DDG-DA paper code (#743)

* Merge data selection to main

* Update trainer for reweighter

* Typos fixed.

* update data selection interface

* successfully run exp after refactor some interface

* data selection share handler &  trainer

* fix meta model time series bug

* fix online workflow set_uri bug

* fix set_uri bug

* updawte ds docs and delay trainer bug

* docs

* resume reweighter

* add reweighting result

* fix qlib model import

* make recorder more friendly

* fix experiment workflow bug

* commit for merging master incase of conflictions

* Successful run DDG-DA with a single command

* remove unused code

* asdd more docs

* Update README.md

* Update & fix some bugs.

* Update configuration & remove debug functions

* Update README.md

* Modfify horizon from code rather than yaml

* Update performance in README.md

* fix part comments

* Remove unfinished TCTS.

* Fix some details.

* Update meta docs

* Update README.md of the benchmarks_dynamic

* Update README.md files

* Add README.md to the rolling_benchmark baseline.

* Refine the docs and link

* Rename README.md in benchmarks_dynamic.

* Remove comments.

* auto download data

Co-authored-by: wendili-cs <wendili.academic@qq.com>
Co-authored-by: demon143 <785696300@qq.com>

qlib/data/dataset/__init__.py

@@ -1,5 +1,5 @@
 from ...utils.serial import Serializable
-from typing import Union, List, Tuple, Dict, Text, Optional
+from typing import Callable, Union, List, Tuple, Dict, Text, Optional
 from ...utils import init_instance_by_config, np_ffill, time_to_slc_point
 from ...log import get_module_logger
 from .handler import DataHandler, DataHandlerLP
@@ -235,6 +235,28 @@ class DatasetH(Dataset):
         else:
             raise NotImplementedError(f"This type of input is not supported")
 
+    # helper functions
+    @staticmethod
+    def get_min_time(segments):
+        return DatasetH._get_extrema(segments, 0, (lambda a, b: a > b))
+
+    @staticmethod
+    def get_max_time(segments):
+        return DatasetH._get_extrema(segments, 1, (lambda a, b: a < b))
+
+    @staticmethod
+    def _get_extrema(segments, idx: int, cmp: Callable, key_func=pd.Timestamp):
+        """it will act like sort and return the max value or None"""
+        candidate = None
+        for k, seg in segments.items():
+            point = seg[idx]
+            if point is None:
+                # None indicates unbounded, return directly
+                return None
+            elif candidate is None or cmp(key_func(candidate), key_func(point)):
+                candidate = point
+        return candidate
+
 
 class TSDataSampler:
     """