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WinstonLiyt
abcf7d913b
* Fixed some bugs introduced during refactoring. * Improved documentation for two factor scenarios. * Update factor_from_report_w_sc.py * Improved some details.
88 lines
3.8 KiB
ReStructuredText
88 lines
3.8 KiB
ReStructuredText
.. _data_copilot_fin:
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=====================
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Finance Data Copilot
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=====================
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**Automated Quantitative Trading & Factors Extraction from Financial Reports📄**
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---------------------------------------------------------------------------------
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Background
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~~~~~~~~~~
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**Research reports** are treasure troves of insights, often unveiling potential **factors** that can drive successful quantitative trading strategies.
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Yet, with the sheer volume of reports available, extracting the most valuable insights efficiently becomes a daunting task.
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Furthermore, rather than hastily replicating factors from a report, it's essential to delve into the underlying logic of their construction.
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Does the factor capture the essential market dynamics? How unique is it compared to the factors already in your library?
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Therefore, there is an urgent need for a systematic approach to design a framework that can effectively manage this process.
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This is where our R&D Agent comes into play.
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Introduction
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~~~~~~~~~~~~
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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.
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This process continually expands and refines the factor library.
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Here's an enhanced outline of the steps:
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**Step 1 : Hypothesis Generation 🔍**
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- Generate and propose initial hypotheses based on insights from financial reports with thorough reasoning and financial justification.
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**Step 2 : Factor Creation ✨**
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- Based on the hypothesis and financial reports, divide the tasks.
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- Each task involves developing, defining, and implementing a new financial factor, including its name, description, formulation, and variables.
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**Step 3 : Factor Implementation 👨💻**
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- Implement the factor code based on the description, evolving it as a developer would.
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- Quantitatively validate the newly created factors.
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**Step 4 : Backtesting with Qlib 📉**
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- Integrate the full dataset into the factor implementation code and prepare the factor library.
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- Perform backtesting using the Alpha158+ model in Qlib to assess the factor's effectiveness and performance.
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+----------------+------------+----------------+----------------------------------------------------+
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| Dataset | Model | Factors | Data Split |
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+================+============+================+====================================================+
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| CSI300 | LGBModel | Alpha158 Plus | +-----------+--------------------------+ |
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| | | | | Train | 2008-01-01 to 2014-12-31 | |
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| | | | +-----------+--------------------------+ |
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| | | | | Valid | 2015-01-01 to 2016-12-31 | |
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| | | | +-----------+--------------------------+ |
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| | | | | Test | 2017-01-01 to 2020-08-01 | |
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| | | | +-----------+--------------------------+ |
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+----------------+------------+----------------+----------------------------------------------------+
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**Step 5 : Feedback Analysis 🔍**
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- Analyze backtest results.
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- Incorporate feedback to refine hypotheses.
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**Step 6 : Knowledge Base Refinement ♻️**
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- Refine the knowledge base based on feedback and repeat the process.
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Demo
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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_w_sc.py.py
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Usage of modules
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~~~~~~~~~~~~~~~~~
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TODO: Show some examples:
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