Remove arctic from Qlib core to Contrib (#865)

* Remove arctic from Qlib core to Contrib

* fix empty df bug

qlib/contrib/data/data.py

@@ -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