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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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, **kwargs)
__init__(market: 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
check()

check self.uri

Raises:ValueError
class qlib.data.storage.file_storage.FileCalendarStorage(freq: str, future: bool, **kwargs)
__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.file_storage.FileInstrumentStorage(market: str, **kwargs)
__init__(market: 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.file_storage.FileFeatureStorage(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)

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:
  1. 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 …
  2. 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(pred, account=1000000000.0, shift=1, benchmark='SH000905', verbose=True, **kwargs)

This function will help you set a reasonable Exchange and provide default value for strategy :param - backtest workflow related or commmon arguments: :param pred: predict should has <datetime, instrument> index and one score column. :type pred: pandas.DataFrame :param account: init account value. :type account: float :param shift: whether to shift prediction by one day. :type shift: int :param benchmark: benchmark code, default is SH000905 CSI 500. :type benchmark: str :param verbose: whether to print log. :type verbose: bool :param - strategy related arguments: :param strategy: strategy used in backtest. :type strategy: Strategy() :param topk: top-N stocks to buy. :type topk: int (Default value: 50) :param margin:

  • if isinstance(margin, int):

    sell_limit = margin

  • else:

    sell_limit = pred_in_a_day.count() * margin

buffer margin, in single score_mode, continue holding stock if it is in nlargest(sell_limit). sell_limit should be no less than topk.

Parameters:
  • n_drop (int) – number of stocks to be replaced in each trading date.
  • risk_degree (float) – 0-1, 0.95 for example, use 95% money to trade.
  • str_type ('amount', 'weight' or 'dropout') – strategy type: TopkAmountStrategy ,TopkWeightStrategy or TopkDropoutStrategy.
  • exchange related arguments (-) –
  • exchange (Exchange()) – pass the exchange for speeding up.
  • subscribe_fields (list) – subscribe fields.
  • open_cost (float) – open transaction cost. The default value is 0.002(0.2%).
  • close_cost (float) – close transaction cost. The default value is 0.002(0.2%).
  • min_cost (float) – min transaction cost.
  • trade_unit (int) – 100 for China A.
  • deal_price (str) – dealing price type: ‘close’, ‘open’, ‘vwap’.
  • limit_threshold (float) – limit move 0.1 (10%) for example, long and short with same limit.
  • extract_codes (bool) –

    will we pass the codes extracted from the pred to the exchange.

    Note

    This will be faster with offline qlib.

  • executor related arguments (-) –
  • executor (BaseExecutor()) – executor used in backtest.
  • verbose (bool) – whether to print log.
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:

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)

report_normal_df, _ = backtest(pred_df, strategy, **bparas)

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_dataD.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:

from qlib.contrib.evaluate import risk_analysis, backtest, long_short_backtest
from qlib.contrib.strategy import TopkDropoutStrategy
from qlib.contrib.report import analysis_position

# backtest parameters
bparas = {}
bparas['limit_threshold'] = 0.095
bparas['account'] = 1000000000

sparas = {}
sparas['topk'] = 50
sparas['n_drop'] = 230
strategy = TopkDropoutStrategy(**sparas)

report_normal_df, positions = backtest(pred_df, strategy, **bparas)
# long_short_map = long_short_backtest(pred_df)
# report_long_short_df = pd.DataFrame(long_short_map)

analysis = dict()
# analysis['pred_long'] = risk_analysis(report_long_short_df['long'])
# analysis['pred_short'] = risk_analysis(report_long_short_df['short'])
# analysis['pred_long_short'] = risk_analysis(report_long_short_df['long_short'])
analysis['excess_return_without_cost'] = risk_analysis(report_normal_df['return'] - report_normal_df['bench'])
analysis['excess_return_with_cost'] = risk_analysis(report_normal_df['return'] - report_normal_df['bench'] - report_normal_df['cost'])
analysis_df = pd.concat(analysis)

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_dataD.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_notebookTrue 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:
  • lagpred.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
  1. 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.

  1. 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:
  1. collect predictions in Recorder
  2. 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