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>

examples/benchmarks_dynamic/baseline/rolling_benchmark.py

@@ -0,0 +1,114 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+from qlib.model.ens.ensemble import RollingEnsemble
+from qlib.utils import init_instance_by_config
+import fire
+import yaml
+from qlib import auto_init
+from pathlib import Path
+from tqdm.auto import tqdm
+from qlib.model.trainer import TrainerR
+from qlib.workflow import R
+from qlib.tests.data import GetData
+
+DIRNAME = Path(__file__).absolute().resolve().parent
+from qlib.workflow.task.gen import task_generator, RollingGen
+from qlib.workflow.task.collect import RecorderCollector
+from qlib.workflow.record_temp import PortAnaRecord, SigAnaRecord
+
+
+class RollingBenchmark:
+    """
+    **NOTE**
+    before running the example, please clean your previous results with following command
+    - `rm -r mlruns`
+
+    """
+
+    def __init__(self, rolling_exp="rolling_models", model_type="linear") -> None:
+        self.step = 20
+        self.horizon = 20
+        self.rolling_exp = rolling_exp
+        self.model_type = model_type
+
+    def basic_task(self):
+        """For fast training rolling"""
+        if self.model_type == "gbdt":
+            conf_path = DIRNAME.parent.parent / "benchmarks" / "LightGBM" / "workflow_config_lightgbm_Alpha158.yaml"
+            # dump the processed data on to disk for later loading to speed up the processing
+            h_path = DIRNAME / "lightgbm_alpha158_handler_horizon{}.pkl".format(self.horizon)
+        elif self.model_type == "linear":
+            conf_path = DIRNAME.parent.parent / "benchmarks" / "Linear" / "workflow_config_linear_Alpha158.yaml"
+            h_path = DIRNAME / "linear_alpha158_handler_horizon{}.pkl".format(self.horizon)
+        else:
+            raise AssertionError("Model type is not supported!")
+        with conf_path.open("r") as f:
+            conf = yaml.safe_load(f)
+
+        # modify dataset horizon
+        conf["task"]["dataset"]["kwargs"]["handler"]["kwargs"]["label"] = [
+            "Ref($close, -{}) / Ref($close, -1) - 1".format(self.horizon + 1)
+        ]
+
+        task = conf["task"]
+
+        if not h_path.exists():
+            h_conf = task["dataset"]["kwargs"]["handler"]
+            h = init_instance_by_config(h_conf)
+            h.to_pickle(h_path, dump_all=True)
+
+        task["dataset"]["kwargs"]["handler"] = f"file://{h_path}"
+        task["record"] = ["qlib.workflow.record_temp.SignalRecord"]
+        return task
+
+    def create_rolling_tasks(self):
+        task = self.basic_task()
+        task_l = task_generator(
+            task, RollingGen(step=self.step, trunc_days=self.horizon + 1)
+        )  # the last two days should be truncated to avoid information leakage
+        return task_l
+
+    def train_rolling_tasks(self, task_l=None):
+        if task_l is None:
+            task_l = self.create_rolling_tasks()
+        trainer = TrainerR(experiment_name=self.rolling_exp)
+        trainer(task_l)
+
+    COMB_EXP = "rolling"
+
+    def ens_rolling(self):
+        rc = RecorderCollector(
+            experiment=self.rolling_exp,
+            artifacts_key=["pred", "label"],
+            process_list=[RollingEnsemble()],
+            # rec_key_func=lambda rec: (self.COMB_EXP, rec.info["id"]),
+            artifacts_path={"pred": "pred.pkl", "label": "label.pkl"},
+        )
+        res = rc()
+        with R.start(experiment_name=self.COMB_EXP):
+            R.log_params(exp_name=self.rolling_exp)
+            R.save_objects(**{"pred.pkl": res["pred"], "label.pkl": res["label"]})
+
+    def update_rolling_rec(self):
+        """
+        Evaluate the combined rolling results
+        """
+        for rid, rec in R.list_recorders(experiment_name=self.COMB_EXP).items():
+            for rt_cls in SigAnaRecord, PortAnaRecord:
+                rt = rt_cls(recorder=rec, skip_existing=True)
+                rt.generate()
+        print(f"Your evaluation results can be found in the experiment named `{self.COMB_EXP}`.")
+
+    def run_all(self):
+        # the results will be  save in mlruns.
+        # 1) each rolling task is saved in rolling_models
+        self.train_rolling_tasks()
+        # 2) combined rolling tasks and evaluation results are saved in rolling
+        self.ens_rolling()
+        self.update_rolling_rec()
+
+
+if __name__ == "__main__":
+    GetData().qlib_data(exists_skip=True)
+    auto_init()
+    fire.Fire(RollingBenchmark)