From e76700513bee29232c93b97414419df330d9be8d Mon Sep 17 00:00:00 2001 From: WinstonLiyt <104308117+WinstonLiyt@users.noreply.github.com> Date: Tue, 6 Aug 2024 11:07:10 +0800 Subject: [PATCH] feat: Remove redundant 'key steps' section in frontend scene display. (#169) * Fixed some bugs introduced during refactoring. * fix a minor bug * build factor source data (price and volumns) from qlib if no source data is provided by the user (#168) * Fixed some bugs introduced during refactoring. * fix a small bug * fix a small bug * Remove redundant 'key steps' section in frontend scene display. --------- Co-authored-by: Xu Yang --- .../qlib/experiment/factor_experiment.py | 25 ------------------- .../factor_from_report_experiment.py | 21 ---------------- 2 files changed, 46 deletions(-) diff --git a/rdagent/scenarios/qlib/experiment/factor_experiment.py b/rdagent/scenarios/qlib/experiment/factor_experiment.py index 8cd7404f..71433863 100644 --- a/rdagent/scenarios/qlib/experiment/factor_experiment.py +++ b/rdagent/scenarios/qlib/experiment/factor_experiment.py @@ -49,31 +49,6 @@ def rich_style_description(self) -> str: 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. -#### Key Steps - -1. **Hypothesis Generation** - - Generate and propose initial hypotheses based on data and domain knowledge. - -2. **Factor Creation** - - Develop, define, and implement new financial factors. - - Test these factors to gather empirical results. - -3. **Factor Validation** - - Quantitatively validate the newly created factors. - -4. **Backtesting with Qlib** - | **Dataset** | **Model** | **Factors** | - |------------------|-------------|----------------| - | 📊 CSI300 | 🤖 LGBModel | 🌟 Alpha158 Plus| - - -5. **Feedback Analysis** - - Analyze backtest results. - - Incorporate feedback to refine hypotheses. - -6. **Hypothesis Refinement** - - Refine hypotheses based on feedback and repeat the process. - #### [Automated R&D](#_rdloops) - **[R (Research)](#_research)** diff --git a/rdagent/scenarios/qlib/experiment/factor_from_report_experiment.py b/rdagent/scenarios/qlib/experiment/factor_from_report_experiment.py index df75c8ba..25a6b42a 100644 --- a/rdagent/scenarios/qlib/experiment/factor_from_report_experiment.py +++ b/rdagent/scenarios/qlib/experiment/factor_from_report_experiment.py @@ -23,27 +23,6 @@ def rich_style_description(self) -> str: This demo showcases the process of extracting factors from financial research reports, implementing these factors, and analyzing their performance through Qlib backtesting, continually expanding and refining the factor library. -#### Key Steps - -1. **Hypothesis Generation** - - Generate and propose initial hypotheses based on insights from financial reports. - -2. **Factor Creation** - - Develop, define, and codify new financial factors derived from the reports. - - Conduct empirical tests to evaluate these factors. - -3. **Factor Validation** - - Quantitatively validate the newly created factors. - -4. **Backtesting with Qlib** - | **Dataset** | **Model** | **Factors** | - |------------------|-------------|----------------| - | 📊 CSI300 | 🤖 LGBModel | 🌟 Alpha158 Plus| - -5. **Feedback Analysis** - - Analyze backtest results. - - Incorporate feedback to refine and enhance the factor hypotheses. - #### [Automated R&D](#_rdloops) - **[R (Research)](#_research)**