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https://github.com/NicolasBohn/NexQuant.git
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WinstonLiyt
25fb571a0f
- 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.
111 lines
2.8 KiB
ReStructuredText
111 lines
2.8 KiB
ReStructuredText
=========================
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Scenarios and Quick Start
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=========================
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Scenario lists
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=========================
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.. list-table::
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:header-rows: 1
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* - Scenario/Target
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- Model Implementation
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- Data Building
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* - 💹 Finance
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- Iteratively Proposing Ideas & Evolving
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- Auto reports reading & implementation
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Iteratively Proposing Ideas & Evolving
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* - 🩺 Medical
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- Iteratively Proposing Ideas & Evolving
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-
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* - 🏭 General
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- Auto paper reading & implementation
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-
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Scnarios' demo & quick start
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=========================
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Scen1
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-----
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🤖 Knowledge-Based Hypothesis Generation and Iteration
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Scen1 Intro
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~~~~~~~~~~~
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In this scenario, our model autonomously generates and tests hypotheses using a knowledge base. The process involves:
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- **🔍 Hypothesis Generation**: The model proposes new hypotheses.
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- **📝 Factor Creation**: Write and define new factors.
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- **✅ Factor Validation**: Validate the factors quantitatively.
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- **📈 Backtesting with Qlib**:
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- **Dataset**: CSI300
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- **Model**: LGBModel
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- **Factors**: Alpha158 +
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- **Data Split**:
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- **Train**: 2008-01-01 to 2014-12-31
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- **Valid**: 2015-01-01 to 2016-12-31
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- **Test**: 2017-01-01 to 2020-08-01
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- **🔄 Feedback Analysis**: Analyze backtest results.
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- **🔧 Hypothesis Refinement**: Refine hypotheses based on feedback and repeat.
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Scen1 Demo
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~~~~~~~~~~
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.. TODO
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Scen1 Quick Start
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~~~~~~~~~~~~~~~~~
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To quickly start the factor extraction process, run the following command in your terminal within the 'rdagent' virtual environment:
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.. code-block:: sh
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python rdagent/app/qlib_rd_loop/factor.py
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Usage of modules
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~~~~~~~~~~~~~~~~~
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TODO: Show some examples:
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Scen2:
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-----
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📄 Research Report-Based Factor Extraction
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Scen2 Intro
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~~~~~~~~~~~
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In this scenario, factors and hypotheses are extracted from research reports. The process includes:
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- **🔍 Factor Extraction**: Extract relevant factors from research reports.
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- **📝 Factor Creation**: Define these extracted factors.
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- **✅ Factor Validation**: Validate the extracted factors.
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- **📈 Backtesting with Qlib**:
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- **Dataset**: CSI300
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- **Model**: LGBModel
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- **Factors**: Alpha158 +
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- **Data Split**:
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- **Train**: 2008-01-01 to 2014-12-31
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- **Valid**: 2015-01-01 to 2016-12-31
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- **Test**: 2017-01-01 to 2020-08-01
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- **🔄 Feedback Analysis**: Analyze backtest results.
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- **🔧 Hypothesis Refinement**: Refine hypotheses based on feedback and continue the cycle.
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Scen2 Demo
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~~~~~~~~~~
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.. TODO
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Scen2 Quick Start
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~~~~~~~~~~~~~~~~~
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To quickly start the factor extraction process, run the following command in your terminal within the 'rdagent' virtual environment:
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.. code-block:: sh
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python rdagent/app/qlib_rd_loop/factor_from_report_sh.py
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Usage of modules
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~~~~~~~~~~~~~~~~~
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TODO: Show some examples: |