API Reference
Here you can find all Qlib interfaces.
Data
Provider
- class qlib.data.data.ProviderBackendMixin
This helper class tries to make the provider based on storage backend more convenient It is not necessary to inherent this class if that provider don’t rely on the backend storage
- class qlib.data.data.CalendarProvider
Calendar provider base class
Provide calendar data.
- 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: Timestamp | str, end_time: Timestamp | str, freq: str, future: bool = False)
Locate the start time index and end time index in a calendar under certain frequency.
- Parameters:
start_time (pd.Timestamp) – start of the time range.
end_time (pd.Timestamp) – 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
Instrument provider base class
Provide instrument data.
- static instruments(market: List | str = 'all', filter_pipe: List | None = 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 :
{'market': 'csi500', 'filter_pipe': [{'filter_type': 'ExpressionDFilter', 'rule_expression': '$open<40', 'filter_start_time': None, 'filter_end_time': None, 'keep': False}, {'filter_type': 'NameDFilter', 'name_rule_re': 'SH[0-9]{4}55', 'filter_start_time': None, 'filter_end_time': None}]}
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.
- abstract 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
Feature provider class
Provide feature data.
- abstract 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.PITProvider
- abstract period_feature(instrument, field, start_index: int, end_index: int, cur_time: Timestamp, period: int | None = None) Series
get the historical periods data series between start_index and end_index
- Parameters:
start_index (int) – start_index is a relative index to the latest period to cur_time
end_index (int) – end_index is a relative index to the latest period to cur_time in most cases, the start_index and end_index will be a non-positive values For example, start_index == -3 end_index == 0 and current period index is cur_idx, then the data between [start_index + cur_idx, end_index + cur_idx] will be retrieved.
period (int) – This is used for query specific period. The period is represented with int in Qlib. (e.g. 202001 may represent the first quarter in 2020) NOTE: period will override start_index and end_index
- Returns:
The index will be integers to indicate the periods of the data An typical examples will be TODO
- Return type:
pd.Series
- Raises:
FileNotFoundError – This exception will be raised if the queried data do not exist.
- class qlib.data.data.ExpressionProvider
Expression provider class
Provide Expression data.
- __init__()
- abstract expression(instrument, field, start_time=None, end_time=None, freq='day') Series
Get Expression data.
The responsibility of expression - parse the field and load the according data. - When loading the data, it should handle the time dependency of the data. get_expression_instance is commonly used in this method
- 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
The data has two types of format
expression with datetime index
expression with integer index
because the datetime is not as good as
- Return type:
pd.Series
- class qlib.data.data.DatasetProvider
Dataset provider class
Provide Dataset data.
- abstract 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 inst_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(remote=False, backend={})
Local calendar data provider class
Provide calendar data from local data source.
- __init__(remote=False, backend={})
- 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(backend={})
Local instrument data provider class
Provide instrument data from local data source.
- __init__(backend={}) None
- 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(remote=False, backend={})
Local feature data provider class
Provide feature data from local data source.
- __init__(remote=False, backend={})
- 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.LocalPITProvider
- period_feature(instrument, field, start_index, end_index, cur_time, period=None)
get the historical periods data series between start_index and end_index
- Parameters:
start_index (int) – start_index is a relative index to the latest period to cur_time
end_index (int) – end_index is a relative index to the latest period to cur_time in most cases, the start_index and end_index will be a non-positive values For example, start_index == -3 end_index == 0 and current period index is cur_idx, then the data between [start_index + cur_idx, end_index + cur_idx] will be retrieved.
period (int) – This is used for query specific period. The period is represented with int in Qlib. (e.g. 202001 may represent the first quarter in 2020) NOTE: period will override start_index and end_index
- Returns:
The index will be integers to indicate the periods of the data An typical examples will be TODO
- Return type:
pd.Series
- Raises:
FileNotFoundError – This exception will be raised if the queried data do not exist.
- class qlib.data.data.LocalExpressionProvider(time2idx=True)
Local expression data provider class
Provide expression data from local data source.
- __init__(time2idx=True)
- expression(instrument, field, start_time=None, end_time=None, freq='day')
Get Expression data.
The responsibility of expression - parse the field and load the according data. - When loading the data, it should handle the time dependency of the data. get_expression_instance is commonly used in this method
- 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
The data has two types of format
expression with datetime index
expression with integer index
because the datetime is not as good as
- Return type:
pd.Series
- class qlib.data.data.LocalDatasetProvider(align_time: bool = True)
Local dataset data provider class
Provide dataset data from local data source.
- __init__(align_time: bool = True)
- Parameters:
align_time (bool) –
Will we align the time to calendar the frequency is flexible in some dataset and can’t be aligned. For the data with fixed frequency with a shared calendar, the align data to the calendar will provides following benefits
Align queries to the same parameters, so the cache can be shared.
- 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__()
- 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__()
- 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__()
- 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 It is a set of interface that allow users to access data. Because PITD is not exposed publicly to users, so it is not included in the interface.
To keep compatible with old qlib provider.
- features(instruments, fields, start_time=None, end_time=None, freq='day', disk_cache=None, inst_processors=[])
- Parameters:
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__()
- qlib.data.data.CalendarProviderWrapper
alias of
CalendarProvider
- qlib.data.data.InstrumentProviderWrapper
alias of
InstrumentProvider
- qlib.data.data.FeatureProviderWrapper
alias of
FeatureProvider
- qlib.data.data.PITProviderWrapper
alias of
PITProvider
- qlib.data.data.ExpressionProviderWrapper
alias of
ExpressionProvider
- qlib.data.data.DatasetProviderWrapper
alias of
DatasetProvider
- qlib.data.data.BaseProviderWrapper
alias of
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__()
- static from_config(config)
Construct an instance from config dict.
- Parameters:
config (dict) – dict of config parameters.
- abstract 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, keep=False)
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, keep=False)
- 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.
keep (bool) – whether to keep the instruments of which features don’t exist in the filter time span.
- 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
- Parameters:
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
- Parameters:
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.
- 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
Expression is designed to handle the calculation of data with the format below data with two dimension for each instrument,
feature
time: it could be observation time or period time.
period time is designed for Point-in-time database. For example, the period time maybe 2014Q4, its value can observed for multiple times(different value may be observed at different time due to amendment).
- load(instrument, start_index, end_index, *args)
load feature This function is responsible for loading feature/expression based on the expression engine.
The concrete implementation will be separated into two parts:
caching data, handle errors.
This part is shared by all the expressions and implemented in Expression
processing and calculating data based on the specific expression.
This part is different in each expression and implemented in each expression
Expression Engine is shared by different data. Different data will have different extra information for args.
- Parameters:
instrument (str) – instrument code.
start_index (str) – feature start index [in calendar].
end_index (str) – feature end index [in calendar].
information (*args may contain following) –
data (2) if is used in PIT) –
- freq: str
feature frequency.
arguments (it contains following) –
- freq: str
feature frequency.
data –
- cur_pit:
it is designed for the point-in-time data.
- period: int
This is used for query specific period. The period is represented with int in Qlib. (e.g. 202001 may represent the first quarter in 2020)
arguments –
- cur_pit:
it is designed for the point-in-time data.
- period: int
This is used for query specific period. The period is represented with int in Qlib. (e.g. 202001 may represent the first quarter in 2020)
- Returns:
feature series: The index of the series is the calendar index
- Return type:
pd.Series
- abstract 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.
- abstract 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)
- 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($close, -1), 1) can not be handled rightly.
So this will only used for detecting the length of historical data needed.
- get_extended_window_size()
get_extend_window_size
For to calculate this Operator in range[start_index, end_index] We have to get the leaf feature in range[start_index - lft_etd, end_index + rght_etd].
- Returns:
lft_etd, rght_etd
- Return type:
(int, int)
- class qlib.data.base.PFeature(name=None)
- class qlib.data.base.ExpressionOps
Operator Expression
This kind of feature will use operator for feature construction on the fly.
Operator
- class qlib.data.ops.ElemOperator(feature)
Element-wise Operator
- Parameters:
feature (Expression) – feature instance
- Returns:
feature operation output
- Return type:
- __init__(feature)
- get_longest_back_rolling()
Get the longest length of historical data the feature has accessed
This is designed for getting the needed range of the data to calculate the features in specific range at first. However, situations like Ref(Ref($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.ChangeInstrument(instrument, feature)
Change Instrument Operator In some case, one may want to change to another instrument when calculating, for example, to calculate beta of a stock with respect to a market index. This would require changing the calculation of features from the stock (original instrument) to the index (reference instrument) :param instrument: i.e., SH000300 (CSI300 index), or ^GPSC (SP500 index). :type instrument: new instrument for which the downstream operations should be performed upon. :param feature: :type feature: the feature to be calculated for the new instrument.
- Returns:
feature operation output
- Return type:
- __init__(instrument, feature)
- load(instrument, start_index, end_index, *args)
load feature This function is responsible for loading feature/expression based on the expression engine.
The concrete implementation will be separated into two parts:
caching data, handle errors.
This part is shared by all the expressions and implemented in Expression
processing and calculating data based on the specific expression.
This part is different in each expression and implemented in each expression
Expression Engine is shared by different data. Different data will have different extra information for args.
- Parameters:
instrument (str) – instrument code.
start_index (str) – feature start index [in calendar].
end_index (str) – feature end index [in calendar].
information (*args may contain following) –
data (2) if is used in PIT) –
- freq: str
feature frequency.
arguments (it contains following) –
- freq: str
feature frequency.
data –
- cur_pit:
it is designed for the point-in-time data.
- period: int
This is used for query specific period. The period is represented with int in Qlib. (e.g. 202001 may represent the first quarter in 2020)
arguments –
- cur_pit:
it is designed for the point-in-time data.
- period: int
This is used for query specific period. The period is represented with int in Qlib. (e.g. 202001 may represent the first quarter in 2020)
- Returns:
feature series: The index of the series is the calendar index
- Return type:
pd.Series
- 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:
- __init__(feature, func)
- class qlib.data.ops.Abs(feature)
Feature Absolute Value
- Parameters:
feature (Expression) – feature instance
- Returns:
a feature instance with absolute output
- Return type:
- __init__(feature)
- class qlib.data.ops.Sign(feature)
Feature Sign
- Parameters:
feature (Expression) – feature instance
- Returns:
a feature instance with sign
- Return type:
- __init__(feature)
- class qlib.data.ops.Log(feature)
Feature Log
- Parameters:
feature (Expression) – feature instance
- Returns:
a feature instance with log
- Return type:
- __init__(feature)
- 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:
- __init__(feature, instrument)
- class qlib.data.ops.Not(feature)
Not Operator
- Parameters:
feature (Expression) – feature instance
- Returns:
feature elementwise not output
- Return type:
- __init__(feature)
- 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
- Returns:
two features’ operation output
- Return type:
- __init__(feature_left, feature_right)
- 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:
- __init__(feature_left, feature_right, func)
- class qlib.data.ops.Power(feature_left, feature_right)
Power Operator
- Parameters:
feature_left (Expression) – feature instance
feature_right (Expression) – feature instance
- Returns:
The bases in feature_left raised to the exponents in feature_right
- Return type:
- __init__(feature_left, feature_right)
- class qlib.data.ops.Add(feature_left, feature_right)
Add Operator
- Parameters:
feature_left (Expression) – feature instance
feature_right (Expression) – feature instance
- Returns:
two features’ sum
- Return type:
- __init__(feature_left, feature_right)
- class qlib.data.ops.Sub(feature_left, feature_right)
Subtract Operator
- Parameters:
feature_left (Expression) – feature instance
feature_right (Expression) – feature instance
- Returns:
two features’ subtraction
- Return type:
- __init__(feature_left, feature_right)
- class qlib.data.ops.Mul(feature_left, feature_right)
Multiply Operator
- Parameters:
feature_left (Expression) – feature instance
feature_right (Expression) – feature instance
- Returns:
two features’ product
- Return type:
- __init__(feature_left, feature_right)
- class qlib.data.ops.Div(feature_left, feature_right)
Division Operator
- Parameters:
feature_left (Expression) – feature instance
feature_right (Expression) – feature instance
- Returns:
two features’ division
- Return type:
- __init__(feature_left, feature_right)
- class qlib.data.ops.Greater(feature_left, feature_right)
Greater Operator
- Parameters:
feature_left (Expression) – feature instance
feature_right (Expression) – feature instance
- Returns:
greater elements taken from the input two features
- Return type:
- __init__(feature_left, feature_right)
- class qlib.data.ops.Less(feature_left, feature_right)
Less Operator
- Parameters:
feature_left (Expression) – feature instance
feature_right (Expression) – feature instance
- Returns:
smaller elements taken from the input two features
- Return type:
- __init__(feature_left, feature_right)
- class qlib.data.ops.Gt(feature_left, feature_right)
Greater Than Operator
- Parameters:
feature_left (Expression) – feature instance
feature_right (Expression) – feature instance
- Returns:
bool series indicate left > right
- Return type:
- __init__(feature_left, feature_right)
- class qlib.data.ops.Ge(feature_left, feature_right)
Greater Equal Than Operator
- Parameters:
feature_left (Expression) – feature instance
feature_right (Expression) – feature instance
- Returns:
bool series indicate left >= right
- Return type:
- __init__(feature_left, feature_right)
- class qlib.data.ops.Lt(feature_left, feature_right)
Less Than Operator
- Parameters:
feature_left (Expression) – feature instance
feature_right (Expression) – feature instance
- Returns:
bool series indicate left < right
- Return type:
- __init__(feature_left, feature_right)
- class qlib.data.ops.Le(feature_left, feature_right)
Less Equal Than Operator
- Parameters:
feature_left (Expression) – feature instance
feature_right (Expression) – feature instance
- Returns:
bool series indicate left <= right
- Return type:
- __init__(feature_left, feature_right)
- class qlib.data.ops.Eq(feature_left, feature_right)
Equal Operator
- Parameters:
feature_left (Expression) – feature instance
feature_right (Expression) – feature instance
- Returns:
bool series indicate left == right
- Return type:
- __init__(feature_left, feature_right)
- class qlib.data.ops.Ne(feature_left, feature_right)
Not Equal Operator
- Parameters:
feature_left (Expression) – feature instance
feature_right (Expression) – feature instance
- Returns:
bool series indicate left != right
- Return type:
- __init__(feature_left, feature_right)
- class qlib.data.ops.And(feature_left, feature_right)
And Operator
- Parameters:
feature_left (Expression) – feature instance
feature_right (Expression) – feature instance
- Returns:
two features’ row by row & output
- Return type:
- __init__(feature_left, feature_right)
- class qlib.data.ops.Or(feature_left, feature_right)
Or Operator
- Parameters:
feature_left (Expression) – feature instance
feature_right (Expression) – feature instance
- Returns:
two features’ row by row | outputs
- Return type:
- __init__(feature_left, feature_right)
- 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)
- 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:
- __init__(feature, N, func)
- 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($close, -1), 1) can not be handled rightly.
So this will only used for detecting the length of historical data needed.
- get_extended_window_size()
get_extend_window_size
For to calculate this Operator in range[start_index, end_index] We have to get the leaf feature in range[start_index - lft_etd, end_index + rght_etd].
- Returns:
lft_etd, rght_etd
- Return type:
(int, int)
- class qlib.data.ops.Ref(feature, N)
Feature Reference
- Parameters:
feature (Expression) – feature instance
N (int) – N = 0, retrieve the first data; N > 0, retrieve data of N periods ago; N < 0, future data
- Returns:
a feature instance with target reference
- Return type:
- __init__(feature, N)
- get_longest_back_rolling()
Get the longest length of historical data the feature has accessed
This is designed for getting the needed range of the data to calculate the features in specific range at first. However, situations like Ref(Ref($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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N, qscore)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature, N)
- 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:
- __init__(feature_left, feature_right, N, func)
- 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:
- __init__(feature_left, feature_right, N)
- 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:
- __init__(feature_left, feature_right, N)
- class qlib.data.ops.TResample(feature, freq, func)
- __init__(feature, freq, func)
Resampling the data to target frequency. The resample function of pandas is used.
the timestamp will be at the start of the time span after resample.
- Parameters:
feature (Expression) – An expression for calculating the feature
freq (str) – It will be passed into the resample method for resampling basedn on given frequency
func (method) – The method to get the resampled values Some expression are high frequently used
- class qlib.data.ops.OpsWrapper
Ops Wrapper
- __init__()
- register(ops_list: List[Type[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)
- property 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: str | Path, freq: str = 'day')
Update expression cache to latest calendar.
Override 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: str | Path, freq: str = 'day')
Update dataset cache to latest calendar.
Override 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)
- 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.
Override 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)
- classmethod read_data_from_cache(cache_path: str | 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: str | 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: str | Path)
- gen_dataset_cache(cache_path: str | Path, instruments, fields, freq, inst_processors=[])
Note
This function does not consider the cache read write lock. Please acquire 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.
Override 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)
- property data: Iterable[str]
get all data
- Raises:
ValueError – If the data(storage) does not exist, raise ValueError
- index(value: str) int
- Raises:
ValueError – If the data(storage) does not exist, raise ValueError
- class qlib.data.storage.storage.InstrumentStorage(market: str, freq: str, **kwargs)
- __init__(market: str, freq: str, **kwargs)
- property data: Dict[str, List[Tuple[str, str]]]
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)
- property data: Series
get all data
Notes
if data(storage) does not exist, return empty pd.Series: return pd.Series(dtype=np.float32)
- property start_index: int | None
get FeatureStorage start index
Notes
If the data(storage) does not exist, return None
- property end_index: int | None
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: List | ndarray | Tuple, 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
feature: 3 4 4 5 5 6 >>> self.write([6, 7], index=6) feature: 3 4 4 5 5 6 6 6 7 7 >>> self.write([8], index=9) feature: 3 4 4 5 5 6 6 6 7 7 8 np.nan 9 8 >>> self.write([1, np.nan], index=3) feature: 3 1 4 np.nan 5 6 6 6 7 7 8 np.nan 9 8
- rebase(start_index: int | None = None, end_index: int | None = None)
Rebase the start_index and end_index of the FeatureStorage.
start_index and end_index are closed intervals: [start_index, end_index]
Examples
feature: 3 4 4 5 5 6 >>> self.rebase(start_index=4) feature: 4 5 5 6 >>> self.rebase(start_index=3) feature: 3 np.nan 4 5 5 6 >>> self.write([3], index=3) feature: 3 3 4 5 5 6 >>> self.rebase(end_index=4) feature: 3 3 4 5 >>> self.write([6, 7, 8], index=4) feature: 3 3 4 6 5 7 6 8 >>> self.rebase(start_index=4, end_index=5) feature: 4 6 5 7
- rewrite(data: List | ndarray | Tuple, index: int)
overwrite all data in FeatureStorage with data
- Parameters:
data (Union[List, np.ndarray, Tuple]) – data
index (int) – data start index
- class qlib.data.storage.file_storage.FileStorageMixin
FileStorageMixin, applicable to FileXXXStorage Subclasses need to have provider_uri, freq, storage_name, file_name attributes
- check()
check self.uri
- Raises:
ValueError –
- class qlib.data.storage.file_storage.FileCalendarStorage(freq: str, future: bool, provider_uri: dict | None = None, **kwargs)
- __init__(freq: str, future: bool, provider_uri: dict | None = None, **kwargs)
- property data: List[str]
get all data
- Raises:
ValueError – If the data(storage) does not exist, raise ValueError
- index(value: str) int
- Raises:
ValueError – If the data(storage) does not exist, raise ValueError
- class qlib.data.storage.file_storage.FileInstrumentStorage(market: str, freq: str, provider_uri: dict | None = None, **kwargs)
- __init__(market: str, freq: str, provider_uri: dict | None = None, **kwargs)
- property data: Dict[str, List[Tuple[str, str]]]
get all data
- Raises:
ValueError – If the data(storage) does not exist, raise ValueError
- update([E, ]**F) None. Update D from mapping/iterable E and F.
Notes
If E present and has a .keys() method, does: for k in E: D[k] = E[k]
If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v
In either case, this is followed by: for k, v in F.items(): D[k] = v
- class qlib.data.storage.file_storage.FileFeatureStorage(instrument: str, field: str, freq: str, provider_uri: dict | None = None, **kwargs)
- __init__(instrument: str, field: str, freq: str, provider_uri: dict | None = None, **kwargs)
- property data: Series
get all data
Notes
if data(storage) does not exist, return empty pd.Series: return pd.Series(dtype=np.float32)
- write(data_array: List | ndarray, index: int | None = 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
feature: 3 4 4 5 5 6 >>> self.write([6, 7], index=6) feature: 3 4 4 5 5 6 6 6 7 7 >>> self.write([8], index=9) feature: 3 4 4 5 5 6 6 6 7 7 8 np.nan 9 8 >>> self.write([1, np.nan], index=3) feature: 3 1 4 np.nan 5 6 6 6 7 7 8 np.nan 9 8
- property start_index: int | None
get FeatureStorage start index
Notes
If the data(storage) does not exist, return None
- property end_index: int | None
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: Dict | DataHandler, segments: Dict[str, Tuple], fetch_kwargs: Dict = {}, **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: Dict | DataHandler, segments: Dict[str, Tuple], fetch_kwargs: Dict = {}, **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:
1) 'segments': { 'train': ("2008-01-01", "2014-12-31"), 'valid': ("2017-01-01", "2020-08-01",), 'test': ("2015-01-01", "2016-12-31",), } 2) 'segments': { 'insample': ("2008-01-01", "2014-12-31"), 'outsample': ("2017-01-01", "2020-08-01",), }
- config(handler_kwargs: dict | None = 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
- segmentsdict
Config of segments which is same as ‘segments’ in self.__init__
- setup_data(handler_kwargs: dict | None = 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: List[str] | Tuple[str] | str | slice | Index, col_set='__all', data_key='infer', **kwargs) List[DataFrame] | 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. TODO: make it automatic:
select DK_I for test data
select DK_L for training 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_colstr
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.
- Return type:
Union[List[pd.DataFrame], pd.DataFrame]
- Raises:
NotImplementedError: –
Data Loader
- class qlib.data.dataset.loader.DataLoader
DataLoader is designed for loading raw data from original data source.
- abstract load(instruments, start_time=None, end_time=None) 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. If the value of instruments is None, it means that no filtering is done.
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
- Raises:
KeyError: – if the instruments filter is not supported, raise KeyError
- class qlib.data.dataset.loader.DLWParser(config: 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: list | tuple | dict)
- Parameters:
config (Union[list, tuple, dict]) –
Config will be used to describe the fields and column names
<config> := { "group_name1": <fields_info1> "group_name2": <fields_info2> } or <config> := <fields_info> <fields_info> := ["expr", ...] | (["expr", ...], ["col_name", ...]) # NOTE: list or tuple will be treated as the things when parsing
- abstract load_group_df(instruments, exprs: list, names: list, start_time: str | Timestamp | None = None, end_time: str | Timestamp | None = None, gp_name: str | None = None) 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) 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. If the value of instruments is None, it means that no filtering is done.
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
- Raises:
KeyError: – if the instruments filter is not supported, raise KeyError
- class qlib.data.dataset.loader.QlibDataLoader(config: Tuple[list, tuple, dict], filter_pipe: List | None = None, swap_level: bool = True, freq: str | dict = 'day', inst_processors: dict | list | None = None)
Same as QlibDataLoader. The fields can be define by config
- __init__(config: Tuple[list, tuple, dict], filter_pipe: List | None = None, swap_level: bool = True, freq: str | dict = 'day', inst_processors: dict | list | None = 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_processors (dict | list) – If inst_processors is not None and type(config) == dict; load config[<group_name>] data using inst_processors[<group_name>] If inst_processors is a list, then it will be applied to all groups.
- load_group_df(instruments, exprs: list, names: list, start_time: str | Timestamp | None = None, end_time: str | Timestamp | None = None, gp_name: str | None = None) 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 | str | DataFrame, join='outer')
DataLoader that supports loading data from file or as provided.
- __init__(config: dict | str | DataFrame, 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) 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. If the value of instruments is None, it means that no filtering is done.
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
- Raises:
KeyError: – if the instruments filter is not supported, raise KeyError
- class qlib.data.dataset.loader.NestedDataLoader(dataloader_l: List[Dict], join='left')
We have multiple DataLoader, we can use this class to combine them.
- __init__(dataloader_l: List[Dict], join='left') None
- Parameters:
dataloader_l (list[dict]) –
A list of dataloader, for exmaple
nd = NestedDataLoader( dataloader_l=[ { "class": "qlib.contrib.data.loader.Alpha158DL", }, { "class": "qlib.contrib.data.loader.Alpha360DL", "kwargs": { "config": { "label": ( ["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"]) } } } ] )
join – it will pass to pd.concat when merging it.
- load(instruments=None, start_time=None, end_time=None) 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. If the value of instruments is None, it means that no filtering is done.
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
- Raises:
KeyError: – if the instruments filter is not supported, raise KeyError
- 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
<handler_config> := { "group_name1": <handler> "group_name2": <handler> } or <handler_config> := <handler> <handler> := DataHandler Instance | DataHandler Config
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) 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. If the value of instruments is None, it means that no filtering is done.
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
- Raises:
KeyError: – if the instruments filter is not supported, raise KeyError
Data Handler
- class qlib.data.dataset.handler.DataHandler(instruments=None, start_time=None, end_time=None, data_loader: dict | str | DataLoader | None = 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 0.0289Tips 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: dict | str | DataLoader | None = None, init_data=True, fetch_orig=True)
- Parameters:
instruments – The stock list to retrieve.
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: Timestamp | slice | str | Index = slice(None, None, None), level: str | int = 'datetime', col_set: str | List[str] = '__all', squeeze: bool = False, proc_func: Callable | None = None) DataFrame
fetch data from underlying data source
Design motivation: - providing a unified interface for underlying data. - Potential to make the interface more friendly. - User can improve performance when fetching data in this extra layer
- Parameters:
selector (Union[pd.Timestamp, slice, str]) –
describe how to select data by index It can be categories as following
fetch single index
fetch a range of index
a slice range
pd.Index for specific indexes
Following conflicts may occur
Does [“20200101”, “20210101”] mean selecting this slice or these two days?
slice have higher priorities
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, pd.Index 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
- 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: 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: int | None = None, **kwargs) Iterator[Tuple[Timestamp, DataFrame]]
get an 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: dict | str | DataLoader | None = None, infer_processors: List = [], learn_processors: List = [], shared_processors: List = [], process_type='append', drop_raw=False, **kwargs)
DataHandler with (L)earnable (P)rocessor
This handler will produce three pieces of data in pd.DataFrame format.
DK_R / self._data: the raw data loaded from the loader
DK_I / self._infer: the data processed for inference
DK_L / self._learn: the data processed for learning model.
The motivation of using different processor workflows for learning and inference Here are some examples.
The instrument universe for learning and inference may be different.
The processing of some samples may rely on label (for example, some samples hit the limit may need extra processing or be dropped).
These processors only apply to the learning phase.
Tips for data handler
To reduce the memory cost
drop_raw=True: this will modify the data inplace on raw data;
Please note processed data like self._infer or self._learn are concepts different from segments in Qlib’s Dataset like “train” and “test”
Processed data like self._infer or self._learn are underlying data processed with different processors
segments in Qlib’s Dataset like “train” and “test” are simply the time segmentations when querying data(“train” are often before “test” in time-series).
For example, you can query data._infer processed by infer_processors in the “train” time segmentation.
- __init__(instruments=None, start_time=None, end_time=None, data_loader: dict | str | DataLoader | None = None, infer_processors: List = [], learn_processors: List = [], shared_processors: List = [], 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>:
1) classname & kwargs: { "class": "MinMaxNorm", "kwargs": { "fit_start_time": "20080101", "fit_end_time": "20121231" } } 2) Only classname: "DropnaFeature" 3) object instance of Processor
learn_processors (list) – similar to infer_processors, but for generating data for learning models
process_type (str) –
PTYPE_I = ‘independent’
self._infer will be processed by infer_processors
self._learn will be processed by learn_processors
PTYPE_A = ‘append’
self._infer will be 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 = 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: Timestamp | slice | str = slice(None, None, None), level: str | int = 'datetime', col_set='__all', data_key: Literal['raw', 'infer', 'learn'] = 'infer', squeeze: bool = False, proc_func: Callable | None = None) 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
- Return type:
pd.DataFrame
- get_cols(col_set='__all', data_key: Literal['raw', 'infer', 'learn'] = 'infer') list
get the column names
- Parameters:
col_set (str) – select a set of meaningful columns.(e.g. features, columns).
data_key (DATA_KEY_TYPE) – the data to fetch: DK_*.
- Returns:
list of column names
- Return type:
list
- classmethod cast(handler: DataHandlerLP) DataHandlerLP
Motivation
A user creates a datahandler in his customized package. Then he wants 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:
- classmethod from_df(df: DataFrame) DataHandlerLP
Motivation: - When user want to get a quick data handler.
The created data handler will have only one shared Dataframe without processors. After creating the handler, user may often want to dump the handler for reuse Here is a typical use case
from qlib.data.dataset import DataHandlerLP dh = DataHandlerLP.from_df(df) dh.to_pickle(fname, dump_all=True)
TODO: - The StaticDataLoader is quite slow. It don’t have to copy the data again…
Processor
- qlib.data.dataset.processor.get_group_columns(df: DataFrame, group: str | None)
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: DataFrame | None = 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 processing
Knowning the readonly information is helpful to the Handler to avoid uncessary copy
- config(**kwargs)
configure the serializable object
- Parameters:
keys (kwargs may include following) –
- dump_allbool
will the object dump all object
- excludelist
What attribute will not be dumped
- includelist
What attribute will be dumped
recursive (bool) – will the configuration be recursive
- class qlib.data.dataset.processor.DropnaProcessor(fields_group=None)
- __init__(fields_group=None)
- readonly()
Does the processor treat the input data readonly (i.e. does not write the input data) when processing
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')
- 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=[])
- readonly()
Does the processor treat the input data readonly (i.e. does not write the input data) when processing
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=[])
- readonly()
Does the processor treat the input data readonly (i.e. does not write the input data) when processing
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)
- 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)
- fit(df: DataFrame | None = 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.
- 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)
- fit(df: DataFrame | None = 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.
- 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:
- __init__(fit_start_time, fit_end_time, fields_group=None, clip_outlier=True)
- fit(df: DataFrame | None = 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.
- class qlib.data.dataset.processor.CSZScoreNorm(fields_group=None, method='zscore')
Cross Sectional ZScore Normalization
- __init__(fields_group=None, method='zscore')
- class qlib.data.dataset.processor.CSRankNorm(fields_group=None)
Cross Sectional Rank Normalization. “Cross Sectional” is often used to describe data operations. The operations across different stocks are often called Cross Sectional Operation.
For example, CSRankNorm is an operation that grouping the data by each day and rank across all the stocks in each day.
Explanation about 3.46 & 0.5
import numpy as np import pandas as pd x = np.random.random(10000) # for any variable x_rank = pd.Series(x).rank(pct=True) # if it is converted to rank, it will be a uniform distributed x_rank_norm = (x_rank - x_rank.mean()) / x_rank.std() # Normally, we will normalize it to make it like normal distribution x_rank.mean() # accounts for 0.5 1 / x_rank.std() # accounts for 3.46
- __init__(fields_group=None)
- class qlib.data.dataset.processor.CSZFillna(fields_group=None)
Cross Sectional Fill Nan
- __init__(fields_group=None)
- class qlib.data.dataset.processor.HashStockFormat
Process the storage of from df into hasing stock format
- class qlib.data.dataset.processor.TimeRangeFlt(start_time: Timestamp | str | None = None, end_time: Timestamp | str | None = None, freq: str = 'day')
This is a filter to filter stock. Only keep the data that exist from start_time to end_time (the existence in the middle is not checked.) WARNING: It may induce leakage!!!
- __init__(start_time: Timestamp | str | None = None, end_time: Timestamp | str | None = None, freq: str = 'day')
- Parameters:
start_time (Optional[Union[pd.Timestamp, str]]) – The data must start earlier (or equal) than start_time None indicates data will not be filtered based on start_time
end_time (Optional[Union[pd.Timestamp, str]]) – similar to start_time
freq (str) – The frequency of the calendar
Contrib
Model
- class qlib.model.base.BaseModel
Modeling things
- abstract predict(*args, **kwargs) object
Make predictions after modeling things
- class qlib.model.base.Model
Learnable Models
- fit(dataset: Dataset, reweighter: Reweighter)
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.
- abstract predict(dataset: Dataset, segment: 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)
- Return type:
Prediction results with certain type such as pandas.Series.
- class qlib.model.base.ModelFT
Model (F)ine(t)unable
- abstract finetune(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
- class qlib.contrib.strategy.TopkDropoutStrategy(*, topk, n_drop, method_sell='bottom', method_buy='top', hold_thresh=1, only_tradable=False, forbid_all_trade_at_limit=True, **kwargs)
- __init__(*, topk, n_drop, method_sell='bottom', method_buy='top', hold_thresh=1, only_tradable=False, forbid_all_trade_at_limit=True, **kwargs)
- Parameters:
topk (int) – the number of stocks in the portfolio.
n_drop (int) – number of stocks to be replaced in each trading date.
method_sell (str) – dropout method_sell, random/bottom.
method_buy (str) – dropout method_buy, random/top.
hold_thresh (int) – minimum holding days before sell stock , will check current.get_stock_count(order.stock_id) >= self.hold_thresh.
only_tradable (bool) –
will the strategy only consider the tradable stock when buying and selling.
if only_tradable:
strategy will make decision with the tradable state of the stock info and avoid buy and sell them.
else:
strategy will make buy sell decision without checking the tradable state of the stock.
forbid_all_trade_at_limit (bool) –
if forbid all trades when limit_up or limit_down reached.
if forbid_all_trade_at_limit:
strategy will not do any trade when price reaches limit up/down, even not sell at limit up nor buy at limit down, though allowed in reality.
else:
strategy will sell at limit up and buy ad limit down.
- generate_trade_decision(execute_result=None)
Generate trade decision in each trading bar
- Parameters:
execute_result (List[object], optional) –
the executed result for trade decision, by default None
When call the generate_trade_decision firstly, execute_result could be None
- class qlib.contrib.strategy.WeightStrategyBase(*, order_generator_cls_or_obj=<class 'qlib.contrib.strategy.order_generator.OrderGenWOInteract'>, **kwargs)
- __init__(*, order_generator_cls_or_obj=<class 'qlib.contrib.strategy.order_generator.OrderGenWOInteract'>, **kwargs)
- signal :
the information to describe a signal. Please refer to the docs of qlib.backtest.signal.create_signal_from the decision of the strategy will base on the given signal
- trade_exchangeExchange
exchange that provides market info, used to deal order and generate report
If trade_exchange is None, self.trade_exchange will be set with common_infra
It allowes different trade_exchanges is used in different executions.
For example:
In daily execution, both daily exchange and minutely are usable, but the daily exchange is recommended because it runs faster.
In minutely execution, the daily exchange is not usable, only the minutely exchange is recommended.
- generate_target_weight_position(score, current, trade_start_time, trade_end_time)
Generate target position from score for this date and the current position.The cash is not considered in the position
- Parameters:
score (pd.Series) – pred score for this trade date, index is stock_id, contain ‘score’ column.
current (Position()) – current position.
trade_start_time (pd.Timestamp) –
trade_end_time (pd.Timestamp) –
- generate_trade_decision(execute_result=None)
Generate trade decision in each trading bar
- Parameters:
execute_result (List[object], optional) –
the executed result for trade decision, by default None
When call the generate_trade_decision firstly, execute_result could be None
- class qlib.contrib.strategy.EnhancedIndexingStrategy(*, riskmodel_root, market='csi500', turn_limit=None, name_mapping={}, optimizer_kwargs={}, verbose=False, **kwargs)
Enhanced Indexing Strategy
Enhanced indexing combines the arts of active management and passive management, with the aim of outperforming a benchmark index (e.g., S&P 500) in terms of portfolio return while controlling the risk exposure (a.k.a. tracking error).
Users need to prepare their risk model data like below:
├── /path/to/riskmodel ├──── 20210101 ├────── factor_exp.{csv|pkl|h5} ├────── factor_cov.{csv|pkl|h5} ├────── specific_risk.{csv|pkl|h5} ├────── blacklist.{csv|pkl|h5} # optionalThe risk model data can be obtained from risk data provider. You can also use qlib.model.riskmodel.structured.StructuredCovEstimator to prepare these data.
- Parameters:
riskmodel_path (str) – risk model path
name_mapping (dict) – alternative file names
- __init__(*, riskmodel_root, market='csi500', turn_limit=None, name_mapping={}, optimizer_kwargs={}, verbose=False, **kwargs)
- signal :
the information to describe a signal. Please refer to the docs of qlib.backtest.signal.create_signal_from the decision of the strategy will base on the given signal
- trade_exchangeExchange
exchange that provides market info, used to deal order and generate report
If trade_exchange is None, self.trade_exchange will be set with common_infra
It allowes different trade_exchanges is used in different executions.
For example:
In daily execution, both daily exchange and minutely are usable, but the daily exchange is recommended because it runs faster.
In minutely execution, the daily exchange is not usable, only the minutely exchange is recommended.
- generate_target_weight_position(score, current, trade_start_time, trade_end_time)
Generate target position from score for this date and the current position.The cash is not considered in the position
- Parameters:
score (pd.Series) – pred score for this trade date, index is stock_id, contain ‘score’ column.
current (Position()) – current position.
trade_start_time (pd.Timestamp) –
trade_end_time (pd.Timestamp) –
- class qlib.contrib.strategy.TWAPStrategy(outer_trade_decision: BaseTradeDecision = None, level_infra: LevelInfrastructure = None, common_infra: CommonInfrastructure = None, trade_exchange: Exchange = None)
TWAP Strategy for trading
Note
This TWAP strategy will celling round when trading. This will make the TWAP trading strategy produce the order earlier when the total trade unit of amount is less than the trading step
- reset(outer_trade_decision: BaseTradeDecision | None = None, **kwargs)
- Parameters:
outer_trade_decision (BaseTradeDecision, optional) –
- generate_trade_decision(execute_result=None)
Generate trade decision in each trading bar
- Parameters:
execute_result (List[object], optional) –
the executed result for trade decision, by default None
When call the generate_trade_decision firstly, execute_result could be None
- class qlib.contrib.strategy.SBBStrategyBase(outer_trade_decision: BaseTradeDecision = None, level_infra: LevelInfrastructure = None, common_infra: CommonInfrastructure = None, trade_exchange: Exchange = None)
(S)elect the (B)etter one among every two adjacent trading (B)ars to sell or buy.
- reset(outer_trade_decision: BaseTradeDecision | None = None, **kwargs)
- Parameters:
outer_trade_decision (BaseTradeDecision, optional) –
- generate_trade_decision(execute_result=None)
Generate trade decision in each trading bar
- Parameters:
execute_result (List[object], optional) –
the executed result for trade decision, by default None
When call the generate_trade_decision firstly, execute_result could be None
- class qlib.contrib.strategy.SBBStrategyEMA(outer_trade_decision: BaseTradeDecision | None = None, instruments: List | str = 'csi300', freq: str = 'day', trade_exchange: Exchange | None = None, level_infra: LevelInfrastructure | None = None, common_infra: CommonInfrastructure | None = None, **kwargs)
(S)elect the (B)etter one among every two adjacent trading (B)ars to sell or buy with (EMA) signal.
- __init__(outer_trade_decision: BaseTradeDecision | None = None, instruments: List | str = 'csi300', freq: str = 'day', trade_exchange: Exchange | None = None, level_infra: LevelInfrastructure | None = None, common_infra: CommonInfrastructure | None = None, **kwargs)
- Parameters:
instruments (Union[List, str], optional) – instruments of EMA signal, by default “csi300”
freq (str, optional) – freq of EMA signal, by default “day” Note: freq may be different from time_per_step
- reset_level_infra(level_infra)
reset level-shared infra - After reset the trade calendar, the signal will be changed
- class qlib.contrib.strategy.SoftTopkStrategy(model, dataset, topk, order_generator_cls_or_obj=<class 'qlib.contrib.strategy.order_generator.OrderGenWInteract'>, max_sold_weight=1.0, risk_degree=0.95, buy_method='first_fill', trade_exchange=None, level_infra=None, common_infra=None, **kwargs)
- __init__(model, dataset, topk, order_generator_cls_or_obj=<class 'qlib.contrib.strategy.order_generator.OrderGenWInteract'>, max_sold_weight=1.0, risk_degree=0.95, buy_method='first_fill', trade_exchange=None, level_infra=None, common_infra=None, **kwargs)
- Parameters:
topk (int) – top-N stocks to buy
risk_degree (float) –
position percentage of total value buy_method:
rank_fill: assign the weight stocks that rank high first(1/topk max) average_fill: assign the weight to the stocks rank high averagely.
- get_risk_degree(trade_step=None)
Return the proportion of your total value you will used in investment. Dynamically risk_degree will result in Market timing
- generate_target_weight_position(score, current, trade_start_time, trade_end_time)
- Parameters:
score – pred score for this trade date, pd.Series, index is stock_id, contain ‘score’ column
current – current position, use Position() class
trade_date –
trade date
generate target position from score for this date and the current position
The cache is not considered in the position
Evaluate
- qlib.contrib.evaluate.risk_analysis(r, N: int | None = None, freq: str = 'day')
Risk Analysis NOTE: The calculation of annulaized return is different from the definition of annualized return. It is implemented by design. Qlib tries to cumulated returns by summation instead of production to avoid the cumulated curve being skewed exponentially. All the calculation of annualized returns follows this principle in Qlib.
TODO: add a parameter to enable calculating metrics with production accumulation of return.
- Parameters:
r (pandas.Series) – daily return series.
N (int) – scaler for annualizing information_ratio (day: 252, week: 50, month: 12), at least one of N and freq should exist
freq (str) – analysis frequency used for calculating the scaler, at least one of N and freq should exist
- qlib.contrib.evaluate.indicator_analysis(df, method='mean')
analyze statistical time-series indicators of trading
- Parameters:
df (pandas.DataFrame) –
- columns: like [‘pa’, ‘pos’, ‘ffr’, ‘deal_amount’, ‘value’].
- Necessary fields:
’pa’ is the price advantage in trade indicators
’pos’ is the positive rate in trade indicators
’ffr’ is the fulfill rate in trade indicators
- Optional fields:
’deal_amount’ is the total deal deal_amount, only necessary when method is ‘amount_weighted’
’value’ is the total trade value, only necessary when method is ‘value_weighted’
index: Index(datetime)
method (str, optional) –
statistics method of pa/ffr, by default “mean”
if method is ‘mean’, count the mean statistical value of each trade indicator
if method is ‘amount_weighted’, count the deal_amount weighted mean statistical value of each trade indicator
if method is ‘value_weighted’, count the value weighted mean statistical value of each trade indicator
Note: statistics method of pos is always “mean”
- Returns:
statistical value of each trade indicators
- Return type:
pd.DataFrame
- qlib.contrib.evaluate.backtest_daily(start_time: str | Timestamp, end_time: str | Timestamp, strategy: str | dict | BaseStrategy, executor: str | dict | BaseExecutor | None = None, account: float | int | Position = 100000000.0, benchmark: str = 'SH000300', exchange_kwargs: dict | None = None, pos_type: str = 'Position')
initialize the strategy and executor, then executor the backtest of daily frequency
- Parameters:
start_time (Union[str, pd.Timestamp]) – closed start time for backtest NOTE: This will be applied to the outmost executor’s calendar.
end_time (Union[str, pd.Timestamp]) – closed end time for backtest NOTE: This will be applied to the outmost executor’s calendar. E.g. Executor[day](Executor[1min]), setting end_time == 20XX0301 will include all the minutes on 20XX0301
strategy (Union[str, dict, BaseStrategy]) –
for initializing outermost portfolio strategy. Please refer to the docs of init_instance_by_config for more information.
E.g.
# dict strategy = { "class": "TopkDropoutStrategy", "module_path": "qlib.contrib.strategy.signal_strategy", "kwargs": { "signal": (model, dataset), "topk": 50, "n_drop": 5, }, } # BaseStrategy pred_score = pd.read_pickle("score.pkl")["score"] STRATEGY_CONFIG = { "topk": 50, "n_drop": 5, "signal": pred_score, } strategy = TopkDropoutStrategy(**STRATEGY_CONFIG) # str example. # 1) specify a pickle object # - path like 'file:///<path to pickle file>/obj.pkl' # 2) specify a class name # - "ClassName": getattr(module, "ClassName")() will be used. # 3) specify module path with class name # - "a.b.c.ClassName" getattr(<a.b.c.module>, "ClassName")() will be used.
executor (Union[str, dict, BaseExecutor]) – for initializing the outermost executor.
benchmark (str) – the benchmark for reporting.
account (Union[float, int, Position]) –
information for describing how to creating the account
For float or int:
Using Account with only initial cash
For Position:
Using Account with a Position
exchange_kwargs (dict) –
the kwargs for initializing Exchange E.g.
exchange_kwargs = { "freq": freq, "limit_threshold": None, # limit_threshold is None, using C.limit_threshold "deal_price": None, # deal_price is None, using C.deal_price "open_cost": 0.0005, "close_cost": 0.0015, "min_cost": 5, }
pos_type (str) – the type of Position.
- Returns:
report_normal (pd.DataFrame) – backtest report
positions_normal (pd.DataFrame) – backtest positions
- qlib.contrib.evaluate.long_short_backtest(pred, topk=50, deal_price=None, shift=1, open_cost=0, close_cost=0, trade_unit=None, limit_threshold=None, min_cost=5, subscribe_fields=[], extract_codes=False)
A backtest for long-short strategy
- Parameters:
pred – The trading signal produced on day T.
topk – The short topk securities and long topk securities.
deal_price – The price to deal the trading.
shift – Whether to shift prediction by one day. The trading day will be T+1 if shift==1.
open_cost – open transaction cost.
close_cost – close transaction cost.
trade_unit – 100 for China A.
limit_threshold – limit move 0.1 (10%) for example, long and short with same limit.
min_cost – min transaction cost.
subscribe_fields – subscribe fields.
extract_codes – bool. will we pass the codes extracted from the pred to the exchange. NOTE: This will be faster with offline qlib.
- Returns:
The result of backtest, it is represented by a dict. { “long”: long_returns(excess), “short”: short_returns(excess), “long_short”: long_short_returns}
Report
Workflow
Experiment Manager
- class qlib.workflow.expm.ExpManager(uri: str, default_exp_name: str | None)
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)
The ExpManager is expected to be a singleton (btw, we can have multiple Experiment`s with different uri. user can get different experiments from different uri, and then compare records of them). Global Config (i.e. `C) is also a singleton.
So we try to align them together. They share the same variable, which is called default uri. Please refer to ExpManager.default_uri for details of variable sharing.
When the user starts an experiment, the user may want to set the uri to a specific uri (it will override default uri during this period), and then unset the specific uri and fallback to the default uri. ExpManager._active_exp_uri is that specific uri.
- __init__(uri: str, default_exp_name: str | None)
- start_exp(*, experiment_id: str | None = None, experiment_name: str | None = None, recorder_id: str | None = None, recorder_name: str | None = None, uri: str | None = None, resume: bool = False, **kwargs) Experiment
Start an experiment. This method includes first get_or_create an experiment, and then set it to be active.
Maintaining _active_exp_uri is included in start_exp, remaining implementation should be included in _end_exp in subclass
- 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.
- Return type:
An active experiment.
- end_exp(recorder_status: str = 'SCHEDULED', **kwargs)
End an active experiment.
Maintaining _active_exp_uri is included in end_exp, remaining implementation should be included in _end_exp in subclass
- 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: str | None = None)
Create an experiment.
- Parameters:
experiment_name (str) – the experiment name, which must be unique.
- Return type:
An experiment object.
- Raises:
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 criteria 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.
- 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.
- property default_uri
Get the default tracking URI from qlib.config.C
- property uri
Get the default tracking URI or current URI.
- Return type:
The tracking URI string.
- list_experiments()
List all the existing experiments.
- 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)
- 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
- 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.
- 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 criteria 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) Recorder
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.
- Return type:
A recorder object.
- list_recorders(rtype: Literal['dict', 'list'] = 'dict', **flt_kwargs) List[Recorder] | Dict[str, Recorder]
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_kwargsdict
filter recorders by conditions e.g. list_recorders(status=Recorder.STATUS_FI)
- Returns:
- if rtype == “dict”:
A dictionary (id -> recorder) of recorder information that being stored.
- elif rtype == “list”:
A list of Recorder.
- Return type:
The return type depends on rtype
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)
- 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.
- 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)
- 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.
- log_artifact(local_path: str, artifact_path: str | None = None)
Log a local file or directory as an artifact of the currently active run.
- Parameters:
local_path (str) – Path to the file to write.
artifact_path (Optional[str]) – If provided, the directory in
artifact_urito write to.
- 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 = None)
List all the artifacts of a recorder.
- Parameters:
artifact_path (str) – the relative path for the artifact to be stored in the URI.
- Return type:
A list of artifacts information (name, path, etc.) that being stored.
- download_artifact(path: str, dst_path: str | None = None) str
Download an artifact file or directory from a run to a local directory if applicable, and return a local path for it.
- Parameters:
path (str) – Relative source path to the desired artifact.
dst_path (Optional[str]) – Absolute path of the local filesystem destination directory to which to download the specified artifacts. This directory must already exist. If unspecified, the artifacts will either be downloaded to a new uniquely-named directory on the local filesystem.
- Returns:
Local path of desired artifact.
- Return type:
str
- list_metrics()
List all the metrics of a recorder.
- Return type:
A dictionary of metrics that being stored.
- list_params()
List all the params of a recorder.
- Return type:
A dictionary of params that being stored.
- list_tags()
List all the tags of a recorder.
- 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)
- 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
- Return type:
The stored records.
- list()
List the supported artifacts. Users don’t have to consider self.get_path
- 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 dependant files complete before generating 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
- Raises:
FileNotFoundError – 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
RecordTempclass.- __init__(model=None, dataset=None, recorder=None)
- 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
- Return type:
A list of all the supported artifacts.
- class qlib.workflow.record_temp.ACRecordTemp(recorder, skip_existing=False)
Automatically checking record template
- __init__(recorder, skip_existing=False)
- generate(*args, **kwargs)
automatically checking the files and then run the concrete generating task
- 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
RecordTempclass.- depend_cls
alias of
SignalRecord
- __init__(recorder, **kwargs)
- 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
- 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
RecordTempclass.- depend_cls
alias of
SignalRecord
- __init__(recorder, ana_long_short=False, ann_scaler=252, label_col=0, skip_existing=False)
- list()
List the supported artifacts. Users don’t have to consider self.get_path
- Return type:
A list of all the supported artifacts.
- class qlib.workflow.record_temp.PortAnaRecord(recorder, config=None, risk_analysis_freq: List | str | None = None, indicator_analysis_freq: List | str | None = None, indicator_analysis_method=None, skip_existing=False, **kwargs)
This is the Portfolio Analysis Record class that generates the analysis results such as those of backtest. This class inherits the
RecordTempclass.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
- depend_cls
alias of
SignalRecord
- __init__(recorder, config=None, risk_analysis_freq: List | str | None = None, indicator_analysis_freq: List | str | None = None, indicator_analysis_method=None, skip_existing=False, **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_freqstr|List[str]
risk analysis freq of report
- indicator_analysis_freqstr|List[str]
indicator analysis freq of report
- indicator_analysis_methodstr, optional, default by None
the candidate values include ‘mean’, ‘amount_weighted’, ‘value_weighted’
- list()
List the supported artifacts. Users don’t have to consider self.get_path
- Return type:
A list of all the supported artifacts.
- class qlib.workflow.record_temp.MultiPassPortAnaRecord(recorder, pass_num=10, shuffle_init_score=True, **kwargs)
This is the Multiple Pass Portfolio Analysis Record class that run backtest multiple times and generates the analysis results such as those of backtest. This class inherits the
PortAnaRecordclass.If shuffle_init_score enabled, the prediction score of the first backtest date will be shuffled, so that initial position will be random. The shuffle_init_score will only works when the signal is used as <PRED> placeholder. The placeholder will be replaced by pred.pkl saved in recorder.
- Parameters:
recorder (Recorder) – The recorder used to save the backtest results.
pass_num (int) – The number of backtest passes.
shuffle_init_score (bool) – Whether to shuffle the prediction score of the first backtest date.
- depend_cls
alias of
SignalRecord
- __init__(recorder, pass_num=10, shuffle_init_score=True, **kwargs)
- Parameters:
recorder (Recorder) – The recorder used to save the backtest results.
pass_num (int) – The number of backtest passes.
shuffle_init_score (bool) – Whether to shuffle the prediction score of the first backtest date.
- list()
List the supported artifacts. Users don’t have to consider self.get_path
- 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.
- 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
- abstract 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:
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
- qlib.workflow.task.gen.trunc_segments(ta: TimeAdjuster, segments: Dict[str, Timestamp], days, test_key='test')
To avoid the leakage of future information, the segments should be truncated according to the test start_time
Note
This function will change segments inplace
- class qlib.workflow.task.gen.RollingGen(step: int = 40, rtype: str = 'expanding', ds_extra_mod_func: None | ~typing.Callable = <function handler_mod>, test_key='test', train_key='train', trunc_days: int | None = None, task_copy_func: ~typing.Callable = <function deepcopy>)
- __init__(step: int = 40, rtype: str = 'expanding', ds_extra_mod_func: None | ~typing.Callable = <function handler_mod>, test_key='test', train_key='train', trunc_days: int | None = None, task_copy_func: ~typing.Callable = <function deepcopy>)
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_modto modify the end time of the handler of a dataset.trunc_days (int) – trunc some data to avoid future information leakage
task_copy_func (Callable) – the function to copy entire task. This is very useful when user want to share something between tasks
- gen_following_tasks(task: dict, test_end: 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 generate 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)
- abstract 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:
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 several 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
- 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. Always 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 = None) 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:
- qlib.model.trainer.end_task_train(rec: Recorder, experiment_name: str) Recorder
Finish task training with real model fitting and saving.
- qlib.model.trainer.task_train(task_config: dict, experiment_name: str, recorder_name: str | None = None) 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__()
- 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 = None, train_func: ~typing.Callable = <function task_train>, call_in_subproc: bool = False, default_rec_name: str | None = None)
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 = None, train_func: ~typing.Callable = <function task_train>, call_in_subproc: bool = False, default_rec_name: str | None = None)
Init TrainerR.
- Parameters:
experiment_name (str, optional) – the default name of experiment.
train_func (Callable, optional) – default training method. Defaults to task_train.
call_in_subproc (bool) – call the process in subprocess to force memory release
- train(tasks: list, train_func: Callable | None = None, experiment_name: str | None = None, **kwargs) List[Recorder]
Given a list of tasks 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]
- class qlib.model.trainer.DelayTrainerR(experiment_name: str | None = None, train_func=<function begin_task_train>, end_train_func=<function end_task_train>, **kwargs)
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 = None, train_func=<function begin_task_train>, end_train_func=<function end_task_train>, **kwargs)
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(models, end_train_func=None, experiment_name: str | None = None, **kwargs) List[Recorder]
Given a list of Recorder and return a list of trained Recorder. This class will finish real data loading and model fitting.
- Parameters:
models (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 recorders 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 = None, task_pool: str | None = None, train_func=<function task_train>, skip_run_task: bool = False, default_rec_name: str | None = None)
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 = None, task_pool: str | None = None, train_func=<function task_train>, skip_run_task: bool = False, default_rec_name: str | None = None)
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 = None, experiment_name: str | None = None, before_status: str = 'waiting', after_status: str = 'done', default_rec_name: str | None = None, **kwargs) List[Recorder]
Given a list of tasks 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 tasks 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[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 = None, experiment_name: str | None = 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 tasks 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 = None, task_pool: str | None = None, train_func=<function begin_task_train>, end_train_func=<function end_task_train>, skip_run_task: bool = False, **kwargs)
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 = None, task_pool: str | None = None, train_func=<function begin_task_train>, end_train_func=<function end_task_train>, skip_run_task: bool = False, **kwargs)
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 = None, **kwargs) List[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 tasks 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 = None, **kwargs) List[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 recorders 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 = 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 recorders 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. The processor order is the same as the list order. For example: [Group1(…, Ensemble1()), Group2(…, Ensemble2())]
- Returns:
the dict after processing.
- Return type:
dict
- class qlib.workflow.task.collect.MergeCollector(collector_dict: Dict[str, 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, 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_keyfrom collector_dict andkeyfrom 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={}, status: Iterable = {'FINISHED'})
- __init__(experiment, process_list=[], rec_key_func=None, rec_filter_func=None, artifacts_path={'pred': 'pred.pkl'}, artifacts_key=None, list_kwargs={}, status: Iterable = {'FINISHED'})
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.
status (Iterable) – only collect recorders with specific status. None indicating collecting all the recorders
- 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: Ensemble | None = None)
Group the objects based on dict
- __init__(group_func=None, ens: Ensemble | None = 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}}
None. (Defaults to) –
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(ens=<qlib.model.ens.ensemble.RollingEnsemble object>)
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__(ens=<qlib.model.ens.ensemble.RollingEnsemble object>)
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}}
None. (Defaults to) –
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() 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)
- 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() 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') 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: dict | tuple) 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 days are based on the test_start. For example, if the label contains the information of 2 days in the near future, the prediction horizon 1 day. (e.g. the prediction target is Ref($close, -2)/Ref($close, -1) - 1) the days should be 2 + 1 == 3 days.
- Returns:
tuple
- Return type:
new segment
- shift(seg: tuple, step: int, rtype='sliding') tuple
Shift the datetime of segment
If there are None (which indicates unbounded index) in the segment, this method will return None.
- 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
- qlib.workflow.task.utils.replace_task_handler_with_cache(task: dict, cache_dir: str | Path = '.') dict
Replace the handler in task with a cache handler. It will automatically cache the file and save it in cache_dir.
>>> import qlib >>> qlib.auto_init() >>> import datetime >>> # it is simplified task >>> task = {"dataset": {"kwargs":{'handler': {'class': 'Alpha158', 'module_path': 'qlib.contrib.data.handler', 'kwargs': {'start_time': datetime.date(2008, 1, 1), 'end_time': datetime.date(2020, 8, 1), 'fit_start_time': datetime.date(2008, 1, 1), 'fit_end_time': datetime.date(2014, 12, 31), 'instruments': 'CSI300'}}}}} >>> new_task = replace_task_handler_with_cache(task) >>> print(new_task) {'dataset': {'kwargs': {'handler': 'file...Alpha158.3584f5f8b4.pkl'}}}
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 that demonstrate the workflow of each situation
- For simplicity
Only one strategy is used in the strategy
update_online_pred is only called in the online mode and is ignored
Online + Trainer
tasks = first_train()
models = trainer.train(tasks)
trainer.end_train(models)
for day in online_trading_days:
# OnlineManager.routine
models = trainer.train(strategy.prepare_tasks()) # for each strategy
strategy.prepare_online_models(models) # for each strategy
trainer.end_train(models)
prepare_signals() # prepare trading signals daily
Online + DelayTrainer: the workflow is the same as Online + Trainer.
Simulation + DelayTrainer
# simulate
tasks = first_train()
models = trainer.train(tasks)
for day in historical_calendars:
# OnlineManager.routine
models = trainer.train(strategy.prepare_tasks()) # for each strategy
strategy.prepare_online_models(models) # for each strategy
# delay_prepare()
# FIXME: Currently the delay_prepare is not implemented in a proper way.
trainer.end_train(<for all previous models>)
prepare_signals()
# Can we simplify current workflow?
Can reduce the number of state of tasks?
For each task, we have three phases (i.e. task, partly trained task, final trained task)
- class qlib.workflow.online.manager.OnlineManager(strategies: OnlineStrategy | List[OnlineStrategy], trainer: Trainer | None = None, begin_time: str | Timestamp | None = 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: OnlineStrategy | List[OnlineStrategy], trainer: Trainer | None = None, begin_time: str | Timestamp | None = 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 (qlib.model.trainer.Trainer) – the trainer to train task. None for using TrainerR.
freq (str, optional) – data frequency. Defaults to “day”.
- first_train(strategies: List[OnlineStrategy] | None = 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: str | Timestamp | None = 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) 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:
- add_strategy(strategies: OnlineStrategy | List[OnlineStrategy])
Add some new strategies to OnlineManager.
- Parameters:
strategy (Union[OnlineStrategy, List[OnlineStrategy]]) – a list of OnlineStrategy
- prepare_signals(prepare_func: ~typing.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() Series | 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={}) Series | 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
DelayTrainerand the delay preparing signals way can bedelay_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 (qlib.model.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.
- class qlib.workflow.online.strategy.RollingStrategy(name_id: str, task_template: dict | List[dict], rolling_gen: RollingGen)
This example strategy always uses the latest rolling model sas online models.
- __init__(name_id: str, task_template: dict | List[dict], rolling_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: 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: 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 = None)
The implementation of OnlineTool based on (R)ecorder.
- __init__(default_exp_name: str | None = None)
Init OnlineToolR.
- Parameters:
default_exp_name (str) – the default experiment name.
- set_online_tag(tag, recorder: Recorder | List)
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: 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: Recorder | List, exp_name: str | None = 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 = 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 = 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: Recorder)
Recorder Model Dataset Loader
- get_dataset(start_time, end_time, segments=None, unprepared_dataset: DatasetH | None = None) 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
unprepared_dataset – Optional[DatasetH] if user don’t want to load dataset from recorder, please specify user’s dataset
- Returns:
the instance of DatasetH
- Return type:
- class qlib.workflow.online.update.RecordUpdater(record: Recorder, *args, **kwargs)
Update a specific recorders
- abstract 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 | None = None, freq='day', fname='pred.pkl', loader_cls: type = <class 'qlib.workflow.online.update.RMDLoader'>)
Dataset-Based Updater
Providing 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 | None = None, freq='day', fname='pred.pkl', loader_cls: type = <class 'qlib.workflow.online.update.RMDLoader'>)
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 If user doesn’t specify this parameter, Updater will try to load dataset to automatically determine the hist_ref
Note
the start_time is not included in the hist_ref; So the hist_ref will be step_len - 1 in most cases
loader_cls – type the class to load the model and dataset
- prepare_data(unprepared_dataset: DatasetH | None = None) DatasetH
Load dataset - if unprepared_dataset is specified, then prepare the dataset directly - Otherwise,
Separating this function will make it easier to reuse the dataset
- Returns:
the instance of DatasetH
- Return type:
- update(dataset: DatasetH | None = None, write: bool = True, ret_new: bool = False) object | None
- Parameters:
dataset (DatasetH) – DatasetH: the instance of DatasetH. None for prepare it again.
write (bool) – will the the write action be executed
ret_new (bool) – will the updated data be returned
- Returns:
the updated dataset
- Return type:
Optional[object]
- abstract get_update_data(dataset: Dataset) 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 | None = None, freq='day', fname='pred.pkl', loader_cls: type = <class 'qlib.workflow.online.update.RMDLoader'>)
Update the prediction in the Recorder
- get_update_data(dataset: Dataset) 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: Recorder, to_date=None, **kwargs)
Update the label in the recorder
Assumption - The label is generated from record_temp.SignalRecord.
- __init__(record: 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 If user doesn’t specify this parameter, Updater will try to load dataset to automatically determine the hist_ref
Note
the start_time is not included in the hist_ref; So the hist_ref will be step_len - 1 in most cases
loader_cls – type the class to load the model and dataset
- get_update_data(dataset: Dataset) 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
- class qlib.utils.serial.Serializable
Serializable will change the behaviors of pickle.
The rule to tell if a attribute will be kept or dropped when dumping. The rule with higher priorities is on the top - in the config attribute list -> always dropped - in the include attribute list -> always kept - in the exclude attribute list -> always dropped - name not starts with _ -> kept - name starts with _ -> kept if dump_all is true else dropped
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
- __init__()
- property dump_all
will the object dump all object
- config(recursive=False, **kwargs)
configure the serializable object
- Parameters:
keys (kwargs may include following) –
- dump_allbool
will the object dump all object
- excludelist
What attribute will not be dumped
- includelist
What attribute will be dumped
recursive (bool) – will the configuration be recursive
- to_pickle(path: Path | str, **kwargs)
Dump self to a pickle file.
path (Union[Path, str]): the path to dump
kwargs may include following keys
- dump_allbool
will the object dump all object
- excludelist
What attribute will not be dumped
- includelist
What attribute will be dumped
- classmethod load(filepath)
Load the serializable class from a filepath.
- Parameters:
filepath (str) – the path of file
- Raises:
TypeError – the pickled file must be type(cls)
- Returns:
the instance of type(cls)
- Return type:
type(cls)
- classmethod get_backend()
Return the real backend of a Serializable class. The pickle_backend value can be “pickle” or “dill”.
- Returns:
pickle or dill module based on pickle_backend
- Return type:
module
- static general_dump(obj, path: Path | str)
A general dumping method for object
- Parameters:
obj (object) – the object to be dumped
path (Union[Path, str]) – the target path the data will be dumped
RL
Base Component
- class qlib.rl.Interpreter
Interpreter is a media between states produced by simulators and states needed by RL policies. Interpreters are two-way:
From simulator state to policy state (aka observation), see
StateInterpreter.From policy action to action accepted by simulator, see
ActionInterpreter.
Inherit one of the two sub-classes to define your own interpreter. This super-class is only used for isinstance check.
Interpreters are recommended to be stateless, meaning that storing temporary information with
self.xxxin interpreter is anti-pattern. In future, we might support register some interpreter-related states by callingself.env.register_state(), but it’s not planned for first iteration.
- class qlib.rl.StateInterpreter(*args, **kwds)
State Interpreter that interpret execution result of qlib executor into rl env state
- validate(obs: ObsType) None
Validate whether an observation belongs to the pre-defined observation space.
- interpret(simulator_state: StateType) ObsType
Interpret the state of simulator.
- Parameters:
simulator_state – Retrieved with
simulator.get_state().- Return type:
State needed by policy. Should conform with the state space defined in
observation_space.
- class qlib.rl.ActionInterpreter(*args, **kwds)
Action Interpreter that interpret rl agent action into qlib orders
- validate(action: PolicyActType) None
Validate whether an action belongs to the pre-defined action space.
- interpret(simulator_state: StateType, action: PolicyActType) ActType
Convert the policy action to simulator action.
- Parameters:
simulator_state – Retrieved with
simulator.get_state().action – Raw action given by policy.
- Return type:
The action needed by simulator,
- class qlib.rl.Reward(*args, **kwds)
Reward calculation component that takes a single argument: state of simulator. Returns a real number: reward.
Subclass should implement
reward(simulator_state)to implement their own reward calculation recipe.- reward(simulator_state: SimulatorState) float
Implement this method for your own reward.
- class qlib.rl.RewardCombination(rewards: Dict[str, Tuple[Reward, float]])
Combination of multiple reward.
- reward(simulator_state: Any) float
Implement this method for your own reward.
- class qlib.rl.Simulator(initial: InitialStateType, **kwargs: Any)
Simulator that resets with
__init__, and transits withstep(action).To make the data-flow clear, we make the following restrictions to Simulator:
The only way to modify the inner status of a simulator is by using
step(action).External modules can read the status of a simulator by using
simulator.get_state(), and check whether the simulator is in the ending state by callingsimulator.done().
A simulator is defined to be bounded with three types:
InitialStateType that is the type of the data used to create the simulator.
StateType that is the type of the status (state) of the simulator.
ActType that is the type of the action, which is the input received in each step.
Different simulators might share the same StateType. For example, when they are dealing with the same task, but with different simulation implementation. With the same type, they can safely share other components in the MDP.
Simulators are ephemeral. The lifecycle of a simulator starts with an initial state, and ends with the trajectory. In another word, when the trajectory ends, simulator is recycled. If simulators want to share context between (e.g., for speed-up purposes), this could be done by accessing the weak reference of environment wrapper.
- env
A reference of env-wrapper, which could be useful in some corner cases. Simulators are discouraged to use this, because it’s prone to induce errors.
- Type:
Optional[EnvWrapper]
- __init__(initial: InitialStateType, **kwargs: Any) None
- step(action: ActType) None
Receives an action of ActType.
Simulator should update its internal state, and return None. The updated state can be retrieved with
simulator.get_state().
- done() bool
Check whether the simulator is in a “done” state. When simulator is in a “done” state, it should no longer receives any
steprequest. As simulators are ephemeral, to reset the simulator, the old one should be destroyed and a new simulator can be created.
Strategy
- class qlib.rl.strategy.SingleOrderStrategy(order: Order, trade_range: TradeRange | None = None)
Strategy used to generate a trade decision with exactly one order.
- __init__(order: Order, trade_range: TradeRange | None = None) None
- Parameters:
outer_trade_decision (BaseTradeDecision, optional) –
the trade decision of outer strategy which this strategy relies, and it will be traded in [start_time, end_time], by default None
If the strategy is used to split trade decision, it will be used
If the strategy is used for portfolio management, it can be ignored
level_infra (LevelInfrastructure, optional) – level shared infrastructure for backtesting, including trade calendar
common_infra (CommonInfrastructure, optional) – common infrastructure for backtesting, including trade_account, trade_exchange, .etc
trade_exchange (Exchange) –
exchange that provides market info, used to deal order and generate report
If trade_exchange is None, self.trade_exchange will be set with common_infra
It allows different trade_exchanges is used in different executions.
For example:
In daily execution, both daily exchange and minutely are usable, but the daily exchange is recommended because it run faster.
In minutely execution, the daily exchange is not usable, only the minutely exchange is recommended.
- generate_trade_decision(execute_result: list | None = None) TradeDecisionWO
Generate trade decision in each trading bar
- Parameters:
execute_result (List[object], optional) –
the executed result for trade decision, by default None
When call the generate_trade_decision firstly, execute_result could be None
Trainer
Train, test, inference utilities.
- class qlib.rl.trainer.Trainer(*, max_iters: int | None = None, val_every_n_iters: int | None = None, loggers: LogWriter | List[LogWriter] | None = None, callbacks: List[Callback] | None = None, finite_env_type: FiniteEnvType = 'subproc', concurrency: int = 2, fast_dev_run: int | None = None)
Utility to train a policy on a particular task.
Different from traditional DL trainer, the iteration of this trainer is “collect”, rather than “epoch”, or “mini-batch”. In each collect,
Collectorcollects a number of policy-env interactions, and accumulates them into a replay buffer. This buffer is used as the “data” to train the policy. At the end of each collect, the policy is updated several times.The API has some resemblence with PyTorch Lightning, but it’s essentially different because this trainer is built for RL applications, and thus most configurations are under RL context. We are still looking for ways to incorporate existing trainer libraries, because it looks like big efforts to build a trainer as powerful as those libraries, and also, that’s not our primary goal.
It’s essentially different tianshou’s built-in trainers, as it’s far much more complicated than that.
- Parameters:
max_iters – Maximum iterations before stopping.
val_every_n_iters – Perform validation every n iterations (i.e., training collects).
logger – Logger to record the backtest results. Logger must be present because without logger, all information will be lost.
finite_env_type – Type of finite env implementation.
concurrency – Parallel workers.
fast_dev_run – Create a subset for debugging. How this is implemented depends on the implementation of training vessel. For
TrainingVessel, if greater than zero, a random subset sizedfast_dev_runwill be used instead oftrain_initial_statesandval_initial_states.
- should_stop: bool
Set to stop the training.
- metrics: dict
Numeric metrics of produced in train/val/test. In the middle of training / validation, metrics will be of the latest episode. When each iteration of training / validation finishes, metrics will be the aggregation of all episodes encountered in this iteration.
Cleared on every new iteration of training.
In fit, validation metrics will be prefixed with
val/.
- current_iter: int
Current iteration (collect) of training.
- __init__(*, max_iters: int | None = None, val_every_n_iters: int | None = None, loggers: LogWriter | List[LogWriter] | None = None, callbacks: List[Callback] | None = None, finite_env_type: FiniteEnvType = 'subproc', concurrency: int = 2, fast_dev_run: int | None = None)
- initialize()
Initialize the whole training process.
The states here should be synchronized with state_dict.
- initialize_iter()
Initialize one iteration / collect.
- state_dict() dict
Putting every states of current training into a dict, at best effort.
It doesn’t try to handle all the possible kinds of states in the middle of one training collect. For most cases at the end of each iteration, things should be usually correct.
Note that it’s also intended behavior that replay buffer data in the collector will be lost.
- load_state_dict(state_dict: dict) None
Load all states into current trainer.
- named_callbacks() Dict[str, Callback]
Retrieve a collection of callbacks where each one has a name. Useful when saving checkpoints.
- named_loggers() Dict[str, LogWriter]
Retrieve a collection of loggers where each one has a name. Useful when saving checkpoints.
- fit(vessel: TrainingVesselBase, ckpt_path: Path | None = None) None
Train the RL policy upon the defined simulator.
- Parameters:
vessel – A bundle of all elements used in training.
ckpt_path – Load a pre-trained / paused training checkpoint.
- test(vessel: TrainingVesselBase) None
Test the RL policy against the simulator.
The simulator will be fed with data generated in
test_seed_iterator.- Parameters:
vessel – A bundle of all related elements.
- venv_from_iterator(iterator: Iterable[InitialStateType]) FiniteVectorEnv
Create a vectorized environment from iterator and the training vessel.
- class qlib.rl.trainer.TrainingVessel(*, simulator_fn: Callable[[InitialStateType], Simulator[InitialStateType, StateType, ActType]], state_interpreter: StateInterpreter[StateType, ObsType], action_interpreter: ActionInterpreter[StateType, PolicyActType, ActType], policy: BasePolicy, reward: Reward, train_initial_states: Sequence[InitialStateType] | None = None, val_initial_states: Sequence[InitialStateType] | None = None, test_initial_states: Sequence[InitialStateType] | None = None, buffer_size: int = 20000, episode_per_iter: int = 1000, update_kwargs: Dict[str, Any] = None)
The default implementation of training vessel.
__init__accepts a sequence of initial states so that iterator can be created.train,validate,testeach do one collect (and also update in train). By default, the train initial states will be repeated infinitely during training, and collector will control the number of episodes for each iteration. In validation and testing, the val / test initial states will be used exactly once.Extra hyper-parameters (only used in train) include:
buffer_size: Size of replay buffer.episode_per_iter: Episodes per collect at training. Can be overridden by fast dev run.update_kwargs: Keyword arguments appearing inpolicy.update. For example,dict(repeat=10, batch_size=64).
- __init__(*, simulator_fn: Callable[[InitialStateType], Simulator[InitialStateType, StateType, ActType]], state_interpreter: StateInterpreter[StateType, ObsType], action_interpreter: ActionInterpreter[StateType, PolicyActType, ActType], policy: BasePolicy, reward: Reward, train_initial_states: Sequence[InitialStateType] | None = None, val_initial_states: Sequence[InitialStateType] | None = None, test_initial_states: Sequence[InitialStateType] | None = None, buffer_size: int = 20000, episode_per_iter: int = 1000, update_kwargs: Dict[str, Any] = None)
- train_seed_iterator() ContextManager[Iterable[InitialStateType]] | Iterable[InitialStateType]
Override this to create a seed iterator for training. If the iterable is a context manager, the whole training will be invoked in the with-block, and the iterator will be automatically closed after the training is done.
- val_seed_iterator() ContextManager[Iterable[InitialStateType]] | Iterable[InitialStateType]
Override this to create a seed iterator for validation.
- test_seed_iterator() ContextManager[Iterable[InitialStateType]] | Iterable[InitialStateType]
Override this to create a seed iterator for testing.
- train(vector_env: FiniteVectorEnv) Dict[str, Any]
Create a collector and collects
episode_per_iterepisodes. Update the policy on the collected replay buffer.
- validate(vector_env: FiniteVectorEnv) Dict[str, Any]
Implement this to validate the policy once.
- test(vector_env: FiniteVectorEnv) Dict[str, Any]
Implement this to evaluate the policy on test environment once.
- class qlib.rl.trainer.TrainingVesselBase(*args, **kwds)
A ship that contains simulator, interpreter, and policy, will be sent to trainer. This class controls algorithm-related parts of training, while trainer is responsible for runtime part.
The ship also defines the most important logic of the core training part, and (optionally) some callbacks to insert customized logics at specific events.
- train_seed_iterator() ContextManager[Iterable[InitialStateType]] | Iterable[InitialStateType]
Override this to create a seed iterator for training. If the iterable is a context manager, the whole training will be invoked in the with-block, and the iterator will be automatically closed after the training is done.
- val_seed_iterator() ContextManager[Iterable[InitialStateType]] | Iterable[InitialStateType]
Override this to create a seed iterator for validation.
- test_seed_iterator() ContextManager[Iterable[InitialStateType]] | Iterable[InitialStateType]
Override this to create a seed iterator for testing.
- train(vector_env: BaseVectorEnv) Dict[str, Any]
Implement this to train one iteration. In RL, one iteration usually refers to one collect.
- validate(vector_env: FiniteVectorEnv) Dict[str, Any]
Implement this to validate the policy once.
- test(vector_env: FiniteVectorEnv) Dict[str, Any]
Implement this to evaluate the policy on test environment once.
- state_dict() Dict
Return a checkpoint of current vessel state.
- load_state_dict(state_dict: Dict) None
Restore a checkpoint from a previously saved state dict.
- class qlib.rl.trainer.Checkpoint(dirpath: Path,