Fix code and docs for issues (#853)

* Docs for model and strategy

* add some docs about workflow and online

* safe_load yaml

* DDG-DA paper link and comments for code

examples/benchmarks_dynamic/DDG-DA/README.md

@@ -1,6 +1,9 @@
 # Introduction
 This is the implementation of `DDG-DA` based on `Meta Controller` component provided by `Qlib`.
 
+Please refer to the paper for more details: *DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation* [[arXiv](https://arxiv.org/abs/2201.04038)]
+
+
 ## Background
 In many real-world scenarios, we often deal with streaming data that is sequentially collected over time. Due to the non-stationary nature of the environment, the streaming data distribution may change in unpredictable ways, which is known as concept drift. To handle concept drift, previous methods first detect when/where the concept drift happens and then adapt models to fit the distribution of the latest data. However, there are still many cases that some underlying factors of environment evolution are predictable, making it possible to model the future concept drift trend of the streaming data, while such cases are not fully explored in previous work.
 

examples/benchmarks_dynamic/DDG-DA/workflow.py

@@ -147,6 +147,9 @@ class DDGDA:
             },
             # "record": ["qlib.workflow.record_temp.SignalRecord"]
         }
+        # the proxy_forecast_model_task will be used to create meta tasks.
+        # The test date of first task will be 2011-01-01. Each test segment will be about 20days
+        # The tasks include all training tasks and test tasks.
 
         # 2) preparing meta dataset
         kwargs = dict(