API Reference¶
Here you can find all Qlib
interfaces.
Data¶
Provider¶
-
class
qlib.data.data.
CalendarProvider
¶ Calendar provider base class
Provide calendar data.
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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
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locate_index
(start_time, end_time, freq, future)¶ Locate the start time index and end time index in a calendar under certain frequency.
Parameters: - start_time (str) – start of the time range.
- end_time (str) – end of the time range.
- freq (str) – time frequency, available: year/quarter/month/week/day.
- future (bool) – whether including future trading day.
Returns: - pd.Timestamp – the real start time.
- pd.Timestamp – the real end time.
- int – the index of start time.
- int – the index of end time.
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-
class
qlib.data.data.
InstrumentProvider
¶ Instrument provider base class
Provide instrument data.
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static
instruments
(market='all', filter_pipe=None)¶ Get the general config dictionary for a base market adding several dynamic filters.
Parameters: - market (str) – market/industry/index shortname, e.g. all/sse/szse/sse50/csi300/csi500.
- filter_pipe (list) – the list of dynamic filters.
Returns: dict of stockpool config. {`market`=>base market name, `filter_pipe`=>list of filters}
example :
Return type: dict
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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
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static
-
class
qlib.data.data.
FeatureProvider
¶ Feature provider class
Provide feature data.
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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
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-
class
qlib.data.data.
ExpressionProvider
¶ Expression provider class
Provide Expression data.
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expression
(instrument, field, start_time=None, end_time=None, freq='day')¶ Get Expression data.
Parameters: - instrument (str) – a certain instrument.
- field (str) – a certain field of feature.
- start_time (str) – start of the time range.
- end_time (str) – end of the time range.
- freq (str) – time frequency, available: year/quarter/month/week/day.
Returns: data of a certain expression
Return type: pd.Series
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-
class
qlib.data.data.
DatasetProvider
¶ Dataset provider class
Provide Dataset data.
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dataset
(instruments, fields, start_time=None, end_time=None, freq='day')¶ 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.
Returns: a pandas dataframe with <instrument, datetime> index.
Return type: pd.DataFrame
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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.
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static
get_column_names
(fields)¶ Get column names from input fields
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static
dataset_processor
(instruments_d, column_names, start_time, end_time, freq)¶ Load and process the data, return the data set. - default using multi-kernel method.
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static
expression_calculator
(inst, start_time, end_time, freq, column_names, spans=None, g_config=None)¶ 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.
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-
class
qlib.data.data.
LocalCalendarProvider
(**kwargs)¶ Local calendar data provider class
Provide calendar data from local data source.
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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
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-
class
qlib.data.data.
LocalInstrumentProvider
¶ Local instrument data provider class
Provide instrument data from local data source.
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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
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-
class
qlib.data.data.
LocalFeatureProvider
(**kwargs)¶ Local feature data provider class
Provide feature data from local data source.
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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
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-
class
qlib.data.data.
LocalExpressionProvider
¶ Local expression data provider class
Provide expression data from local data source.
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expression
(instrument, field, start_time=None, end_time=None, freq='day')¶ Get Expression data.
Parameters: - instrument (str) – a certain instrument.
- field (str) – a certain field of feature.
- start_time (str) – start of the time range.
- end_time (str) – end of the time range.
- freq (str) – time frequency, available: year/quarter/month/week/day.
Returns: data of a certain expression
Return type: pd.Series
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-
class
qlib.data.data.
LocalDatasetProvider
¶ Local dataset data provider class
Provide dataset data from local data source.
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dataset
(instruments, fields, start_time=None, end_time=None, freq='day')¶ 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.
Returns: a pandas dataframe with <instrument, datetime> index.
Return type: pd.DataFrame
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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.
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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.
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-
class
qlib.data.data.
ClientCalendarProvider
¶ Client calendar data provider class
Provide calendar data by requesting data from server as a client.
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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
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-
class
qlib.data.data.
ClientInstrumentProvider
¶ Client instrument data provider class
Provide instrument data by requesting data from server as a client.
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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
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-
class
qlib.data.data.
ClientDatasetProvider
¶ Client dataset data provider class
Provide dataset data by requesting data from server as a client.
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dataset
(instruments, fields, start_time=None, end_time=None, freq='day', disk_cache=0, return_uri=False)¶ 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.
Returns: a pandas dataframe with <instrument, datetime> index.
Return type: pd.DataFrame
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-
class
qlib.data.data.
BaseProvider
¶ Local provider class
To keep compatible with old qlib provider.
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features
(instruments, fields, start_time=None, end_time=None, freq='day', disk_cache=None)¶ - 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.
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-
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 –
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-
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 reponse 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.
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qlib.data.data.
register_all_wrappers
()¶
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
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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
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static
-
class
qlib.data.filter.
SeriesDFilter
(fstart_time=None, fend_time=None)¶ Dynamic Instruments Filter Abstract class to filter a series of certain features
Filters should provide parameters:
- filter start time
- filter end time
- filter rule
Override __init__ to assign a certain rule to filter the series.
Override _getFilterSeries to use the rule to filter the series and get a dict of {inst => series}, or override filter_main for more advanced series filter rule
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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
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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.
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static
from_config
(config)¶ Construct an instance from config dict.
Parameters: config (dict) – dict of config parameters.
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to_config
()¶ Construct an instance from config dict.
Returns: return the dict of config parameters. Return type: dict
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static
-
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’
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from_config
()¶ Construct an instance from config dict.
Parameters: config (dict) – dict of config parameters.
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to_config
()¶ Construct an instance from config dict.
Returns: return the dict of config parameters. Return type: dict
Class¶
-
class
qlib.data.base.
Expression
¶ Expression base class
-
load
(instrument, start_index, end_index, freq)¶ load feature
Parameters: - instrument (str) – instrument code.
- start_index (str) – feature start index [in calendar].
- end_index (str) – feature end index [in calendar].
- freq (str) – feature frequency.
Returns: feature series: The index of the series is the calendar index
Return type: pd.Series
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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($close, -1), 1) can not be handled rightly.
So this will only used for detecting the length of historical data needed.
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get_extended_window_size
()¶ get_extend_window_size
For to calculate this Operator in range[start_index, end_index] We have to get the leaf feature in range[start_index - lft_etd, end_index + rght_etd].
Returns: lft_etd, rght_etd Return type: (int, int)
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-
class
qlib.data.base.
Feature
(name=None)¶ Static Expression
This kind of feature will load data from provider
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get_longest_back_rolling
()¶ Get the longest length of historical data the feature has accessed
This is designed for getting the needed range of the data to calculate the features in specific range at first. However, situations like Ref(Ref($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)
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-
class
qlib.data.base.
ExpressionOps
¶ Operator Expression
This kind of feature will use operator for feature construction on the fly.
Operator¶
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class
qlib.data.ops.
Abs
(feature)¶ Feature Absolute Value
Parameters: feature (Expression) – feature instance Returns: a feature instance with absolute output Return type: Expression
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class
qlib.data.ops.
Sign
(feature)¶ Feature Sign
Parameters: feature (Expression) – feature instance Returns: a feature instance with sign Return type: Expression
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class
qlib.data.ops.
Log
(feature)¶ Feature Log
Parameters: feature (Expression) – feature instance Returns: a feature instance with log Return type: Expression
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class
qlib.data.ops.
Power
(feature, exponent)¶ Feature Power
Parameters: feature (Expression) – feature instance Returns: a feature instance with power Return type: Expression
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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:
-
class
qlib.data.ops.
Not
(feature)¶ Not Operator
Parameters: - feature_left (Expression) – feature instance
- feature_right (Expression) – feature instance
Returns: feature elementwise not output
Return type:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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
-
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($close, -1), 1) can not be handled rightly.
So this will only used for detecting the length of historical data needed.
-
get_extended_window_size
()¶ get_extend_window_size
For to calculate this Operator in range[start_index, end_index] We have to get the leaf feature in range[start_index - lft_etd, end_index + rght_etd].
Returns: lft_etd, rght_etd Return type: (int, int)
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class
qlib.data.ops.
Ref
(feature, N)¶ Feature Reference
Parameters: - feature (Expression) – feature instance
- N (int) – N = 0, retrieve the first data; N > 0, retrieve data of N periods ago; N < 0, future data
Returns: a feature instance with target reference
Return type: -
get_longest_back_rolling
()¶ Get the longest length of historical data the feature has accessed
This is designed for getting the needed range of the data to calculate the features in specific range at first. However, situations like Ref(Ref($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)
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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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
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:
-
class
qlib.data.ops.
Slope
(feature, N)¶ Rolling Slope
Parameters: - feature (Expression) – feature instance
- N (int) – rolling window size
Returns: a feature instance with regression slope of given window
Return type:
-
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 regression r-value square of given window
Return type:
-
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:
-
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:
-
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:
-
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:
-
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:
Cache¶
-
class
qlib.data.cache.
MemCacheUnit
(*args, **kwargs)¶ Memory Cache Unit.
-
class
qlib.data.cache.
MemCache
(mem_cache_size_limit=None, limit_type='length')¶ Memory cache.
-
class
qlib.data.cache.
ExpressionCache
(provider)¶ Expression cache mechanism base class.
This class is used to wrap expression provider with self-defined expression cache mechanism.
Note
Override the _uri and _expression method to create your own expression cache mechanism.
-
expression
(instrument, field, start_time, end_time, freq)¶ Get expression data.
Note
Same interface as expression method in expression provider
-
update
(cache_uri)¶ Update expression cache to latest calendar.
Overide this method to define how to update expression cache corresponding to users’ own cache mechanism.
Parameters: cache_uri (str) – the complete uri of expression cache file (include dir path). Returns: 0(successful update)/ 1(no need to update)/ 2(update failure). Return type: int
-
-
class
qlib.data.cache.
DatasetCache
(provider)¶ Dataset cache mechanism base class.
This class is used to wrap dataset provider with self-defined dataset cache mechanism.
Note
Override the _uri and _dataset method to create your own dataset cache mechanism.
-
dataset
(instruments, fields, start_time=None, end_time=None, freq='day', disk_cache=1)¶ Get feature dataset.
Note
Same interface as dataset method in dataset provider
Note
The server use redis_lock to make sure read-write conflicts will not be triggered
but client readers are not considered.
-
update
(cache_uri)¶ Update dataset cache to latest calendar.
Overide this method to define how to update dataset cache corresponding to users’ own cache mechanism.
Parameters: cache_uri (str) – the complete uri of dataset cache file (include dir path). Returns: 0(successful update)/ 1(no need to update)/ 2(update failure) Return type: int
-
static
cache_to_origin_data
(data, fields)¶ cache data to origin data
Parameters: - data – pd.DataFrame, cache data.
- fields – feature fields.
Returns: pd.DataFrame.
-
static
normalize_uri_args
(instruments, fields, freq)¶ normalize uri args
-
-
class
qlib.data.cache.
DiskExpressionCache
(provider, **kwargs)¶ Prepared cache mechanism for server.
-
gen_expression_cache
(expression_data, cache_path, instrument, field, freq, last_update)¶ use bin file to save like feature-data.
-
update
(sid, cache_uri)¶ Update expression cache to latest calendar.
Overide this method to define how to update expression cache corresponding to users’ own cache mechanism.
Parameters: cache_uri (str) – the complete uri of expression cache file (include dir path). Returns: 0(successful update)/ 1(no need to update)/ 2(update failure). Return type: int
-
-
class
qlib.data.cache.
DiskDatasetCache
(provider, **kwargs)¶ Prepared cache mechanism for server.
-
classmethod
read_data_from_cache
(cache_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)¶ The lock is not considered in the class. Please consider the lock outside the code. This class is the proxy of the disk data.
-
gen_dataset_cache
(cache_path, instruments, fields, freq)¶ Note
This function does not consider the cache read write lock. Please
Aquire the lock outside this function
The format the cache contains 3 parts(followed by typical filename).
index : cache/d41366901e25de3ec47297f12e2ba11d.index
The content of the file may be in following format(pandas.Series)
start end 1999-11-10 00:00:00 0 1 1999-11-11 00:00:00 1 2 1999-11-12 00:00:00 2 3 ...
Note
The start is closed. The end is open!!!!!
- Each line contains two element <timestamp, end_index>
- It indicates the end_index 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.
:return type pd.DataFrame; The fields of the returned DataFrame are consistent with the parameters of the function.
-
update
(cache_uri)¶ Update dataset cache to latest calendar.
Overide this method to define how to update dataset cache corresponding to users’ own cache mechanism.
Parameters: cache_uri (str) – the complete uri of dataset cache file (include dir path). Returns: 0(successful update)/ 1(no need to update)/ 2(update failure) Return type: int
-
classmethod
Dataset¶
Dataset Class¶
-
class
qlib.data.dataset.__init__.
Dataset
(*args, **kwargs)¶ Preparing data for model training and inferencing.
-
setup_data
(*args, **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(Note the init function is skiped).
- User call setup_data to load new data.
- User prepare data for model based on previous status.
-
prepare
(*args, **kwargs) → object¶ The type of dataset depends on the model. (It could be pd.DataFrame, pytorch.DataLoader, etc.) The parameters should specify the scope for the prepared data The method should: - process the data
- return the processed data
Returns: return the object Return type: object
-
-
class
qlib.data.dataset.__init__.
DatasetH
(handler: Union[dict, qlib.data.dataset.handler.DataHandler], segments: list)¶ 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.
-
setup_data
(handler: Union[dict, qlib.data.dataset.handler.DataHandler], segments: list)¶ Setup the underlying data.
Parameters: - handler (Union[dict, DataHandler]) –
handler could be:
- insntance of DataHandler
- config of DataHandler. Please refer to DataHandler
- segments (list) – Describe the options to segment the data. Here are some examples:
- handler (Union[dict, DataHandler]) –
-
prepare
(segments: Union[List[str], Tuple[str], str, slice], col_set='__all', data_key='infer', **kwargs) → Union[List[pandas.core.frame.DataFrame], pandas.core.frame.DataFrame]¶ Prepare the data for learning and inference.
Parameters: - segments (Union[List[str], Tuple[str], str, slice]) –
Describe the scope of the data to be prepared Here are some examples:
- ’train’
- [‘train’, ‘valid’]
- col_set (str) – The col_set will be passed to self._handler when fetching data.
- data_key (str) – The data to fetch: DK_* Default is DK_I, which indicate fetching data for inference.
Returns: Return type: Union[List[pd.DataFrame], pd.DataFrame]
Raises: NotImplementedError:
- segments (Union[List[str], Tuple[str], str, slice]) –
-
class
qlib.data.dataset.__init__.
TSDataSampler
(data: pandas.core.frame.DataFrame, start, end, step_len: int, fillna_type: str = 'none')¶ (T)ime-(S)eries DataSampler This is the result of TSDatasetH
It works like torch.data.utils.Dataset, it provides a very convient interface for constructing time-series dataset based on tabular data.
If user have further requirements for processing data, user could process them based on TSDataSampler or create more powerful subclasses.
Known Issues: - For performance issues, this Sampler will convert dataframe into arrays for better performance. This could result
in a different data type-
get_index
()¶ Get the pandas index of the data, it will be useful in following scenarios - Special sampler will be used (e.g. user want to sample day by day)
-
static
build_index
(data: pandas.core.frame.DataFrame) → dict¶ The relation of the data
Parameters: data (pd.DataFrame) – The dataframe with <datetime, DataFrame> Returns: {<index>: <prev_index or None>} # get the previous index of a line given index Return type: dict
-
-
class
qlib.data.dataset.__init__.
TSDatasetH
(step_len=30, *args, **kwargs)¶ (T)ime-(S)eries Dataset (H)andler
Covnert the tabular data to Time-Series data
Requirements analysis
The typical workflow of a user to get time-series data for an sample - process features - slice proper data from data handler: dimension of sample <feature, > - Build relation of samples by <time, instrument> index
- Be able to sample times series of data <timestep, feature>
- It will be better if the interface is like “torch.utils.data.Dataset”
- User could build customized batch based on the data
- The dimension of a batch of data <batch_idx, feature, timestep>
-
setup_data
(*args, **kwargs)¶ Setup the underlying data.
Parameters: - handler (Union[dict, DataHandler]) –
handler could be:
- insntance of DataHandler
- config of DataHandler. Please refer to DataHandler
- segments (list) – Describe the options to segment the data. Here are some examples:
- handler (Union[dict, DataHandler]) –
Data Loader¶
-
class
qlib.data.dataset.loader.
DataLoader
¶ DataLoader is designed for loading raw data from original data source.
-
load
(instruments, start_time=None, end_time=None) → pandas.core.frame.DataFrame¶ load the data as pd.DataFrame.
Example of the data (The multi-index of the columns is optional.):
feature label $close $volume Ref($close, 1) Mean($close, 3) $high-$low LABEL0 datetime instrument 2010-01-04 SH600000 81.807068 17145150.0 83.737389 83.016739 2.741058 0.0032 SH600004 13.313329 11800983.0 13.313329 13.317701 0.183632 0.0042 SH600005 37.796539 12231662.0 38.258602 37.919757 0.970325 0.0289
Parameters: - instruments (str or dict) – it can either be the market name or the config file of instruments generated by InstrumentProvider.
- start_time (str) – start of the time range.
- end_time (str) – end of the time range.
Returns: data load from the under layer source
Return type: pd.DataFrame
-
-
class
qlib.data.dataset.loader.
DLWParser
(config: Tuple[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.
-
load_group_df
(instruments, exprs: list, names: list, start_time=None, end_time=None) → pandas.core.frame.DataFrame¶ load the dataframe for specific group
Parameters: - instruments – the instruments.
- exprs (list) – the expressions to describe the content of the data.
- names (list) – the name of the data.
Returns: the queried dataframe.
Return type: pd.DataFrame
-
load
(instruments=None, start_time=None, end_time=None) → pandas.core.frame.DataFrame¶ load the data as pd.DataFrame.
Example of the data (The multi-index of the columns is optional.):
feature label $close $volume Ref($close, 1) Mean($close, 3) $high-$low LABEL0 datetime instrument 2010-01-04 SH600000 81.807068 17145150.0 83.737389 83.016739 2.741058 0.0032 SH600004 13.313329 11800983.0 13.313329 13.317701 0.183632 0.0042 SH600005 37.796539 12231662.0 38.258602 37.919757 0.970325 0.0289
Parameters: - instruments (str or dict) – it can either be the market name or the config file of instruments generated by InstrumentProvider.
- start_time (str) – start of the time range.
- end_time (str) – end of the time range.
Returns: data load from the under layer source
Return type: pd.DataFrame
-
-
class
qlib.data.dataset.loader.
QlibDataLoader
(config: Tuple[list, tuple, dict], filter_pipe=None)¶ Same as QlibDataLoader. The fields can be define by config
-
load_group_df
(instruments, exprs: list, names: list, start_time=None, end_time=None) → pandas.core.frame.DataFrame¶ load the dataframe for specific group
Parameters: - instruments – the instruments.
- exprs (list) – the expressions to describe the content of the data.
- names (list) – the name of the data.
Returns: the queried dataframe.
Return type: pd.DataFrame
-
-
class
qlib.data.dataset.loader.
StaticDataLoader
(config: dict, join='outer')¶ DataLoader that supports loading data from file or as provided.
-
load
(instruments=None, start_time=None, end_time=None) → pandas.core.frame.DataFrame¶ load the data as pd.DataFrame.
Example of the data (The multi-index of the columns is optional.):
feature label $close $volume Ref($close, 1) Mean($close, 3) $high-$low LABEL0 datetime instrument 2010-01-04 SH600000 81.807068 17145150.0 83.737389 83.016739 2.741058 0.0032 SH600004 13.313329 11800983.0 13.313329 13.317701 0.183632 0.0042 SH600005 37.796539 12231662.0 38.258602 37.919757 0.970325 0.0289
Parameters: - instruments (str or dict) – it can either be the market name or the config file of instruments generated by InstrumentProvider.
- start_time (str) – start of the time range.
- end_time (str) – end of the time range.
Returns: data load from the under layer source
Return type: pd.DataFrame
-
Data Handler¶
-
class
qlib.data.dataset.handler.
DataHandler
(instruments=None, start_time=None, end_time=None, data_loader: Tuple[dict, str, qlib.data.dataset.loader.DataLoader] = None, init_data=True, fetch_orig=True)¶ The steps to using a handler 1. initialized data handler (call by init). 2. use the data.
The data handler try to maintain a handler with 2 level. datetime & instruments.
Any order of the index level can be suported(The order will implied in the data). The order <datetime, instruments> will be used when the dataframe index name is missed.
Example of the data: The multi-index of the columns is optional.
feature label $close $volume Ref($close, 1) Mean($close, 3) $high-$low LABEL0 datetime instrument 2010-01-04 SH600000 81.807068 17145150.0 83.737389 83.016739 2.741058 0.0032 SH600004 13.313329 11800983.0 13.313329 13.317701 0.183632 0.0042 SH600005 37.796539 12231662.0 38.258602 37.919757 0.970325 0.0289
-
init
(enable_cache: bool = True)¶ initialize the data. In case of running intialization for multiple time, it will do nothing for the second 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
- if enable_cache == True:
-
fetch
(selector: Union[pandas._libs.tslibs.timestamps.Timestamp, slice, str] = slice(None, None, None), level: Union[str, int] = 'datetime', col_set: Union[str, List[str]] = '__all', squeeze: bool = False) → pandas.core.frame.DataFrame¶ fetch data from underlying data source
Parameters: - selector (Union[pd.Timestamp, slice, str]) – describe how to select data by index
- level (Union[str, int]) – which index level to select the data
- col_set (Union[str, List[str]]) –
- if isinstance(col_set, str):select a set of meaningful columns.(e.g. features, columns)
- if cal_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
- if isinstance(col_set, str):
- squeeze (bool) – whether squeeze columns and index
Returns: Return type: pd.DataFrame.
-
get_cols
(col_set='__all') → list¶ get the column names
Parameters: col_set (str) – select a set of meaningful columns.(e.g. features, columns) Returns: list of column names Return type: list
-
get_range_selector
(cur_date: Union[pandas._libs.tslibs.timestamps.Timestamp, str], periods: int) → slice¶ get range selector by number of periods
Parameters: - cur_date (pd.Timestamp or str) – current date
- periods (int) – number of periods
-
get_range_iterator
(periods: int, min_periods: Optional[int] = None, **kwargs) → Iterator[Tuple[pandas._libs.tslibs.timestamps.Timestamp, pandas.core.frame.DataFrame]]¶ get a iterator of sliced data with given periods
Parameters: - periods (int) – number of periods.
- min_periods (int) – minimum periods for sliced dataframe.
- kwargs (dict) – will be passed to self.fetch.
-
-
class
qlib.data.dataset.handler.
DataHandlerLP
(instruments=None, start_time=None, end_time=None, data_loader: Tuple[dict, str, qlib.data.dataset.loader.DataLoader] = None, infer_processors=[], learn_processors=[], process_type='append', **kwargs)¶ DataHandler with (L)earnable (P)rocessor
-
fit_process_data
()¶ fit and process data
The input of the fit will be the output of the previous processor
-
process_data
(with_fit: bool = False)¶ process_data data. Fun processor.fit if necessary
Parameters: with_fit (bool) – The input of the fit will be the output of the previous processor
-
init
(init_type: str = 'fit_seq', enable_cache: bool = False)¶ Initialize the data of Qlib
Parameters: - init_type (str) – The type IT_* listed above.
- enable_cache (bool) –
default value is false:
- if enable_cache == True:the processed data will be saved on disk, and handler will load the cached data from the disk directly when we call init next time
- if enable_cache == True:
-
fetch
(selector: Union[pandas._libs.tslibs.timestamps.Timestamp, slice, str] = slice(None, None, None), level: Union[str, int] = 'datetime', col_set='__all', data_key: str = 'infer') → pandas.core.frame.DataFrame¶ fetch data from underlying data source
Parameters: - selector (Union[pd.Timestamp, slice, str]) – describe how to select data by index.
- level (Union[str, int]) – which index level to select the data.
- col_set (str) – select a set of meaningful columns.(e.g. features, columns).
- data_key (str) – the data to fetch: DK_*.
Returns: Return type: pd.DataFrame
-
get_cols
(col_set='__all', data_key: str = 'infer') → list¶ get the column names
Parameters: - col_set (str) – select a set of meaningful columns.(e.g. features, columns).
- data_key (str) – the data to fetch: DK_*.
Returns: list of column names
Return type: list
-
Processor¶
-
qlib.data.dataset.processor.
get_group_columns
(df: pandas.core.frame.DataFrame, group: str)¶ get a group of columns from multi-index columns DataFrame
Parameters: - df (pd.DataFrame) – with multi of columns.
- group (str) – the name of the feature group, i.e. the first level value of the group index.
-
class
qlib.data.dataset.processor.
Processor
¶ -
fit
(df: pandas.core.frame.DataFrame = None)¶ learn data processing parameters
Parameters: df (pd.DataFrame) – When we fit and process data with processor one by one. The fit function reiles on the output of previous processor, i.e. df.
-
is_for_infer
() → bool¶ Is this processor usable for inference Some processors are not usable for inference.
Returns: if it is usable for infenrece. Return type: bool
-
-
class
qlib.data.dataset.processor.
DropnaProcessor
(fields_group=None)¶
-
class
qlib.data.dataset.processor.
DropnaLabel
(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=[])¶
-
class
qlib.data.dataset.processor.
FilterCol
(fields_group='feature', col_list=[])¶
-
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
-
class
qlib.data.dataset.processor.
MinMaxNorm
(fit_start_time, fit_end_time, fields_group=None)¶ -
fit
(df)¶ learn data processing parameters
Parameters: df (pd.DataFrame) – When we fit and process data with processor one by one. The fit function reiles on the output of previous processor, i.e. df.
-
-
class
qlib.data.dataset.processor.
ZScoreNorm
(fit_start_time, fit_end_time, fields_group=None)¶ ZScore Normalization
-
fit
(df)¶ learn data processing parameters
Parameters: df (pd.DataFrame) – When we fit and process data with processor one by one. The fit function reiles on the output of previous processor, i.e. df.
-
-
class
qlib.data.dataset.processor.
RobustZScoreNorm
(fit_start_time, fit_end_time, fields_group=None, clip_outlier=True)¶ Robust ZScore Normalization
- Use robust statistics for Z-Score normalization:
- mean(x) = median(x) std(x) = MAD(x) * 1.4826
- Reference:
- https://en.wikipedia.org/wiki/Median_absolute_deviation.
-
fit
(df)¶ learn data processing parameters
Parameters: df (pd.DataFrame) – When we fit and process data with processor one by one. The fit function reiles on the output of previous processor, i.e. df.
-
class
qlib.data.dataset.processor.
CSZScoreNorm
(fields_group=None)¶ Cross Sectional ZScore Normalization
-
class
qlib.data.dataset.processor.
CSRankNorm
(fields_group=None)¶ Cross Sectional Rank Normalization
-
class
qlib.data.dataset.processor.
CSZFillna
(fields_group=None)¶ Cross Sectional Fill Nan
Contrib¶
Model¶
-
class
qlib.model.base.
BaseModel
¶ Modeling things
-
predict
(*args, **kwargs) → object¶ Make predictions after modeling things
-
-
class
qlib.model.base.
Model
¶ Learnable Models
-
fit
(dataset: qlib.data.dataset.Dataset)¶ Learn model from the base model
Note
The attribute names of learned model should not start with ‘_’. So that the model could be dumped to disk.
Parameters: - dataset (Dataset) – dataset will generate the processed data from model training.
- following code example shows how to retrieve x_train, y_train and w_train from the dataset (The) –
# 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)
-
-
class
qlib.model.base.
ModelFT
¶ Model (F)ine(t)unable
-
finetune
(dataset: qlib.data.dataset.Dataset)¶ finetune model based given dataset
A typical use case of finetuning model with qlib.workflow.R
# start exp to train init model with R.start(experiment_name="init models"): model.fit(dataset) R.save_objects(init_model=model) rid = R.get_recorder().id # Finetune model based on previous trained model with R.start(experiment_name="finetune model"): recorder = R.get_recorder(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.strategy.
StrategyWrapper
(inner_strategy)¶ StrategyWrapper is a wrapper of another strategy. By overriding some methods to make some changes on the basic strategy Cost control and risk control will base on this class.
-
class
qlib.contrib.strategy.strategy.
AdjustTimer
¶ Responsible for timing of position adjusting
This is designed as multiple inheritance mechanism due to: - the is_adjust may need access to the internel state of a strategy.
- it can be reguard as a enhancement to the existing strategy.
-
is_adjust
(trade_date)¶ Return if the strategy can adjust positions on trade_date Will normally be used in strategy do trading with trade frequency
-
class
qlib.contrib.strategy.strategy.
ListAdjustTimer
(adjust_dates=None)¶ -
is_adjust
(trade_date)¶ Return if the strategy can adjust positions on trade_date Will normally be used in strategy do trading with trade frequency
-
-
class
qlib.contrib.strategy.strategy.
WeightStrategyBase
(order_generator_cls_or_obj=<class 'qlib.contrib.strategy.order_generator.OrderGenWInteract'>, *args, **kwargs)¶ -
generate_target_weight_position
(score, current, trade_date)¶ 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_exchange (Exchange()) –
- trade_date (pd.Timestamp) – trade date.
-
generate_order_list
(score_series, current, trade_exchange, pred_date, trade_date)¶ Parameters: - score_series (pd.Seires) – stock_id , score.
- current (Position()) – current of account.
- trade_exchange (Exchange()) – exchange.
- trade_date (pd.Timestamp) – date.
-
-
class
qlib.contrib.strategy.strategy.
TopkDropoutStrategy
(topk, n_drop, method_sell='bottom', method_buy='top', risk_degree=0.95, thresh=1, hold_thresh=1, only_tradable=False, **kwargs)¶ -
get_risk_degree
(date)¶ Return the proportion of your total value you will used in investment. Dynamically risk_degree will result in Market timing.
-
generate_order_list
(score_series, current, trade_exchange, pred_date, trade_date)¶ Gnererate order list according to score_series at trade_date, will not change current.
Parameters: - score_series (pd.Series) – stock_id , score.
- current (Position()) – current of account.
- trade_exchange (Exchange()) – exchange.
- pred_date (pd.Timestamp) – predict date.
- trade_date (pd.Timestamp) – trade date.
-
Evaluate¶
-
qlib.contrib.evaluate.
risk_analysis
(r, N=252)¶ Risk Analysis
Parameters: - r (pandas.Series) – daily return series.
- N (int) – scaler for annualizing information_ratio (day: 250, week: 50, month: 12).
-
qlib.contrib.evaluate.
get_strategy
(strategy=None, topk=50, margin=0.5, n_drop=5, risk_degree=0.95, str_type='amount', adjust_dates=None)¶ Parameters: - strategy (Strategy()) – strategy used in backtest.
- topk (int (Default value: 50)) – top-N stocks to buy.
- margin (int or float(Default value: 0.5)) –
- if isinstance(margin, int):sell_limit = margin
- else:sell_limit = pred_in_a_day.count() * margin
buffer margin, in single score_mode, continue holding stock if it is in nlargest(sell_limit). sell_limit should be no less than topk.
- if isinstance(margin, int):
- n_drop (int) – number of stocks to be replaced in each trading date.
- risk_degree (float) – 0-1, 0.95 for example, use 95% money to trade.
- str_type ('amount', 'weight' or 'dropout') – strategy type: TopkAmountStrategy ,TopkWeightStrategy or TopkDropoutStrategy.
Returns: - class: Strategy
- an initialized strategy object
-
qlib.contrib.evaluate.
get_exchange
(pred, exchange=None, subscribe_fields=[], open_cost=0.0015, close_cost=0.0025, min_cost=5.0, trade_unit=None, limit_threshold=None, deal_price=None, extract_codes=False, shift=1)¶ Parameters: - exchange related arguments (#) –
- exchange (Exchange()) –
- subscribe_fields (list) – subscribe fields.
- open_cost (float) – open transaction cost.
- close_cost (float) – close transaction cost.
- min_cost (float) – min transaction cost.
- trade_unit (int) – 100 for China A.
- deal_price (str) – dealing price type: ‘close’, ‘open’, ‘vwap’.
- limit_threshold (float) – limit move 0.1 (10%) for example, long and short with same limit.
- extract_codes (bool) – will we pass the codes extracted from the pred to the exchange. NOTE: This will be faster with offline qlib.
Returns: - class: Exchange
- an initialized Exchange object
-
qlib.contrib.evaluate.
backtest
(pred, account=1000000000.0, shift=1, benchmark='SH000905', verbose=True, **kwargs)¶ This function will help you set a reasonable Exchange and provide default value for strategy :param - backtest workflow related or commmon arguments: :param pred: predict should has <datetime, instrument> index and one score column. :type pred: pandas.DataFrame :param account: init account value. :type account: float :param shift: whether to shift prediction by one day. :type shift: int :param benchmark: benchmark code, default is SH000905 CSI 500. :type benchmark: str :param verbose: whether to print log. :type verbose: bool :param - strategy related arguments: :param strategy: strategy used in backtest. :type strategy: Strategy() :param topk: top-N stocks to buy. :type topk: int (Default value: 50) :param margin:
if isinstance(margin, int):
sell_limit = margin
else:
sell_limit = pred_in_a_day.count() * margin
buffer margin, in single score_mode, continue holding stock if it is in nlargest(sell_limit). sell_limit should be no less than topk.
Parameters: - n_drop (int) – number of stocks to be replaced in each trading date.
- risk_degree (float) – 0-1, 0.95 for example, use 95% money to trade.
- str_type ('amount', 'weight' or 'dropout') – strategy type: TopkAmountStrategy ,TopkWeightStrategy or TopkDropoutStrategy.
- exchange related arguments (-) –
- exchange (Exchange()) – pass the exchange for speeding up.
- subscribe_fields (list) – subscribe fields.
- open_cost (float) – open transaction cost. The default value is 0.002(0.2%).
- close_cost (float) – close transaction cost. The default value is 0.002(0.2%).
- min_cost (float) – min transaction cost.
- trade_unit (int) – 100 for China A.
- deal_price (str) – dealing price type: ‘close’, ‘open’, ‘vwap’.
- limit_threshold (float) – limit move 0.1 (10%) for example, long and short with same limit.
- extract_codes (bool) –
will we pass the codes extracted from the pred to the exchange.
Note
This will be faster with offline qlib.
-
qlib.contrib.evaluate.
long_short_backtest
(pred, topk=50, deal_price=None, shift=1, open_cost=0, close_cost=0, trade_unit=None, limit_threshold=None, min_cost=5, subscribe_fields=[], extract_codes=False)¶ A backtest for long-short strategy
Parameters: - pred – The trading signal produced on day T.
- topk – The short topk securities and long topk securities.
- deal_price – The price to deal the trading.
- shift – Whether to shift prediction by one day. The trading day will be T+1 if shift==1.
- open_cost – open transaction cost.
- close_cost – close transaction cost.
- trade_unit – 100 for China A.
- limit_threshold – limit move 0.1 (10%) for example, long and short with same limit.
- min_cost – min transaction cost.
- subscribe_fields – subscribe fields.
- extract_codes – bool. will we pass the codes extracted from the pred to the exchange. NOTE: This will be faster with offline qlib.
Returns: The result of backtest, it is represented by a dict. { “long”: long_returns(excess),
”short”: short_returns(excess), “long_short”: long_short_returns}
Report¶
-
qlib.contrib.report.analysis_position.report.
report_graph
(report_df: pandas.core.frame.DataFrame, show_notebook: bool = True) → [<class 'list'>, <class 'tuple'>]¶ display backtest report
Example:
from qlib.contrib.evaluate import backtest from qlib.contrib.strategy import TopkDropoutStrategy # backtest parameters bparas = {} bparas['limit_threshold'] = 0.095 bparas['account'] = 1000000000 sparas = {} sparas['topk'] = 50 sparas['n_drop'] = 230 strategy = TopkDropoutStrategy(**sparas) report_normal_df, _ = backtest(pred_df, strategy, **bparas) qcr.report_graph(report_normal_df)
Parameters: - report_df –
df.index.name must be date, df.columns must contain return, turnover, cost, bench.
return cost bench turnover date 2017-01-04 0.003421 0.000864 0.011693 0.576325 2017-01-05 0.000508 0.000447 0.000721 0.227882 2017-01-06 -0.003321 0.000212 -0.004322 0.102765 2017-01-09 0.006753 0.000212 0.006874 0.105864 2017-01-10 -0.000416 0.000440 -0.003350 0.208396
- show_notebook – whether to display graphics in notebook, the default is True.
Returns: if show_notebook is True, display in notebook; else return plotly.graph_objs.Figure list.
- report_df –
-
qlib.contrib.report.analysis_position.score_ic.
score_ic_graph
(pred_label: pandas.core.frame.DataFrame, show_notebook: bool = True) → [<class 'list'>, <class 'tuple'>]¶ score IC
Example:
from qlib.data import D from qlib.contrib.report import analysis_position pred_df_dates = pred_df.index.get_level_values(level='datetime') features_df = D.features(D.instruments('csi500'), ['Ref($close, -2)/Ref($close, -1)-1'], pred_df_dates.min(), pred_df_dates.max()) features_df.columns = ['label'] pred_label = pd.concat([features_df, pred], axis=1, sort=True).reindex(features_df.index) analysis_position.score_ic_graph(pred_label)
Parameters: - pred_label –
index is pd.MultiIndex, index name is [instrument, datetime]; columns names is [score, label].
instrument datetime score label SH600004 2017-12-11 -0.013502 -0.013502 2017-12-12 -0.072367 -0.072367 2017-12-13 -0.068605 -0.068605 2017-12-14 0.012440 0.012440 2017-12-15 -0.102778 -0.102778
- show_notebook – whether to display graphics in notebook, the default is True.
Returns: if show_notebook is True, display in notebook; else return plotly.graph_objs.Figure list.
- pred_label –
-
qlib.contrib.report.analysis_position.cumulative_return.
cumulative_return_graph
(position: dict, report_normal: pandas.core.frame.DataFrame, label_data: pandas.core.frame.DataFrame, show_notebook=True, start_date=None, end_date=None) → Iterable[plotly.graph_objs._figure.Figure]¶ Backtest buy, sell, and holding cumulative return graph
Example:
from qlib.data import D from qlib.contrib.evaluate import risk_analysis, backtest, long_short_backtest from qlib.contrib.strategy import TopkDropoutStrategy # backtest parameters bparas = {} bparas['limit_threshold'] = 0.095 bparas['account'] = 1000000000 sparas = {} sparas['topk'] = 50 sparas['n_drop'] = 5 strategy = TopkDropoutStrategy(**sparas) report_normal_df, positions = backtest(pred_df, strategy, **bparas) pred_df_dates = pred_df.index.get_level_values(level='datetime') features_df = D.features(D.instruments('csi500'), ['Ref($close, -1)/$close - 1'], pred_df_dates.min(), pred_df_dates.max()) features_df.columns = ['label'] qcr.cumulative_return_graph(positions, report_normal_df, features_df)
- Graph desc:
- Axis X: Trading day.
- Axis Y:
- Above axis Y: (((Ref($close, -1)/$close - 1) * weight).sum() / weight.sum()).cumsum().
- Below axis Y: Daily weight sum.
- In the sell graph, y < 0 stands for profit; in other cases, y > 0 stands for profit.
- In the buy_minus_sell graph, the y value of the weight graph at the bottom is buy_weight + sell_weight.
- In each graph, the red line in the histogram on the right represents the average.
Parameters: - position – position data
- report_normal –
return cost bench turnover date 2017-01-04 0.003421 0.000864 0.011693 0.576325 2017-01-05 0.000508 0.000447 0.000721 0.227882 2017-01-06 -0.003321 0.000212 -0.004322 0.102765 2017-01-09 0.006753 0.000212 0.006874 0.105864 2017-01-10 -0.000416 0.000440 -0.003350 0.208396
- label_data – D.features result; index is pd.MultiIndex, index name is [instrument, datetime]; columns names is [label].
The label T is the change from T to T+1, it is recommended to use
close
, example: D.features(D.instruments(‘csi500’), [‘Ref($close, -1)/$close-1’])label instrument datetime SH600004 2017-12-11 -0.013502 2017-12-12 -0.072367 2017-12-13 -0.068605 2017-12-14 0.012440 2017-12-15 -0.102778
Parameters: - show_notebook – True or False. If True, show graph in notebook, else return figures
- start_date – start date
- end_date – end date
Returns:
-
qlib.contrib.report.analysis_position.risk_analysis.
risk_analysis_graph
(analysis_df: pandas.core.frame.DataFrame = None, report_normal_df: pandas.core.frame.DataFrame = None, report_long_short_df: pandas.core.frame.DataFrame = None, show_notebook: bool = True) → Iterable[plotly.graph_objs._figure.Figure]¶ Generate analysis graph and monthly analysis
Example:
from qlib.contrib.evaluate import risk_analysis, backtest, long_short_backtest from qlib.contrib.strategy import TopkDropoutStrategy from qlib.contrib.report import analysis_position # backtest parameters bparas = {} bparas['limit_threshold'] = 0.095 bparas['account'] = 1000000000 sparas = {} sparas['topk'] = 50 sparas['n_drop'] = 230 strategy = TopkDropoutStrategy(**sparas) report_normal_df, positions = backtest(pred_df, strategy, **bparas) # long_short_map = long_short_backtest(pred_df) # report_long_short_df = pd.DataFrame(long_short_map) analysis = dict() # analysis['pred_long'] = risk_analysis(report_long_short_df['long']) # analysis['pred_short'] = risk_analysis(report_long_short_df['short']) # analysis['pred_long_short'] = risk_analysis(report_long_short_df['long_short']) analysis['excess_return_without_cost'] = risk_analysis(report_normal_df['return'] - report_normal_df['bench']) analysis['excess_return_with_cost'] = risk_analysis(report_normal_df['return'] - report_normal_df['bench'] - report_normal_df['cost']) analysis_df = pd.concat(analysis) analysis_position.risk_analysis_graph(analysis_df, report_normal_df)
Parameters: - analysis_df –
analysis data, index is pd.MultiIndex; columns names is [risk].
risk excess_return_without_cost mean 0.000692 std 0.005374 annualized_return 0.174495 information_ratio 2.045576 max_drawdown -0.079103 excess_return_with_cost mean 0.000499 std 0.005372 annualized_return 0.125625 information_ratio 1.473152 max_drawdown -0.088263
- report_normal_df –
df.index.name must be date, df.columns must contain return, turnover, cost, bench.
return cost bench turnover date 2017-01-04 0.003421 0.000864 0.011693 0.576325 2017-01-05 0.000508 0.000447 0.000721 0.227882 2017-01-06 -0.003321 0.000212 -0.004322 0.102765 2017-01-09 0.006753 0.000212 0.006874 0.105864 2017-01-10 -0.000416 0.000440 -0.003350 0.208396
- report_long_short_df –
df.index.name must be date, df.columns contain long, short, long_short.
long short long_short date 2017-01-04 -0.001360 0.001394 0.000034 2017-01-05 0.002456 0.000058 0.002514 2017-01-06 0.000120 0.002739 0.002859 2017-01-09 0.001436 0.001838 0.003273 2017-01-10 0.000824 -0.001944 -0.001120
- show_notebook – Whether to display graphics in a notebook, default True. If True, show graph in notebook If False, return graph figure
Returns: - analysis_df –
-
qlib.contrib.report.analysis_position.rank_label.
rank_label_graph
(position: dict, label_data: pandas.core.frame.DataFrame, start_date=None, end_date=None, show_notebook=True) → Iterable[plotly.graph_objs._figure.Figure]¶ Ranking percentage of stocks buy, sell, and holding on the trading day. Average rank-ratio(similar to sell_df[‘label’].rank(ascending=False) / len(sell_df)) of daily trading
Example:
from qlib.data import D from qlib.contrib.evaluate import backtest from qlib.contrib.strategy import TopkDropoutStrategy # backtest parameters bparas = {} bparas['limit_threshold'] = 0.095 bparas['account'] = 1000000000 sparas = {} sparas['topk'] = 50 sparas['n_drop'] = 230 strategy = TopkDropoutStrategy(**sparas) _, positions = backtest(pred_df, strategy, **bparas) pred_df_dates = pred_df.index.get_level_values(level='datetime') features_df = D.features(D.instruments('csi500'), ['Ref($close, -1)/$close-1'], pred_df_dates.min(), pred_df_dates.max()) features_df.columns = ['label'] qcr.rank_label_graph(positions, features_df, pred_df_dates.min(), pred_df_dates.max())
Parameters: - position – position data; qlib.contrib.backtest.backtest.backtest result.
- label_data – D.features result; index is pd.MultiIndex, index name is [instrument, datetime]; columns names is [label].
The label T is the change from T to T+1, it is recommended to use
close
, example: D.features(D.instruments(‘csi500’), [‘Ref($close, -1)/$close-1’]).label instrument datetime SH600004 2017-12-11 -0.013502 2017-12-12 -0.072367 2017-12-13 -0.068605 2017-12-14 0.012440 2017-12-15 -0.102778
Parameters: - start_date – start date
- end_date – end_date
- show_notebook – True or False. If True, show graph in notebook, else return figures.
Returns:
-
qlib.contrib.report.analysis_model.analysis_model_performance.
ic_figure
(ic_df: pandas.core.frame.DataFrame, show_nature_day=True, **kwargs) → plotly.graph_objs._figure.Figure¶ IC figure
Parameters: - ic_df – ic DataFrame
- show_nature_day – whether to display the abscissa of non-trading day
Returns: plotly.graph_objs.Figure
-
qlib.contrib.report.analysis_model.analysis_model_performance.
model_performance_graph
(pred_label: pandas.core.frame.DataFrame, lag: int = 1, N: int = 5, reverse=False, rank=False, graph_names: list = ['group_return', 'pred_ic', 'pred_autocorr'], show_notebook: bool = True, show_nature_day=True) → [<class 'list'>, <class 'tuple'>]¶ Model performance
Parameters: pred_label – index is pd.MultiIndex, index name is [instrument, datetime]; columns names is **[score, label]**. It is usually same as the label of model training(e.g. “Ref($close, -2)/Ref($close, -1) - 1”).
instrument datetime score label SH600004 2017-12-11 -0.013502 -0.013502 2017-12-12 -0.072367 -0.072367 2017-12-13 -0.068605 -0.068605 2017-12-14 0.012440 0.012440 2017-12-15 -0.102778 -0.102778
Parameters: - lag – pred.groupby(level=’instrument’)[‘score’].shift(lag). It will be only used in the auto-correlation computing.
- N – group number, default 5.
- reverse – if True, pred[‘score’] *= -1.
- rank – if True, calculate rank ic.
- graph_names – graph names; default [‘cumulative_return’, ‘pred_ic’, ‘pred_autocorr’, ‘pred_turnover’].
- show_notebook – whether to display graphics in notebook, the default is True.
- show_nature_day – whether to display the abscissa of non-trading day.
Returns: if show_notebook is True, display in notebook; else return plotly.graph_objs.Figure list.
Workflow¶
Experiment Manager¶
-
class
qlib.workflow.expm.
ExpManager
(uri, default_exp_name)¶ 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)
-
start_exp
(experiment_name=None, recorder_name=None, uri=None, **kwargs)¶ Start an experiment. This method includes first get_or_create an experiment, and then set it to be active.
Parameters: - experiment_name (str) – name of the active experiment.
- recorder_name (str) – name of the recorder to be started.
- uri (str) – the current tracking URI.
Returns: Return type: An active experiment.
-
end_exp
(recorder_status: str = 'SCHEDULED', **kwargs)¶ End an active experiment.
Parameters: - experiment_name (str) – name of the active experiment.
- recorder_status (str) – the status of the active recorder of the experiment.
-
create_exp
(experiment_name=None)¶ Create an experiment.
Parameters: experiment_name (str) – the experiment name, which must be unique. Returns: Return type: An experiment object.
-
search_records
(experiment_ids=None, **kwargs)¶ Get a pandas DataFrame of records that fit the search criteria of the experiment. Inputs are the search critera user want to apply.
Returns: - A pandas.DataFrame of records, where each metric, parameter, and tag
- are expanded into their own columns named metrics., params.*, and tags.**
- respectively. For records that don’t have a particular metric, parameter, or tag, their
- value will be (NumPy) Nan, None, or None respectively.
-
get_exp
(experiment_id=None, experiment_name=None, create: bool = True)¶ Retrieve an experiment. This method includes getting an active experiment, and get_or_create a specific experiment. The returned experiment will be active.
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, and 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, and 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.
Returns: Return type: An experiment object.
-
delete_exp
(experiment_id=None, experiment_name=None)¶ Delete an experiment.
Parameters: - experiment_id (str) – the experiment id.
- experiment_name (str) – the experiment name.
-
get_uri
()¶ Get the default tracking URI or current URI.
Returns: Return type: The tracking URI string.
-
list_experiments
()¶ List all the existing experiments.
Returns: Return type: A dictionary (name -> experiment) of experiments information that being stored.
-
Experiment¶
-
class
qlib.workflow.exp.
Experiment
(id, name)¶ This is the Experiment class for each experiment being run. The API is designed similar to mlflow. (The link: https://mlflow.org/docs/latest/python_api/mlflow.html)
-
start
(recorder_name=None)¶ Start the experiment and set it to be active. This method will also start a new recorder.
Parameters: recorder_name (str) – the name of the recorder to be created. Returns: Return type: An active recorder.
-
end
(recorder_status='SCHEDULED')¶ End the experiment.
Parameters: recorder_status (str) – the status the recorder to be set with when ending (SCHEDULED, RUNNING, FINISHED, FAILED).
-
create_recorder
(name=None)¶ Create a recorder for each experiment.
Parameters: name (str) – the name of the recorder to be created. Returns: Return type: A recorder object.
-
search_records
(**kwargs)¶ Get a pandas DataFrame of records that fit the search criteria of the experiment. Inputs are the search critera user want to apply.
Returns: - A pandas.DataFrame of records, where each metric, parameter, and tag
- are expanded into their own columns named metrics., params.*, and tags.**
- respectively. For records that don’t have a particular metric, parameter, or tag, their
- value will be (NumPy) Nan, None, or None respectively.
-
delete_recorder
(recorder_id)¶ Create a recorder for each experiment.
Parameters: recorder_id (str) – the id of the recorder to be deleted.
-
get_recorder
(recorder_id=None, recorder_name=None, create: bool = True)¶ 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, and the recorder shoud 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, and the recorder shoud 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.
Returns: Return type: A recorder object.
-
list_recorders
()¶ 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.
Returns: Return type: A dictionary (id -> recorder) of recorder information that being stored.
-
Recorder¶
-
class
qlib.workflow.recorder.
Recorder
(experiment_id, name)¶ This is the Recorder class for logging the experiments. The API is designed similar to mlflow. (The link: https://mlflow.org/docs/latest/python_api/mlflow.html)
The status of the recorder can be SCHEDULED, RUNNING, FINISHED, FAILED.
-
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).
Parameters: - local_path (str) – if provided, them save the file or directory to the artifact URI.
- artifact_path=None (str) – the relative path for the artifact to be stored in the URI.
-
load_object
(name)¶ Load objects such as prediction file or model checkpoints.
Parameters: name (str) – name of the file to be loaded. Returns: Return type: The saved object.
-
start_run
()¶ Start running or resuming the Recorder. The return value can be used as a context manager within a with block; otherwise, you must call end_run() to terminate the current run. (See ActiveRun class in mlflow)
Returns: Return type: An active running object (e.g. mlflow.ActiveRun object)
-
end_run
()¶ End an active Recorder.
-
log_params
(**kwargs)¶ Log a batch of params for the current run.
Parameters: arguments (keyword) – key, value pair to be logged as parameters.
-
log_metrics
(step=None, **kwargs)¶ Log multiple metrics for the current run.
Parameters: arguments (keyword) – key, value pair to be logged as metrics.
Log a batch of tags for the current run.
Parameters: arguments (keyword) – key, value pair to be logged as tags.
Delete some tags from a run.
Parameters: keys (series of strs of the keys) – all the name of the tag to be deleted.
-
list_artifacts
(artifact_path: str = None)¶ List all the artifacts of a recorder.
Parameters: artifact_path (str) – the relative path for the artifact to be stored in the URI. Returns: Return type: A list of artifacts information (name, path, etc.) that being stored.
-
list_metrics
()¶ List all the metrics of a recorder.
Returns: Return type: A dictionary of metrics that being stored.
-
list_params
()¶ List all the params of a recorder.
Returns: Return type: A dictionary of params that being stored.
List all the tags of a recorder.
Returns: Return type: A dictionary of tags that being stored.
-
Record Template¶
-
class
qlib.workflow.record_temp.
RecordTemp
(recorder)¶ This is the Records Template class that enables user to generate experiment results such as IC and backtest in a certain format.
-
generate
(**kwargs)¶ Generate certain records such as IC, backtest etc., and save them.
Parameters: kwargs –
-
load
(name)¶ Load the stored records. Due to the fact that some problems occured when we tried to balancing a clean API with the Python’s inheritance. This method has to be used in a rather ugly way, and we will try to fix them in the future:
sar = SigAnaRecord(recorder) ic = sar.load(sar.get_path("ic.pkl"))
Parameters: name (str) – the name for the file to be load. Returns: Return type: The stored records.
-
list
()¶ List the stored records.
Returns: Return type: A list of all the stored records.
-
check
(parent=False)¶ Check if the records is properly generated and saved.
FileExistsError: whether the records are stored properly.
-
-
class
qlib.workflow.record_temp.
SignalRecord
(model=None, dataset=None, recorder=None, **kwargs)¶ This is the Signal Record class that generates the signal prediction. This class inherits the
RecordTemp
class.-
generate
(**kwargs)¶ Generate certain records such as IC, backtest etc., and save them.
Parameters: kwargs –
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list
()¶ List the stored records.
Returns: Return type: A list of all the stored records.
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load
(name='pred.pkl')¶ Load the stored records. Due to the fact that some problems occured when we tried to balancing a clean API with the Python’s inheritance. This method has to be used in a rather ugly way, and we will try to fix them in the future:
sar = SigAnaRecord(recorder) ic = sar.load(sar.get_path("ic.pkl"))
Parameters: name (str) – the name for the file to be load. Returns: Return type: The stored records.
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class
qlib.workflow.record_temp.
SigAnaRecord
(recorder, ana_long_short=False, ann_scaler=252, **kwargs)¶ This is the Signal Analysis Record class that generates the analysis results such as IC and IR. This class inherits the
RecordTemp
class.-
generate
()¶ Generate certain records such as IC, backtest etc., and save them.
Parameters: kwargs –
-
list
()¶ List the stored records.
Returns: Return type: A list of all the stored records.
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class
qlib.workflow.record_temp.
PortAnaRecord
(recorder, config, **kwargs)¶ This is the Portfolio Analysis Record class that generates the analysis results such as those of backtest. This class inherits the
RecordTemp
class.-
generate
(**kwargs)¶ Generate certain records such as IC, backtest etc., and save them.
Parameters: kwargs –
-
list
()¶ List the stored records.
Returns: Return type: A list of all the stored records.
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