doc: Improved documentation for two factor scenarios. (#172)

* Fixed some bugs introduced during refactoring.

* Improved documentation for two factor scenarios.

* Update factor_from_report_w_sc.py

* Improved some details.

docs/scens/data_agent_fin.rst

@@ -4,42 +4,79 @@
 Finance Data Agent
 =====================
 
-Scen1
------
-🤖 Knowledge-Based Hypothesis Generation and Iteration
-
-Scen1 Intro
-~~~~~~~~~~~
-In this scenario, our model autonomously generates and tests hypotheses using a knowledge base. The process involves:
-
-- **🔍 Hypothesis Generation**: The model proposes new hypotheses.
-- **📝 Factor Creation**: Write and define new factors.
-- **✅ Factor Validation**: Validate the factors quantitatively.
-- **📈 Backtesting with Qlib**: 
-
-  - **Dataset**: CSI300
-  - **Model**: LGBModel
-  - **Factors**: Alpha158 +
-  - **Data Split**:
-
-    - **Train**: 2008-01-01 to 2014-12-31
-    - **Valid**: 2015-01-01 to 2016-12-31
-    - **Test**: 2017-01-01 to 2020-08-01
-- **🔄 Feedback Analysis**: Analyze backtest results.
-- **🔧 Hypothesis Refinement**: Refine hypotheses based on feedback and repeat.
-
-Scen1 Demo
+
+**Automated Quantitative Trading & Iterative Factors Evolution 🤖**
+-------------------------------------------------------------------
+
+Background
+~~~~~~~~~~
+In the dynamic world of quantitative trading, **factors** are the secret weapons that traders use to harness market inefficiencies. 
+
+These powerful tools—ranging from straightforward metrics like price-to-earnings ratios to intricate discounted cash flow models—unlock the potential to predict stock prices with remarkable precision. 
+By tapping into this rich vein of data, quantitative traders craft sophisticated strategies that not only capitalize on market patterns but also drastically enhance trading efficiency and accuracy. 
+
+Embrace the power of factors, and you're not just trading; you're strategically outsmarting the market.
+
+
+Introduction
+~~~~~~~~~~~~
+In this scenario, our agent illustrates the iterative process of hypothesis generation, knowledge construction, and decision-making. 
+
+It highlights how financial factors evolve through continuous feedback and refinement. 
+
+Here's an enhanced outline of the steps:
+
+**Step 1 : Hypothesis Generation 🔍**
+
+- Generate and propose initial hypotheses based on previous experiment analysis and domain expertise, with thorough reasoning and financial justification.
+
+**Step 2 : Factor Creation ✨**
+
+- Based on the hypothesis, divide the tasks.
+- Each task involves developing, defining, and implementing a new financial factor, including its name, description, formulation, and variables.
+
+**Step 3 : Factor Implementation 👨‍💻**
+
+- Implement the factor code based on the description, evolving it as a developer would.
+- Quantitatively validate the newly created factors.
+
+**Step 4 : Backtesting with Qlib 📉**
+
+- Integrate the full dataset into the factor implementation code and prepare the factor library.
+- Perform backtesting using the Alpha158+ model in Qlib to assess the factor's effectiveness and performance.
+
++----------------+------------+----------------+----------------------------------------------------+
+| Dataset        | Model      | Factors        | Data Split                                         |
++================+============+================+====================================================+
+| CSI300         | LGBModel   | Alpha158 Plus  | +-----------+--------------------------+           |
+|                |            |                | | Train     | 2008-01-01 to 2014-12-31 |           |
+|                |            |                | +-----------+--------------------------+           |
+|                |            |                | | Valid     | 2015-01-01 to 2016-12-31 |           |
+|                |            |                | +-----------+--------------------------+           |
+|                |            |                | | Test      | 2017-01-01 to 2020-08-01 |           |
+|                |            |                | +-----------+--------------------------+           |
++----------------+------------+----------------+----------------------------------------------------+
+
+
+**Step 5 : Feedback Analysis 🔍**
+   - Analyze backtest results.
+   - Incorporate feedback to refine hypotheses.
+
+**Step 6 :Hypothesis Refinement ♻️**
+   - Refine hypotheses based on feedback and repeat the process.
+
+Demo
 ~~~~~~~~~~
 .. TODO
 
-Scen1 Quick Start
+Quick Start
 ~~~~~~~~~~~~~~~~~
 
-To quickly start the factor extraction process, run the following command in your terminal within the 'rdagent' virtual environment:
+To quickly start the Automated Quantitative Trading & Iterative Factors Evolution process, run the following command in your terminal within the `rdagent` virtual environment:
 
 .. code-block:: sh
 
-    python rdagent/app/qlib_rd_loop/factor.py
+    python rdagent/app/qlib_rd_loop/factor_w_sc.py
 
 
 Usage of modules

docs/scens/data_copilot_fin.rst

@@ -5,42 +5,80 @@ Finance Data Copilot
 =====================
 
 
-Scen2: 
-------
-📄 Research Report-Based Factor Extraction
-
-Scen2 Intro
-~~~~~~~~~~~
-In this scenario, factors and hypotheses are extracted from research reports. The process includes:
-
-- **🔍 Factor Extraction**: Extract relevant factors from research reports.
-- **📝 Factor Creation**: Define these extracted factors.
-- **✅ Factor Validation**: Validate the extracted factors.
-- **📈 Backtesting with Qlib**: 
-
-  - **Dataset**: CSI300
-  - **Model**: LGBModel
-  - **Factors**: Alpha158 +
-  - **Data Split**:
-
-    - **Train**: 2008-01-01 to 2014-12-31
-    - **Valid**: 2015-01-01 to 2016-12-31
-    - **Test**: 2017-01-01 to 2020-08-01
-- **🔄 Feedback Analysis**: Analyze backtest results.
-- **🔧 Hypothesis Refinement**: Refine hypotheses based on feedback and continue the cycle.
-
-Scen2 Demo
+**Automated Quantitative Trading & Factors Extraction from Financial Reports📄**
+---------------------------------------------------------------------------------
+
+Background
+~~~~~~~~~~
+**Research reports** are treasure troves of insights, often unveiling potential **factors** that can drive successful quantitative trading strategies. 
+Yet, with the sheer volume of reports available, extracting the most valuable insights efficiently becomes a daunting task.
+
+Furthermore, rather than hastily replicating factors from a report, it's essential to delve into the underlying logic of their construction. 
+Does the factor capture the essential market dynamics? How unique is it compared to the factors already in your library?
+
+Therefore, there is an urgent need for a systematic approach to design a framework that can effectively manage this process. 
+This is where our R&D Agent comes into play.
+
+
+
+Introduction
+~~~~~~~~~~~~
+In this scenario, our agent demonstrates the process of extracting factors from financial research reports, implementing these factors, and analyzing their performance through Qlib backtesting. 
+This process continually expands and refines the factor library.
+
+Here's an enhanced outline of the steps:
+
+**Step 1 : Hypothesis Generation 🔍**
+
+- Generate and propose initial hypotheses based on insights from financial reports with thorough reasoning and financial justification.
+
+**Step 2 : Factor Creation ✨**
+
+- Based on the hypothesis and financial reports, divide the tasks. 
+- Each task involves developing, defining, and implementing a new financial factor, including its name, description, formulation, and variables.
+
+**Step 3 : Factor Implementation 👨‍💻**
+
+- Implement the factor code based on the description, evolving it as a developer would.
+- Quantitatively validate the newly created factors.
+
+**Step 4 : Backtesting with Qlib 📉**
+
+- Integrate the full dataset into the factor implementation code and prepare the factor library.
+- Perform backtesting using the Alpha158+ model in Qlib to assess the factor's effectiveness and performance.
+
++----------------+------------+----------------+----------------------------------------------------+
+| Dataset        | Model      | Factors        | Data Split                                         |
++================+============+================+====================================================+
+| CSI300         | LGBModel   | Alpha158 Plus  | +-----------+--------------------------+           |
+|                |            |                | | Train     | 2008-01-01 to 2014-12-31 |           |
+|                |            |                | +-----------+--------------------------+           |
+|                |            |                | | Valid     | 2015-01-01 to 2016-12-31 |           |
+|                |            |                | +-----------+--------------------------+           |
+|                |            |                | | Test      | 2017-01-01 to 2020-08-01 |           |
+|                |            |                | +-----------+--------------------------+           |
++----------------+------------+----------------+----------------------------------------------------+
+
+
+**Step 5 : Feedback Analysis 🔍**
+   - Analyze backtest results.
+   - Incorporate feedback to refine hypotheses.
+
+**Step 6 : Knowledge Base Refinement ♻️**
+   - Refine the knowledge base based on feedback and repeat the process.
+
+Demo
 ~~~~~~~~~~
 .. TODO
 
 Scen2 Quick Start
 ~~~~~~~~~~~~~~~~~
 
-To quickly start the factor extraction process, run the following command in your terminal within the 'rdagent' virtual environment:
+To quickly start the factor extraction process, run the following command in your terminal within the  `rdagent` virtual environment:
 
 .. code-block:: sh
 
-    python rdagent/app/qlib_rd_loop/factor_from_report_sh.py
+    python rdagent/app/qlib_rd_loop/factor_from_report_w_sc.py.py
 
 
 Usage of modules

rdagent/scenarios/qlib/experiment/factor_experiment.py

@@ -43,7 +43,7 @@ def simulator(self) -> str:
     @property
     def rich_style_description(self) -> str:
         return """
-### R&D Agent-Qlib: Automated Quantitative Trading & Iterative Factor Evolution Demo
+### R&D Agent-Qlib: Automated Quantitative Trading & Iterative Factors Evolution Demo
 
 #### [Overview](#_summary)
 

rdagent/scenarios/qlib/experiment/factor_from_report_experiment.py

@@ -17,7 +17,7 @@ class QlibFactorFromReportScenario(QlibFactorScenario):
     @property
     def rich_style_description(self) -> str:
         return """
-### R&D Agent-Qlib: Automated Quantitative Trading & Factor Extraction from Financial Reports Demo
+### R&D Agent-Qlib: Automated Quantitative Trading & Factors Extraction from Financial Reports Demo
 
 #### [Overview](#_summary)