mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-06 11:37:44 +00:00
WinstonLiyt
420dbbe584
* Optimize factor hypothesis prompt * Optimize the factor feedback prompt. * Improve the prompts in feedback(factor). * change some prompts * Added QlibFactorFromReportScenario and improved the report-factor loop. * reformat * reformat * reformat * reformat
105 lines
3.3 KiB
Python
105 lines
3.3 KiB
Python
from pathlib import Path
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from rdagent.components.coder.factor_coder.factor import (
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FactorExperiment,
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FactorFBWorkspace,
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FactorTask,
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)
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from rdagent.components.coder.factor_coder.utils import get_data_folder_intro
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from rdagent.core.prompts import Prompts
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from rdagent.core.scenario import Scenario
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from rdagent.scenarios.qlib.experiment.workspace import QlibFBWorkspace
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prompt_dict = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
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class QlibFactorExperiment(FactorExperiment[FactorTask, QlibFBWorkspace, FactorFBWorkspace]):
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def __init__(self, *args, **kwargs) -> None:
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super().__init__(*args, **kwargs)
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self.experiment_workspace = QlibFBWorkspace(template_folder_path=Path(__file__).parent / "factor_template")
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class QlibFactorScenario(Scenario):
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@property
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def background(self) -> str:
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return prompt_dict["qlib_factor_background"]
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@property
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def source_data(self) -> str:
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return get_data_folder_intro()
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@property
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def output_format(self) -> str:
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return prompt_dict["qlib_factor_output_format"]
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@property
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def interface(self) -> str:
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return prompt_dict["qlib_factor_interface"]
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@property
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def simulator(self) -> str:
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return prompt_dict["qlib_factor_simulator"]
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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 & Iterative Factor Evolution Demo
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#### [Overview](#_summary)
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The demo showcases the iterative process of hypothesis generation, knowledge construction, and decision-making. It highlights how financial factors evolve through continuous feedback and refinement.
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#### Key Steps
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1. **Hypothesis Generation**
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- Generate and propose initial hypotheses based on data and domain knowledge.
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2. **Factor Creation**
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- Develop, define, and implement new financial factors.
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- Test these factors to gather empirical results.
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3. **Factor Validation**
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- Quantitatively validate the newly created factors.
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4. **Backtesting with Qlib**
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| **Dataset** | **Model** | **Factors** |
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|------------------|-------------|----------------|
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| 📊 CSI300 | 🤖 LGBModel | 🌟 Alpha158 Plus|
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5. **Feedback Analysis**
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- Analyze backtest results.
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- Incorporate feedback to refine hypotheses.
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6. **Hypothesis Refinement**
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- Refine hypotheses based on feedback and repeat the process.
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#### [Automated R&D](#_rdloops)
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- **[R (Research)](#_research)**
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- Iterative development of ideas and hypotheses.
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- Continuous learning and knowledge construction.
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- **[D (Development)](#_development)**
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- Progressive implementation and code generation of factors.
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- Automated testing and validation of financial factors.
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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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return f"""Background of the scenario:
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{self.background}
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The source data you can use:
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{self.source_data}
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The interface you should follow to write the runnable code:
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{self.interface}
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The output of your code should be in the format:
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{self.output_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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