put all the property in scenarios into local attribute to enable stat…

…ic scenario content (#175)

rdagent/core/scenario.py

@@ -37,6 +37,6 @@ def get_scenario_all_desc(self) -> str:
         """Combine all the description together"""
 
     @property
-    @abstractmethod
-    def get_experiment_setting(self) -> str:
-        """Get experiment setting and return as rich text string"""
+    def experiment_setting(self) -> str | None:
+        """Get experiment setting and return as rich text string"""
+        return None

rdagent/scenarios/general_model/prompts.yaml

@@ -70,5 +70,20 @@ general_model_output_format: |-
 general_model_simulator: |-
   The models are not loaded and backtested. That said, pay attention to its architecture.
 
+general_model_rich_style_description: |-
+  ### [Model Research & Development Co-Pilot](#_scenario)
 
-  
+  #### [Overview](#_summary)
+
+  This demo automates the extraction and development of PyTorch models from academic papers. It supports various model types through two main components: Reader and Coder.
+  
+  #### [Workflow Components](#_rdloops)
+  
+  1. **[Reader](#_research)**
+      - Extracts model information from papers, including architectures and parameters.
+      - Converts content into a structured format using Large Language Models.
+  
+  2. **[Evolving Coder](#_development)**
+      - Translates structured information into executable PyTorch code.
+      - Ensures correct tensor shapes with an evolving coding mechanism.
+      - Refines the code to match source specifications.

rdagent/scenarios/general_model/scenario.py

@@ -1,3 +1,4 @@
+from copy import deepcopy
 from pathlib import Path
 
 from rdagent.core.prompts import Prompts
@@ -7,47 +8,37 @@
 
 
 class GeneralModelScenario(Scenario):
+    def __init__(self) -> None:
+        super().__init__()
+        self._background = deepcopy(prompt_dict["general_model_background"])
+        self._output_format = deepcopy(prompt_dict["general_model_output_format"])
+        self._interface = deepcopy(prompt_dict["general_model_interface"])
+        self._simulator = deepcopy(prompt_dict["general_model_simulator"])
+        self._rich_style_description = deepcopy(prompt_dict["general_model_rich_style_description"])
+
     @property
     def background(self) -> str:
-        return prompt_dict["general_model_background"]
+        return self._background
 
     @property
     def source_data(self) -> str:
         raise NotImplementedError("source_data of GeneralModelScenario is not implemented")
 
     @property
     def output_format(self) -> str:
-        return prompt_dict["general_model_output_format"]
+        return self._output_format
 
     @property
     def interface(self) -> str:
-        return prompt_dict["general_model_interface"]
+        return self._interface
 
     @property
     def simulator(self) -> str:
-        return prompt_dict["general_model_simulator"]
+        return self._simulator
 
     @property
     def rich_style_description(self) -> str:
-        return """
-### [Model Research & Development Co-Pilot](#_scenario)
-
-#### [Overview](#_summary)
-
-This demo automates the extraction and development of PyTorch models from academic papers. It supports various model types through two main components: Reader and Coder.
- 
-#### [Workflow Components](#_rdloops)
- 
-1. **[Reader](#_research)**
-    - Extracts model information from papers, including architectures and parameters.
-    - Converts content into a structured format using Large Language Models.
- 
-2. **[Evolving Coder](#_development)**
-    - Translates structured information into executable PyTorch code.
-    - Ensures correct tensor shapes with an evolving coding mechanism.
-    - Refines the code to match source specifications.
-
-        """
+        return self._rich_style_description
 
     def get_scenario_all_desc(self) -> str:
         return f"""Background of the scenario:

rdagent/scenarios/qlib/experiment/factor_experiment.py

@@ -1,3 +1,4 @@
+from copy import deepcopy
 from pathlib import Path
 
 from rdagent.components.coder.factor_coder.factor import (
@@ -20,49 +21,43 @@ def __init__(self, *args, **kwargs) -> None:
 
 
 class QlibFactorScenario(Scenario):
+    def __init__(self) -> None:
+        super().__init__()
+        self._background = deepcopy(prompt_dict["qlib_factor_background"])
+        self._source_data = deepcopy(get_data_folder_intro())
+        self._output_format = deepcopy(prompt_dict["qlib_factor_output_format"])
+        self._interface = deepcopy(prompt_dict["qlib_factor_interface"])
+        self._simulator = deepcopy(prompt_dict["qlib_factor_simulator"])
+        self._rich_style_description = deepcopy(prompt_dict["qlib_factor_rich_style_description"])
+        self._experiment_setting = deepcopy(prompt_dict["qlib_factor_experiment_setting"])
+
     @property
     def background(self) -> str:
-        return prompt_dict["qlib_factor_background"]
+        return self._background
 
     @property
     def source_data(self) -> str:
-        return get_data_folder_intro()
+        return self._source_data
 
     @property
     def output_format(self) -> str:
-        return prompt_dict["qlib_factor_output_format"]
+        return self._output_format
 
     @property
     def interface(self) -> str:
-        return prompt_dict["qlib_factor_interface"]
+        return self._interface
 
     @property
     def simulator(self) -> str:
-        return prompt_dict["qlib_factor_simulator"]
+        return self._simulator
 
     @property
     def rich_style_description(self) -> str:
-        return """
-### R&D Agent-Qlib: Automated Quantitative Trading & Iterative Factors Evolution Demo
-
-#### [Overview](#_summary)
-
-The demo showcases the iterative process of hypothesis generation, knowledge construction, and decision-making. It highlights how financial factors evolve through continuous feedback and refinement.
-
-#### [Automated R&D](#_rdloops)
-
-- **[R (Research)](#_research)**
-  - Iterative development of ideas and hypotheses.
-  - Continuous learning and knowledge construction.
+        return self._rich_style_description
 
-- **[D (Development)](#_development)**
-  - Progressive implementation and code generation of factors.
-  - Automated testing and validation of financial factors.
-
-#### [Objective](#_summary)
-
-To demonstrate the dynamic evolution of financial factors through the Qlib platform, emphasizing how each iteration enhances the accuracy and reliability of the resulting financial factors.
-        """
+    @property
+    def experiment_setting(self) -> str:
+        return self._experiment_setting
 
     def get_scenario_all_desc(self) -> str:
         return f"""Background of the scenario:
@@ -76,11 +71,3 @@ def get_scenario_all_desc(self) -> str:
 The simulator user can use to test your factor:
 {self.simulator}
 """
-
-    @property
-    def get_experiment_setting(self) -> str:
-        return """
-| Dataset 📊 | Model 🤖    | Factors 🌟       | Data Split  🧮                                   |
-|---------|----------|---------------|-------------------------------------------------|
-| CSI300  | LGBModel | Alpha158 Plus | Train: 2008-01-01 to 2014-12-31 <br> Valid: 2015-01-01 to 2016-12-31 <br> Test &nbsp;: 2017-01-01 to 2020-08-01 |
-        """

rdagent/scenarios/qlib/experiment/factor_from_report_experiment.py

@@ -1,3 +1,4 @@
+from copy import deepcopy
 from pathlib import Path
 
 from rdagent.components.coder.factor_coder.factor import (
@@ -14,48 +15,10 @@
 
 
 class QlibFactorFromReportScenario(QlibFactorScenario):
-    @property
-    def rich_style_description(self) -> str:
-        return """
-### R&D Agent-Qlib: Automated Quantitative Trading & Factor Extraction from Financial Reports Demo
-
-
-#### [Overview](#_summary)
-
-This demo showcases the process of extracting factors from financial research reports, implementing these factors, and analyzing their performance through Qlib backtesting, continually expanding and refining the factor library.
-
-#### [Automated R&D](#_rdloops)
-
-- **[R (Research)](#_research)**
-  - Iterative development of ideas and hypotheses from financial reports.
-  - Continuous learning and knowledge construction.
-
-- **[D (Development)](#_development)**
-  - Progressive factor extraction and code generation.
-  - Automated implementation and testing of financial factors.
-
-#### [Objective](#_summary)
-
-<table border="1" style="width:100%; border-collapse: collapse;">
-  <tr>
-    <td>💡 <strong>Innovation </strong></td>
-    <td>Tool to quickly extract and test factors from research reports.</td>
-  </tr>
-  <tr>
-    <td>⚡ <strong>Efficiency </strong></td>
-    <td>Rapid identification of valuable factors from numerous reports.</td>
-  </tr>
-  <tr>
-    <td>🗃️ <strong>Outputs </strong></td>
-    <td>Expand and refine the factor library to support further research.</td>
-  </tr>
-</table>
-        """
+    def __init__(self) -> None:
+        super().__init__()
+        self._rich_style_description = deepcopy(prompt_dict["qlib_factor_from_report_rich_style_description"])
 
     @property
-    def get_experiment_setting(self) -> str:
-        return """
-| Dataset 📊 | Model 🤖    | Factors 🌟       | Data Split  🧮                                   |
-|---------|----------|---------------|-------------------------------------------------|
-| CSI300  | LGBModel | Alpha158 Plus | Train: 2008-01-01 to 2014-12-31 <br> Valid: 2015-01-01 to 2016-12-31 <br> Test &nbsp;: 2017-01-01 to 2020-08-01 |
-        """
+    def rich_style_description(self) -> str:
+        return self._rich_style_description

rdagent/scenarios/qlib/experiment/model_experiment.py

@@ -1,3 +1,4 @@
+from copy import deepcopy
 from pathlib import Path
 
 from rdagent.components.coder.model_coder.model import (
@@ -19,49 +20,42 @@ def __init__(self, *args, **kwargs) -> None:
 
 
 class QlibModelScenario(Scenario):
+    def __init__(self) -> None:
+        super().__init__()
+        self._background = deepcopy(prompt_dict["qlib_model_background"])
+        self._output_format = deepcopy(prompt_dict["qlib_model_output_format"])
+        self._interface = deepcopy(prompt_dict["qlib_model_interface"])
+        self._simulator = deepcopy(prompt_dict["qlib_model_simulator"])
+        self._rich_style_description = deepcopy(prompt_dict["qlib_model_rich_style_description"])
+        self._experiment_setting = deepcopy(prompt_dict["qlib_model_experiment_setting"])
+
     @property
     def background(self) -> str:
-        return prompt_dict["qlib_model_background"]
+        return self._background
 
     @property
     def source_data(self) -> str:
         raise NotImplementedError("source_data of QlibModelScenario is not implemented")
 
     @property
     def output_format(self) -> str:
-        return prompt_dict["qlib_model_output_format"]
+        return self._output_format
 
     @property
     def interface(self) -> str:
-        return prompt_dict["qlib_model_interface"]
+        return self._interface
 
     @property
     def simulator(self) -> str:
-        return prompt_dict["qlib_model_simulator"]
+        return self._simulator
 
     @property
     def rich_style_description(self) -> str:
-        return """
-### Qlib Model Evolving Automatic R&D Demo
- 
-#### [Overview](#_summary)
- 
-The demo showcases the iterative process of hypothesis generation, knowledge construction, and decision-making in model construction in quantitative finance. It highlights how models evolve through continuous feedback and refinement.
- 
-#### [Automated R&D](#_rdloops)
- 
-- **[R (Research)](#_research)**
-  - Iteration of ideas and hypotheses.
-  - Continuous learning and knowledge construction.
- 
-- **[D (Development)](#_development)**
-  - Evolving code generation and model refinement.
-  - Automated implementation and testing of models.
- 
-#### [Objective](#_summary)
- 
-To demonstrate the dynamic evolution of models through the Qlib platform, emphasizing how each iteration enhances the accuracy and reliability of the resulting models.    
-    """
+        return self._rich_style_description
+
+    @property
+    def experiment_setting(self) -> str:
+        return self._experiment_setting
 
     def get_scenario_all_desc(self) -> str:
         return f"""Background of the scenario:
@@ -73,10 +67,3 @@ def get_scenario_all_desc(self) -> str:
 The simulator user can use to test your model:
 {self.simulator}
 """
-
-    def get_experiment_setting(self) -> str:
-        return """
-| Dataset 📊 | Model 🤖    | Factors 🌟       | Data Split  🧮                                   |
-|---------|----------|---------------|-------------------------------------------------|
-| CSI300  | RDAgent-dev | 20 factors (Alpha158)  | Train: 2008-01-01 to 2014-12-31 <br> Valid: 2015-01-01 to 2016-12-31 <br> Test &nbsp;: 2017-01-01 to 2020-08-01 |
-        """