diff --git a/patches/qlib_experiment_prompts.yaml b/patches/qlib_experiment_prompts.yaml index 1fa8da89..f43ec898 100644 --- a/patches/qlib_experiment_prompts.yaml +++ b/patches/qlib_experiment_prompts.yaml @@ -76,24 +76,21 @@ qlib_factor_strategy: |- qlib_factor_output_format: |- Your output should be a pandas dataframe similar to the following example information: - MultiIndex: 40914 entries, (Timestamp('2020-01-02 00:00:00'), 'SH600000') to (Timestamp('2021-12-31 00:00:00'), 'SZ300059') + MultiIndex: 2261923 entries, (Timestamp('2020-01-01 17:00:00'), 'EURUSD') to (Timestamp('2026-03-20 15:58:00'), 'EURUSD') Data columns (total 1 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 your factor name 40914 non-null float64 + 0 your factor name 2261923 non-null float64 dtypes: float64(1) memory usage: Notice: The non-null count is OK to be different to the total number of entries since some instruments may not have the factor value on some days. One possible format of `result.h5` may be like following: datetime instrument - 2020-01-02 SZ000001 -0.001796 - SZ000166 0.005780 - SZ000686 0.004228 - SZ000712 0.001298 - SZ000728 0.005330 - ... - 2021-12-31 SZ000750 0.000000 - SZ000776 0.002459 + 2020-01-01 EURUSD 1.094240 + 2020-01-02 EURUSD 1.094280 + 2020-01-03 EURUSD 1.095920 + ... + 2026-03-20 EURUSD 1.083150 qlib_factor_simulator: |- The factors will be sent into Qlib to train a model to predict the next several days return based on the factor values of the previous days. @@ -162,7 +159,7 @@ qlib_factor_from_report_rich_style_description : |- qlib_factor_experiment_setting: |- | Dataset 📊 | Model 🤖 | Factors 🌟 | Data Split 🧮 | |---------|----------|---------------|-------------------------------------------------| - | CSI300 | LGBModel | Alpha158 Plus | Train: 2008-01-01 to 2014-12-31
Valid: 2015-01-01 to 2016-12-31
Test  : 2017-01-01 to 2020-08-01 | + | EURUSD | LGBModel | Alpha158 Plus | Train: 2022-01-01 to 2024-06-30
Valid: 2024-07-01 to 2024-12-31
Test  : 2025-01-01 to 2026-03-20 | qlib_model_background: |- @@ -257,4 +254,4 @@ qlib_model_rich_style_description: |- qlib_model_experiment_setting: |- | Dataset 📊 | Model 🤖 | Factors 🌟 | Data Split 🧮 | |---------|----------|---------------|-------------------------------------------------| - | CSI300 | RDAgent-dev | 20 factors (Alpha158) | Train: 2008-01-01 to 2014-12-31
Valid: 2015-01-01 to 2016-12-31
Test  : 2017-01-01 to 2020-08-01 | \ No newline at end of file + | EURUSD | RDAgent-dev | 20 factors (Alpha158) | Train: 2022-01-01 to 2024-06-30
Valid: 2024-07-01 to 2024-12-31
Test  : 2025-01-01 to 2026-03-20 | \ No newline at end of file diff --git a/rdagent/scenarios/qlib/experiment/factor_template/conf_baseline.yaml b/rdagent/scenarios/qlib/experiment/factor_template/conf_baseline.yaml index 4583dc3e..af553d5b 100644 --- a/rdagent/scenarios/qlib/experiment/factor_template/conf_baseline.yaml +++ b/rdagent/scenarios/qlib/experiment/factor_template/conf_baseline.yaml @@ -3,7 +3,7 @@ qlib_init: region: cn market: &market csi300 -benchmark: &benchmark SH000300 +benchmark: &benchmark EURUSD data_handler_config: &data_handler_config start_time: {{ train_start | default("2008-01-01", true) }} @@ -35,7 +35,6 @@ data_handler_config: &data_handler_config fields_group: feature learn_processors: - class: DropnaLabel - - class: CSZScoreNorm kwargs: fields_group: label diff --git a/rdagent/scenarios/qlib/experiment/factor_template/conf_combined_factors.yaml b/rdagent/scenarios/qlib/experiment/factor_template/conf_combined_factors.yaml index df535694..8114ddbf 100644 --- a/rdagent/scenarios/qlib/experiment/factor_template/conf_combined_factors.yaml +++ b/rdagent/scenarios/qlib/experiment/factor_template/conf_combined_factors.yaml @@ -3,7 +3,7 @@ qlib_init: region: cn market: &market csi300 -benchmark: &benchmark SH000300 +benchmark: &benchmark EURUSD data_handler_config: &data_handler_config start_time: {{ train_start | default("2008-01-01", true) }} @@ -28,7 +28,6 @@ data_handler_config: &data_handler_config learn_processors: - class: DropnaLabel - - class: CSZScoreNorm kwargs: fields_group: label diff --git a/rdagent/scenarios/qlib/experiment/factor_template/conf_combined_factors_sota_model.yaml b/rdagent/scenarios/qlib/experiment/factor_template/conf_combined_factors_sota_model.yaml index f47941e0..94f76a7b 100644 --- a/rdagent/scenarios/qlib/experiment/factor_template/conf_combined_factors_sota_model.yaml +++ b/rdagent/scenarios/qlib/experiment/factor_template/conf_combined_factors_sota_model.yaml @@ -3,7 +3,7 @@ qlib_init: region: cn market: &market csi300 -benchmark: &benchmark SH000300 +benchmark: &benchmark EURUSD data_handler_config: &data_handler_config start_time: {{ train_start | default("2008-01-01", true) }} @@ -38,7 +38,6 @@ data_handler_config: &data_handler_config fields_group: feature learn_processors: - class: DropnaLabel - - class: CSZScoreNorm kwargs: fields_group: label diff --git a/rdagent/scenarios/qlib/experiment/model_template/conf_baseline_factors_model.yaml b/rdagent/scenarios/qlib/experiment/model_template/conf_baseline_factors_model.yaml index ef6cb0a9..b5a919bb 100644 --- a/rdagent/scenarios/qlib/experiment/model_template/conf_baseline_factors_model.yaml +++ b/rdagent/scenarios/qlib/experiment/model_template/conf_baseline_factors_model.yaml @@ -2,7 +2,7 @@ qlib_init: provider_uri: "~/.qlib/qlib_data/cn_data" region: cn market: &market csi300 -benchmark: &benchmark SH000300 +benchmark: &benchmark EURUSD data_handler_config: &data_handler_config start_time: {{ train_start | default("2008-01-01", true) }} @@ -34,7 +34,6 @@ data_handler_config: &data_handler_config fields_group: feature learn_processors: - class: DropnaLabel - - class: CSZScoreNorm kwargs: fields_group: label diff --git a/rdagent/scenarios/qlib/experiment/model_template/conf_sota_factors_model.yaml b/rdagent/scenarios/qlib/experiment/model_template/conf_sota_factors_model.yaml index f47941e0..94f76a7b 100644 --- a/rdagent/scenarios/qlib/experiment/model_template/conf_sota_factors_model.yaml +++ b/rdagent/scenarios/qlib/experiment/model_template/conf_sota_factors_model.yaml @@ -3,7 +3,7 @@ qlib_init: region: cn market: &market csi300 -benchmark: &benchmark SH000300 +benchmark: &benchmark EURUSD data_handler_config: &data_handler_config start_time: {{ train_start | default("2008-01-01", true) }} @@ -38,7 +38,6 @@ data_handler_config: &data_handler_config fields_group: feature learn_processors: - class: DropnaLabel - - class: CSZScoreNorm kwargs: fields_group: label diff --git a/rdagent/scenarios/qlib/experiment/prompts.yaml b/rdagent/scenarios/qlib/experiment/prompts.yaml index ae173a77..a86b8199 100644 --- a/rdagent/scenarios/qlib/experiment/prompts.yaml +++ b/rdagent/scenarios/qlib/experiment/prompts.yaml @@ -75,24 +75,21 @@ qlib_factor_strategy: |- qlib_factor_output_format: |- Your output should be a pandas dataframe similar to the following example information: - MultiIndex: 40914 entries, (Timestamp('2020-01-02 00:00:00'), 'SH600000') to (Timestamp('2021-12-31 00:00:00'), 'SZ300059') + MultiIndex: 2261923 entries, (Timestamp('2020-01-01 17:00:00'), 'EURUSD') to (Timestamp('2026-03-20 15:58:00'), 'EURUSD') Data columns (total 1 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 your factor name 40914 non-null float64 + 0 your factor name 2261923 non-null float64 dtypes: float64(1) memory usage: Notice: The non-null count is OK to be different to the total number of entries since some instruments may not have the factor value on some days. One possible format of `result.h5` may be like following: datetime instrument - 2020-01-02 SZ000001 -0.001796 - SZ000166 0.005780 - SZ000686 0.004228 - SZ000712 0.001298 - SZ000728 0.005330 - ... - 2021-12-31 SZ000750 0.000000 - SZ000776 0.002459 + 2020-01-01 EURUSD 1.094240 + 2020-01-02 EURUSD 1.094280 + 2020-01-03 EURUSD 1.095920 + ... + 2026-03-20 EURUSD 1.083150 qlib_factor_simulator: |- The factors will be sent into Qlib to train a model to predict the next several days return based on the factor values of the previous days. @@ -161,7 +158,7 @@ qlib_factor_from_report_rich_style_description : |- qlib_factor_experiment_setting: |- | Dataset 📊 | Model 🤖 | Factors 🌟 | Data Split 🧮 | |---------|----------|---------------|-------------------------------------------------| - | CSI300 | LGBModel | Alpha158 Plus | Train: {{ train_start }} to {{ train_end }}
Valid: {{ valid_start }} to {{ valid_end }}
Test  : {{ test_start }} to {{ test_end }} | + | EURUSD | LGBModel | Alpha158 Plus | Train: {{ train_start }} to {{ train_end }}
Valid: {{ valid_start }} to {{ valid_end }}
Test  : {{ test_start }} to {{ test_end }} | qlib_model_background: |- @@ -256,4 +253,4 @@ qlib_model_rich_style_description: |- qlib_model_experiment_setting: |- | Dataset 📊 | Model 🤖 | Factors 🌟 | Data Split 🧮 | |---------|----------|---------------|-------------------------------------------------| - | CSI300 | RDAgent-dev | 20 factors (Alpha158) | Train: {{ train_start }} to {{ train_end }}
Valid: {{ valid_start }} to {{ valid_end }}
Test  : {{ test_start }} to {{ test_end }} | \ No newline at end of file + | EURUSD | RDAgent-dev | 20 factors (Alpha158) | Train: {{ train_start }} to {{ train_end }}
Valid: {{ valid_start }} to {{ valid_end }}
Test  : {{ test_start }} to {{ test_end }} | \ No newline at end of file diff --git a/rdagent/scenarios/qlib/factor_experiment_loader/prompts.yaml b/rdagent/scenarios/qlib/factor_experiment_loader/prompts.yaml index 2c49a14b..8a96f6b5 100644 --- a/rdagent/scenarios/qlib/factor_experiment_loader/prompts.yaml +++ b/rdagent/scenarios/qlib/factor_experiment_loader/prompts.yaml @@ -97,7 +97,7 @@ classify_system: |- factor_viability_system: |- User has designed several factors in quant investment. Please help the user to check the viability of these factors. - These factors are used to build a daily frequency strategy in China A-share market. + These factors are used to build a daily frequency strategy in EURUSD 1min intraday FX market. User will provide a pandas dataframe like table containing following information: 1. The name of the factor; @@ -145,7 +145,7 @@ factor_viability_system: |- factor_relevance_system: |- User has designed several factors in quant investment. Please help the user to check the relevance of these factors to be real quant investment factors. - These factors are used to build a daily frequency strategy in China A-share market. + These factors are used to build a daily frequency strategy in EURUSD 1min intraday FX market. User will provide a pandas dataframe like table containing following information: 1. The name of the factor; @@ -186,7 +186,7 @@ factor_relevance_system: |- factor_duplicate_system: |- User has designed several factors in quant investment. Please help the user to duplicate these factors. - These factors are used to build a daily frequency strategy in China A-share market. + These factors are used to build a daily frequency strategy in EURUSD 1min intraday FX market. User will provide a pandas dataframe like table containing following information: 1. The name of the factor;