mirror of
https://github.com/microsoft/qlib.git
synced 2026-07-04 03:21:00 +08:00
adjust data and model interface
This commit is contained in:
Young
@@ -6,12 +6,10 @@ from __future__ import division
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from __future__ import print_function
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import abc
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import six
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import pandas as pd
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@six.add_metaclass(abc.ABCMeta)
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class Expression(object):
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class Expression(abc.ABC):
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"""Expression base class"""
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def __str__(self):
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@@ -218,7 +216,6 @@ class Feature(Expression):
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return 0, 0
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@six.add_metaclass(abc.ABCMeta)
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class ExpressionOps(Expression):
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"""Operator Expression
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@@ -7,7 +7,6 @@ from __future__ import print_function
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import os
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import abc
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import six
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import time
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import queue
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import bisect
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@@ -27,8 +26,7 @@ from .base import Feature
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from .cache import DiskDatasetCache, DiskExpressionCache
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@six.add_metaclass(abc.ABCMeta)
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class CalendarProvider(object):
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class CalendarProvider(abc.ABC):
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"""Calendar provider base class
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Provide calendar data.
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@@ -128,8 +126,7 @@ class CalendarProvider(object):
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return hash_args(start_time, end_time, freq, future)
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@six.add_metaclass(abc.ABCMeta)
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class InstrumentProvider(object):
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class InstrumentProvider(abc.ABC):
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"""Instrument provider base class
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Provide instrument data.
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@@ -214,8 +211,7 @@ class InstrumentProvider(object):
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raise ValueError(f"Unknown instrument type {inst}")
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@six.add_metaclass(abc.ABCMeta)
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class FeatureProvider(object):
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class FeatureProvider(abc.ABC):
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"""Feature provider class
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Provide feature data.
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@@ -246,8 +242,7 @@ class FeatureProvider(object):
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raise NotImplementedError("Subclass of FeatureProvider must implement `feature` method")
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@six.add_metaclass(abc.ABCMeta)
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class ExpressionProvider(object):
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class ExpressionProvider(abc.ABC):
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"""Expression provider class
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Provide Expression data.
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@@ -298,8 +293,7 @@ class ExpressionProvider(object):
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raise NotImplementedError("Subclass of ExpressionProvider must implement `Expression` method")
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@six.add_metaclass(abc.ABCMeta)
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class DatasetProvider(object):
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class DatasetProvider(abc.ABC):
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"""Dataset provider class
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Provide Dataset data.
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@@ -0,0 +1,8 @@
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class Dataset:
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'''
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Preparing data for model training.
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The type of dataset depends on the model. (It could be pd.DataFrame, pytorch.DataLoader, etc.)
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'''
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def generate(self):
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pass
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@@ -16,6 +16,17 @@ class DataLoader(ABC):
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"""
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load the data as pd.DataFrame
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Parameters
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----------
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self : [TODO:type]
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[TODO:description]
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instruments : [TODO:type]
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[TODO:description]
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start_time : [TODO:type]
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[TODO:description]
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end_time : [TODO:type]
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[TODO:description]
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Returns
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-------
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pd.DataFrame:
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@@ -35,240 +46,51 @@ class DataLoader(ABC):
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class QlibDataLoader(DataLoader):
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'''Same as QlibDataLoader. The fields can be define by config'''
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def __init__(self, config: Tuple[list, tuple, dict], group_fields: bool = False, filter_pipe=None):
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def __init__(self, config: Tuple[list, tuple, dict], filter_pipe=None):
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"""
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Parameters
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----------
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config : Tuple[list ,tuple, dict]
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Config will be used to describe the fields and column names
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if `group_fields`:
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<config> := {
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"group_name1": <fields_info1>
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"group_name2": <fields_info2>
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}
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else:
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<config> := <fields_info>
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<config> := {
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"group_name1": <fields_info1>
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"group_name2": <fields_info2>
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}
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<fields_info> := ["expr", ...] | (["expr", ...], ["col_name", ...]) | <fields_info_config>
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<config> := <fields_info>
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<fields_info_config> is a config with dict type which could be parsed by `parse_config_to_fields`
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<fields_info> := ["expr", ...] | (["expr", ...], ["col_name", ...])
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Here is a few examples to describe the fields
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Here is a few examples to describe the fields
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TODO:
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group_fields : bool
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Will the fields be grouped. Multi-index will be used for the group
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"""
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if group_fields:
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fields_all = []
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name_grp_info = []
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for grp, fields_info in config.items():
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fields, names = self._parse_fields_info(fields_info)
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fields_all.extend(fields)
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name_grp_info.extend([(grp, n) for n in names])
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self.fields, self.names = fields_all, name_grp_info
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else:
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self.fields, self.names = self._parse_fields_info(fields_info)
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self.is_group = isinstance(config, dict)
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if self.is_group:
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self.fields = {grp: self._parse_fields_info(fields_info) for grp, fields_info in config.items()}
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else:
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self.fields = self._parse_fields_info(fields_info)
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self.group_fields = group_fields
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self.filter_pipe = filter_pipe
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def _parse_fields_info(self, fields_info: Tuple[list, tuple, dict]) -> Tuple[list, list]:
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if isinstance(fields_info, dict):
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fields, names = parse_config_to_fields(fields_info)
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elif isinstance(fields_info, list):
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fields = fields_info
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names = fields
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def _parse_fields_info(self, fields_info: Tuple[list, tuple]) -> Tuple[list, list]:
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if isinstance(fields_info, list):
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exprs = names = fields_info
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elif isinstance(fields_info, tuple):
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fields, names = fields_info
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exprs, names = fields_info
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else:
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raise NotImplementedError(f"This type of input is not supported")
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return fields, names
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return exprs, names
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def load(self,
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instruments,
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config: Tuple[list, tuple, dict],
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group_fields=False,
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start_time=None,
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end_time=None) -> Tuple[pd.DataFrame, dict]:
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df = D.features(D.instruments(instruments, filter_pipe=self.filter_pipe), self.fields, start_time, end_time)
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df.columns = pd.MultiIndex.from_tuples(self.names) if self.group_fields else self.names
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def load(self, instruments, start_time=None, end_time=None) -> pd.DataFrame:
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def _get_df(exprs, names):
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df = D.features(D.instruments(instruments, filter_pipe=self.filter_pipe), exprs, start_time, end_time)
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df.columns = names
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return df
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if self.is_group:
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df = pd.concat({grp: _get_df(exprs, names) for grp, (exprs, names) in self.fields.items()}, axis=1)
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else:
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df = _get_df(exprs, names)
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df = df.swaplevel().sort_index()
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return df
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# TODO: make it easier to understand the config language
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def parse_config_to_fields(config):
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"""create factors from config
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config = {
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'kbar': {}, # whether to use some hard-code kbar features
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'price': { # whether to use raw price features
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'windows': [0, 1, 2, 3, 4], # use price at n days ago
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'feature': ['OPEN', 'HIGH', 'LOW'] # which price field to use
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},
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'volume': { # whether to use raw volume features
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'windows': [0, 1, 2, 3, 4], # use volume at n days ago
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},
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'rolling': { # whether to use rolling operator based features
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'windows': [5, 10, 20, 30, 60], # rolling windows size
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'include': ['ROC', 'MA', 'STD'], # rolling operator to use
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#if include is None we will use default operators
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'exclude': ['RANK'], # rolling operator not to use
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}
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}
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"""
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fields = []
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names = []
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if "kbar" in config:
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fields += [
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"($close-$open)/$open",
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"($high-$low)/$open",
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"($close-$open)/($high-$low+1e-12)",
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"($high-Greater($open, $close))/$open",
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"($high-Greater($open, $close))/($high-$low+1e-12)",
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"(Less($open, $close)-$low)/$open",
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"(Less($open, $close)-$low)/($high-$low+1e-12)",
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"(2*$close-$high-$low)/$open",
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"(2*$close-$high-$low)/($high-$low+1e-12)",
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]
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names += [
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"KMID",
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"KLEN",
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"KMID2",
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"KUP",
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"KUP2",
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"KLOW",
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"KLOW2",
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"KSFT",
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"KSFT2",
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]
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if "price" in config:
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windows = config["price"].get("windows", range(5))
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feature = config["price"].get("feature", ["OPEN", "HIGH", "LOW", "CLOSE", "VWAP"])
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for field in feature:
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field = field.lower()
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fields += ["Ref($%s, %d)/$close" % (field, d) if d != 0 else "$%s/$close" % field for d in windows]
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names += [field.upper() + str(d) for d in windows]
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if "volume" in config:
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windows = config["volume"].get("windows", range(5))
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fields += ["Ref($volume, %d)/$volume" % d if d != 0 else "$volume/$volume" for d in windows]
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names += ["VOLUME" + str(d) for d in windows]
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if "rolling" in config:
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windows = config["rolling"].get("windows", [5, 10, 20, 30, 60])
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include = config["rolling"].get("include", None)
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exclude = config["rolling"].get("exclude", [])
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# `exclude` in dataset config unnecessary filed
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# `include` in dataset config necessary field
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use = lambda x: x not in exclude and (include is None or x in include)
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if use("ROC"):
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fields += ["Ref($close, %d)/$close" % d for d in windows]
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names += ["ROC%d" % d for d in windows]
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if use("MA"):
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fields += ["Mean($close, %d)/$close" % d for d in windows]
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names += ["MA%d" % d for d in windows]
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if use("STD"):
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fields += ["Std($close, %d)/$close" % d for d in windows]
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names += ["STD%d" % d for d in windows]
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if use("BETA"):
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fields += ["Slope($close, %d)/$close" % d for d in windows]
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names += ["BETA%d" % d for d in windows]
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if use("RSQR"):
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fields += ["Rsquare($close, %d)" % d for d in windows]
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names += ["RSQR%d" % d for d in windows]
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if use("RESI"):
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fields += ["Resi($close, %d)/$close" % d for d in windows]
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names += ["RESI%d" % d for d in windows]
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if use("MAX"):
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fields += ["Max($high, %d)/$close" % d for d in windows]
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names += ["MAX%d" % d for d in windows]
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if use("LOW"):
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fields += ["Min($low, %d)/$close" % d for d in windows]
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names += ["MIN%d" % d for d in windows]
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if use("QTLU"):
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fields += ["Quantile($close, %d, 0.8)/$close" % d for d in windows]
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names += ["QTLU%d" % d for d in windows]
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if use("QTLD"):
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fields += ["Quantile($close, %d, 0.2)/$close" % d for d in windows]
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names += ["QTLD%d" % d for d in windows]
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if use("RANK"):
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fields += ["Rank($close, %d)" % d for d in windows]
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names += ["RANK%d" % d for d in windows]
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if use("RSV"):
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fields += ["($close-Min($low, %d))/(Max($high, %d)-Min($low, %d)+1e-12)" % (d, d, d) for d in windows]
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names += ["RSV%d" % d for d in windows]
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if use("IMAX"):
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fields += ["IdxMax($high, %d)/%d" % (d, d) for d in windows]
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names += ["IMAX%d" % d for d in windows]
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if use("IMIN"):
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fields += ["IdxMin($low, %d)/%d" % (d, d) for d in windows]
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names += ["IMIN%d" % d for d in windows]
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if use("IMXD"):
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fields += ["(IdxMax($high, %d)-IdxMin($low, %d))/%d" % (d, d, d) for d in windows]
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names += ["IMXD%d" % d for d in windows]
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if use("CORR"):
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fields += ["Corr($close, Log($volume+1), %d)" % d for d in windows]
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names += ["CORR%d" % d for d in windows]
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if use("CORD"):
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fields += ["Corr($close/Ref($close,1), Log($volume/Ref($volume, 1)+1), %d)" % d for d in windows]
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names += ["CORD%d" % d for d in windows]
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if use("CNTP"):
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fields += ["Mean($close>Ref($close, 1), %d)" % d for d in windows]
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names += ["CNTP%d" % d for d in windows]
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if use("CNTN"):
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fields += ["Mean($close<Ref($close, 1), %d)" % d for d in windows]
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names += ["CNTN%d" % d for d in windows]
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if use("CNTD"):
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fields += ["Mean($close>Ref($close, 1), %d)-Mean($close<Ref($close, 1), %d)" % (d, d) for d in windows]
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names += ["CNTD%d" % d for d in windows]
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if use("SUMP"):
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fields += [
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"Sum(Greater($close-Ref($close, 1), 0), %d)/(Sum(Abs($close-Ref($close, 1)), %d)+1e-12)" % (d, d)
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for d in windows
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]
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names += ["SUMP%d" % d for d in windows]
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if use("SUMN"):
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fields += [
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"Sum(Greater(Ref($close, 1)-$close, 0), %d)/(Sum(Abs($close-Ref($close, 1)), %d)+1e-12)" % (d, d)
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for d in windows
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]
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names += ["SUMN%d" % d for d in windows]
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if use("SUMD"):
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fields += [
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"(Sum(Greater($close-Ref($close, 1), 0), %d)-Sum(Greater(Ref($close, 1)-$close, 0), %d))"
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"/(Sum(Abs($close-Ref($close, 1)), %d)+1e-12)" % (d, d, d) for d in windows
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]
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names += ["SUMD%d" % d for d in windows]
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if use("VMA"):
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fields += ["Mean($volume, %d)/($volume+1e-12)" % d for d in windows]
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names += ["VMA%d" % d for d in windows]
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if use("VSTD"):
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fields += ["Std($volume, %d)/($volume+1e-12)" % d for d in windows]
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names += ["VSTD%d" % d for d in windows]
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if use("WVMA"):
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fields += [
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"Std(Abs($close/Ref($close, 1)-1)*$volume, %d)/(Mean(Abs($close/Ref($close, 1)-1)*$volume, %d)+1e-12)" %
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(d, d) for d in windows
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]
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names += ["WVMA%d" % d for d in windows]
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if use("VSUMP"):
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fields += [
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"Sum(Greater($volume-Ref($volume, 1), 0), %d)/(Sum(Abs($volume-Ref($volume, 1)), %d)+1e-12)" % (d, d)
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for d in windows
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]
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names += ["VSUMP%d" % d for d in windows]
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if use("VSUMN"):
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fields += [
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"Sum(Greater(Ref($volume, 1)-$volume, 0), %d)/(Sum(Abs($volume-Ref($volume, 1)), %d)+1e-12)" % (d, d)
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for d in windows
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]
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names += ["VSUMN%d" % d for d in windows]
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if use("VSUMD"):
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fields += [
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"(Sum(Greater($volume-Ref($volume, 1), 0), %d)-Sum(Greater(Ref($volume, 1)-$volume, 0), %d))"
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"/(Sum(Abs($volume-Ref($volume, 1)), %d)+1e-12)" % (d, d, d) for d in windows
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]
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names += ["VSUMD%d" % d for d in windows]
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return fields, names
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@@ -165,113 +165,3 @@ class CSZScoreNorm(Processor):
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cols = get_group_columns(df,self.fields_group)
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df[cols] = df[cols].groupby('datetime').apply(lambda df: (df - df.mean()).div(df.std()))
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return df
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# TODO: make the config language easier to understand
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class ConfigSectionProcessor(Processor):
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# TODO: this class is not well tested
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# FIXME: this will raise error when multi-index is passed in
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def __init__(self, fields_group=None, **kwargs):
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super().__init__()
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# Options
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self.fillna_feature = kwargs.get("fillna_feature", True)
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self.fillna_label = kwargs.get("fillna_label", True)
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self.clip_feature_outlier = kwargs.get("clip_feature_outlier", False)
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self.shrink_feature_outlier = kwargs.get("shrink_feature_outlier", True)
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self.clip_label_outlier = kwargs.get("clip_label_outlier", False)
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self.fields_group = None
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def __call__(self, df):
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return self._transform(df)
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def _transform(self, df):
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def _label_norm(x):
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x = x - x.mean() # copy
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x /= x.std()
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if self.clip_label_outlier:
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x.clip(-3, 3, inplace=True)
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if self.fillna_label:
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x.fillna(0, inplace=True)
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return x
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def _feature_norm(x):
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x = x - x.median() # copy
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x /= x.abs().median() * 1.4826
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if self.clip_feature_outlier:
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x.clip(-3, 3, inplace=True)
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if self.shrink_feature_outlier:
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x.where(x <= 3, 3 + (x - 3).div(x.max() - 3) * 0.5, inplace=True)
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x.where(x >= -3, -3 - (x + 3).div(x.min() + 3) * 0.5, inplace=True)
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if self.fillna_feature:
|
||||
x.fillna(0, inplace=True)
|
||||
return x
|
||||
|
||||
TimeInspector.set_time_mark()
|
||||
|
||||
# Copy the focus part and change it to single level
|
||||
selected_cols = get_group_columns(df, self.fields_group)
|
||||
df_focus = df[selected_cols].copy()
|
||||
if len(df_focus.columns.levels) > 1:
|
||||
df_focus = df_focus.droplevel(level=0)
|
||||
|
||||
# Label
|
||||
cols = df_focus.columns[df_focus.columns.str.contains("^LABEL")]
|
||||
df_focus[cols] = df_focus[cols].groupby(level="datetime").apply(_label_norm)
|
||||
|
||||
# Features
|
||||
cols = df_focus.columns[df_focus.columns.str.contains("^KLEN|^KLOW|^KUP")]
|
||||
df_focus[cols] = df_focus[cols].apply(lambda x: x ** 0.25).groupby(level="datetime").apply(_feature_norm)
|
||||
|
||||
cols = df_focus.columns[df_focus.columns.str.contains("^KLOW2|^KUP2")]
|
||||
df_focus[cols] = df_focus[cols].apply(lambda x: x ** 0.5).groupby(level="datetime").apply(_feature_norm)
|
||||
|
||||
_cols = [
|
||||
"KMID",
|
||||
"KSFT",
|
||||
"OPEN",
|
||||
"HIGH",
|
||||
"LOW",
|
||||
"CLOSE",
|
||||
"VWAP",
|
||||
"ROC",
|
||||
"MA",
|
||||
"BETA",
|
||||
"RESI",
|
||||
"QTLU",
|
||||
"QTLD",
|
||||
"RSV",
|
||||
"SUMP",
|
||||
"SUMN",
|
||||
"SUMD",
|
||||
"VSUMP",
|
||||
"VSUMN",
|
||||
"VSUMD",
|
||||
]
|
||||
pat = "|".join(["^" + x for x in _cols])
|
||||
cols = df_focus.columns[df_focus.columns.str.contains(pat) & (~df_focus.columns.isin(["HIGH0", "LOW0"]))]
|
||||
df_focus[cols] = df_focus[cols].groupby(level="datetime").apply(_feature_norm)
|
||||
|
||||
cols = df_focus.columns[df_focus.columns.str.contains("^STD|^VOLUME|^VMA|^VSTD")]
|
||||
df_focus[cols] = df_focus[cols].apply(np.log).groupby(level="datetime").apply(_feature_norm)
|
||||
|
||||
cols = df_focus.columns[df_focus.columns.str.contains("^RSQR")]
|
||||
df_focus[cols] = df_focus[cols].fillna(0).groupby(level="datetime").apply(_feature_norm)
|
||||
|
||||
cols = df_focus.columns[df_focus.columns.str.contains("^MAX|^HIGH0")]
|
||||
df_focus[cols] = df_focus[cols].apply(lambda x: (x - 1) ** 0.5).groupby(level="datetime").apply(_feature_norm)
|
||||
|
||||
cols = df_focus.columns[df_focus.columns.str.contains("^MIN|^LOW0")]
|
||||
df_focus[cols] = df_focus[cols].apply(lambda x: (1 - x) ** 0.5).groupby(level="datetime").apply(_feature_norm)
|
||||
|
||||
cols = df_focus.columns[df_focus.columns.str.contains("^CORR|^CORD")]
|
||||
df_focus[cols] = df_focus[cols].apply(np.exp).groupby(level="datetime").apply(_feature_norm)
|
||||
|
||||
cols = df_focus.columns[df_focus.columns.str.contains("^WVMA")]
|
||||
df_focus[cols] = df_focus[cols].apply(np.log1p).groupby(level="datetime").apply(_feature_norm)
|
||||
|
||||
df[selected_cols] = df_focus.values
|
||||
|
||||
TimeInspector.log_cost_time("Finished preprocessing data.")
|
||||
|
||||
return df
|
||||
|
||||
@@ -7,14 +7,12 @@ from abc import abstractmethod
|
||||
import re
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import six
|
||||
import abc
|
||||
|
||||
from .data import Cal, DatasetD
|
||||
|
||||
|
||||
@six.add_metaclass(abc.ABCMeta)
|
||||
class BaseDFilter(object):
|
||||
class BaseDFilter(abc.ABC):
|
||||
"""Dynamic Instruments Filter Abstract class
|
||||
|
||||
Users can override this class to construct their own filter
|
||||
@@ -50,7 +48,6 @@ class BaseDFilter(object):
|
||||
raise NotImplementedError("Subclass of BaseDFilter must reimplement `to_config` method")
|
||||
|
||||
|
||||
@six.add_metaclass(abc.ABCMeta)
|
||||
class SeriesDFilter(BaseDFilter):
|
||||
"""Dynamic Instruments Filter Abstract class to filter a series of certain features
|
||||
|
||||
|
||||
Reference in New Issue
Block a user