docs: update readme for v0.5.0 release (#973)

README.md

@@ -196,6 +196,11 @@ Ensure the current user can run Docker commands **without using sudo**. You can
 
 The **[🖥️ Live Demo](https://rdagent.azurewebsites.net/)** is implemented by the following commands(each item represents one demo, you can select the one you prefer):
 
+- Run the **Automated Quantitative Trading & Iterative Factors Model Joint Evolution**:  [Qlib](http://github.com/microsoft/qlib) self-loop factor & model proposal and implementation application
+  ```sh
+  rdagent fin_quant
+  ```
+
 - Run the **Automated Quantitative Trading & Iterative Factors Evolution**:  [Qlib](http://github.com/microsoft/qlib) self-loop factor proposal and implementation application
   ```sh
   rdagent fin_factor
@@ -206,19 +211,6 @@ The **[🖥️ Live Demo](https://rdagent.azurewebsites.net/)** is implemented b
   rdagent fin_model
   ```
 
-- Run the **Automated Medical Prediction Model Evolution**: Medical self-loop model proposal and implementation application
-  >(1) Apply for an account at [PhysioNet](https://physionet.org/). <br /> (2) Request access to FIDDLE preprocessed data: [FIDDLE Dataset](https://physionet.org/content/mimic-eicu-fiddle-feature/1.0.0/). <br />
-  (3) Place your username and password in `.env`.
-  ```bash
-  cat << EOF  >> .env
-  DM_USERNAME=<your_username>
-  DM_PASSWORD=<your_password>
-  EOF
-  ```
-  ```sh
-  rdagent med_model
-  ```
-
 - Run the **Automated Quantitative Trading & Factors Extraction from Financial Reports**:  Run the [Qlib](http://github.com/microsoft/qlib) factor extraction and implementation application based on financial reports
   ```sh
   # 1. Generally, you can run this scenario using the following command:

docs/scens/data_science.rst

@@ -61,13 +61,13 @@ The Data Science Agent is an agent that can automatically perform feature engine
 
 - 🔧 **Set up Environment for Custom User-defined Dataset**
 
-    .. code-block:: sh
+  .. code-block:: sh
 
-        dotenv set DS_SCEN rdagent.scenarios.data_science.scen.DataScienceScen
-        dotenv set DS_LOCAL_DATA_PATH <your local directory>/ds_data (e.g. rdagent/scenarios/data_science/example)
-        dotenv set DS_IF_USING_MLE_DATA False
-        dotenv set DS_CODER_ON_WHOLE_PIPELINE True
-        dotenv set DS_CODER_COSTEER_ENV_TYPE docker
+      dotenv set DS_SCEN rdagent.scenarios.data_science.scen.DataScienceScen
+      dotenv set DS_LOCAL_DATA_PATH rdagent/scenarios/data_science/example
+      dotenv set DS_IF_USING_MLE_DATA False
+      dotenv set DS_CODER_ON_WHOLE_PIPELINE True
+      dotenv set DS_CODER_COSTEER_ENV_TYPE docker
 
 🔍 MLE-bench Guide: Running ML Engineering via MLE-bench
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~