A draft of the "Quick Start" section for your README. Enable logging of backtesting in Qlib and store rich-text descriptions in Trace. Support one-step debugging for factor extraction. (#105)

- A draft of the "Quick Start" section for your README. 
- Enable logging of backtesting in Qlib and store rich-text descriptions in Trace. 
- Support one-step debugging for factor extraction.

docs/installation.rst

@@ -13,3 +13,27 @@ Configuration
 =============
 
 Quick configuration
+
+
+
+
+Azure OpenAI
+------------
+
+
+
+USE_AZURE_TOKEN_PROVIDER
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+### ☁️ Azure Configuration
+- Install Azure CLI:
+  ```sh
+  curl -L https://aka.ms/InstallAzureCli | bash
+  ```
+- Log in to Azure:
+  ```sh
+  az login --use-device-code
+  ```
+
+- `exit` and re-login to your environment (this step may not be necessary).
+

docs/scenarios_and_quickstart.rst

@@ -5,29 +5,107 @@ Scenarios and Quick Start
 Scenario lists
 =========================
 
-TODO: Copy the content in the README.md
+.. list-table:: 
+   :header-rows: 1
 
+   * - Scenario/Target
+     - Model Implementation
+     - Data Building
+   * - 💹 Finance
+     - Iteratively Proposing Ideas & Evolving
+     - Auto reports reading & implementation
+       Iteratively Proposing Ideas & Evolving
+   * - 🩺 Medical
+     - Iteratively Proposing Ideas & Evolving
+     - 
+   * - 🏭 General
+     - Auto paper reading & implementation
+     - 
 
 Scnarios' demo & quick start
 =========================
 
 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
 ~~~~~~~~~~
+.. TODO
 
 Scen1 Quick Start
 ~~~~~~~~~~~~~~~~~
 
+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.py
 
 
 Usage of modules
-================
+~~~~~~~~~~~~~~~~~
 TODO: Show some examples:
 
+
+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
+~~~~~~~~~~
+.. TODO
+
+Scen2 Quick Start
+~~~~~~~~~~~~~~~~~
+
+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
+
+
+Usage of modules
+~~~~~~~~~~~~~~~~~
+TODO: Show some examples: