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feat: Add description for scenario experiments. (#174)
* Fixed some bugs introduced during refactoring. * Add description for scenario experiments. * Update factor_from_report_w_sc.py * fix some ci bugs. * add @property to get_experiment_setting * fix a ci error.
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@@ -35,3 +35,8 @@ class Scenario(ABC):
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@abstractmethod
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def get_scenario_all_desc(self) -> str:
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"""Combine all the description together"""
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@property
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@abstractmethod
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def get_experiment_setting(self) -> str:
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"""Get experiment setting and return as rich text string"""
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@@ -531,13 +531,13 @@ with st.container():
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st.header("Scenario Description📖", divider="violet", anchor="_scenario")
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# TODO: other scenarios
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if state.log_type == "Qlib Model":
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st.markdown(QlibModelScenario().rich_style_description)
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st.markdown(QlibModelScenario().rich_style_description, unsafe_allow_html=True)
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elif state.log_type == "Data Mining":
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st.markdown(DMModelScenario().rich_style_description)
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elif state.log_type == "Qlib Factor":
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st.markdown(QlibFactorScenario().rich_style_description)
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st.markdown(QlibFactorScenario().rich_style_description, unsafe_allow_html=True)
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elif state.log_type == "Model from Paper":
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st.markdown(GeneralModelScenario().rich_style_description)
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st.markdown(GeneralModelScenario().rich_style_description, unsafe_allow_html=True)
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# Summary Window
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@@ -62,7 +62,6 @@ The demo showcases the iterative process of hypothesis generation, knowledge con
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#### [Objective](#_summary)
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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.
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"""
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def get_scenario_all_desc(self) -> str:
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@@ -77,3 +76,11 @@ The output of your code should be in the format:
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The simulator user can use to test your factor:
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{self.simulator}
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"""
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@property
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def get_experiment_setting(self) -> str:
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return """
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| Dataset 📊 | Model 🤖 | Factors 🌟 | Data Split 🧮 |
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|---------|----------|---------------|-------------------------------------------------|
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| CSI300 | LGBModel | Alpha158 Plus | Train: 2008-01-01 to 2014-12-31 <br> Valid: 2015-01-01 to 2016-12-31 <br> Test : 2017-01-01 to 2020-08-01 |
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"""
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@@ -17,7 +17,8 @@ class QlibFactorFromReportScenario(QlibFactorScenario):
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@property
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def rich_style_description(self) -> str:
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return """
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### R&D Agent-Qlib: Automated Quantitative Trading & Factors Extraction from Financial Reports Demo
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### R&D Agent-Qlib: Automated Quantitative Trading & Factor Extraction from Financial Reports Demo
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#### [Overview](#_summary)
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@@ -35,10 +36,26 @@ This demo showcases the process of extracting factors from financial research re
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#### [Objective](#_summary)
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| Objective | Description |
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|-------------------|---------------------------------------------------------------------------------------------------------|
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| **Convenience** | Provide a tool for financial and quantitative practitioners or enthusiasts to quickly extract and test factors from research reports. |
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| **Efficiency** | Enable rapid identification of factors from a vast number of reports that could enhance the current factor library. |
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| **Research Facilitation** | Support further research by continuously expanding and refining the factor library. |
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| **Innovation** | Foster innovation in financial analysis by leveraging automated R&D processes to iterate and improve financial factors. |
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<table border="1" style="width:100%; border-collapse: collapse;">
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<tr>
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<td>💡 <strong>Innovation </strong></td>
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<td>Tool to quickly extract and test factors from research reports.</td>
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</tr>
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<tr>
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<td>⚡ <strong>Efficiency </strong></td>
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<td>Rapid identification of valuable factors from numerous reports.</td>
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</tr>
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<tr>
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<td>🗃️ <strong>Outputs </strong></td>
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<td>Expand and refine the factor library to support further research.</td>
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</tr>
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</table>
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"""
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@property
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def get_experiment_setting(self) -> str:
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return """
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| Dataset 📊 | Model 🤖 | Factors 🌟 | Data Split 🧮 |
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|---------|----------|---------------|-------------------------------------------------|
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| CSI300 | LGBModel | Alpha158 Plus | Train: 2008-01-01 to 2014-12-31 <br> Valid: 2015-01-01 to 2016-12-31 <br> Test : 2017-01-01 to 2020-08-01 |
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"""
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@@ -60,7 +60,8 @@ The demo showcases the iterative process of hypothesis generation, knowledge con
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#### [Objective](#_summary)
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To demonstrate the dynamic evolution of models through the Qlib platform, emphasizing how each iteration enhances the accuracy and reliability of the resulting models. """
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To demonstrate the dynamic evolution of models through the Qlib platform, emphasizing how each iteration enhances the accuracy and reliability of the resulting models.
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"""
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def get_scenario_all_desc(self) -> str:
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return f"""Background of the scenario:
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@@ -72,3 +73,10 @@ The output of your code should be in the format:
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The simulator user can use to test your model:
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{self.simulator}
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"""
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def get_experiment_setting(self) -> str:
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return """
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| Dataset 📊 | Model 🤖 | Factors 🌟 | Data Split 🧮 |
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|---------|----------|---------------|-------------------------------------------------|
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| 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 : 2017-01-01 to 2020-08-01 |
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"""
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