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

Here is a typical example of Qlib data workflow

Users download data and converting data into Qlib format(with filename suffix .bin). In this step, typically only some basic data are stored on disk(such as OHLCV).
Creating some basic features based on Qlib's expression Engine(e.g. "Ref($close, 60) / $close", the return of last 60 trading days). Supported operators in the expression engine can be found here. This step is typically implemented in Qlib's Data Loader which is a component of Data Handler .
If users require more complicated data processing (e.g. data normalization), Data Handler support user-customized processors to process data(some predefined processors can be found here). The processors are different from operators in expression engine. It is designed for some complicated data processing methods which is hard to supported in operators in expression engine.
At last, Dataset is responsible to prepare model-specific dataset from the processed data of Data Handler

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. The price volume data look different from the actual dealling price because of they are adjusted (adjusted price). And then you may find that the adjusted price may be different from different data sources. This is because different data sources may vary in the way of adjusting prices. Qlib normalize the price on first trading day of each stock to 1 when adjusting them. Users can leverage $factor to get the original trading price (e.g. $close / $factor to get the original close price).

# 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:
  symbol,close SH600000,120
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:
```
symbol,date,close,open,volume
SH600000,2020-11-01,120,121,12300000
SH600000,2020-11-02,123,120,12300000
```

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.constant 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:

qlib.data.dataset.loader.DataLoader

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 learnable 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:

qlib.data.dataset.handler.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:

qlib.data.dataset.__init__.DatasetH

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.

qlib.data.cache.MemCacheUnit

qlib.data.cache.MemCache

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:

qlib.data.cache.ExpressionCache

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:

qlib.data.cache.DatasetCache

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
        - ...

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data.rst

data.rst

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

Interface

API

Data Handler

DataHandlerLP

Interface

Processor

Example

API

Dataset

API

Cache

Global Memory Cache

ExpressionCache

DatasetCache

Data and Cache File Structure

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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

Interface

API

Data Handler

DataHandlerLP

Interface

Processor

Example

API

Dataset

API

Cache

Global Memory Cache

ExpressionCache

DatasetCache

Data and Cache File Structure