mirror of
https://github.com/microsoft/qlib.git
synced 2026-07-07 04:50:56 +08:00
Remove arctic from Qlib core to Contrib (#865)
* Remove arctic from Qlib core to Contrib * fix empty df bug
This commit is contained in:
@@ -17,6 +17,7 @@ Current version of script with default value tries to connect localhost **via de
|
||||
Run following command to install necessary libraries
|
||||
```
|
||||
pip install pytest
|
||||
pip install arctic # NOTE: pip may fail to resolve the right package dependency !!! Please make sure the dependency are satisfied.
|
||||
```
|
||||
|
||||
# Importing example data
|
||||
|
||||
@@ -25,7 +25,11 @@ class TestClass(unittest.TestCase):
|
||||
mem_cache_type="sizeof",
|
||||
kernels=1,
|
||||
expression_provider={"class": "LocalExpressionProvider", "kwargs": {"time2idx": False}},
|
||||
feature_provider={"class": "ArcticFeatureProvider", "kwargs": {"uri": "127.0.0.1"}},
|
||||
feature_provider={
|
||||
"class": "ArcticFeatureProvider",
|
||||
"module_path": "qlib.contrib.data.data",
|
||||
"kwargs": {"uri": "127.0.0.1"},
|
||||
},
|
||||
dataset_provider={
|
||||
"class": "LocalDatasetProvider",
|
||||
"kwargs": {
|
||||
|
||||
55
qlib/contrib/data/data.py
Normal file
55
qlib/contrib/data/data.py
Normal file
@@ -0,0 +1,55 @@
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
|
||||
# We remove arctic from core framework of Qlib to contrib due to
|
||||
# - Arctic has very strict limitation on pandas and numpy version
|
||||
# - https://github.com/man-group/arctic/pull/908
|
||||
# - pip fail to computing the right version number!!!!
|
||||
# - Maybe we can solve this problem by poetry
|
||||
|
||||
# FIXME: So if you want to use arctic-based provider, please install arctic manually
|
||||
# `pip install arctic` may not be enough.
|
||||
from arctic import Arctic
|
||||
import pandas as pd
|
||||
import pymongo
|
||||
|
||||
from qlib.data.data import FeatureProvider
|
||||
|
||||
|
||||
class ArcticFeatureProvider(FeatureProvider):
|
||||
def __init__(
|
||||
self, uri="127.0.0.1", retry_time=0, market_transaction_time_list=[("09:15", "11:30"), ("13:00", "15:00")]
|
||||
):
|
||||
super().__init__()
|
||||
self.uri = uri
|
||||
# TODO:
|
||||
# retry connecting if error occurs
|
||||
# does it real matters?
|
||||
self.retry_time = retry_time
|
||||
# NOTE: this is especially important for TResample operator
|
||||
self.market_transaction_time_list = market_transaction_time_list
|
||||
|
||||
def feature(self, instrument, field, start_index, end_index, freq):
|
||||
field = str(field)[1:]
|
||||
with pymongo.MongoClient(self.uri) as client:
|
||||
# TODO: this will result in frequently connecting the server and performance issue
|
||||
arctic = Arctic(client)
|
||||
|
||||
if freq not in arctic.list_libraries():
|
||||
raise ValueError("lib {} not in arctic".format(freq))
|
||||
|
||||
if instrument not in arctic[freq].list_symbols():
|
||||
# instruments does not exist
|
||||
return pd.Series()
|
||||
else:
|
||||
df = arctic[freq].read(instrument, columns=[field], chunk_range=(start_index, end_index))
|
||||
s = df[field]
|
||||
|
||||
if not s.empty:
|
||||
s = pd.concat(
|
||||
[
|
||||
s.between_time(time_tuple[0], time_tuple[1])
|
||||
for time_tuple in self.market_transaction_time_list
|
||||
]
|
||||
)
|
||||
return s
|
||||
@@ -15,7 +15,6 @@ from .data import (
|
||||
LocalCalendarProvider,
|
||||
LocalInstrumentProvider,
|
||||
LocalFeatureProvider,
|
||||
ArcticFeatureProvider,
|
||||
LocalExpressionProvider,
|
||||
LocalDatasetProvider,
|
||||
ClientCalendarProvider,
|
||||
|
||||
@@ -17,11 +17,9 @@ import pandas as pd
|
||||
from multiprocessing import Pool
|
||||
from typing import Iterable, Union
|
||||
from typing import List, Union
|
||||
from arctic import Arctic
|
||||
|
||||
# For supporting multiprocessing in outer code, joblib is used
|
||||
from joblib import delayed
|
||||
import pymongo
|
||||
|
||||
from .cache import H
|
||||
from ..config import C
|
||||
@@ -582,7 +580,7 @@ class DatasetProvider(abc.ABC):
|
||||
data.index = _calendar[data.index.values.astype(int)]
|
||||
data.index.names = ["datetime"]
|
||||
|
||||
if spans is not None:
|
||||
if not data.empty and spans is not None:
|
||||
mask = np.zeros(len(data), dtype=bool)
|
||||
for begin, end in spans:
|
||||
mask |= (data.index >= begin) & (data.index <= end)
|
||||
@@ -702,45 +700,6 @@ class LocalFeatureProvider(FeatureProvider, ProviderBackendMixin):
|
||||
return self.backend_obj(instrument=instrument, field=field, freq=freq)[start_index : end_index + 1]
|
||||
|
||||
|
||||
class ArcticFeatureProvider(FeatureProvider):
|
||||
def __init__(
|
||||
self, uri="127.0.0.1", retry_time=0, market_transaction_time_list=[("09:15", "11:30"), ("13:00", "15:00")]
|
||||
):
|
||||
super().__init__()
|
||||
self.uri = uri
|
||||
# TODO:
|
||||
# retry connecting if error occurs
|
||||
# does it real matters?
|
||||
self.retry_time = retry_time
|
||||
# NOTE: this is especially important for TResample operator
|
||||
self.market_transaction_time_list = market_transaction_time_list
|
||||
|
||||
def feature(self, instrument, field, start_index, end_index, freq):
|
||||
field = str(field)[1:]
|
||||
with pymongo.MongoClient(self.uri) as client:
|
||||
# TODO: this will result in frequently connecting the server and performance issue
|
||||
arctic = Arctic(client)
|
||||
|
||||
if freq not in arctic.list_libraries():
|
||||
raise ValueError("lib {} not in arctic".format(freq))
|
||||
|
||||
if instrument not in arctic[freq].list_symbols():
|
||||
# instruments does not exist
|
||||
return pd.Series()
|
||||
else:
|
||||
df = arctic[freq].read(instrument, columns=[field], chunk_range=(start_index, end_index))
|
||||
s = df[field]
|
||||
|
||||
if not s.empty:
|
||||
s = pd.concat(
|
||||
[
|
||||
s.between_time(time_tuple[0], time_tuple[1])
|
||||
for time_tuple in self.market_transaction_time_list
|
||||
]
|
||||
)
|
||||
return s
|
||||
|
||||
|
||||
class LocalExpressionProvider(ExpressionProvider):
|
||||
"""Local expression data provider class
|
||||
|
||||
|
||||
Reference in New Issue
Block a user