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 dataset, which can be accessed through this link:

Dataset US Market China Market
Alpha360
Alpha158

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.

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

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/csv_data/cn_data directory and ~/.qlib/csv_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.

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.

Users can download the demo china-stock data in CSV format as follows for reference to the CSV format.

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

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.

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/csv_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 china-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/csv_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)
      

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’

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: Tuple[dict, str, qlib.data.dataset.loader.DataLoader] = None, infer_processors=[], learn_processors=[], process_type='append', drop_raw=False, **kwargs)

DataHandler with (L)earnable (P)rocessor

__init__(instruments=None, start_time=None, end_time=None, data_loader: Tuple[dict, str, qlib.data.dataset.loader.DataLoader] = None, infer_processors=[], learn_processors=[], 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_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

Parameters:with_fit (bool) – The input of the fit will be the output of the previous processor
init(init_type: str = 'fit_seq', enable_cache: bool = False)

Initialize the data of Qlib

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') → 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_*.
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

If users want to load features and labels by config, users can inherit qlib.data.dataset.handler.ConfigDataHandler, Qlib also provides some preprocess method in this subclass.

If users want to use qlib data, QLibDataHandler is recommended. Users can inherit their custom class from QLibDataHandler, which is also a subclass of ConfigDataHandler.

Processor

The Processor module in Qlib is designed to be learnable and it is responsible for handling data processing such as normalization and drop none/nan features/labels.

Qlib provides the following Processors:

  • DropnaProcessor: processor that drops N/A features.
  • DropnaLabel: processor that drops N/A labels.
  • TanhProcess: processor that uses tanh to process noise data.
  • ProcessInf: processor that handles infinity values, it will be replaces by the mean of the column.
  • Fillna: processor that handles N/A values, which will fill the N/A value by 0 or other given number.
  • MinMaxNorm: processor that applies min-max normalization.
  • ZscoreNorm: processor that applies z-score normalization.
  • 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"))

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, qlib.data.dataset.handler.DataHandler], segments: dict)

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, qlib.data.dataset.handler.DataHandler], segments: dict)
Parameters:
  • handler (Union[dict, DataHandler]) – handler will be passed into setup_data.
  • segments (dict) – handler will be passed into setup_data.
init(**kwargs)

Initialize the DatasetH, Only parameters belonging to handler.init will be passed in

setup_data(handler: Union[dict, qlib.data.dataset.handler.DataHandler], segments: dict)

Setup the underlying data.

Parameters:
  • handler (Union[dict, DataHandler]) –

    handler could be:

    • insntance of DataHandler
    • config of DataHandler. Please refer to DataHandler
  • segments (dict) – Describe the options to segment the data. Here are some examples:
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[str], Tuple[str], str, slice]) –

    Describe the scope of the data to be prepared Here are some examples:

    • ’train’
    • [‘train’, ‘valid’]
  • col_set (str) – The col_set will be passed to self.handler when fetching data.
  • data_key (str) – The data to fetch: DK_* Default is DK_I, which indicate fetching data for inference.
Returns:

Return type:

Union[List[pd.DataFrame], pd.DataFrame]

Raises:

NotImplementedError:

API

To know more about Dataset, please refer to `Dataset API <../reference/api.html#module-qlib.data.dataset.__init__>`_.

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)

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) – the complete uri of expression cache file (include dir path).
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)

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)

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) – the complete uri of dataset cache file (include dir path).
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
        - ...