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 useQlib
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 initializeQlib
as follows.from qlib.config import REG_CN qlib.init(provider_uri='~/.qlib/qlib_data/cn_data', region=REG_CN)
- Initialize
- If users use
- 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 useQlib
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 initializeQlib
as follows.from qlib.config import REG_US qlib.init(provider_uri='~/.qlib/qlib_data/us_data', region=REG_US)
- Initialize
- If users use
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, $close, .etc, which should correspond with the arguments of –include_fields, please refer to section Converting CSV Format into Qlib Format.
- ExpressionOps
- ExpressionOps will use operator for feature construction.
To know more about
Operator
, please refer to Operator API. Also,Qlib
supports users to define their own customOperator
, an example has been given intests/test_register_ops.py
.
To know more about Feature
, please refer to Feature API.
Filter¶
Qlib
provides NameDFilter and ExpressionDFilter to filter the instruments according to users’ needs.
- NameDFilter
- Name dynamic instrument filter. Filter the instruments based on a regulated name format. A name rule regular expression is required.
- ExpressionDFilter
- Expression dynamic instrument filter. Filter the instruments based on a certain expression. An expression rule indicating a certain feature field is required.
- basic features filter: rule_expression = ‘$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.
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
- infer_processors (list) –
-
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
- if enable_cache == True:
-
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:
- handler (Union[dict, DataHandler]) –
-
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:
- segments (Union[List[str], Tuple[str], str, slice]) –
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.
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
- ...