – end of the time range. freq (str) – time frequency. inst_processors (Iterable[Union[dict, InstProcessor]]) – the operations performed on each instrument
Returns:	a pandas dataframe with <instrument, datetime> index.
Return type:	pd.DataFrame

class qlib.data.data.BaseProvider¶

Local provider class

To keep compatible with old qlib provider.

features(instruments, fields, start_time=None, end_time=None, freq='day', disk_cache=None, inst_processors=[])¶

disk_cache : int: whether to skip(0)/use(1)/replace(2) disk_cache

This function will try to use cache method which has a keyword disk_cache, and will use provider method if a type error is raised because the DatasetD instance is a provider class.

class qlib.data.data.LocalProvider¶

features_uri(instruments, fields, start_time, end_time, freq, disk_cache=1)¶

Return the uri of the generated cache of features/dataset

Parameters:	disk_cache – instruments – fields – start_time – end_time – freq –

class qlib.data.data.ClientProvider¶

Client Provider

Requesting data from server as a client. Can propose requests:

Calendar : Directly respond a list of calendars
Instruments (without filter): Directly respond a list/dict of instruments
Instruments (with filters): Respond a list/dict of instruments
Features : Respond a cache uri

The general workflow is described as follows: When the user use client provider to propose a request, the client provider will connect the server and send the request. The client will start to wait for the response. The response will be made instantly indicating whether the cache is available. The waiting procedure will terminate only when the client get the response saying feature_available is true. BUG : Everytime we make request for certain data we need to connect to the server, wait for the response and disconnect from it. We can’t make a sequence of requests within one connection. You can refer to https://python-socketio.readthedocs.io/en/latest/client.html for documentation of python-socketIO client.

__init__()¶: Initialize self. See help(type(self)) for accurate signature.

qlib.data.data.CalendarProviderWrapper¶: alias of qlib.data.data.CalendarProvider

qlib.data.data.InstrumentProviderWrapper¶: alias of qlib.data.data.InstrumentProvider

qlib.data.data.FeatureProviderWrapper¶: alias of qlib.data.data.FeatureProvider

qlib.data.data.ExpressionProviderWrapper¶: alias of qlib.data.data.ExpressionProvider

qlib.data.data.DatasetProviderWrapper¶: alias of qlib.data.data.DatasetProvider

qlib.data.data.BaseProviderWrapper¶: alias of qlib.data.data.BaseProvider

qlib.data.data.register_all_wrappers(C)¶

Filter¶

class qlib.data.filter.BaseDFilter¶

Dynamic Instruments Filter Abstract class

Users can override this class to construct their own filter

Override __init__ to input filter regulations

Override filter_main to use the regulations to filter instruments

__init__()¶: Initialize self. See help(type(self)) for accurate signature.

static from_config(config)¶

Construct an instance from config dict.

Parameters:	config (dict) – dict of config parameters.

to_config()¶

Construct an instance from config dict.

Returns:	return the dict of config parameters.
Return type:	dict

class qlib.data.filter.SeriesDFilter(fstart_time=None, fend_time=None)¶

Dynamic Instruments Filter Abstract class to filter a series of certain features

Filters should provide parameters:

filter start time
filter end time
filter rule

Override __init__ to assign a certain rule to filter the series.

Override _getFilterSeries to use the rule to filter the series and get a dict of {inst => series}, or override filter_main for more advanced series filter rule

__init__(fstart_time=None, fend_time=None)¶

Init function for filter base class.: Filter a set of instruments based on a certain rule within a certain period assigned by fstart_time and fend_time.

Parameters:	fstart_time (str) – 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.

filter_main(instruments, start_time=None, end_time=None)¶

Implement this method to filter the instruments.

Parameters:	instruments (dict) – input instruments to be filtered. start_time (str) – start of the time range. end_time (str) – end of the time range.
Returns:	filtered instruments, same structure as input instruments.
Return type:	dict

class qlib.data.filter.NameDFilter(name_rule_re, fstart_time=None, fend_time=None)¶

Name dynamic instrument filter

Filter the instruments based on a regulated name format.

A name rule regular expression is required.

__init__(name_rule_re, fstart_time=None, fend_time=None)¶

Init function for name filter class

name_rule_re: str: regular expression for the name rule.

static from_config(config)¶

Construct an instance from config dict.

Parameters:	config (dict) – dict of config parameters.

to_config()¶

Construct an instance from config dict.

Returns:	return the dict of config parameters.
Return type:	dict

class qlib.data.filter.ExpressionDFilter(rule_expression, fstart_time=None, fend_time=None, keep=False)¶

Expression dynamic instrument filter

Filter the instruments based on a certain expression.

An expression rule indicating a certain feature field is required.

Examples

basic features filter : rule_expression = ‘$close/$open>5’
cross-sectional features filter : rule_expression = ‘$rank($close)<10’
time-sequence features filter : rule_expression = ‘$Ref($close, 3)>100’

__init__(rule_expression, fstart_time=None, fend_time=None, keep=False)¶

Init function for expression filter class

fstart_time: str: filter the feature starting from this time.
fend_time: str: filter the feature ending by this time.
rule_expression: str: an input expression for the rule.
keep: bool: whether to keep the instruments of which features don’t exist in the filter time span.

static from_config(config)¶

Construct an instance from config dict.

Parameters:	config (dict) – dict of config parameters.

to_config()¶

Construct an instance from config dict.

Returns:	return the dict of config parameters.
Return type:	dict

Class¶

class qlib.data.base.Expression¶

Expression base class

load(instrument, start_index, end_index, freq)¶

load feature

Parameters:	instrument (str) – instrument code. start_index (str) – feature start index [in calendar]. end_index (str) – feature end index [in calendar]. freq (str) – feature frequency.
Returns:	feature series: The index of the series is the calendar index
Return type:	pd.Series

get_longest_back_rolling()¶

Get the longest length of historical data the feature has accessed

This is designed for getting the needed range of the data to calculate the features in specific range at first. However, situations like Ref(Ref($close, -1), 1) can not be handled rightly.

So this will only used for detecting the length of historical data needed.

get_extended_window_size()¶

get_extend_window_size

For to calculate this Operator in range[start_index, end_index] We have to get the leaf feature in range[start_index - lft_etd, end_index + rght_etd].

Returns:	lft_etd, rght_etd
Return type:	(int, int)

class qlib.data.base.Feature(name=None)¶

Static Expression

This kind of feature will load data from provider

__init__(name=None)¶: Initialize self. See help(type(self)) for accurate signature.

get_longest_back_rolling()¶

Get the longest length of historical data the feature has accessed

This is designed for getting the needed range of the data to calculate the features in specific range at first. However, situations like Ref(Ref($close, -1), 1) can not be handled rightly.

So this will only used for detecting the length of historical data needed.

get_extended_window_size()¶

get_extend_window_size

For to calculate this Operator in range[start_index, end_index] We have to get the leaf feature in range[start_index - lft_etd, end_index + rght_etd].

Returns:	lft_etd, rght_etd
Return type:	(int, int)

class qlib.data.base.ExpressionOps¶

Operator Expression

This kind of feature will use operator for feature construction on the fly.

Operator¶

class qlib.data.ops.ElemOperator(feature)¶

Element-wise Operator

Parameters:	feature (Expression) – feature instance
Returns:	feature operation output
Return type:	Expression

__init__(feature)¶: Initialize self. See help(type(self)) for accurate signature.

get_longest_back_rolling()¶

Get the longest length of historical data the feature has accessed

This is designed for getting the needed range of the data to calculate the features in specific range at first. However, situations like Ref(Ref($close, -1), 1) can not be handled rightly.

So this will only used for detecting the length of historical data needed.

get_extended_window_size()¶

get_extend_window_size

For to calculate this Operator in range[start_index, end_index] We have to get the leaf feature in range[start_index - lft_etd, end_index + rght_etd].

Returns:	lft_etd, rght_etd
Return type:	(int, int)

class qlib.data.ops.NpElemOperator(feature, func)¶

Numpy Element-wise Operator

Parameters:	feature (Expression) – feature instance func (str) – numpy feature operation method
Returns:	feature operation output
Return type:	Expression

__init__(feature, func)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Abs(feature)¶

Feature Absolute Value

Parameters:	feature (Expression) – feature instance
Returns:	a feature instance with absolute output
Return type:	Expression

__init__(feature)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Sign(feature)¶

Feature Sign

Parameters:	feature (Expression) – feature instance
Returns:	a feature instance with sign
Return type:	Expression

__init__(feature)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Log(feature)¶

Feature Log

Parameters:	feature (Expression) – feature instance
Returns:	a feature instance with log
Return type:	Expression

__init__(feature)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Power(feature, exponent)¶

Feature Power

Parameters:	feature (Expression) – feature instance
Returns:	a feature instance with power
Return type:	Expression

__init__(feature, exponent)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Mask(feature, instrument)¶

Feature Mask

Parameters:	feature (Expression) – feature instance instrument (str) – instrument mask
Returns:	a feature instance with masked instrument
Return type:	Expression

__init__(feature, instrument)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Not(feature)¶

Not Operator

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance
Returns:	feature elementwise not output
Return type:	Feature

__init__(feature)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.PairOperator(feature_left, feature_right)¶

Pair-wise operator

Parameters:	feature_left (Expression) – feature instance or numeric value feature_right (Expression) – feature instance or numeric value func (str) – operator function
Returns:	two features’ operation output
Return type:	Feature

__init__(feature_left, feature_right)¶: Initialize self. See help(type(self)) for accurate signature.

get_longest_back_rolling()¶

Get the longest length of historical data the feature has accessed

This is designed for getting the needed range of the data to calculate the features in specific range at first. However, situations like Ref(Ref($close, -1), 1) can not be handled rightly.

So this will only used for detecting the length of historical data needed.

get_extended_window_size()¶

get_extend_window_size

For to calculate this Operator in range[start_index, end_index] We have to get the leaf feature in range[start_index - lft_etd, end_index + rght_etd].

Returns:	lft_etd, rght_etd
Return type:	(int, int)

class qlib.data.ops.NpPairOperator(feature_left, feature_right, func)¶

Numpy Pair-wise operator

Parameters:	feature_left (Expression) – feature instance or numeric value feature_right (Expression) – feature instance or numeric value func (str) – operator function
Returns:	two features’ operation output
Return type:	Feature

__init__(feature_left, feature_right, func)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Add(feature_left, feature_right)¶

Add Operator

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance
Returns:	two features’ sum
Return type:	Feature

__init__(feature_left, feature_right)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Sub(feature_left, feature_right)¶

Subtract Operator

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance
Returns:	two features’ subtraction
Return type:	Feature

__init__(feature_left, feature_right)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Mul(feature_left, feature_right)¶

Multiply Operator

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance
Returns:	two features’ product
Return type:	Feature

__init__(feature_left, feature_right)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Div(feature_left, feature_right)¶

Division Operator

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance
Returns:	two features’ division
Return type:	Feature

__init__(feature_left, feature_right)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Greater(feature_left, feature_right)¶

Greater Operator

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance
Returns:	greater elements taken from the input two features
Return type:	Feature

__init__(feature_left, feature_right)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Less(feature_left, feature_right)¶

Less Operator

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance
Returns:	smaller elements taken from the input two features
Return type:	Feature

__init__(feature_left, feature_right)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Gt(feature_left, feature_right)¶

Greater Than Operator

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance
Returns:	bool series indicate left > right
Return type:	Feature

__init__(feature_left, feature_right)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Ge(feature_left, feature_right)¶

Greater Equal Than Operator

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance
Returns:	bool series indicate left >= right
Return type:	Feature

__init__(feature_left, feature_right)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Lt(feature_left, feature_right)¶

Less Than Operator

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance
Returns:	bool series indicate left < right
Return type:	Feature

__init__(feature_left, feature_right)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Le(feature_left, feature_right)¶

Less Equal Than Operator

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance
Returns:	bool series indicate left <= right
Return type:	Feature

__init__(feature_left, feature_right)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Eq(feature_left, feature_right)¶

Equal Operator

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance
Returns:	bool series indicate left == right
Return type:	Feature

__init__(feature_left, feature_right)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Ne(feature_left, feature_right)¶

Not Equal Operator

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance
Returns:	bool series indicate left != right
Return type:	Feature

__init__(feature_left, feature_right)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.And(feature_left, feature_right)¶

And Operator

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance
Returns:	two features’ row by row & output
Return type:	Feature

__init__(feature_left, feature_right)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Or(feature_left, feature_right)¶

Or Operator

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance
Returns:	two features’ row by row \| outputs
Return type:	Feature

__init__(feature_left, feature_right)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.If(condition, feature_left, feature_right)¶

If Operator

Parameters:	condition (Expression) – feature instance with bool values as condition feature_left (Expression) – feature instance feature_right (Expression) – feature instance

__init__(condition, feature_left, feature_right)¶: Initialize self. See help(type(self)) for accurate signature.

get_longest_back_rolling()¶

Get the longest length of historical data the feature has accessed

This is designed for getting the needed range of the data to calculate the features in specific range at first. However, situations like Ref(Ref($close, -1), 1) can not be handled rightly.

So this will only used for detecting the length of historical data needed.

get_extended_window_size()¶

get_extend_window_size

For to calculate this Operator in range[start_index, end_index] We have to get the leaf feature in range[start_index - lft_etd, end_index + rght_etd].

Returns:	lft_etd, rght_etd
Return type:	(int, int)

class qlib.data.ops.Rolling(feature, N, func)¶

Rolling Operator The meaning of rolling and expanding is the same in pandas. When the window is set to 0, the behaviour of the operator should follow expanding Otherwise, it follows rolling

Parameters:	feature (Expression) – feature instance N (int) – rolling window size func (str) – rolling method
Returns:	rolling outputs
Return type:	Expression

__init__(feature, N, func)¶: Initialize self. See help(type(self)) for accurate signature.

get_longest_back_rolling()¶

Get the longest length of historical data the feature has accessed

This is designed for getting the needed range of the data to calculate the features in specific range at first. However, situations like Ref(Ref($close, -1), 1) can not be handled rightly.

So this will only used for detecting the length of historical data needed.

get_extended_window_size()¶

get_extend_window_size

For to calculate this Operator in range[start_index, end_index] We have to get the leaf feature in range[start_index - lft_etd, end_index + rght_etd].

Returns:	lft_etd, rght_etd
Return type:	(int, int)

class qlib.data.ops.Ref(feature, N)¶

Feature Reference

Parameters:	feature (Expression) – feature instance N (int) – N = 0, retrieve the first data; N > 0, retrieve data of N periods ago; N < 0, future data
Returns:	a feature instance with target reference
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

get_longest_back_rolling()¶

Get the longest length of historical data the feature has accessed

This is designed for getting the needed range of the data to calculate the features in specific range at first. However, situations like Ref(Ref($close, -1), 1) can not be handled rightly.

So this will only used for detecting the length of historical data needed.

get_extended_window_size()¶

get_extend_window_size

For to calculate this Operator in range[start_index, end_index] We have to get the leaf feature in range[start_index - lft_etd, end_index + rght_etd].

Returns:	lft_etd, rght_etd
Return type:	(int, int)

class qlib.data.ops.Mean(feature, N)¶

Rolling Mean (MA)

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling average
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Sum(feature, N)¶

Rolling Sum

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling sum
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Std(feature, N)¶

Rolling Std

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling std
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Var(feature, N)¶

Rolling Variance

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling variance
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Skew(feature, N)¶

Rolling Skewness

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling skewness
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Kurt(feature, N)¶

Rolling Kurtosis

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling kurtosis
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Max(feature, N)¶

Rolling Max

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling max
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.IdxMax(feature, N)¶

Rolling Max Index

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling max index
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Min(feature, N)¶

Rolling Min

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling min
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.IdxMin(feature, N)¶

Rolling Min Index

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling min index
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Quantile(feature, N, qscore)¶

Rolling Quantile

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling quantile
Return type:	Expression

__init__(feature, N, qscore)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Med(feature, N)¶

Rolling Median

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling median
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Mad(feature, N)¶

Rolling Mean Absolute Deviation

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling mean absolute deviation
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Rank(feature, N)¶

Rolling Rank (Percentile)

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling rank
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Count(feature, N)¶

Rolling Count

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling count of number of non-NaN elements
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Delta(feature, N)¶

Rolling Delta

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with end minus start in rolling window
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Slope(feature, N)¶

Rolling Slope This operator calculate the slope between idx and feature. (e.g. [<feature_t1>, <feature_t2>, <feature_t3>] and [1, 2, 3])

Usage Example: - “Slope($close, %d)/$close”

# TODO: # Some users may want pair-wise rolling like Slope(A, B, N)

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with linear regression slope of given window
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Rsquare(feature, N)¶

Rolling R-value Square

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with linear regression r-value square of given window
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Resi(feature, N)¶

Rolling Regression Residuals

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with regression residuals of given window
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.WMA(feature, N)¶

Rolling WMA

Parameters:	feature (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with weighted moving average output
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.EMA(feature, N)¶

Rolling Exponential Mean (EMA)

Parameters:	feature (Expression) – feature instance N (int, float) – rolling window size
Returns:	a feature instance with regression r-value square of given window
Return type:	Expression

__init__(feature, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.PairRolling(feature_left, feature_right, N, func)¶

Pair Rolling Operator

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling output of two input features
Return type:	Expression

__init__(feature_left, feature_right, N, func)¶: Initialize self. See help(type(self)) for accurate signature.

get_longest_back_rolling()¶

Get the longest length of historical data the feature has accessed

This is designed for getting the needed range of the data to calculate the features in specific range at first. However, situations like Ref(Ref($close, -1), 1) can not be handled rightly.

So this will only used for detecting the length of historical data needed.

get_extended_window_size()¶

get_extend_window_size

For to calculate this Operator in range[start_index, end_index] We have to get the leaf feature in range[start_index - lft_etd, end_index + rght_etd].

Returns:	lft_etd, rght_etd
Return type:	(int, int)

class qlib.data.ops.Corr(feature_left, feature_right, N)¶

Rolling Correlation

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling correlation of two input features
Return type:	Expression

__init__(feature_left, feature_right, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.Cov(feature_left, feature_right, N)¶

Rolling Covariance

Parameters:	feature_left (Expression) – feature instance feature_right (Expression) – feature instance N (int) – rolling window size
Returns:	a feature instance with rolling max of two input features
Return type:	Expression

__init__(feature_left, feature_right, N)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.ops.OpsWrapper¶

Ops Wrapper

__init__()¶: Initialize self. See help(type(self)) for accurate signature.

register(ops_list: List[Union[Type[qlib.data.base.ExpressionOps], dict]])¶

register operator

Parameters:

ops_list (List[Union[Type[ExpressionOps], dict]]) –

if type(ops_list) is List[Type[ExpressionOps]], each element of ops_list represents the operator class, which should be the subclass of ExpressionOps.
if type(ops_list) is List[dict], each element of ops_list represents the config of operator, which has the following format:

{

“class”: class_name, “module_path”: path,

} Note: class should be the class name of operator, module_path should be a python module or path of file.

qlib.data.ops.register_all_ops(C)¶: register all operator

Cache¶

class qlib.data.cache.MemCacheUnit(*args, **kwargs)¶

Memory Cache Unit.

__init__(*args, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

limited¶: whether memory cache is limited

class qlib.data.cache.MemCache(mem_cache_size_limit=None, limit_type='length')¶

Memory cache.

__init__(mem_cache_size_limit=None, limit_type='length')¶

Parameters:	mem_cache_size_limit (cache max size.) – limit_type (length or sizeof; length(call fun: len), size(call fun: sys.getsizeof)) –

class qlib.data.cache.ExpressionCache(provider)¶

Expression cache mechanism base class.

This class is used to wrap expression provider with self-defined expression cache mechanism.

Note

Override the _uri and _expression method to create your own expression cache mechanism.

expression(instrument, field, start_time, end_time, freq)¶: Get expression data.

Note

Same interface as expression method in expression provider

update(cache_uri: Union[str, pathlib.Path], freq: str = 'day')¶

Update expression cache to latest calendar.

Overide this method to define how to update expression cache corresponding to users’ own cache mechanism.

Parameters:	cache_uri (str or Path) – the complete uri of expression cache file (include dir path). freq (str) –
Returns:	0(successful update)/ 1(no need to update)/ 2(update failure).
Return type:	int

class qlib.data.cache.DatasetCache(provider)¶

Dataset cache mechanism base class.

This class is used to wrap dataset provider with self-defined dataset cache mechanism.

Note

Override the _uri and _dataset method to create your own dataset cache mechanism.

dataset(instruments, fields, start_time=None, end_time=None, freq='day', disk_cache=1, inst_processors=[])¶: Get feature dataset.

Note

Same interface as dataset method in dataset provider

Note

The server use redis_lock to make sure read-write conflicts will not be triggered

but client readers are not considered.

update(cache_uri: Union[str, pathlib.Path], freq: str = 'day')¶

Update dataset cache to latest calendar.

Overide this method to define how to update dataset cache corresponding to users’ own cache mechanism.

Parameters:	cache_uri (str or Path) – the complete uri of dataset cache file (include dir path). freq (str) –
Returns:	0(successful update)/ 1(no need to update)/ 2(update failure)
Return type:	int

static cache_to_origin_data(data, fields)¶

cache data to origin data

Parameters:	data – pd.DataFrame, cache data. fields – feature fields.
Returns:	pd.DataFrame.

static normalize_uri_args(instruments, fields, freq)¶: normalize uri args

class qlib.data.cache.DiskExpressionCache(provider, **kwargs)¶

Prepared cache mechanism for server.

__init__(provider, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

gen_expression_cache(expression_data, cache_path, instrument, field, freq, last_update)¶: use bin file to save like feature-data.

update(sid, cache_uri, freq: str = 'day')¶

Update expression cache to latest calendar.

Overide this method to define how to update expression cache corresponding to users’ own cache mechanism.

Parameters:	cache_uri (str or Path) – the complete uri of expression cache file (include dir path). freq (str) –
Returns:	0(successful update)/ 1(no need to update)/ 2(update failure).
Return type:	int

class qlib.data.cache.DiskDatasetCache(provider, **kwargs)¶

Prepared cache mechanism for server.

__init__(provider, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

classmethod read_data_from_cache(cache_path: Union[str, pathlib.Path], start_time, end_time, fields)¶

read_cache_from

This function can read data from the disk cache dataset

Parameters:	cache_path – start_time – end_time – fields – The fields order of the dataset cache is sorted. So rearrange the columns to make it consistent.
Returns:

class IndexManager(cache_path: Union[str, pathlib.Path])¶

The lock is not considered in the class. Please consider the lock outside the code. This class is the proxy of the disk data.

__init__(cache_path: Union[str, pathlib.Path])¶: Initialize self. See help(type(self)) for accurate signature.

gen_dataset_cache(cache_path: Union[str, pathlib.Path], instruments, fields, freq, inst_processors=[])¶

Note

This function does not consider the cache read write lock. Please

Aquire the lock outside this function

The format the cache contains 3 parts(followed by typical filename).

index : cache/d41366901e25de3ec47297f12e2ba11d.index
- The content of the file may be in following format(pandas.Series)
  start end 1999-11-10 00:00:00 0 1 1999-11-11 00:00:00 1 2 1999-11-12 00:00:00 2 3 ...
Note

The start is closed. The end is open!!!!!
- Each line contains two element <start_index, end_index> with a timestamp as its index.
- It indicates the start_index`(included) and `end_index`(excluded) of the data for `timestamp
meta data: cache/d41366901e25de3ec47297f12e2ba11d.meta
data : cache/d41366901e25de3ec47297f12e2ba11d
- This is a hdf file sorted by datetime

Parameters:	cache_path – The path to store the cache. instruments – The instruments to store the cache. fields – The fields to store the cache. freq – The freq to store the cache. inst_processors – Instrument processors.

:return type pd.DataFrame; The fields of the returned DataFrame are consistent with the parameters of the function.

update(cache_uri, freq: str = 'day')¶

Update dataset cache to latest calendar.

Overide this method to define how to update dataset cache corresponding to users’ own cache mechanism.

Parameters:	cache_uri (str or Path) – the complete uri of dataset cache file (include dir path). freq (str) –
Returns:	0(successful update)/ 1(no need to update)/ 2(update failure)
Return type:	int

Storage¶

class qlib.data.storage.storage.BaseStorage¶

class qlib.data.storage.storage.CalendarStorage(freq: str, future: bool, **kwargs)¶

The behavior of CalendarStorage’s methods and List’s methods of the same name remain consistent

__init__(freq: str, future: bool, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

data¶

get all data

Raises:	`ValueError` – If the data(storage) does not exist, raise ValueError

index(value: str) → int¶

Raises:	`ValueError` – If the data(storage) does not exist, raise ValueError

class qlib.data.storage.storage.InstrumentStorage(market: str, freq: str, **kwargs)¶

__init__(market: str, freq: str, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

data¶

get all data

Raises:	`ValueError` – If the data(storage) does not exist, raise ValueError

update([E, ]**F) → None. Update D from mapping/iterable E and F.¶

Notes

If E present and has a .keys() method, does: for k in E: D[k] = E[k]

If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v

In either case, this is followed by: for k, v in F.items(): D[k] = v

class qlib.data.storage.storage.FeatureStorage(instrument: str, field: str, freq: str, **kwargs)¶

__init__(instrument: str, field: str, freq: str, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

data¶

get all data

Notes

if data(storage) does not exist, return empty pd.Series: return pd.Series(dtype=np.float32)

start_index¶

get FeatureStorage start index

Notes

If the data(storage) does not exist, return None

end_index¶

get FeatureStorage end index

Notes

The right index of the data range (both sides are closed)

The next data appending point will be end_index + 1

If the data(storage) does not exist, return None

write(data_array: Union[List[T], numpy.ndarray, Tuple], index: int = None)¶

Write data_array to FeatureStorage starting from index.

Notes

If index is None, append data_array to feature.

If len(data_array) == 0; return

If (index - self.end_index) >= 1, self[end_index+1: index] will be filled with np.nan

Examples

rebase(start_index: int = None, end_index: int = None)¶

Rebase the start_index and end_index of the FeatureStorage.

start_index and end_index are closed intervals: [start_index, end_index]

Examples

rewrite(data: Union[List[T], numpy.ndarray, Tuple], index: int)¶

overwrite all data in FeatureStorage with data

Parameters:	data (Union[List, np.ndarray, Tuple]) – data index (int) – data start index

class qlib.data.storage.file_storage.FileStorageMixin¶

FileStorageMixin, applicable to FileXXXStorage Subclasses need to have provider_uri, freq, storage_name, file_name attributes

check()¶

check self.uri

Raises:	`ValueError`

class qlib.data.storage.file_storage.FileCalendarStorage(freq: str, future: bool, provider_uri: dict, **kwargs)¶

__init__(freq: str, future: bool, provider_uri: dict, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

data¶

get all data

Raises:	`ValueError` – If the data(storage) does not exist, raise ValueError

index(value: str) → int¶

Raises:	`ValueError` – If the data(storage) does not exist, raise ValueError

class qlib.data.storage.file_storage.FileInstrumentStorage(market: str, freq: str, provider_uri: dict, **kwargs)¶

__init__(market: str, freq: str, provider_uri: dict, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

data¶

get all data

Raises:	`ValueError` – If the data(storage) does not exist, raise ValueError

update([E, ]**F) → None. Update D from mapping/iterable E and F.¶

Notes

If E present and has a .keys() method, does: for k in E: D[k] = E[k]

If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v

In either case, this is followed by: for k, v in F.items(): D[k] = v

class qlib.data.storage.file_storage.FileFeatureStorage(instrument: str, field: str, freq: str, provider_uri: dict, **kwargs)¶

__init__(instrument: str, field: str, freq: str, provider_uri: dict, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

data¶

get all data

Notes

if data(storage) does not exist, return empty pd.Series: return pd.Series(dtype=np.float32)

write(data_array: Union[List[T], numpy.ndarray], index: int = None) → None¶

Write data_array to FeatureStorage starting from index.

Notes

If index is None, append data_array to feature.

If len(data_array) == 0; return

If (index - self.end_index) >= 1, self[end_index+1: index] will be filled with np.nan

Examples

start_index¶

get FeatureStorage start index

Notes

If the data(storage) does not exist, return None

end_index¶

get FeatureStorage end index

Notes

The right index of the data range (both sides are closed)

The next data appending point will be end_index + 1

If the data(storage) does not exist, return None

Dataset¶

Dataset Class¶

class qlib.data.dataset.__init__.Dataset(**kwargs)¶

Preparing data for model training and inferencing.

__init__(**kwargs)¶

init is designed to finish following steps:

init the sub instance and 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.
setup data
- The data related attributes’ names should start with ‘_’ so that it will not be saved on disk when serializing.

The data could specify the info to calculate the essential data for preparation

config(**kwargs)¶: config is designed to configure and parameters that cannot be learned from the data

setup_data(**kwargs)¶

Setup the data.

We split the setup_data function for following situation:

User have a Dataset object with learned status on disk.
User load the Dataset object from the disk.
User call setup_data to load new data.
User prepare data for model based on previous status.

prepare(**kwargs) → object¶

The type of dataset depends on the model. (It could be pd.DataFrame, pytorch.DataLoader, etc.) The parameters should specify the scope for the prepared data The method should: - process the data

return the processed data

Returns:	return the object
Return type:	object

class qlib.data.dataset.__init__.DatasetH(handler: Union[Dict[KT, VT], qlib.data.dataset.handler.DataHandler], segments: Dict[str, Tuple], **kwargs)¶

Dataset with Data(H)andler

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.

__init__(handler: Union[Dict[KT, VT], qlib.data.dataset.handler.DataHandler], segments: Dict[str, Tuple], **kwargs)¶

Setup the underlying data.

Parameters:	handler (Union[dict, DataHandler]) – handler could be: instance of DataHandler config of DataHandler. Please refer to DataHandler segments (dict) – Describe the options to segment the data. Here are some examples:

config(handler_kwargs: dict = None, **kwargs)¶

Initialize the DatasetH

Parameters:	handler_kwargs (dict) – Config of DataHandler, which could include the following arguments: arguments of DataHandler.conf_data, such as ‘instruments’, ‘start_time’ and ‘end_time’. kwargs (dict) – Config of DatasetH, such as segments : dict Config of segments which is same as ‘segments’ in self.__init__

setup_data(handler_kwargs: dict = None, **kwargs)¶

Setup the Data

Parameters:

handler_kwargs (dict) –

init arguments of DataHandler, which could include the following arguments:

init_type : Init Type of Handler
enable_cache : whether to enable cache

prepare(segments: Union[List[str], Tuple[str], str, slice], col_set='__all', data_key='infer', **kwargs) → Union[List[pandas.core.frame.DataFrame], pandas.core.frame.DataFrame]¶

Prepare the data for learning and inference.

Parameters:	segments (Union[List[Text], Tuple[Text], Text, 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. kwargs – The parameters that kwargs may contain: flt_col : str It only exists in TSDatasetH, can be used to add a column of data(True or False) to filter data. This parameter is only supported when it is an instance of TSDatasetH.
Returns:
Return type:	Union[List[pd.DataFrame], pd.DataFrame]
Raises:	NotImplementedError:

class qlib.data.dataset.__init__.TSDataSampler(data: pandas.core.frame.DataFrame, start, end, step_len: int, fillna_type: str = 'none', dtype=None, flt_data=None)¶

(T)ime-(S)eries DataSampler This is the result of TSDatasetH

It works like torch.data.utils.Dataset, it provides a very convenient interface for constructing time-series dataset based on tabular data. - On time step dimension, the smaller index indicates the historical data and the larger index indicates the future

data.

If user have further requirements for processing data, user could process them based on TSDataSampler or create more powerful subclasses.

Known Issues: - For performance issues, this Sampler will convert dataframe into arrays for better performance. This could result

in a different data type

__init__(data: pandas.core.frame.DataFrame, start, end, step_len: int, fillna_type: str = 'none', dtype=None, flt_data=None)¶

Build a dataset which looks like torch.data.utils.Dataset.

Parameters:

data (pd.DataFrame) – The raw tabular data
start – The indexable start time
end – The indexable end time
step_len (int) – The length of the time-series step
fillna_type (int) –
How will qlib handle the sample if there is on sample in a specific date. none:

fill with np.nan

ffill:

ffill with previous sample

ffill+bfill:

ffill with previous samples first and fill with later samples second
flt_data (pd.Series) –
a column of data(True or False) to filter data. None:

kepp all data

get_index()¶: Get the pandas index of the data, it will be useful in following scenarios - Special sampler will be used (e.g. user want to sample day by day)

static build_index(data: pandas.core.frame.DataFrame) → Tuple[pandas.core.frame.DataFrame, dict]¶

The relation of the data

Parameters:	data (pd.DataFrame) – The dataframe with <datetime, DataFrame>
Returns:	the first element: reshape the original index into a <datetime(row), instrument(column)> 2D dataframe instrument SH600000 SH600004 SH600006 SH600007 SH600008 SH600009 … datetime 2021-01-11 0 1 2 3 4 5 … 2021-01-12 4146 4147 4148 4149 4150 4151 … 2021-01-13 8293 8294 8295 8296 8297 8298 … 2021-01-14 12441 12442 12443 12444 12445 12446 … the second element: {<original index>: <row, col>}
Return type:	Tuple[pd.DataFrame, dict]

class qlib.data.dataset.__init__.TSDatasetH(step_len=30, **kwargs)¶

(T)ime-(S)eries Dataset (H)andler

Convert the tabular data to Time-Series data

Requirements analysis

The typical workflow of a user to get time-series data for an sample - process features - slice proper data from data handler: dimension of sample <feature, > - Build relation of samples by <time, instrument> index

Be able to sample times series of data <timestep, feature>

It will be better if the interface is like “torch.utils.data.Dataset”

User could build customized batch based on the data
- The dimension of a batch of data <batch_idx, feature, timestep>

__init__(step_len=30, **kwargs)¶

Setup the underlying data.

Parameters:	handler (Union[dict, DataHandler]) – handler could be: instance of DataHandler config of DataHandler. Please refer to DataHandler segments (dict) – Describe the options to segment the data. Here are some examples:

config(**kwargs)¶

Initialize the DatasetH

Parameters:	handler_kwargs (dict) – Config of DataHandler, which could include the following arguments: arguments of DataHandler.conf_data, such as ‘instruments’, ‘start_time’ and ‘end_time’. kwargs (dict) – Config of DatasetH, such as segments : dict Config of segments which is same as ‘segments’ in self.__init__

setup_data(**kwargs)¶

Setup the Data

Parameters:

handler_kwargs (dict) –

init arguments of DataHandler, which could include the following arguments:

init_type : Init Type of Handler
enable_cache : whether to enable cache

Data Loader¶

class qlib.data.dataset.loader.DataLoader¶

DataLoader is designed for loading raw data from original data source.

load(instruments, start_time=None, end_time=None) → pandas.core.frame.DataFrame¶

load the data as pd.DataFrame.

Example of the data (The multi-index of the columns is optional.):

                        feature                                                             label
                        $close     $volume     Ref($close, 1)  Mean($close, 3)  $high-$low  LABEL0
datetime    instrument
2010-01-04  SH600000    81.807068  17145150.0       83.737389        83.016739    2.741058  0.0032
            SH600004    13.313329  11800983.0       13.313329        13.317701    0.183632  0.0042
            SH600005    37.796539  12231662.0       38.258602        37.919757    0.970325  0.0289

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:	data load from the under layer source
Return type:	pd.DataFrame

class qlib.data.dataset.loader.DLWParser(config: Union[list, tuple, dict])¶

(D)ata(L)oader (W)ith (P)arser for features and names

Extracting this class so that QlibDataLoader and other dataloaders(such as QdbDataLoader) can share the fields.

__init__(config: Union[list, tuple, dict])¶

Parameters:	config (Union[list, tuple, dict]) – Config will be used to describe the fields and column names

load_group_df(instruments, exprs: list, names: list, start_time: Union[str, pandas._libs.tslibs.timestamps.Timestamp] = None, end_time: Union[str, pandas._libs.tslibs.timestamps.Timestamp] = None, gp_name: str = None) → pandas.core.frame.DataFrame¶

load the dataframe for specific group

Parameters:	instruments – the instruments. exprs (list) – the expressions to describe the content of the data. names (list) – the name of the data.
Returns:	the queried dataframe.
Return type:	pd.DataFrame

load(instruments=None, start_time=None, end_time=None) → pandas.core.frame.DataFrame¶

load the data as pd.DataFrame.

Example of the data (The multi-index of the columns is optional.):

                        feature                                                             label
                        $close     $volume     Ref($close, 1)  Mean($close, 3)  $high-$low  LABEL0
datetime    instrument
2010-01-04  SH600000    81.807068  17145150.0       83.737389        83.016739    2.741058  0.0032
            SH600004    13.313329  11800983.0       13.313329        13.317701    0.183632  0.0042
            SH600005    37.796539  12231662.0       38.258602        37.919757    0.970325  0.0289

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:	data load from the under layer source
Return type:	pd.DataFrame

class qlib.data.dataset.loader.QlibDataLoader(config: Tuple[list, tuple, dict], filter_pipe: List[T] = None, swap_level: bool = True, freq: Union[str, dict] = 'day', inst_processor: dict = None)¶

Same as QlibDataLoader. The fields can be define by config

__init__(config: Tuple[list, tuple, dict], filter_pipe: List[T] = None, swap_level: bool = True, freq: Union[str, dict] = 'day', inst_processor: dict = None)¶

Parameters:

config (Tuple[list, tuple, dict]) – Please refer to the doc of DLWParser
filter_pipe – Filter pipe for the instruments
swap_level – Whether to swap level of MultiIndex
freq (dict or str) – If type(config) == dict and type(freq) == str, load config data using freq. If type(config) == dict and type(freq) == dict, load config[<group_name>] data using freq[<group_name>]
inst_processor (dict) – If inst_processor is not None and type(config) == dict; load config[<group_name>] data using inst_processor[<group_name>]

load_group_df(instruments, exprs: list, names: list, start_time: Union[str, pandas._libs.tslibs.timestamps.Timestamp] = None, end_time: Union[str, pandas._libs.tslibs.timestamps.Timestamp] = None, gp_name: str = None) → pandas.core.frame.DataFrame¶

load the dataframe for specific group

Parameters:	instruments – the instruments. exprs (list) – the expressions to describe the content of the data. names (list) – the name of the data.
Returns:	the queried dataframe.
Return type:	pd.DataFrame

class qlib.data.dataset.loader.StaticDataLoader(config: dict, join='outer')¶

DataLoader that supports loading data from file or as provided.

__init__(config: dict, join='outer')¶

Parameters:	config (dict) – {fields_group: <path or object>} join (str) – How to align different dataframes

load(instruments=None, start_time=None, end_time=None) → pandas.core.frame.DataFrame¶

load the data as pd.DataFrame.

Example of the data (The multi-index of the columns is optional.):

                        feature                                                             label
                        $close     $volume     Ref($close, 1)  Mean($close, 3)  $high-$low  LABEL0
datetime    instrument
2010-01-04  SH600000    81.807068  17145150.0       83.737389        83.016739    2.741058  0.0032
            SH600004    13.313329  11800983.0       13.313329        13.317701    0.183632  0.0042
            SH600005    37.796539  12231662.0       38.258602        37.919757    0.970325  0.0289

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:	data load from the under layer source
Return type:	pd.DataFrame

class qlib.data.dataset.loader.DataLoaderDH(handler_config: dict, fetch_kwargs: dict = {}, is_group=False)¶

DataLoader based on (D)ata (H)andler It is designed to load multiple data from data handler - If you just want to load data from single datahandler, you can write them in single data handler

TODO: What make this module not that easy to use. - For online scenario

The underlayer data handler should be configured. But data loader doesn’t provide such interface & hook.

__init__(handler_config: dict, fetch_kwargs: dict = {}, is_group=False)¶

Parameters:	handler_config (dict) – handler_config will be used to describe the handlers fetch_kwargs (dict) – fetch_kwargs will be used to describe the different arguments of fetch method, such as col_set, squeeze, data_key, etc. is_group (bool) – is_group will be used to describe whether the key of handler_config is group

load(instruments=None, start_time=None, end_time=None) → pandas.core.frame.DataFrame¶

load the data as pd.DataFrame.

Example of the data (The multi-index of the columns is optional.):

                        feature                                                             label
                        $close     $volume     Ref($close, 1)  Mean($close, 3)  $high-$low  LABEL0
datetime    instrument
2010-01-04  SH600000    81.807068  17145150.0       83.737389        83.016739    2.741058  0.0032
            SH600004    13.313329  11800983.0       13.313329        13.317701    0.183632  0.0042
            SH600005    37.796539  12231662.0       38.258602        37.919757    0.970325  0.0289

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:	data load from the under layer source
Return type:	pd.DataFrame

Data Handler¶

class qlib.data.dataset.handler.DataHandler(instruments=None, start_time=None, end_time=None, data_loader: Union[dict, str, qlib.data.dataset.loader.DataLoader] = None, init_data=True, fetch_orig=True)¶

The steps to using a handler 1. initialized data handler (call by init). 2. use the data.

The data handler try to maintain a handler with 2 level. datetime & instruments.

Any order of the index level can be supported (The order will be implied in the data). The order <datetime, instruments> will be used when the dataframe index name is missed.

Example of the data: The multi-index of the columns is optional.

                        feature                                                            label
                        $close     $volume  Ref($close, 1)  Mean($close, 3)  $high-$low  LABEL0
datetime   instrument
2010-01-04 SH600000    81.807068  17145150.0       83.737389        83.016739    2.741058  0.0032
           SH600004    13.313329  11800983.0       13.313329        13.317701    0.183632  0.0042
           SH600005    37.796539  12231662.0       38.258602        37.919757    0.970325  0.0289

Tips for improving the performance of datahandler - Fetching data with col_set=CS_RAW will return the raw data and may avoid pandas from copying the data when calling loc

__init__(instruments=None, start_time=None, end_time=None, data_loader: Union[dict, str, qlib.data.dataset.loader.DataLoader] = None, init_data=True, fetch_orig=True)¶

Parameters:	instruments – The stock list to retrive. start_time – start_time of the original data. end_time – end_time of the original data. data_loader (Union[dict, str, DataLoader]) – data loader to load the data. init_data – initialize the original data in the constructor. fetch_orig (bool) – Return the original data instead of copy if possible.

config(**kwargs)¶

configuration of data. # what data to be loaded from data source

This method will be used when loading pickled handler from dataset. The data will be initialized with different time range.

setup_data(enable_cache: bool = False)¶

Set Up the data in case of running initialization for multiple time

It is responsible for maintaining following variable 1) self._data

Parameters:

enable_cache (bool) –

default value is false:

if enable_cache == True:

the processed data will be saved on disk, and handler will load the cached data from the disk directly when we call init next time

fetch(selector: Union[pandas._libs.tslibs.timestamps.Timestamp, slice, str] = slice(None, None, None), level: Union[str, int] = 'datetime', col_set: Union[str, List[str]] = '__all', squeeze: bool = False, proc_func: Callable = None) → pandas.core.frame.DataFrame¶

fetch 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 (Union[str, List[str]]) – if isinstance(col_set, str): select a set of meaningful columns.(e.g. features, columns) if col_set == CS_RAW: the raw dataset will be returned. if isinstance(col_set, List[str]): select several sets of meaningful columns, the returned data has multiple levels proc_func (Callable) – Give a hook for processing data before fetching An example to explain the necessity of the hook: A Dataset learned some processors to process data which is related to data segmentation It will apply them every time when preparing data. The learned processor require the dataframe remains the same format when fitting and applying However the data format will change according to the parameters. So the processors should be applied to the underlayer data. squeeze (bool) – whether squeeze columns and index
Returns:
Return type:	pd.DataFrame.

get_cols(col_set='__all') → list¶

get the column names

Parameters:	col_set (str) – select a set of meaningful columns.(e.g. features, columns)
Returns:	list of column names
Return type:	list

get_range_selector(cur_date: Union[pandas._libs.tslibs.timestamps.Timestamp, str], periods: int) → slice¶

get range selector by number of periods

Parameters:	cur_date (pd.Timestamp or str) – current date periods (int) – number of periods

get_range_iterator(periods: int, min_periods: Optional[int] = None, **kwargs) → Iterator[Tuple[pandas._libs.tslibs.timestamps.Timestamp, pandas.core.frame.DataFrame]]¶

get a iterator of sliced data with given periods

Parameters:	periods (int) – number of periods. min_periods (int) – minimum periods for sliced dataframe. kwargs (dict) – will be passed to self.fetch.

class qlib.data.dataset.handler.DataHandlerLP(instruments=None, start_time=None, end_time=None, data_loader: Union[dict, str, qlib.data.dataset.loader.DataLoader] = None, infer_processors: List[T] = [], learn_processors: List[T] = [], shared_processors: List[T] = [], process_type='append', drop_raw=False, **kwargs)¶

DataHandler with (L)earnable (P)rocessor

Tips to improving the performance of data handler - To reduce the memory cost

drop_raw=True: this will modify the data inplace on raw data;

__init__(instruments=None, start_time=None, end_time=None, data_loader: Union[dict, str, qlib.data.dataset.loader.DataLoader] = None, infer_processors: List[T] = [], learn_processors: List[T] = [], shared_processors: List[T] = [], process_type='append', drop_raw=False, **kwargs)¶

Parameters:

infer_processors (list) –
- list of <description info> of processors to generate data for inference
- example of <description info>:
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 )
drop_raw (bool) – Whether to drop the raw data

fit()¶: fit data without processing the data

fit_process_data()¶

fit and process data

The input of the fit will be the output of the previous processor

process_data(with_fit: bool = False)¶

process_data data. Fun processor.fit if necessary

Notation: (data) [processor]

# data processing flow of self.process_type == DataHandlerLP.PTYPE_I (self._data)-[shared_processors]-(_shared_df)-[learn_processors]-(_learn_df)

-[infer_processors]-(_infer_df)

# data processing flow of self.process_type == DataHandlerLP.PTYPE_A (self._data)-[shared_processors]-(_shared_df)-[infer_processors]-(_infer_df)-[learn_processors]-(_learn_df)

Parameters:	with_fit (bool) – The input of the fit will be the output of the previous processor

config(processor_kwargs: dict = None, **kwargs)¶

configuration of data. # what data to be loaded from data source

This method will be used when loading pickled handler from dataset. The data will be initialized with different time range.

setup_data(init_type: str = 'fit_seq', **kwargs)¶

Set up the data in case of running initialization for multiple time

Parameters:	init_type (str) – The type IT_* listed above. enable_cache (bool) – default value is false: if enable_cache == True: the processed data will be saved on disk, and handler will load the cached data from the disk directly when we call init next time

fetch(selector: Union[pandas._libs.tslibs.timestamps.Timestamp, slice, str] = slice(None, None, None), level: Union[str, int] = 'datetime', col_set='__all', data_key: str = 'infer', proc_func: Callable = None) → pandas.core.frame.DataFrame¶

fetch 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_. proc_func* (Callable) – please refer to the doc of DataHandler.fetch
Returns:
Return type:	pd.DataFrame

get_cols(col_set='__all', data_key: str = 'infer') → list¶

get 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:	list of column names
Return type:	list

classmethod cast(handler: qlib.data.dataset.handler.DataHandlerLP) → qlib.data.dataset.handler.DataHandlerLP¶

Motivation - A user create a datahandler in his customized package. Then he want to share the processed handler to other users without introduce the package dependency and complicated data processing logic. - This class make it possible by casting the class to DataHandlerLP and only keep the processed data

Parameters:	handler (DataHandlerLP) – A subclass of DataHandlerLP
Returns:	the converted processed data
Return type:	DataHandlerLP

Processor¶

qlib.data.dataset.processor.get_group_columns(df: pandas.core.frame.DataFrame, group: Optional[str])¶

get a group of columns from multi-index columns DataFrame

Parameters:	df (pd.DataFrame) – with multi of columns. group (str) – the name of the feature group, i.e. the first level value of the group index.

class qlib.data.dataset.processor.Processor¶

fit(df: pandas.core.frame.DataFrame = None)¶

learn data processing parameters

Parameters:	df (pd.DataFrame) – When we fit and process data with processor one by one. The fit function reiles on the output of previous processor, i.e. df.

is_for_infer() → bool¶

Is this processor usable for inference Some processors are not usable for inference.

Returns:	if it is usable for infenrece.
Return type:	bool

readonly() → bool¶

Does the processor treat the input data readonly (i.e. does not write the input data) when processsing

Knowning the readonly information is helpful to the Handler to avoid uncessary copy

config(**kwargs)¶

configure the serializable object

Parameters:	dump_all (bool) – will the object dump all object exclude (list) – What attribute will not be dumped recursive (bool) – will the configuration be recursive

class qlib.data.dataset.processor.DropnaProcessor(fields_group=None)¶

__init__(fields_group=None)¶: Initialize self. See help(type(self)) for accurate signature.

readonly()¶

Does the processor treat the input data readonly (i.e. does not write the input data) when processsing

Knowning the readonly information is helpful to the Handler to avoid uncessary copy

class qlib.data.dataset.processor.DropnaLabel(fields_group='label')¶

__init__(fields_group='label')¶: Initialize self. See help(type(self)) for accurate signature.

is_for_infer() → bool¶: The samples are dropped according to label. So it is not usable for inference

class qlib.data.dataset.processor.DropCol(col_list=[])¶

__init__(col_list=[])¶: Initialize self. See help(type(self)) for accurate signature.

readonly()¶

Does the processor treat the input data readonly (i.e. does not write the input data) when processsing

Knowning the readonly information is helpful to the Handler to avoid uncessary copy

class qlib.data.dataset.processor.FilterCol(fields_group='feature', col_list=[])¶

__init__(fields_group='feature', col_list=[])¶: Initialize self. See help(type(self)) for accurate signature.

readonly()¶

Does the processor treat the input data readonly (i.e. does not write the input data) when processsing

Knowning the readonly information is helpful to the Handler to avoid uncessary copy

class qlib.data.dataset.processor.TanhProcess¶: Use tanh to process noise data

class qlib.data.dataset.processor.ProcessInf¶: Process infinity

class qlib.data.dataset.processor.Fillna(fields_group=None, fill_value=0)¶

Process NaN

__init__(fields_group=None, fill_value=0)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.dataset.processor.MinMaxNorm(fit_start_time, fit_end_time, fields_group=None)¶

__init__(fit_start_time, fit_end_time, fields_group=None)¶: Initialize self. See help(type(self)) for accurate signature.

fit(df)¶

learn data processing parameters

Parameters:	df (pd.DataFrame) – When we fit and process data with processor one by one. The fit function reiles on the output of previous processor, i.e. df.

class qlib.data.dataset.processor.ZScoreNorm(fit_start_time, fit_end_time, fields_group=None)¶

ZScore Normalization

__init__(fit_start_time, fit_end_time, fields_group=None)¶: Initialize self. See help(type(self)) for accurate signature.

fit(df)¶

learn data processing parameters

Parameters:	df (pd.DataFrame) – When we fit and process data with processor one by one. The fit function reiles on the output of previous processor, i.e. df.

class qlib.data.dataset.processor.RobustZScoreNorm(fit_start_time, fit_end_time, fields_group=None, clip_outlier=True)¶

Robust ZScore Normalization

Use robust statistics for Z-Score normalization:: mean(x) = median(x) std(x) = MAD(x) * 1.4826
Reference:: https://en.wikipedia.org/wiki/Median_absolute_deviation.

__init__(fit_start_time, fit_end_time, fields_group=None, clip_outlier=True)¶: Initialize self. See help(type(self)) for accurate signature.

fit(df)¶

learn data processing parameters

Parameters:	df (pd.DataFrame) – When we fit and process data with processor one by one. The fit function reiles on the output of previous processor, i.e. df.

class qlib.data.dataset.processor.CSZScoreNorm(fields_group=None)¶

Cross Sectional ZScore Normalization

__init__(fields_group=None)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.dataset.processor.CSRankNorm(fields_group=None)¶

Cross Sectional Rank Normalization

__init__(fields_group=None)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.dataset.processor.CSZFillna(fields_group=None)¶

Cross Sectional Fill Nan

__init__(fields_group=None)¶: Initialize self. See help(type(self)) for accurate signature.

class qlib.data.dataset.processor.HashStockFormat¶: Process the storage of from df into hasing stock format

Contrib¶

Model¶

class qlib.model.base.BaseModel¶

Modeling things

predict(*args, **kwargs) → object¶: Make predictions after modeling things

class qlib.model.base.Model¶

Learnable Models

fit(dataset: qlib.data.dataset.Dataset)¶

Learn model from the base model

Note

The attribute names of learned model should not start with ‘_’. So that the model could be dumped to disk.

The following code example shows how to retrieve x_train, y_train and w_train from the dataset:

# 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"]

# 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)

Parameters:	dataset (Dataset) – dataset will generate the processed data from model training.

predict(dataset: qlib.data.dataset.Dataset, segment: Union[str, slice] = 'test') → object¶

give prediction given Dataset

Parameters:	dataset (Dataset) – dataset will generate the processed dataset from model training. segment (Text or slice) – dataset will use this segment to prepare data. (default=test)
Returns:
Return type:	Prediction results with certain type such as pandas.Series.

class qlib.model.base.ModelFT¶

Model (F)ine(t)unable

finetune(dataset: qlib.data.dataset.Dataset)¶

finetune model based given dataset

A typical use case of finetuning model with qlib.workflow.R

# start exp to train init model
with R.start(experiment_name="init models"):
    model.fit(dataset)
    R.save_objects(init_model=model)
    rid = R.get_recorder().id

# Finetune model based on previous trained model
with R.start(experiment_name="finetune model"):
    recorder = R.get_recorder(recorder_id=rid, experiment_name="init models")
    model = recorder.load_object("init_model")
    model.finetune(dataset, num_boost_round=10)

Parameters:	dataset (Dataset) – dataset will generate the processed dataset from model training.

Strategy¶

Evaluate¶

qlib.contrib.evaluate.risk_analysis(r, N: int = None, freq: str = 'day')¶

Risk Analysis

Parameters:	r (pandas.Series) – daily return series. N (int) – scaler for annualizing information_ratio (day: 252, week: 50, month: 12), at least one of N and freq should exist freq (str) – analysis frequency used for calculating the scaler, at least one of N and freq should exist

qlib.contrib.evaluate.indicator_analysis(df, method='mean')¶

analyze statistical time-series indicators of trading

Parameters:	df (pandas.DataFrame) – columns: like [‘pa’, ‘pos’, ‘ffr’, ‘deal_amount’, ‘value’]. Necessary fields: ’pa’ is the price advantage in trade indicators ’pos’ is the positive rate in trade indicators ’ffr’ is the fulfill rate in trade indicators Optional fields: ’deal_amount’ is the total deal deal_amount, only necessary when method is ‘amount_weighted’ ’value’ is the total trade value, only necessary when method is ‘value_weighted’ index: Index(datetime) method (str, optional) – statistics method of pa/ffr, by default “mean” - if method is ‘mean’, count the mean statistical value of each trade indicator - if method is ‘amount_weighted’, count the deal_amount weighted mean statistical value of each trade indicator - if method is ‘value_weighted’, count the value weighted mean statistical value of each trade indicator Note: statistics method of pos is always “mean”
Returns:	statistical value of each trade indicators
Return type:	pd.DataFrame

qlib.contrib.evaluate.backtest_daily(start_time: Union[str, pandas._libs.tslibs.timestamps.Timestamp], end_time: Union[str, pandas._libs.tslibs.timestamps.Timestamp], strategy: Union[str, dict, qlib.strategy.base.BaseStrategy], executor: Union[str, dict, qlib.backtest.executor.BaseExecutor] = None, account: Union[float, int, qlib.backtest.position.Position] = 100000000.0, benchmark: str = 'SH000300', exchange_kwargs: dict = None, pos_type: str = 'Position')¶

initialize the strategy and executor, then executor the backtest of daily frequency

Parameters:

start_time (Union[str, pd.Timestamp]) – closed start time for backtest NOTE: This will be applied to the outmost executor’s calendar.
end_time (Union[str, pd.Timestamp]) – closed end time for backtest NOTE: This will be applied to the outmost executor’s calendar. E.g. Executor[day](Executor[1min]), setting end_time == 20XX0301 will include all the minutes on 20XX0301
strategy (Union[str, dict, BaseStrategy]) –
for initializing outermost portfolio strategy. Please refer to the docs of init_instance_by_config for more information.

E.g.

executor : Union[str, dict, BaseExecutor]

for initializing the outermost executor.

benchmark: str

the benchmark for reporting.

account : Union[float, int, Position]

information for describing how to creating the account For float or int:

Using Account with only initial cash

For Position:: Using Account with a Position

exchange_kwargs : dict

the kwargs for initializing Exchange E.g.

exchange_kwargs = {
    "freq": freq,
    "limit_threshold": None, # limit_threshold is None, using C.limit_threshold
    "deal_price": None, # deal_price is None, using C.deal_price
    "open_cost": 0.0005,
    "close_cost": 0.0015,
    "min_cost": 5,
}

pos_type : str

the type of Position.

Returns:	report_normal (pd.DataFrame) – backtest report positions_normal (pd.DataFrame) – backtest positions

qlib.contrib.evaluate.long_short_backtest(pred, topk=50, deal_price=None, shift=1, open_cost=0, close_cost=0, trade_unit=None, limit_threshold=None, min_cost=5, subscribe_fields=[], extract_codes=False)¶

A backtest for long-short strategy

Parameters:

pred – The trading signal produced on day T.
topk – The short topk securities and long topk securities.
deal_price – The price to deal the trading.
shift – Whether to shift prediction by one day. The trading day will be T+1 if shift==1.
open_cost – open transaction cost.
close_cost – close transaction cost.
trade_unit – 100 for China A.
limit_threshold – limit move 0.1 (10%) for example, long and short with same limit.
min_cost – min transaction cost.
subscribe_fields – subscribe fields.
extract_codes – bool. will we pass the codes extracted from the pred to the exchange. NOTE: This will be faster with offline qlib.

Returns:

The result of backtest, it is represented by a dict. { “long”: long_returns(excess),

”short”: short_returns(excess), “long_short”: long_short_returns}

Report¶

qlib.contrib.report.analysis_position.report.report_graph(report_df: pandas.core.frame.DataFrame, show_notebook: bool = True) → [<class 'list'>, <class 'tuple'>]¶

display backtest report

Example:

import qlib
import pandas as pd
from qlib.utils.time import Freq
from qlib.utils import flatten_dict
from qlib.backtest import backtest, executor
from qlib.contrib.evaluate import risk_analysis
from qlib.contrib.strategy import TopkDropoutStrategy

# init qlib
qlib.init(provider_uri=<qlib data dir>)

CSI300_BENCH = "SH000300"
FREQ = "day"
STRATEGY_CONFIG = {
    "topk": 50,
    "n_drop": 5,
    # pred_score, pd.Series
    "signal": pred_score,
}

EXECUTOR_CONFIG = {
    "time_per_step": "day",
    "generate_portfolio_metrics": True,
}

backtest_config = {
    "start_time": "2017-01-01",
    "end_time": "2020-08-01",
    "account": 100000000,
    "benchmark": CSI300_BENCH,
    "exchange_kwargs": {
        "freq": FREQ,
        "limit_threshold": 0.095,
        "deal_price": "close",
        "open_cost": 0.0005,
        "close_cost": 0.0015,
        "min_cost": 5,
    },
}

# strategy object
strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
# executor object
executor_obj = executor.SimulatorExecutor(**EXECUTOR_CONFIG)
# backtest
portfolio_metric_dict, indicator_dict = backtest(executor=executor_obj, strategy=strategy_obj, **backtest_config)
analysis_freq = "{0}{1}".format(*Freq.parse(FREQ))
# backtest info
report_normal_df, positions_normal = portfolio_metric_dict.get(analysis_freq)

qcr.analysis_position.report_graph(report_normal_df)

Parameters:

report_df –

df.index.name must be date, df.columns must contain return, turnover, cost, bench.

            return      cost        bench       turnover
date
2017-01-04  0.003421    0.000864    0.011693    0.576325
2017-01-05  0.000508    0.000447    0.000721    0.227882
2017-01-06  -0.003321   0.000212    -0.004322   0.102765
2017-01-09  0.006753    0.000212    0.006874    0.105864
2017-01-10  -0.000416   0.000440    -0.003350   0.208396

show_notebook – whether to display graphics in notebook, the default is True.

Returns:

if show_notebook is True, display in notebook; else return plotly.graph_objs.Figure list.

qlib.contrib.report.analysis_position.score_ic.score_ic_graph(pred_label: pandas.core.frame.DataFrame, show_notebook: bool = True) → [<class 'list'>, <class 'tuple'>]¶

score IC

Example:

from qlib.data import D
from qlib.contrib.report import analysis_position
pred_df_dates = pred_df.index.get_level_values(level='datetime')
features_df = D.features(D.instruments('csi500'), ['Ref($close, -2)/Ref($close, -1)-1'], pred_df_dates.min(), pred_df_dates.max())
features_df.columns = ['label']
pred_label = pd.concat([features_df, pred], axis=1, sort=True).reindex(features_df.index)
analysis_position.score_ic_graph(pred_label)

Parameters:

pred_label –

index is pd.MultiIndex, index name is [instrument, datetime]; columns names is [score, label].

instrument  datetime        score         label
SH600004  2017-12-11     -0.013502       -0.013502
            2017-12-12   -0.072367       -0.072367
            2017-12-13   -0.068605       -0.068605
            2017-12-14    0.012440        0.012440
            2017-12-15   -0.102778       -0.102778

show_notebook – whether to display graphics in notebook, the default is True.

Returns:

if show_notebook is True, display in notebook; else return plotly.graph_objs.Figure list.

qlib.contrib.report.analysis_position.cumulative_return.cumulative_return_graph(position: dict, report_normal: pandas.core.frame.DataFrame, label_data: pandas.core.frame.DataFrame, show_notebook=True, start_date=None, end_date=None) → Iterable[plotly.graph_objs._figure.Figure]¶

Backtest buy, sell, and holding cumulative return graph

Example:
from qlib.data import D
from qlib.contrib.evaluate import risk_analysis, backtest, long_short_backtest
from qlib.contrib.strategy import TopkDropoutStrategy

# backtest parameters
bparas = {}
bparas['limit_threshold'] = 0.095
bparas['account'] = 1000000000

sparas = {}
sparas['topk'] = 50
sparas['n_drop'] = 5
strategy = TopkDropoutStrategy(**sparas)

report_normal_df, positions = backtest(pred_df, strategy, **bparas)

pred_df_dates = pred_df.index.get_level_values(level='datetime')
features_df = D.features(D.instruments('csi500'), ['Ref($close, -1)/$close - 1'], pred_df_dates.min(), pred_df_dates.max())
features_df.columns = ['label']

qcr.analysis_position.cumulative_return_graph(positions, report_normal_df, features_df)
Graph desc:

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.

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.

Parameters:

position – position data

report_normal –

                return      cost        bench       turnover
date
2017-01-04  0.003421    0.000864    0.011693    0.576325
2017-01-05  0.000508    0.000447    0.000721    0.227882
2017-01-06  -0.003321   0.000212    -0.004322   0.102765
2017-01-09  0.006753    0.000212    0.006874    0.105864
2017-01-10  -0.000416   0.000440    -0.003350   0.208396

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’])

                                label
instrument  datetime
SH600004        2017-12-11  -0.013502
                2017-12-12  -0.072367
                2017-12-13  -0.068605
                2017-12-14  0.012440
                2017-12-15  -0.102778

Parameters:	show_notebook – True or False. If True, show graph in notebook, else return figures start_date – start date end_date – end date
Returns:

qlib.contrib.report.analysis_position.risk_analysis.risk_analysis_graph(analysis_df: pandas.core.frame.DataFrame = None, report_normal_df: pandas.core.frame.DataFrame = None, report_long_short_df: pandas.core.frame.DataFrame = None, show_notebook: bool = True) → Iterable[plotly.graph_objs._figure.Figure]¶

Generate analysis graph and monthly analysis

Example:

import qlib
import pandas as pd
from qlib.utils.time import Freq
from qlib.utils import flatten_dict
from qlib.backtest import backtest, executor
from qlib.contrib.evaluate import risk_analysis
from qlib.contrib.strategy import TopkDropoutStrategy

# init qlib
qlib.init(provider_uri=<qlib data dir>)

CSI300_BENCH = "SH000300"
FREQ = "day"
STRATEGY_CONFIG = {
    "topk": 50,
    "n_drop": 5,
    # pred_score, pd.Series
    "signal": pred_score,
}

EXECUTOR_CONFIG = {
    "time_per_step": "day",
    "generate_portfolio_metrics": True,
}

backtest_config = {
    "start_time": "2017-01-01",
    "end_time": "2020-08-01",
    "account": 100000000,
    "benchmark": CSI300_BENCH,
    "exchange_kwargs": {
        "freq": FREQ,
        "limit_threshold": 0.095,
        "deal_price": "close",
        "open_cost": 0.0005,
        "close_cost": 0.0015,
        "min_cost": 5,
    },
}

# strategy object
strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
# executor object
executor_obj = executor.SimulatorExecutor(**EXECUTOR_CONFIG)
# backtest
portfolio_metric_dict, indicator_dict = backtest(executor=executor_obj, strategy=strategy_obj, **backtest_config)
analysis_freq = "{0}{1}".format(*Freq.parse(FREQ))
# backtest info
report_normal_df, positions_normal = portfolio_metric_dict.get(analysis_freq)
analysis = dict()
analysis["excess_return_without_cost"] = risk_analysis(
    report_normal_df["return"] - report_normal_df["bench"], freq=analysis_freq
)
analysis["excess_return_with_cost"] = risk_analysis(
    report_normal_df["return"] - report_normal_df["bench"] - report_normal_df["cost"], freq=analysis_freq
)

analysis_df = pd.concat(analysis)  # type: pd.DataFrame
analysis_position.risk_analysis_graph(analysis_df, report_normal_df)

Parameters:

analysis_df –

analysis data, index is pd.MultiIndex; columns names is [risk].

                                                  risk
excess_return_without_cost mean               0.000692
                           std                0.005374
                           annualized_return  0.174495
                           information_ratio  2.045576
                           max_drawdown      -0.079103
excess_return_with_cost    mean               0.000499
                           std                0.005372
                           annualized_return  0.125625
                           information_ratio  1.473152
                           max_drawdown      -0.088263

report_normal_df –

df.index.name must be date, df.columns must contain return, turnover, cost, bench.

            return      cost        bench       turnover
date
2017-01-04  0.003421    0.000864    0.011693    0.576325
2017-01-05  0.000508    0.000447    0.000721    0.227882
2017-01-06  -0.003321   0.000212    -0.004322   0.102765
2017-01-09  0.006753    0.000212    0.006874    0.105864
2017-01-10  -0.000416   0.000440    -0.003350   0.208396

report_long_short_df –

df.index.name must be date, df.columns contain long, short, long_short.

            long        short       long_short
date
2017-01-04  -0.001360   0.001394    0.000034
2017-01-05  0.002456    0.000058    0.002514
2017-01-06  0.000120    0.002739    0.002859
2017-01-09  0.001436    0.001838    0.003273
2017-01-10  0.000824    -0.001944   -0.001120

show_notebook – Whether to display graphics in a notebook, default True. If True, show graph in notebook If False, return graph figure

Returns:

qlib.contrib.report.analysis_position.rank_label.rank_label_graph(position: dict, label_data: pandas.core.frame.DataFrame, start_date=None, end_date=None, show_notebook=True) → Iterable[plotly.graph_objs._figure.Figure]¶

Ranking percentage of stocks buy, sell, and holding on the trading day. Average rank-ratio(similar to sell_df[‘label’].rank(ascending=False) / len(sell_df)) of daily trading

Example:

from qlib.data import D
from qlib.contrib.evaluate import backtest
from qlib.contrib.strategy import TopkDropoutStrategy

# backtest parameters
bparas = {}
bparas['limit_threshold'] = 0.095
bparas['account'] = 1000000000

sparas = {}
sparas['topk'] = 50
sparas['n_drop'] = 230
strategy = TopkDropoutStrategy(**sparas)

_, positions = backtest(pred_df, strategy, **bparas)

pred_df_dates = pred_df.index.get_level_values(level='datetime')
features_df = D.features(D.instruments('csi500'), ['Ref($close, -1)/$close-1'], pred_df_dates.min(), pred_df_dates.max())
features_df.columns = ['label']

qcr.analysis_position.rank_label_graph(positions, features_df, pred_df_dates.min(), pred_df_dates.max())

Parameters:	position – position data; qlib.backtest.backtest result. 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’]).

                                label
instrument  datetime
SH600004        2017-12-11  -0.013502
                2017-12-12  -0.072367
                2017-12-13  -0.068605
                2017-12-14  0.012440
                2017-12-15  -0.102778

Parameters:	start_date – start date end_date – end_date show_notebook – True or False. If True, show graph in notebook, else return figures.
Returns:

qlib.contrib.report.analysis_model.analysis_model_performance.ic_figure(ic_df: pandas.core.frame.DataFrame, show_nature_day=True, **kwargs) → plotly.graph_objs._figure.Figure¶

IC figure

Parameters:	ic_df – ic DataFrame show_nature_day – whether to display the abscissa of non-trading day
Returns:	plotly.graph_objs.Figure

qlib.contrib.report.analysis_model.analysis_model_performance.model_performance_graph(pred_label: pandas.core.frame.DataFrame, lag: int = 1, N: int = 5, reverse=False, rank=False, graph_names: list = ['group_return', 'pred_ic', 'pred_autocorr'], show_notebook: bool = True, show_nature_day=True) → [<class 'list'>, <class 'tuple'>]¶

Model performance

Parameters:	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”).

instrument  datetime        score       label
SH600004    2017-12-11  -0.013502       -0.013502
                2017-12-12  -0.072367       -0.072367
                2017-12-13  -0.068605       -0.068605
                2017-12-14  0.012440        0.012440
                2017-12-15  -0.102778       -0.102778

Parameters:

lag – pred.groupby(level=’instrument’)[‘score’].shift(lag). It will be only used in the auto-correlation computing.
N – group number, default 5.
reverse – if True, pred[‘score’] *= -1.
rank – if True, calculate rank ic.
graph_names – graph names; default [‘cumulative_return’, ‘pred_ic’, ‘pred_autocorr’, ‘pred_turnover’].
show_notebook – whether to display graphics in notebook, the default is True.
show_nature_day – whether to display the abscissa of non-trading day.

Returns:

if show_notebook is True, display in notebook; else return plotly.graph_objs.Figure list.

Workflow¶

Experiment Manager¶

class qlib.workflow.expm.ExpManager(uri: str, default_exp_name: Optional[str])¶

This is the ExpManager class for managing experiments. The API is designed similar to mlflow. (The link: https://mlflow.org/docs/latest/python_api/mlflow.html)

__init__(uri: str, default_exp_name: Optional[str])¶: Initialize self. See help(type(self)) for accurate signature.

start_exp(*, experiment_id: Optional[str] = None, experiment_name: Optional[str] = None, recorder_id: Optional[str] = None, recorder_name: Optional[str] = None, uri: Optional[str] = None, resume: bool = False, **kwargs)¶

Start an experiment. This method includes first get_or_create an experiment, and then set it to be active.

Parameters:	experiment_id (str) – id of the active experiment. experiment_name (str) – name of the active experiment. recorder_id (str) – id of the recorder to be started. recorder_name (str) – name of the recorder to be started. uri (str) – the current tracking URI. resume (boolean) – whether to resume the experiment and recorder.
Returns:
Return type:	An active experiment.

end_exp(recorder_status: str = 'SCHEDULED', **kwargs)¶

End an active experiment.

Parameters:	experiment_name (str) – name of the active experiment. recorder_status (str) – the status of the active recorder of the experiment.

create_exp(experiment_name: Optional[str] = None)¶

Create an experiment.

Parameters:	experiment_name (str) – the experiment name, which must be unique.
Returns:	An experiment object. Raise —– ExpAlreadyExistError

search_records(experiment_ids=None, **kwargs)¶

Get a pandas DataFrame of records that fit the search criteria of the experiment. Inputs are the search critera user want to apply.

Returns:	A pandas.DataFrame of records, where each metric, parameter, and tag are expanded into their own columns named metrics., params., and tags.* respectively. For records that don’t have a particular metric, parameter, or tag, their value will be (NumPy) Nan, None, or None respectively.

get_exp(*, experiment_id=None, experiment_name=None, create: bool = True, start: bool = False)¶

Retrieve an experiment. This method includes getting an active experiment, and get_or_create a specific experiment.

When user specify experiment id and name, the method will try to return the specific experiment. When user does not provide recorder id or name, the method will try to return the current active experiment. The create argument determines whether the method will automatically create a new experiment according to user’s specification if the experiment hasn’t been created before.

If create is True:
- If active experiment exists:
  no id or name specified, return the active experiment.
  
  if id or name is specified, return the specified experiment. If no such exp found, create a new experiment with given id or name. If start is set to be True, the experiment is set to be active.
- If active experiment not exists:
  no id or name specified, create a default experiment.
  
  if id or name is specified, return the specified experiment. If no such exp found, create a new experiment with given id or name. If start is set to be True, the experiment is set to be active.
Else If create is False:
- If active experiment exists:
  no id or name specified, return the active experiment.
  
  if id or name is specified, return the specified experiment. If no such exp found, raise Error.
- If active experiment not exists:
  no id or name specified. If the default experiment exists, return it, otherwise, raise Error.
  
  if id or name is specified, return the specified experiment. If no such exp found, raise Error.

Parameters:	experiment_id (str) – id of the experiment to return. experiment_name (str) – name of the experiment to return. create (boolean) – create the experiment it if hasn’t been created before. start (boolean) – start the new experiment if one is created.
Returns:
Return type:	An experiment object.

delete_exp(experiment_id=None, experiment_name=None)¶

Delete an experiment.

Parameters:	experiment_id (str) – the experiment id. experiment_name (str) – the experiment name.

default_uri¶: Get the default tracking URI from qlib.config.C

uri¶

Get the default tracking URI or current URI.

Returns:
Return type:	The tracking URI string.

set_uri(uri: Optional[str] = None)¶

Set the current tracking URI and the corresponding variables.

Parameters:	uri (str) –

list_experiments()¶

List all the existing experiments.

Returns:
Return type:	A dictionary (name -> experiment) of experiments information that being stored.

Experiment¶

class qlib.workflow.exp.Experiment(id, name)¶

This is the Experiment class for each experiment being run. The API is designed similar to mlflow. (The link: https://mlflow.org/docs/latest/python_api/mlflow.html)

__init__(id, name)¶: Initialize self. See help(type(self)) for accurate signature.

start(*, recorder_id=None, recorder_name=None, resume=False)¶

Start the experiment and set it to be active. This method will also start a new recorder.

Parameters:	recorder_id (str) – the id of the recorder to be created. recorder_name (str) – the name of the recorder to be created. resume (bool) – whether to resume the first recorder
Returns:
Return type:	An active recorder.

end(recorder_status='SCHEDULED')¶

End the experiment.

Parameters:	recorder_status (str) – the status the recorder to be set with when ending (SCHEDULED, RUNNING, FINISHED, FAILED).

create_recorder(recorder_name=None)¶

Create a recorder for each experiment.

Parameters:	recorder_name (str) – the name of the recorder to be created.
Returns:
Return type:	A recorder object.

search_records(**kwargs)¶

Get a pandas DataFrame of records that fit the search criteria of the experiment. Inputs are the search critera user want to apply.

Returns:	A pandas.DataFrame of records, where each metric, parameter, and tag are expanded into their own columns named metrics., params., and tags.* respectively. For records that don’t have a particular metric, parameter, or tag, their value will be (NumPy) Nan, None, or None respectively.

delete_recorder(recorder_id)¶

Create a recorder for each experiment.

Parameters:	recorder_id (str) – the id of the recorder to be deleted.

get_recorder(recorder_id=None, recorder_name=None, create: bool = True, start: bool = False)¶

Retrieve a Recorder for user. When user specify recorder id and name, the method will try to return the specific recorder. When user does not provide recorder id or name, the method will try to return the current active recorder. The create argument determines whether the method will automatically create a new recorder according to user’s specification if the recorder hasn’t been created before.

If create is True:
- If active recorder exists:
  no id or name specified, return the active recorder.
  
  if id or name is specified, return the specified recorder. If no such exp found, create a new recorder with given id or name. If start is set to be True, the recorder is set to be active.
- If active recorder not exists:
  no id or name specified, create a new recorder.
  
  if id or name is specified, return the specified experiment. If no such exp found, create a new recorder with given id or name. If start is set to be True, the recorder is set to be active.
Else If create is False:
- If active recorder exists:
  no id or name specified, return the active recorder.
  
  if id or name is specified, return the specified recorder. If no such exp found, raise Error.
- If active recorder not exists:
  no id or name specified, raise Error.
  
  if id or name is specified, return the specified recorder. If no such exp found, raise Error.

Parameters:	recorder_id (str) – the id of the recorder to be deleted. recorder_name (str) – the name of the recorder to be deleted. create (boolean) – create the recorder if it hasn’t been created before. start (boolean) – start the new recorder if one is created.
Returns:
Return type:	A recorder object.

list_recorders(**flt_kwargs)¶

List all the existing recorders of this experiment. Please first get the experiment instance before calling this method. If user want to use the method R.list_recorders(), please refer to the related API document in QlibRecorder.

flt_kwargs : dict: filter recorders by conditions e.g. list_recorders(status=Recorder.STATUS_FI)

Returns:
Return type:	A dictionary (id -> recorder) of recorder information that being stored.

Recorder¶

class qlib.workflow.recorder.Recorder(experiment_id, name)¶

This is the Recorder class for logging the experiments. The API is designed similar to mlflow. (The link: https://mlflow.org/docs/latest/python_api/mlflow.html)

The status of the recorder can be SCHEDULED, RUNNING, FINISHED, FAILED.

__init__(experiment_id, name)¶: Initialize self. See help(type(self)) for accurate signature.

save_objects(local_path=None, artifact_path=None, **kwargs)¶

Save objects such as prediction file or model checkpoints to the artifact URI. User can save object through keywords arguments (name:value).

Please refer to the docs of qlib.workflow:R.save_objects

Parameters:	local_path (str) – if provided, them save the file or directory to the artifact URI. artifact_path=None (str) – the relative path for the artifact to be stored in the URI.

load_object(name)¶

Load objects such as prediction file or model checkpoints.

Parameters:	name (str) – name of the file to be loaded.
Returns:
Return type:	The saved object.

start_run()¶

Start running or resuming the Recorder. The return value can be used as a context manager within a with block; otherwise, you must call end_run() to terminate the current run. (See ActiveRun class in mlflow)

Returns:
Return type:	An active running object (e.g. mlflow.ActiveRun object)

end_run()¶: End an active Recorder.

log_params(**kwargs)¶

Log a batch of params for the current run.

Parameters:	arguments (keyword) – key, value pair to be logged as parameters.

log_metrics(step=None, **kwargs)¶

Log multiple metrics for the current run.

Parameters:	arguments (keyword) – key, value pair to be logged as metrics.

set_tags(**kwargs)¶

Log a batch of tags for the current run.

Parameters:	arguments (keyword) – key, value pair to be logged as tags.

delete_tags(*keys)¶

Delete some tags from a run.

Parameters:	keys (series of strs of the keys) – all the name of the tag to be deleted.

list_artifacts(artifact_path: str = None)¶

List all the artifacts of a recorder.

Parameters:	artifact_path (str) – the relative path for the artifact to be stored in the URI.
Returns:
Return type:	A list of artifacts information (name, path, etc.) that being stored.

list_metrics()¶

List all the metrics of a recorder.

Returns:
Return type:	A dictionary of metrics that being stored.

list_params()¶

List all the params of a recorder.

Returns:
Return type:	A dictionary of params that being stored.

list_tags()¶

List all the tags of a recorder.

Returns:
Return type:	A dictionary of tags that being stored.

Record Template¶

class qlib.workflow.record_temp.RecordTemp(recorder)¶

This is the Records Template class that enables user to generate experiment results such as IC and backtest in a certain format.

save(**kwargs)¶: It behaves the same as self.recorder.save_objects. But it is an easier interface because users don’t have to care about get_path and artifact_path

__init__(recorder)¶: Initialize self. See help(type(self)) for accurate signature.

generate(**kwargs)¶

Generate certain records such as IC, backtest etc., and save them.

Parameters:	kwargs –

load(name: str, parents: bool = True)¶

It behaves the same as self.recorder.load_object. But it is an easier interface because users don’t have to care about get_path and artifact_path

Parameters:	name (str) – the name for the file to be load. parents (bool) – Each recorder has different artifact_path. So parents recursively find the path in parents Sub classes has higher priority
Returns:
Return type:	The stored records.

list()¶

List the supported artifacts. Users don’t have to consider self.get_path

Returns:
Return type:	A list of all the supported artifacts.

check(include_self: bool = False, parents: bool = True)¶

Check if the records is properly generated and saved. It is useful in following examples - checking if the depended files complete before genrating new things. - checking if the final files is completed

Parameters:	include_self (bool) – is the file generated by self included parents (bool) – will we check parents Raise – ------ – FileNotFoundError –

:param : :type : whether the records are stored properly.

class qlib.workflow.record_temp.SignalRecord(model=None, dataset=None, recorder=None)¶

This is the Signal Record class that generates the signal prediction. This class inherits the RecordTemp class.

__init__(model=None, dataset=None, recorder=None)¶: Initialize self. See help(type(self)) for accurate signature.

generate(**kwargs)¶

Generate certain records such as IC, backtest etc., and save them.

Parameters:	kwargs –

list()¶

List the supported artifacts. Users don’t have to consider self.get_path

Returns:
Return type:	A list of all the supported artifacts.

class qlib.workflow.record_temp.HFSignalRecord(recorder, **kwargs)¶

This is the Signal Analysis Record class that generates the analysis results such as IC and IR. This class inherits the RecordTemp class.

depend_cls¶: alias of SignalRecord

__init__(recorder, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

generate()¶

Generate certain records such as IC, backtest etc., and save them.

Parameters:	kwargs –

list()¶

List the supported artifacts. Users don’t have to consider self.get_path

Returns:
Return type:	A list of all the supported artifacts.

class qlib.workflow.record_temp.SigAnaRecord(recorder, ana_long_short=False, ann_scaler=252, label_col=0, skip_existing=False)¶

This is the Signal Analysis Record class that generates the analysis results such as IC and IR. This class inherits the RecordTemp class.

depend_cls¶: alias of SignalRecord

__init__(recorder, ana_long_short=False, ann_scaler=252, label_col=0, skip_existing=False)¶: Initialize self. See help(type(self)) for accurate signature.

generate(label: Optional[pandas.core.frame.DataFrame] = None, **kwargs)¶

Parameters:	label (Optional[pd.DataFrame]) – Label should be a dataframe.

list()¶

List the supported artifacts. Users don’t have to consider self.get_path

Returns:
Return type:	A list of all the supported artifacts.

class qlib.workflow.record_temp.PortAnaRecord(recorder, config, risk_analysis_freq: Union[List[T], str] = None, indicator_analysis_freq: Union[List[T], str] = None, indicator_analysis_method=None, **kwargs)¶

This is the Portfolio Analysis Record class that generates the analysis results such as those of backtest. This class inherits the RecordTemp class.

The following files will be stored in recorder - report_normal.pkl & positions_normal.pkl:

The return report and detailed positions of the backtest, returned by qlib/contrib/evaluate.py:backtest

port_analysis.pkl : The risk analysis of your portfolio, returned by qlib/contrib/evaluate.py:risk_analysis

__init__(recorder, config, risk_analysis_freq: Union[List[T], str] = None, indicator_analysis_freq: Union[List[T], str] = None, indicator_analysis_method=None, **kwargs)¶

config[“strategy”] : dict: define the strategy class as well as the kwargs.
config[“executor”] : dict: define the executor class as well as the kwargs.
config[“backtest”] : dict: define the backtest kwargs.
risk_analysis_freq : str|List[str]: risk analysis freq of report
indicator_analysis_freq : str|List[str]: indicator analysis freq of report
indicator_analysis_method : str, optional, default by None: the candidated values include ‘mean’, ‘amount_weighted’, ‘value_weighted’

generate(**kwargs)¶

Generate certain records such as IC, backtest etc., and save them.

Parameters:	kwargs –

list()¶

List the supported artifacts. Users don’t have to consider self.get_path

Returns:
Return type:	A list of all the supported artifacts.

Task Management¶

TaskGen¶

TaskGenerator module can generate many tasks based on TaskGen and some task templates.

qlib.workflow.task.gen.task_generator(tasks, generators) → list¶

Use a list of TaskGen and a list of task templates to generate different tasks.

For examples:

There are 3 task templates a,b,c and 2 TaskGen A,B. A will generates 2 tasks from a template and B will generates 3 tasks from a template. task_generator([a, b, c], [A, B]) will finally generate 3*2*3 = 18 tasks.

Parameters:	tasks (List[dict] or dict) – a list of task templates or a single task generators (List[TaskGen] or TaskGen) – a list of TaskGen or a single TaskGen
Returns:	a list of tasks
Return type:	list

class qlib.workflow.task.gen.TaskGen¶

The base class for generating different tasks

Example 1:

input: a specific task template and rolling steps

output: rolling version of the tasks

Example 2:

input: a specific task template and losses list

output: a set of tasks with different losses

generate(task: dict) → List[dict]¶

Generate different tasks based on a task template

Parameters:	task (dict) – a task template
Returns:	A list of tasks
Return type:	typing.List[dict]

qlib.workflow.task.gen.handler_mod(task: dict, rolling_gen)¶

Help to modify the handler end time when using RollingGen It try to handle the following case - Hander’s data end_time is earlier than dataset’s test_data’s segments.

To handle this, handler’s data’s end_time is extended.

If the handler’s end_time is None, then it is not necessary to change it’s end time.

Parameters:	task (dict) – a task template rg (RollingGen) – an instance of RollingGen

class qlib.workflow.task.gen.RollingGen(step: int = 40, rtype: str = 'expanding', ds_extra_mod_func: Union[None, Callable] = <function handler_mod>)¶

__init__(step: int = 40, rtype: str = 'expanding', ds_extra_mod_func: Union[None, Callable] = <function handler_mod>)¶

Generate tasks for rolling

Parameters:	step (int) – step to rolling rtype (str) – rolling type (expanding, sliding) ds_extra_mod_func (Callable) – A method like: handler_mod(task: dict, rg: RollingGen) Do some extra action after generating a task. For example, use `handler_mod` to modify the end time of the handler of a dataset.

gen_following_tasks(task: dict, test_end: pandas._libs.tslibs.timestamps.Timestamp) → List[dict]¶

generating following rolling tasks for task until test_end

Parameters:	task (dict) – Qlib task format test_end (pd.Timestamp) – the latest rolling task includes test_end
Returns:	the following tasks of task`(`task itself is excluded)
Return type:	List[dict]

generate(task: dict) → List[dict]¶

Converting the task into a rolling task.

Parameters:

task (dict) –

A dict describing a task. For example.

DEFAULT_TASK = {
    "model": {
        "class": "LGBModel",
        "module_path": "qlib.contrib.model.gbdt",
    },
    "dataset": {
        "class": "DatasetH",
        "module_path": "qlib.data.dataset",
        "kwargs": {
            "handler": {
                "class": "Alpha158",
                "module_path": "qlib.contrib.data.handler",
                "kwargs": {
                    "start_time": "2008-01-01",
                    "end_time": "2020-08-01",
                    "fit_start_time": "2008-01-01",
                    "fit_end_time": "2014-12-31",
                    "instruments": "csi100",
                },
            },
            "segments": {
                "train": ("2008-01-01", "2014-12-31"),
                "valid": ("2015-01-01", "2016-12-20"),  # Please avoid leaking the future test data into validation
                "test": ("2017-01-01", "2020-08-01"),
            },
        },
    },
    "record": [
        {
            "class": "SignalRecord",
            "module_path": "qlib.workflow.record_temp",
        },
    ]
}

Returns: List[dict]

Return type: a list of tasks

class qlib.workflow.task.gen.MultiHorizonGenBase(horizon: List[int] = [5], label_leak_n=2)¶

__init__(horizon: List[int] = [5], label_leak_n=2)¶

This task generator tries to genrate tasks for different horizons based on an existing task

Parameters:

horizon (List[int]) – the possible horizons of the tasks
label_leak_n (int) – How many future days it will take to get complete label after the day making prediction For example: - User make prediction on day T`(after getting the close price on `T) - The label is the return of buying stock on T + 1 and selling it on T + 2 - the label_leak_n will be 2 (e.g. two days of information is leaked to leverage this sample)

set_horizon(task: dict, hr: int)¶

This method is designed to change the task in place

Parameters:	task (dict) – Qlib’s task hr (int) – the horizon of task

generate(task: dict)¶

Generate different tasks based on a task template

Parameters:	task (dict) – a task template
Returns:	A list of tasks
Return type:	typing.List[dict]

TaskManager¶

TaskManager can fetch unused tasks automatically and manage the lifecycle of a set of tasks with error handling. These features can run tasks concurrently and ensure every task will be used only once. Task Manager will store all tasks in MongoDB. Users MUST finished the configuration of MongoDB when using this module.

A task in TaskManager consists of 3 parts - tasks description: the desc will define the task - tasks status: the status of the task - tasks result: A user can get the task with the task description and task result.

class qlib.workflow.task.manage.TaskManager(task_pool: str)¶

Here is what will a task looks like when it created by TaskManager

{
    'def': pickle serialized task definition.  using pickle will make it easier
    'filter': json-like data. This is for filtering the tasks.
    'status': 'waiting' | 'running' | 'done'
    'res': pickle serialized task result,
}

The tasks manager assumes that you will only update the tasks you fetched. The mongo fetch one and update will make it date updating secure.

This class can be used as a tool from commandline. Here are serveral examples. You can view the help of manage module with the following commands: python -m qlib.workflow.task.manage -h # show manual of manage module CLI python -m qlib.workflow.task.manage wait -h # show manual of the wait command of manage

python -m qlib.workflow.task.manage -t <pool_name> wait
python -m qlib.workflow.task.manage -t <pool_name> task_stat

Note

Assumption: the data in MongoDB was encoded and the data out of MongoDB was decoded

Here are four status which are:

STATUS_WAITING: waiting for training

STATUS_RUNNING: training

STATUS_PART_DONE: finished some step and waiting for next step

STATUS_DONE: all work done

__init__(task_pool: str)¶

Init Task Manager, remember to make the statement of MongoDB url and database name firstly. A TaskManager instance serves a specific task pool. The static method of this module serves the whole MongoDB.

Parameters:	task_pool (str) – the name of Collection in MongoDB

static list() → list¶

List the all collection(task_pool) of the db.

Returns:	list

replace_task(task, new_task)¶

Use a new task to replace a old one

Parameters:	task – old task new_task – new task

insert_task(task)¶

Insert a task.

Parameters:	task – the task waiting for insert
Returns:	pymongo.results.InsertOneResult

insert_task_def(task_def)¶

Insert a task to task_pool

Parameters:	task_def (dict) – the task definition
Returns:
Return type:	pymongo.results.InsertOneResult

create_task(task_def_l, dry_run=False, print_nt=False) → List[str]¶

If the tasks in task_def_l are new, then insert new tasks into the task_pool, and record inserted_id. If a task is not new, then just query its _id.

Parameters:	task_def_l (list) – a list of task dry_run (bool) – if insert those new tasks to task pool print_nt (bool) – if print new task
Returns:	a list of the _id of task_def_l
Return type:	List[str]

fetch_task(query={}, status='waiting') → dict¶

Use query to fetch tasks.

Parameters:	query (dict, optional) – query dict. Defaults to {}. status (str, optional) – [description]. Defaults to STATUS_WAITING.
Returns:	a task(document in collection) after decoding
Return type:	dict

safe_fetch_task(query={}, status='waiting')¶

Fetch task from task_pool using query with contextmanager

Parameters:	query (dict) – the dict of query
Returns:	dict
Return type:	a task(document in collection) after decoding

query(query={}, decode=True)¶

Query task in collection. This function may raise exception pymongo.errors.CursorNotFound: cursor id not found if it takes too long to iterate the generator

python -m qlib.workflow.task.manage -t <your task pool> query ‘{“_id”: “615498be837d0053acbc5d58”}’

Parameters:	query (dict) – the dict of query decode (bool) –
Returns:	dict
Return type:	a task(document in collection) after decoding

re_query(_id) → dict¶

Use _id to query task.

Parameters:	_id (str) – _id of a document
Returns:	a task(document in collection) after decoding
Return type:	dict

commit_task_res(task, res, status='done')¶

Commit the result to task[‘res’].

Parameters:	task ([type]) – [description] res (object) – the result you want to save status (str, optional) – STATUS_WAITING, STATUS_RUNNING, STATUS_DONE, STATUS_PART_DONE. Defaults to STATUS_DONE.

return_task(task, status='waiting')¶

Return a task to status. Alway using in error handling.

Parameters:	task ([type]) – [description] status (str, optional) – STATUS_WAITING, STATUS_RUNNING, STATUS_DONE, STATUS_PART_DONE. Defaults to STATUS_WAITING.

remove(query={})¶

Remove the task using query

Parameters:	query (dict) – the dict of query

task_stat(query={}) → dict¶

Count the tasks in every status.

Parameters:	query (dict, optional) – the query dict. Defaults to {}.
Returns:	dict

reset_waiting(query={})¶

Reset all running task into waiting status. Can be used when some running task exit unexpected.

Parameters:	query (dict, optional) – the query dict. Defaults to {}.

prioritize(task, priority: int)¶

Set priority for task

Parameters:	task (dict) – The task query from the database priority (int) – the target priority

wait(query={})¶

When multiprocessing, the main progress may fetch nothing from TaskManager because there are still some running tasks. So main progress should wait until all tasks are trained well by other progress or machines.

Parameters:	query (dict, optional) – the query dict. Defaults to {}.

qlib.workflow.task.manage.run_task(task_func: Callable, task_pool: str, query: dict = {}, force_release: bool = False, before_status: str = 'waiting', after_status: str = 'done', **kwargs)¶

While the task pool is not empty (has WAITING tasks), use task_func to fetch and run tasks in task_pool

After running this method, here are 4 situations (before_status -> after_status):

STATUS_WAITING -> STATUS_DONE: use task[“def”] as task_func param, it means that the task has not been started

STATUS_WAITING -> STATUS_PART_DONE: use task[“def”] as task_func param

STATUS_PART_DONE -> STATUS_PART_DONE: use task[“res”] as task_func param, it means that the task has been started but not completed

STATUS_PART_DONE -> STATUS_DONE: use task[“res”] as task_func param

Parameters:

task_func (Callable) –

def (task_def, **kwargs) -> <res which will be committed>

the function to run the task
task_pool (str) – the name of the task pool (Collection in MongoDB)
query (dict) – will use this dict to query task_pool when fetching task
force_release (bool) – will the program force to release the resource
before_status (str:) – the tasks in before_status will be fetched and trained. Can be STATUS_WAITING, STATUS_PART_DONE.
after_status (str:) – the tasks after trained will become after_status. Can be STATUS_WAITING, STATUS_PART_DONE.
kwargs – the params for task_func

Trainer¶

The Trainer will train a list of tasks and return a list of model recorders. There are two steps in each Trainer including ``train``(make model recorder) and ``end_train``(modify model recorder).

This is a concept called DelayTrainer, which can be used in online simulating for parallel training. In DelayTrainer, the first step is only to save some necessary info to model recorders, and the second step which will be finished in the end can do some concurrent and time-consuming operations such as model fitting.

Qlib offer two kinds of Trainer, TrainerR is the simplest way and TrainerRM is based on TaskManager to help manager tasks lifecycle automatically.

qlib.model.trainer.begin_task_train(task_config: dict, experiment_name: str, recorder_name: str = None) → qlib.workflow.recorder.Recorder¶

Begin task training to start a recorder and save the task config.

Parameters:	task_config (dict) – the config of a task experiment_name (str) – the name of experiment recorder_name (str) – the given name will be the recorder name. None for using rid.
Returns:	the model recorder
Return type:	Recorder

qlib.model.trainer.fill_placeholder(config: dict, config_extend: dict)¶

Detect placeholder in config and fill them with config_extend. The item of dict must be single item(int, str, etc), dict and list. Tuples are not supported.

Parameters:	config (dict) – the parameter dict will be filled config_extend (dict) – the value of all placeholders
Returns:	the parameter dict
Return type:	dict

qlib.model.trainer.end_task_train(rec: qlib.workflow.recorder.Recorder, experiment_name: str) → qlib.workflow.recorder.Recorder¶

Finish task training with real model fitting and saving.

Parameters:	rec (Recorder) – the recorder will be resumed experiment_name (str) – the name of experiment
Returns:	the model recorder
Return type:	Recorder

qlib.model.trainer.task_train(task_config: dict, experiment_name: str, recorder_name: str = None) → qlib.workflow.recorder.Recorder¶

Task based training, will be divided into two steps.

Parameters:	task_config (dict) – The config of a task. experiment_name (str) – The name of experiment recorder_name (str) – The name of recorder
Returns:	Recorder
Return type:	The instance of the recorder

class qlib.model.trainer.Trainer¶

The trainer can train a list of models. There are Trainer and DelayTrainer, which can be distinguished by when it will finish real training.

__init__()¶: Initialize self. See help(type(self)) for accurate signature.

train(tasks: list, *args, **kwargs) → list¶

Given a list of task definitions, begin training, and return the models.

For Trainer, it finishes real training in this method. For DelayTrainer, it only does some preparation in this method.

Parameters:	tasks – a list of tasks
Returns:	a list of models
Return type:	list

end_train(models: list, *args, **kwargs) → list¶

Given a list of models, finished something at the end of training if you need. The models may be Recorder, txt file, database, and so on.

For Trainer, it does some finishing touches in this method. For DelayTrainer, it finishes real training in this method.

Parameters:	models – a list of models
Returns:	a list of models
Return type:	list

is_delay() → bool¶

If Trainer will delay finishing end_train.

Returns:	if DelayTrainer
Return type:	bool

has_worker() → bool¶

Some trainer has backend worker to support parallel training This method can tell if the worker is enabled.

Returns:	if the worker is enabled
Return type:	bool

worker()¶

start the worker

Raises:	NotImplementedError: – If the worker is not supported

class qlib.model.trainer.TrainerR(experiment_name: str = None, train_func: Callable = <function task_train>)¶

Trainer based on (R)ecorder. It will train a list of tasks and return a list of model recorders in a linear way.

Assumption: models were defined by task and the results will be saved to Recorder.

__init__(experiment_name: str = None, train_func: Callable = <function task_train>)¶

Init TrainerR.

Parameters:	experiment_name (str, optional) – the default name of experiment. train_func (Callable, optional) – default training method. Defaults to task_train.

train(tasks: list, train_func: Callable = None, experiment_name: str = None, **kwargs) → List[qlib.workflow.recorder.Recorder]¶

Given a list of `task`s and return a list of trained Recorder. The order can be guaranteed.

Parameters:	tasks (list) – a list of definitions based on task dict train_func (Callable) – the training method which needs at least tasks and experiment_name. None for the default training method. experiment_name (str) – the experiment name, None for use default name. kwargs – the params for train_func.
Returns:	a list of Recorders
Return type:	List[Recorder]

end_train(recs: list, **kwargs) → List[qlib.workflow.recorder.Recorder]¶

Set STATUS_END tag to the recorders.

Parameters:	recs (list) – a list of trained recorders.
Returns:	the same list as the param.
Return type:	List[Recorder]

class qlib.model.trainer.DelayTrainerR(experiment_name: str = None, train_func=<function begin_task_train>, end_train_func=<function end_task_train>)¶

A delayed implementation based on TrainerR, which means train method may only do some preparation and end_train method can do the real model fitting.

__init__(experiment_name: str = None, train_func=<function begin_task_train>, end_train_func=<function end_task_train>)¶

Init TrainerRM.

Parameters:	experiment_name (str) – the default name of experiment. train_func (Callable, optional) – default train method. Defaults to begin_task_train. end_train_func (Callable, optional) – default end_train method. Defaults to end_task_train.

end_train(recs, end_train_func=None, experiment_name: str = None, **kwargs) → List[qlib.workflow.recorder.Recorder]¶

Given a list of Recorder and return a list of trained Recorder. This class will finish real data loading and model fitting.

Parameters:	recs (list) – a list of Recorder, the tasks have been saved to them end_train_func (Callable, optional) – the end_train method which needs at least recorder`s and `experiment_name. Defaults to None for using self.end_train_func. experiment_name (str) – the experiment name, None for use default name. kwargs – the params for end_train_func.
Returns:	a list of Recorders
Return type:	List[Recorder]

class qlib.model.trainer.TrainerRM(experiment_name: str = None, task_pool: str = None, train_func=<function task_train>, skip_run_task: bool = False)¶

Trainer based on (R)ecorder and Task(M)anager. It can train a list of tasks and return a list of model recorders in a multiprocessing way.

Assumption: task will be saved to TaskManager and task will be fetched and trained from TaskManager

__init__(experiment_name: str = None, task_pool: str = None, train_func=<function task_train>, skip_run_task: bool = False)¶

Init TrainerR.

Parameters:	experiment_name (str) – the default name of experiment. task_pool (str) – task pool name in TaskManager. None for use same name as experiment_name. train_func (Callable, optional) – default training method. Defaults to task_train. skip_run_task (bool) – If skip_run_task == True: Only run_task in the worker. Otherwise skip run_task.

train(tasks: list, train_func: Callable = None, experiment_name: str = None, before_status: str = 'waiting', after_status: str = 'done', **kwargs) → List[qlib.workflow.recorder.Recorder]¶

Given a list of `task`s and return a list of trained Recorder. The order can be guaranteed.

This method defaults to a single process, but TaskManager offered a great way to parallel training. Users can customize their train_func to realize multiple processes or even multiple machines.

Parameters:	tasks (list) – a list of definitions based on task dict train_func (Callable) – the training method which needs at least task`s and `experiment_name. None for the default training method. experiment_name (str) – the experiment name, None for use default name. before_status (str) – the tasks in before_status will be fetched and trained. Can be STATUS_WAITING, STATUS_PART_DONE. after_status (str) – the tasks after trained will become after_status. Can be STATUS_WAITING, STATUS_PART_DONE. kwargs – the params for train_func.
Returns:	a list of Recorders
Return type:	List[Recorder]

end_train(recs: list, **kwargs) → List[qlib.workflow.recorder.Recorder]¶

Set STATUS_END tag to the recorders.

Parameters:	recs (list) – a list of trained recorders.
Returns:	the same list as the param.
Return type:	List[Recorder]

worker(train_func: Callable = None, experiment_name: str = None)¶

The multiprocessing method for train. It can share a same task_pool with train and can run in other progress or other machines.

Parameters:	train_func (Callable) – the training method which needs at least task`s and `experiment_name. None for the default training method. experiment_name (str) – the experiment name, None for use default name.

has_worker() → bool¶

Some trainer has backend worker to support parallel training This method can tell if the worker is enabled.

Returns:	if the worker is enabled
Return type:	bool

class qlib.model.trainer.DelayTrainerRM(experiment_name: str = None, task_pool: str = None, train_func=<function begin_task_train>, end_train_func=<function end_task_train>, skip_run_task: bool = False)¶

A delayed implementation based on TrainerRM, which means train method may only do some preparation and end_train method can do the real model fitting.

__init__(experiment_name: str = None, task_pool: str = None, train_func=<function begin_task_train>, end_train_func=<function end_task_train>, skip_run_task: bool = False)¶

Init DelayTrainerRM.

Parameters:

experiment_name (str) – the default name of experiment.
task_pool (str) – task pool name in TaskManager. None for use same name as experiment_name.
train_func (Callable, optional) – default train method. Defaults to begin_task_train.
end_train_func (Callable, optional) – default end_train method. Defaults to end_task_train.
skip_run_task (bool) – If skip_run_task == True: Only run_task in the worker. Otherwise skip run_task. E.g. Starting trainer on a CPU VM and then waiting tasks to be finished on GPU VMs.

train(tasks: list, train_func=None, experiment_name: str = None, **kwargs) → List[qlib.workflow.recorder.Recorder]¶

Same as train of TrainerRM, after_status will be STATUS_PART_DONE.

Parameters:	tasks (list) – a list of definition based on task dict train_func (Callable) – the train method which need at least task`s and `experiment_name. Defaults to None for using self.train_func. experiment_name (str) – the experiment name, None for use default name.
Returns:	a list of Recorders
Return type:	List[Recorder]

end_train(recs, end_train_func=None, experiment_name: str = None, **kwargs) → List[qlib.workflow.recorder.Recorder]¶

Given a list of Recorder and return a list of trained Recorder. This class will finish real data loading and model fitting.

Parameters:	recs (list) – a list of Recorder, the tasks have been saved to them. end_train_func (Callable, optional) – the end_train method which need at least recorder`s and `experiment_name. Defaults to None for using self.end_train_func. experiment_name (str) – the experiment name, None for use default name. kwargs – the params for end_train_func.
Returns:	a list of Recorders
Return type:	List[Recorder]

worker(end_train_func=None, experiment_name: str = None)¶

The multiprocessing method for end_train. It can share a same task_pool with end_train and can run in other progress or other machines.

Parameters:	end_train_func (Callable, optional) – the end_train method which need at least recorder`s and `experiment_name. Defaults to None for using self.end_train_func. experiment_name (str) – the experiment name, None for use default name.

has_worker() → bool¶

Some trainer has backend worker to support parallel training This method can tell if the worker is enabled.

Returns:	if the worker is enabled
Return type:	bool

Collector¶

Collector module can collect objects from everywhere and process them such as merging, grouping, averaging and so on.

class qlib.workflow.task.collect.Collector(process_list=[])¶

The collector to collect different results

__init__(process_list=[])¶

Init Collector.

Parameters:	process_list (list or Callable) – the list of processors or the instance of a processor to process dict.

collect() → dict¶

Collect the results and return a dict like {key: things}

Returns:

the dict after collecting.

For example:

{“prediction”: pd.Series}

{“IC”: {“Xgboost”: pd.Series, “LSTM”: pd.Series}}

Return type: dict

static process_collect(collected_dict, process_list=[], *args, **kwargs) → dict¶

Do a series of processing to the dict returned by collect and return a dict like {key: things} For example, you can group and ensemble.

Parameters:	collected_dict (dict) – the dict return by collect process_list (list or Callable) – the list of processors or the instance of a processor to process dict. processor order is the same as the list order. (The) – For example: [Group1(…, Ensemble1()), Group2(…, Ensemble2())]
Returns:	the dict after processing.
Return type:	dict

class qlib.workflow.task.collect.MergeCollector(collector_dict: Dict[str, qlib.workflow.task.collect.Collector], process_list: List[Callable] = [], merge_func=None)¶

A collector to collect the results of other Collectors

For example:

We have 2 collector, which named A and B. A can collect {“prediction”: pd.Series} and B can collect {“IC”: {“Xgboost”: pd.Series, “LSTM”: pd.Series}}. Then after this class’s collect, we can collect {“A_prediction”: pd.Series, “B_IC”: {“Xgboost”: pd.Series, “LSTM”: pd.Series}}

__init__(collector_dict: Dict[str, qlib.workflow.task.collect.Collector], process_list: List[Callable] = [], merge_func=None)¶

Init MergeCollector.

Parameters:

collector_dict (Dict[str,Collector]) – the dict like {collector_key, Collector}
process_list (List[Callable]) – the list of processors or the instance of processor to process dict.
merge_func (Callable) – a method to generate outermost key. The given params are collector_key from collector_dict and key from every collector after collecting. None for using tuple to connect them, such as “ABC”+(“a”,”b”) -> (“ABC”, (“a”,”b”)).

collect() → dict¶

Collect all results of collector_dict and change the outermost key to a recombination key.

Returns:	the dict after collecting.
Return type:	dict

class qlib.workflow.task.collect.RecorderCollector(experiment, process_list=[], rec_key_func=None, rec_filter_func=None, artifacts_path={'pred': 'pred.pkl'}, artifacts_key=None, list_kwargs={})¶

__init__(experiment, process_list=[], rec_key_func=None, rec_filter_func=None, artifacts_path={'pred': 'pred.pkl'}, artifacts_key=None, list_kwargs={})¶

Init RecorderCollector.

Parameters:

experiment – (Experiment or str): an instance of an Experiment or the name of an Experiment (Callable): an callable function, which returns a list of experiments
process_list (list or Callable) – the list of processors or the instance of a processor to process dict.
rec_key_func (Callable) – a function to get the key of a recorder. If None, use recorder id.
rec_filter_func (Callable, optional) – filter the recorder by return True or False. Defaults to None.
artifacts_path (dict, optional) – The artifacts name and its path in Recorder. Defaults to {“pred”: “pred.pkl”, “IC”: “sig_analysis/ic.pkl”}.
artifacts_key (str or List, optional) – the artifacts key you want to get. If None, get all artifacts.
list_kwargs (str) – arguments for list_recorders function.

collect(artifacts_key=None, rec_filter_func=None, only_exist=True) → dict¶

Collect different artifacts based on recorder after filtering.

Parameters:	artifacts_key (str or List, optional) – the artifacts key you want to get. If None, use the default. rec_filter_func (Callable, optional) – filter the recorder by return True or False. If None, use the default. only_exist (bool, optional) – if only collect the artifacts when a recorder really has. If True, the recorder with exception when loading will not be collected. But if False, it will raise the exception.
Returns:	the dict after collected like {artifact: {rec_key: object}}
Return type:	dict

get_exp_name() → str¶

Get experiment name

Returns:	experiment name
Return type:	str

Group¶

Group can group a set of objects based on group_func and change them to a dict. After group, we provide a method to reduce them.

For example:

group: {(A,B,C1): object, (A,B,C2): object} -> {(A,B): {C1: object, C2: object}} reduce: {(A,B): {C1: object, C2: object}} -> {(A,B): object}

class qlib.model.ens.group.Group(group_func=None, ens: qlib.model.ens.ensemble.Ensemble = None)¶

Group the objects based on dict

__init__(group_func=None, ens: qlib.model.ens.ensemble.Ensemble = None)¶

Init Group.

Parameters:	group_func (Callable, optional) – Given a dict and return the group key and one of the group elements. For example: {(A,B,C1): object, (A,B,C2): object} -> {(A,B): {C1: object, C2: object}} to None. (Defaults) – ens (Ensemble, optional) – If not None, do ensemble for grouped value after grouping.

group(*args, **kwargs) → dict¶

Group a set of objects and change them to a dict.

For example: {(A,B,C1): object, (A,B,C2): object} -> {(A,B): {C1: object, C2: object}}

Returns:	grouped dict
Return type:	dict

reduce(*args, **kwargs) → dict¶

Reduce grouped dict.

For example: {(A,B): {C1: object, C2: object}} -> {(A,B): object}

Returns:	reduced dict
Return type:	dict

class qlib.model.ens.group.RollingGroup¶

Group the rolling dict

group(rolling_dict: dict) → dict¶

Given an rolling dict likes {(A,B,R): things}, return the grouped dict likes {(A,B): {R:things}}

NOTE: There is an assumption which is the rolling key is at the end of the key tuple, because the rolling results always need to be ensemble firstly.

Parameters:	rolling_dict (dict) – an rolling dict. If the key is not a tuple, then do nothing.
Returns:	grouped dict
Return type:	dict

__init__()¶

Init Group.

Parameters:	group_func (Callable, optional) – Given a dict and return the group key and one of the group elements. For example: {(A,B,C1): object, (A,B,C2): object} -> {(A,B): {C1: object, C2: object}} to None. (Defaults) – ens (Ensemble, optional) – If not None, do ensemble for grouped value after grouping.

Ensemble¶

Ensemble module can merge the objects in an Ensemble. For example, if there are many submodels predictions, we may need to merge them into an ensemble prediction.

class qlib.model.ens.ensemble.Ensemble¶

Merge the ensemble_dict into an ensemble object.

For example: {Rollinga_b: object, Rollingb_c: object} -> object

When calling this class:

Args:

ensemble_dict (dict): the ensemble dict like {name: things} waiting for merging

Returns:

object: the ensemble object

class qlib.model.ens.ensemble.SingleKeyEnsemble¶

Extract the object if there is only one key and value in the dict. Make the result more readable. {Only key: Only value} -> Only value

If there is more than 1 key or less than 1 key, then do nothing. Even you can run this recursively to make dict more readable.

NOTE: Default runs recursively.

When calling this class:

Args:

ensemble_dict (dict): the dict. The key of the dict will be ignored.

Returns:

dict: the readable dict.

class qlib.model.ens.ensemble.RollingEnsemble¶

Merge a dict of rolling dataframe like prediction or IC into an ensemble.

NOTE: The values of dict must be pd.DataFrame, and have the index “datetime”.

When calling this class:

Args:

ensemble_dict (dict): a dict like {“A”: pd.DataFrame, “B”: pd.DataFrame}. The key of the dict will be ignored.

Returns:

pd.DataFrame: the complete result of rolling.

class qlib.model.ens.ensemble.AverageEnsemble¶

Average and standardize a dict of same shape dataframe like prediction or IC into an ensemble.

NOTE: The values of dict must be pd.DataFrame, and have the index “datetime”. If it is a nested dict, then flat it.

When calling this class:

Args:

ensemble_dict (dict): a dict like {“A”: pd.DataFrame, “B”: pd.DataFrame}. The key of the dict will be ignored.

Returns:

pd.DataFrame: the complete result of averaging and standardizing.

Utils¶

Some tools for task management.

qlib.workflow.task.utils.get_mongodb() → pymongo.database.Database¶

Get database in MongoDB, which means you need to declare the address and the name of a database at first.

For example:

Using qlib.init():

mongo_conf = {

“task_url”: task_url, # your MongoDB url “task_db_name”: task_db_name, # database name

} qlib.init(…, mongo=mongo_conf)

After qlib.init():

C[“mongo”] = {

“task_url” : “mongodb://localhost:27017/”, “task_db_name” : “rolling_db”

}

Returns:	the Database instance
Return type:	Database

qlib.workflow.task.utils.list_recorders(experiment, rec_filter_func=None)¶

List all recorders which can pass the filter in an experiment.

Parameters:	experiment (str or Experiment) – the name of an Experiment or an instance rec_filter_func (Callable, optional) – return True to retain the given recorder. Defaults to None.
Returns:	a dict {rid: recorder} after filtering.
Return type:	dict

class qlib.workflow.task.utils.TimeAdjuster(future=True, end_time=None)¶

Find appropriate date and adjust date.

__init__(future=True, end_time=None)¶: Initialize self. See help(type(self)) for accurate signature.

set_end_time(end_time=None)¶

Set end time. None for use calendar’s end time.

Parameters:	end_time –

get(idx: int)¶

Get datetime by index.

Parameters:	idx (int) – index of the calendar

max() → pandas._libs.tslibs.timestamps.Timestamp¶: Return the max calendar datetime

align_idx(time_point, tp_type='start') → int¶

Align the index of time_point in the calendar.

Parameters:	time_point – tp_type (str) –
Returns:	index
Return type:	int

cal_interval(time_point_A, time_point_B) → int¶

Calculate the trading day interval (time_point_A - time_point_B)

Parameters:	time_point_A – time_point_A time_point_B – time_point_B (is the past of time_point_A)
Returns:	the interval between A and B
Return type:	int

align_time(time_point, tp_type='start') → pandas._libs.tslibs.timestamps.Timestamp¶

Align time_point to trade date of calendar

Parameters:	time_point – Time point tp_type – str time point type (“start”, “end”)
Returns:	pd.Timestamp

align_seg(segment: Union[dict, tuple]) → Union[dict, tuple]¶

Align the given date to the trade date

for example:

input: {'train': ('2008-01-01', '2014-12-31'), 'valid': ('2015-01-01', '2016-12-31'), 'test': ('2017-01-01', '2020-08-01')}

output: {'train': (Timestamp('2008-01-02 00:00:00'), Timestamp('2014-12-31 00:00:00')),
        'valid': (Timestamp('2015-01-05 00:00:00'), Timestamp('2016-12-30 00:00:00')),
        'test': (Timestamp('2017-01-03 00:00:00'), Timestamp('2020-07-31 00:00:00'))}

Parameters:	segment –
Returns:	Union[dict, tuple]
Return type:	the start and end trade date (pd.Timestamp) between the given start and end date.

truncate(segment: tuple, test_start, days: int) → tuple¶

Truncate the segment based on the test_start date

Parameters:	segment (tuple) – time segment test_start – days (int) – The trading days to be truncated the data in this segment may need ‘days’ data
Returns:	tuple
Return type:	new segment

shift(seg: tuple, step: int, rtype='sliding') → tuple¶

Shift the datatime of segment

Parameters:	seg – datetime segment step (int) – rolling step rtype (str) – rolling type (“sliding” or “expanding”)
Returns:	tuple
Return type:	new segment
Raises:	KeyError: – shift will raise error if the index(both start and end) is out of self.cal

Online Serving¶

Online Manager¶

OnlineManager can manage a set of Online Strategy and run them dynamically.

With the change of time, the decisive models will be also changed. In this module, we call those contributing models online models. In every routine(such as every day or every minute), the online models may be changed and the prediction of them needs to be updated. So this module provides a series of methods to control this process.

This module also provides a method to simulate Online Strategy in history. Which means you can verify your strategy or find a better one.

There are 4 total situations for using different trainers in different situations:

Situations	Description
Online + Trainer	When you want to do a REAL routine, the Trainer will help you train the models. It will train models task by task and strategy by strategy.
Online + DelayTrainer	DelayTrainer will skip concrete training until all tasks have been prepared by different strategies. It makes users can parallelly train all tasks at the end of routine or first_train. Otherwise, these functions will get stuck when each strategy prepare tasks.
Simulation + Trainer	It will behave in the same way as Online + Trainer. The only difference is that it is for simulation/backtesting instead of online trading
Simulation + DelayTrainer	When your models don’t have any temporal dependence, you can use DelayTrainer for the ability to multitasking. It means all tasks in all routines can be REAL trained at the end of simulating. The signals will be prepared well at different time segments (based on whether or not any new model is online).

Here is some pseudo code the demonstrate the workflow of each situation

For simplicity

Only one strategy is used in the strategy
update_online_pred is only called in the online mode and is ignored

Online + Trainer

tasks = first_train()
models = trainer.train(tasks)
trainer.end_train(models)
for day in online_trading_days:
    # OnlineManager.routine
    models = trainer.train(strategy.prepare_tasks())  # for each strategy
    strategy.prepare_online_models(models)  # for each strategy

    trainer.end_train(models)
    prepare_signals()  # prepare trading signals daily

Online + DelayTrainer: the workflow is the same as Online + Trainer.

Simulation + DelayTrainer

# simulate
tasks = first_train()
models = trainer.train(tasks)
for day in historical_calendars:
    # OnlineManager.routine
    models = trainer.train(strategy.prepare_tasks())  # for each strategy
    strategy.prepare_online_models(models)  # for each strategy
# delay_prepare()
# FIXME: Currently the delay_prepare is not implemented in a proper way.
trainer.end_train(<for all previous models>)
prepare_signals()

# Can we simplify current workflow? - Can reduce the number of state of tasks?

For each task, we have three phases (i.e. task, partly trained task, final trained task)

class qlib.workflow.online.manager.OnlineManager(strategies: Union[qlib.workflow.online.strategy.OnlineStrategy, List[qlib.workflow.online.strategy.OnlineStrategy]], trainer: qlib.model.trainer.Trainer = None, begin_time: Union[str, pandas._libs.tslibs.timestamps.Timestamp] = None, freq='day')¶

OnlineManager can manage online models with Online Strategy. It also provides a history recording of which models are online at what time.

__init__(strategies: Union[qlib.workflow.online.strategy.OnlineStrategy, List[qlib.workflow.online.strategy.OnlineStrategy]], trainer: qlib.model.trainer.Trainer = None, begin_time: Union[str, pandas._libs.tslibs.timestamps.Timestamp] = None, freq='day')¶

Init OnlineManager. One OnlineManager must have at least one OnlineStrategy.

Parameters:

strategies (Union[OnlineStrategy, List[OnlineStrategy]]) – an instance of OnlineStrategy or a list of OnlineStrategy
begin_time (Union[str,pd.Timestamp], optional) – the OnlineManager will begin at this time. Defaults to None for using the latest date.
trainer (Trainer) – the trainer to train task. None for using TrainerR.
freq (str, optional) – data frequency. Defaults to “day”.

first_train(strategies: List[qlib.workflow.online.strategy.OnlineStrategy] = None, model_kwargs: dict = {})¶

Get tasks from every strategy’s first_tasks method and train them. If using DelayTrainer, it can finish training all together after every strategy’s first_tasks.

Parameters:	strategies (List[OnlineStrategy]) – the strategies list (need this param when adding strategies). None for use default strategies. model_kwargs (dict) – the params for prepare_online_models

routine(cur_time: Union[str, pandas._libs.tslibs.timestamps.Timestamp] = None, task_kwargs: dict = {}, model_kwargs: dict = {}, signal_kwargs: dict = {})¶

Typical update process for every strategy and record the online history.

The typical update process after a routine, such as day by day or month by month. The process is: Update predictions -> Prepare tasks -> Prepare online models -> Prepare signals.

If using DelayTrainer, it can finish training all together after every strategy’s prepare_tasks.

Parameters:	cur_time (Union[str,pd.Timestamp], optional) – run routine method in this time. Defaults to None. task_kwargs (dict) – the params for prepare_tasks model_kwargs (dict) – the params for prepare_online_models signal_kwargs (dict) – the params for prepare_signals

get_collector(**kwargs) → qlib.workflow.task.collect.MergeCollector¶

Get the instance of Collector to collect results from every strategy. This collector can be a basis as the signals preparation.

Parameters:	**kwargs – the params for get_collector.
Returns:	the collector to merge other collectors.
Return type:	MergeCollector

add_strategy(strategies: Union[qlib.workflow.online.strategy.OnlineStrategy, List[qlib.workflow.online.strategy.OnlineStrategy]])¶

Add some new strategies to OnlineManager.

Parameters:	strategy (Union[OnlineStrategy, List[OnlineStrategy]]) – a list of OnlineStrategy

prepare_signals(prepare_func: Callable = <qlib.model.ens.ensemble.AverageEnsemble object>, over_write=False)¶

After preparing the data of the last routine (a box in box-plot) which means the end of the routine, we can prepare trading signals for the next routine.

NOTE: Given a set prediction, all signals before these prediction end times will be prepared well.

Even if the latest signal already exists, the latest calculation result will be overwritten.

Note

Given a prediction of a certain time, all signals before this time will be prepared well.

Parameters:	prepare_func (Callable, optional) – Get signals from a dict after collecting. Defaults to AverageEnsemble(), the results collected by MergeCollector must be {xxx:pred}. over_write (bool, optional) – If True, the new signals will overwrite. If False, the new signals will append to the end of signals. Defaults to False.
Returns:	the signals.
Return type:	pd.DataFrame

get_signals() → Union[pandas.core.series.Series, pandas.core.frame.DataFrame]¶

Get prepared online signals.

Returns:	pd.Series for only one signals every datetime. pd.DataFrame for multiple signals, for example, buy and sell operations use different trading signals.
Return type:	Union[pd.Series, pd.DataFrame]

simulate(end_time=None, frequency='day', task_kwargs={}, model_kwargs={}, signal_kwargs={}) → Union[pandas.core.series.Series, pandas.core.frame.DataFrame]¶

Starting from the current time, this method will simulate every routine in OnlineManager until the end time.

Considering the parallel training, the models and signals can be prepared after all routine simulating.

The delay training way can be DelayTrainer and the delay preparing signals way can be delay_prepare.

Parameters:	end_time – the time the simulation will end frequency – the calendar frequency task_kwargs (dict) – the params for prepare_tasks model_kwargs (dict) – the params for prepare_online_models signal_kwargs (dict) – the params for prepare_signals
Returns:	pd.Series for only one signals every datetime. pd.DataFrame for multiple signals, for example, buy and sell operations use different trading signals.
Return type:	Union[pd.Series, pd.DataFrame]

delay_prepare(model_kwargs={}, signal_kwargs={})¶

Prepare all models and signals if something is waiting for preparation.

Parameters:	model_kwargs – the params for end_train signal_kwargs – the params for prepare_signals

Online Strategy¶

OnlineStrategy module is an element of online serving.

class qlib.workflow.online.strategy.OnlineStrategy(name_id: str)¶

OnlineStrategy is working with Online Manager, responding to how the tasks are generated, the models are updated and signals are prepared.

__init__(name_id: str)¶

Init OnlineStrategy. This module MUST use Trainer to finishing model training.

Parameters:	name_id (str) – a unique name or id. trainer (Trainer, optional) – a instance of Trainer. Defaults to None.

prepare_tasks(cur_time, **kwargs) → List[dict]¶

After the end of a routine, check whether we need to prepare and train some new tasks based on cur_time (None for latest).. Return the new tasks waiting for training.

You can find the last online models by OnlineTool.online_models.

prepare_online_models(trained_models, cur_time=None) → List[object]¶

Select some models from trained models and set them to online models. This is a typical implementation to online all trained models, you can override it to implement the complex method. You can find the last online models by OnlineTool.online_models if you still need them.

NOTE: Reset all online models to trained models. If there are no trained models, then do nothing.

NOTE:: Current implementation is very naive. Here is a more complex situation which is more closer to the practical scenarios. 1. Train new models at the day before test_start (at time stamp T) 2. Switch models at the test_start (at time timestamp T + 1 typically)

Parameters:	models (list) – a list of models. cur_time (pd.Dataframe) – current time from OnlineManger. None for the latest.
Returns:	a list of online models.
Return type:	List[object]

first_tasks() → List[dict]¶: Generate a series of tasks firstly and return them.

get_collector() → qlib.workflow.task.collect.Collector¶

Get the instance of Collector to collect different results of this strategy.

For example:

collect predictions in Recorder
collect signals in a txt file

Returns:	Collector

class qlib.workflow.online.strategy.RollingStrategy(name_id: str, task_template: Union[dict, List[dict]], rolling_gen: qlib.workflow.task.gen.RollingGen)¶

This example strategy always uses the latest rolling model sas online models.

__init__(name_id: str, task_template: Union[dict, List[dict]], rolling_gen: qlib.workflow.task.gen.RollingGen)¶

Init RollingStrategy.

Assumption: the str of name_id, the experiment name, and the trainer’s experiment name are the same.

Parameters:	name_id (str) – a unique name or id. Will be also the name of the Experiment. task_template (Union[dict, List[dict]]) – a list of task_template or a single template, which will be used to generate many tasks using rolling_gen. rolling_gen (RollingGen) – an instance of RollingGen

get_collector(process_list=[<qlib.model.ens.group.RollingGroup object>], rec_key_func=None, rec_filter_func=None, artifacts_key=None)¶

Get the instance of Collector to collect results. The returned collector must distinguish results in different models.

Assumption: the models can be distinguished based on the model name and rolling test segments. If you do not want this assumption, please implement your method or use another rec_key_func.

Parameters:	rec_key_func (Callable) – a function to get the key of a recorder. If None, use recorder id. rec_filter_func (Callable, optional) – filter the recorder by return True or False. Defaults to None. artifacts_key (List[str], optional) – the artifacts key you want to get. If None, get all artifacts.

first_tasks() → List[dict]¶

Use rolling_gen to generate different tasks based on task_template.

Returns:	a list of tasks
Return type:	List[dict]

prepare_tasks(cur_time) → List[dict]¶

Prepare new tasks based on cur_time (None for the latest).

You can find the last online models by OnlineToolR.online_models.

Returns:	a list of new tasks.
Return type:	List[dict]

Online Tool¶

OnlineTool is a module to set and unset a series of online models. The online models are some decisive models in some time points, which can be changed with the change of time. This allows us to use efficient submodels as the market-style changing.

class qlib.workflow.online.utils.OnlineTool¶

OnlineTool will manage online models in an experiment that includes the model recorders.

__init__()¶: Init OnlineTool.

set_online_tag(tag, recorder: Union[list, object])¶

Set tag to the model to sign whether online.

Parameters:	tag (str) – the tags in ONLINE_TAG, OFFLINE_TAG recorder (Union[list,object]) – the model’s recorder

get_online_tag(recorder: object) → str¶

Given a model recorder and return its online tag.

Parameters:	recorder (Object) – the model’s recorder
Returns:	the online tag
Return type:	str

reset_online_tag(recorder: Union[list, object])¶

Offline all models and set the recorders to ‘online’.

Parameters:	recorder (Union[list,object]) – the recorder you want to reset to ‘online’.

online_models() → list¶

Get current online models

Returns:	a list of online models.
Return type:	list

update_online_pred(to_date=None)¶

Update the predictions of online models to to_date.

Parameters:	to_date (pd.Timestamp) – the pred before this date will be updated. None for updating to the latest.

class qlib.workflow.online.utils.OnlineToolR(default_exp_name: str = None)¶

The implementation of OnlineTool based on (R)ecorder.

__init__(default_exp_name: str = None)¶

Init OnlineToolR.

Parameters:	default_exp_name (str) – the default experiment name.

set_online_tag(tag, recorder: Union[qlib.workflow.recorder.Recorder, List[T]])¶

Set tag to the model’s recorder to sign whether online.

Parameters:	tag (str) – the tags in ONLINE_TAG, NEXT_ONLINE_TAG, OFFLINE_TAG recorder (Union[Recorder, List]) – a list of Recorder or an instance of Recorder

get_online_tag(recorder: qlib.workflow.recorder.Recorder) → str¶

Given a model recorder and return its online tag.

Parameters:	recorder (Recorder) – an instance of recorder
Returns:	the online tag
Return type:	str

reset_online_tag(recorder: Union[qlib.workflow.recorder.Recorder, List[T]], exp_name: str = None)¶

Offline all models and set the recorders to ‘online’.

Parameters:	recorder (Union[Recorder, List]) – the recorder you want to reset to ‘online’. exp_name (str) – the experiment name. If None, then use default_exp_name.

online_models(exp_name: str = None) → list¶

Get current online models

Parameters:	exp_name (str) – the experiment name. If None, then use default_exp_name.
Returns:	a list of online models.
Return type:	list

update_online_pred(to_date=None, from_date=None, exp_name: str = None)¶

Update the predictions of online models to to_date.

Parameters:	to_date (pd.Timestamp) – the pred before this date will be updated. None for updating to latest time in Calendar. exp_name (str) – the experiment name. If None, then use default_exp_name.

RecordUpdater¶

Updater is a module to update artifacts such as predictions when the stock data is updating.

class qlib.workflow.online.update.RMDLoader(rec: qlib.workflow.recorder.Recorder)¶

Recorder Model Dataset Loader

__init__(rec: qlib.workflow.recorder.Recorder)¶: Initialize self. See help(type(self)) for accurate signature.

get_dataset(start_time, end_time, segments=None) → qlib.data.dataset.DatasetH¶

Load, config and setup dataset.

This dataset is for inference.

Parameters:	start_time – the start_time of underlying data end_time – the end_time of underlying data segments – dict the segments config for dataset Due to the time series dataset (TSDatasetH), the test segments maybe different from start_time and end_time
Returns:	the instance of DatasetH
Return type:	DatasetH

class qlib.workflow.online.update.RecordUpdater(record: qlib.workflow.recorder.Recorder, *args, **kwargs)¶

Update a specific recorders

__init__(record: qlib.workflow.recorder.Recorder, *args, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

update(*args, **kwargs)¶: Update info for specific recorder

class qlib.workflow.online.update.DSBasedUpdater(record: qlib.workflow.recorder.Recorder, to_date=None, from_date=None, hist_ref: int = 0, freq='day', fname='pred.pkl')¶

Dataset-Based Updater - Provding updating feature for Updating data based on Qlib Dataset

Assumption - Based on Qlib dataset - The data to be updated is a multi-level index pd.DataFrame. For example label , prediction.

LABEL0

datetime instrument 2021-05-10 SH600000 0.006965

SH600004 0.003407

… … 2021-05-28 SZ300498 0.015748

SZ300676 -0.001321

__init__(record: qlib.workflow.recorder.Recorder, to_date=None, from_date=None, hist_ref: int = 0, freq='day', fname='pred.pkl')¶

Init PredUpdater.

Expected behavior in following cases: - if to_date is greater than the max date in the calendar, the data will be updated to the latest date - if there are data before from_date or after to_date, only the data between from_date and to_date are affected.

Parameters:

record – Recorder
to_date –
update to prediction to the to_date if to_date is None:

data will updated to the latest date.
from_date –
the update will start from from_date if from_date is None:

the updating will occur on the next tick after the latest data in historical data
hist_ref –
int Sometimes, the dataset will have historical depends. Leave the problem to users to set the length of historical dependency

Note

the start_time is not included in the hist_ref

prepare_data() → qlib.data.dataset.DatasetH¶

Load dataset

Separating this function will make it easier to reuse the dataset

Returns:	the instance of DatasetH
Return type:	DatasetH

update(dataset: qlib.data.dataset.DatasetH = None)¶

Update the data in a recorder.

Parameters:	DatasetH – the instance of DatasetH. None for reprepare.

get_update_data(dataset: qlib.data.dataset.Dataset) → pandas.core.frame.DataFrame¶

return the updated data based on the given dataset

The difference between get_update_data and update - update_date only include some data specific feature - update include some general routine steps(e.g. prepare dataset, checking)

class qlib.workflow.online.update.PredUpdater(record: qlib.workflow.recorder.Recorder, to_date=None, from_date=None, hist_ref: int = 0, freq='day', fname='pred.pkl')¶

Update the prediction in the Recorder

get_update_data(dataset: qlib.data.dataset.Dataset) → pandas.core.frame.DataFrame¶

return the updated data based on the given dataset

The difference between get_update_data and update - update_date only include some data specific feature - update include some general routine steps(e.g. prepare dataset, checking)

class qlib.workflow.online.update.LabelUpdater(record: qlib.workflow.recorder.Recorder, to_date=None, **kwargs)¶

Update the label in the recorder

Assumption - The label is generated from record_temp.SignalRecord.

__init__(record: qlib.workflow.recorder.Recorder, to_date=None, **kwargs)¶

Init PredUpdater.

Expected behavior in following cases: - if to_date is greater than the max date in the calendar, the data will be updated to the latest date - if there are data before from_date or after to_date, only the data between from_date and to_date are affected.

Parameters:

record – Recorder
to_date –
update to prediction to the to_date if to_date is None:

data will updated to the latest date.
from_date –
the update will start from from_date if from_date is None:

the updating will occur on the next tick after the latest data in historical data
hist_ref –
int Sometimes, the dataset will have historical depends. Leave the problem to users to set the length of historical dependency

Note

the start_time is not included in the hist_ref

get_update_data(dataset: qlib.data.dataset.Dataset) → pandas.core.frame.DataFrame¶

return the updated data based on the given dataset

The difference between get_update_data and update - update_date only include some data specific feature - update include some general routine steps(e.g. prepare dataset, checking)

Utils¶

Serializable¶

Serializable will change the behaviors of pickle. - It only saves the state whose name does not start with _ It provides a syntactic sugar for distinguish the attributes which user doesn’t want. - For examples, a learnable Datahandler just wants to save the parameters without data when dumping to disk

qlib.utils.serial.Serializable.dump_all¶: will the object dump all object

qlib.utils.serial.Serializable.exclude¶: What attribute will not be dumped

Qlib FAQ¶

Qlib Frequently Asked Questions¶

1. RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase…
2. qlib.data.cache.QlibCacheException: It sees the key(…) of the redis lock has existed in your redis db now.
3. ModuleNotFoundError: No module named ‘qlib.data._libs.rolling’
4. BadNamespaceError: / is not a connected namespace
5. TypeError: send() got an unexpected keyword argument ‘binary’

1. RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase…¶

RuntimeError:
        An attempt has been made to start a new process before the
        current process has finished its bootstrapping phase.

        This probably means that you are not using fork to start your
        child processes and you have forgotten to use the proper idiom
        in the main module:

            if __name__ == '__main__':
                freeze_support()
                ...

        The "freeze_support()" line can be omitted if the program
        is not going to be frozen to produce an executable.

This is caused by the limitation of multiprocessing under windows OS. Please refer to here for more info.

Solution: To select a start method you use the D.features in the if __name__ == ‘__main__’ clause of the main module. For example:

import qlib
from qlib.data import D


if __name__ == "__main__":
    qlib.init()
    instruments = ["SH600000"]
    fields = ["$close", "$change"]
    df = D.features(instruments, fields, start_time='2010-01-01', end_time='2012-12-31')
    print(df.head())

2. qlib.data.cache.QlibCacheException: It sees the key(…) of the redis lock has existed in your redis db now.¶

It sees the key of the redis lock has existed in your redis db now. You can use the following command to clear your redis keys and rerun your commands

$ redis-cli
> select 1
> flushdb

If the issue is not resolved, use keys * to find if multiple keys exist. If so, try using flushall to clear all the keys.

Note

qlib.config.redis_task_db defaults is 1, users can use qlib.init(redis_task_db=<other_db>) settings.

Also, feel free to post a new issue in our GitHub repository. We always check each issue carefully and try our best to solve them.

3. ModuleNotFoundError: No module named ‘qlib.data._libs.rolling’¶

#### Do not import qlib package in the repository directory in case of importing qlib from . without compiling #####
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "qlib/qlib/__init__.py", line 19, in init
    from .data.cache import H
File "qlib/qlib/data/__init__.py", line 8, in <module>
    from .data import (
File "qlib/qlib/data/data.py", line 20, in <module>
    from .cache import H
File "qlib/qlib/data/cache.py", line 36, in <module>
    from .ops import Operators
File "qlib/qlib/data/ops.py", line 19, in <module>
    from ._libs.rolling import rolling_slope, rolling_rsquare, rolling_resi
ModuleNotFoundError: No module named 'qlib.data._libs.rolling'

If the error occurs when importing qlib package with PyCharm IDE, users can execute the following command in the project root folder to compile Cython files and generate executable files:
python setup.py build_ext --inplace
If the error occurs when importing qlib package with command python , users need to change the running directory to ensure that the script does not run in the project directory.

4. BadNamespaceError: / is not a connected namespace¶

File "qlib_online.py", line 35, in <module>
  cal = D.calendar()
File "e:\code\python\microsoft\qlib_latest\qlib\qlib\data\data.py", line 973, in calendar
  return Cal.calendar(start_time, end_time, freq, future=future)
File "e:\code\python\microsoft\qlib_latest\qlib\qlib\data\data.py", line 798, in calendar
  self.conn.send_request(
File "e:\code\python\microsoft\qlib_latest\qlib\qlib\data\client.py", line 101, in send_request
  self.sio.emit(request_type + "_request", request_content)
File "G:\apps\miniconda\envs\qlib\lib\site-packages\python_socketio-5.3.0-py3.8.egg\socketio\client.py", line 369, in emit
  raise exceptions.BadNamespaceError(
BadNamespaceError: / is not a connected namespace.

The version of python-socketio in qlib needs to be the same as the version of python-socketio in qlib-server:
pip install -U python-socketio==<qlib-server python-socketio version>

5. TypeError: send() got an unexpected keyword argument ‘binary’¶

File "qlib_online.py", line 35, in <module>
  cal = D.calendar()
File "e:\code\python\microsoft\qlib_latest\qlib\qlib\data\data.py", line 973, in calendar
  return Cal.calendar(start_time, end_time, freq, future=future)
File "e:\code\python\microsoft\qlib_latest\qlib\qlib\data\data.py", line 798, in calendar
  self.conn.send_request(
File "e:\code\python\microsoft\qlib_latest\qlib\qlib\data\client.py", line 101, in send_request
  self.sio.emit(request_type + "_request", request_content)
File "G:\apps\miniconda\envs\qlib\lib\site-packages\socketio\client.py", line 263, in emit
  self._send_packet(packet.Packet(packet.EVENT, namespace=namespace,
File "G:\apps\miniconda\envs\qlib\lib\site-packages\socketio\client.py", line 339, in _send_packet
  self.eio.send(ep, binary=binary)
TypeError: send() got an unexpected keyword argument 'binary'

The python-engineio version needs to be compatible with the python-socketio version, reference: https://github.com/miguelgrinberg/python-socketio#version-compatibility
pip install -U python-engineio==<compatible python-socketio version> # or pip install -U python-socketio==3.1.2 python-engineio==3.13.2

Changelog¶

Here you can see the full list of changes between each QLib release.

Version 0.1.0¶

This is the initial release of QLib library.

Version 0.1.1¶

Performance optimize. Add more features and operators.

Version 0.1.2¶

Support operator syntax. Now High() - Low() is equivalent to Sub(High(), Low()).
Add more technical indicators.

Version 0.1.3¶

Bug fix and add instruments filtering mechanism.

Version 0.2.0¶

Redesign LocalProvider database format for performance improvement.
Support load features as string fields.
Add scripts for database construction.
More operators and technical indicators.

Version 0.2.1¶

Support registering user-defined Provider.
Support use operators in string format, e.g. ['Ref($close, 1)'] is valid field format.
Support dynamic fields in $some_field format. And exising fields like Close() may be deprecated in the future.

Version 0.2.2¶

Add disk_cache for reusing features (enabled by default).
Add qlib.contrib for experimental model construction and evaluation.

Version 0.2.3¶

Add backtest module
Decoupling the Strategy, Account, Position, Exchange from the backtest module

Version 0.2.4¶

Add profit attribution module
Add rick_control and cost_control strategies

Version 0.3.0¶

Add estimator module

Version 0.3.1¶

Add filter module

Version 0.3.2¶

Add real price trading, if the factor field in the data set is incomplete, use adj_price trading
Refactor handler launcher trainer code
Support backtest configuration parameters in the configuration file
Fix bug in position amount is 0
Fix bug of filter module

Version 0.3.3¶

Fix bug of filter module

Version 0.3.4¶

Support for finetune model
Refactor fetcher code

Version 0.3.5¶

Support multi-label training, you can provide multiple label in handler. (But LightGBM doesn’t support due to the algorithm itself)
Refactor handler code, dataset.py is no longer used, and you can deploy your own labels and features in feature_label_config
Handler only offer DataFrame. Also, trainer and model.py only receive DataFrame
Change split_rolling_data, we roll the data on market calender now, not on normal date
Move some date config from handler to trainer

Version 0.4.0¶

Add data package that holds all data-related codes
Reform the data provider structure
Create a server for data centralized management `qlib-server<https://amc-msra.visualstudio.com/trading-algo/_git/qlib-server>`_
Add a ClientProvider to work with server
Add a pluggable cache mechanism
Add a recursive backtracking algorithm to inspect the furthest reference date for an expression

Note

The D.instruments function does not support start_time, end_time, and as_list parameters, if you want to get the results of previous versions of D.instruments, you can do this:

>>> from qlib.data import D
>>> instruments = D.instruments(market='csi500')
>>> D.list_instruments(instruments=instruments, start_time='2015-01-01', end_time='2016-02-15', as_list=True)

Version 0.4.1¶

Add support Windows
Fix instruments type bug
Fix features is empty bug(It will cause failure in updating)
Fix cache lock and update bug
Fix use the same cache for the same field (the original space will add a new cache)
Change “logger handler” from config
Change model load support 0.4.0 later
The default value of the method parameter of risk_analysis function is changed from ci to si

Version 0.4.2¶

Refactor DataHandler
Add Alpha360 DataHandler

Version 0.4.3¶

Implementing Online Inference and Trading Framework
Refactoring The interfaces of backtest and strategy module.

Version 0.4.4¶

Optimize cache generation performance
Add report module
Fix bug when using ServerDatasetCache offline.
In the previous version of long_short_backtest, there is a case of np.nan in long_short. The current version 0.4.4 has been fixed, so long_short_backtest will be different from the previous version.
In the 0.4.2 version of risk_analysis function, N is 250, and N is 252 from 0.4.3, so 0.4.2 is 0.002122 smaller than the 0.4.3 the backtest result is slightly different between 0.4.2 and 0.4.3.
refactor the argument of backtest function.
- NOTE: - The default arguments of topk margin strategy is changed. Please pass the arguments explicitly if you want to get the same backtest result as previous version. - The TopkWeightStrategy is changed slightly. It will try to sell the stocks more than topk. (The backtest result of TopkAmountStrategy remains the same)
The margin ratio mechanism is supported in the Topk Margin strategies.

Version 0.4.5¶

Add multi-kernel implementation for both client and server.
- Support a new way to load data from client which skips dataset cache.
- Change the default dataset method from single kernel implementation to multi kernel implementation.
Accelerate the high frequency data reading by optimizing the relative modules.
Support a new method to write config file by using dict.

Version 0.4.6¶

Some bugs are fixed
- The default config in Version 0.4.5 is not friendly to daily frequency data.
- Backtest error in TopkWeightStrategy when WithInteract=True.

Version 0.5.0¶

First opensource version
- Refine the docs, code
- Add baselines
- public data crawler

Version 0.8.0¶

The backtest is greatly refactored.
- Nested decision execution framework is supported
- There are lots of changes for daily trading, it is hard to list all of them. But a few important changes could be noticed
  
  The trading limitation is more accurate;
  
  In previous version, longing and shorting actions share the same action.
  
  In current verison, the trading limitation is different between loging and shorting action.
  
  The constant is different when calculating annualized metrics.
  
  Current version uses more accurate constant than previous version
  
  A new version of data is released. Due to the unstability of Yahoo data source, the data may be different after downloading data again.
  
  Users could chec kout the backtesting results between Current version and previous version

Other Versions¶

Please refer to Github release Notes

Qlib Documentation¶

Document Structure¶

Qlib: Quantitative Platform¶

Introduction¶

Framework¶

Quick Start¶

Introduction¶

Installation¶

Prepare Data¶

Auto Quant Research Workflow¶

Custom Model Integration¶

Installation¶

Qlib Installation¶

Qlib Initialization¶

Initialization¶

Parameters¶

Data Retrieval¶

Introduction¶

Examples¶

API¶

Custom Model Integration¶

Introduction¶

Custom Model Class¶

Configuration File¶

Model Testing¶

Reference¶

Workflow: Workflow Management¶

Introduction¶

Complete Example¶

Configuration File¶

Qlib Init Section¶

Task Section¶

Model Section¶

Dataset Section¶

Record Section¶

Data Layer: Data Framework & Usage¶

Introduction¶

Data Preparation¶

Qlib Format Data¶

Qlib Format Dataset¶

Automatic update of daily frequency data¶

Converting CSV Format into Qlib Format¶

Stock Pool (Market)¶

Multiple Stock Modes¶

Data API¶

Data Retrieval¶

Feature¶

Filter¶

Reference¶

Data Loader¶

QlibDataLoader¶

StaticDataLoader¶

Interface¶

API¶

Data Handler¶

DataHandlerLP¶

Interface¶

Processor¶

Example¶

API¶

Dataset¶

API¶

Cache¶

Global Memory Cache¶

ExpressionCache¶

DatasetCache¶

Data and Cache File Structure¶

Forecast Model: Model Training & Prediction¶

Introduction¶

Base Class & Interface¶

Example¶

Custom Model¶

API¶

Portfolio Strategy: Portfolio Management¶

Introduction¶

Base Class & Interface¶

BaseStrategy¶

WeightStrategyBase¶

Implemented Strategy¶

TopkDropoutStrategy¶

`Qlib` Documentation¶

`Qlib`: Quantitative Platform¶

`Qlib` Installation¶

Building Formulaic Alphas in `Qlib`¶

`Online` & `Offline` mode¶