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fix: remove all Chinese stock references, replace with EURUSD 1min FX

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- experiment/prompts.yaml: SH/SZ examples -> EURUSD, CSI300 -> EURUSD
- patches/qlib_experiment_prompts.yaml: complete EURUSD migration
- factor_experiment_loader/prompts.yaml: A-share -> EURUSD 1min intraday
- conf_*.yaml: benchmark SH000300 -> EURUSD, removed CSZFillNan/CSZScoreNorm
This commit is contained in:
TPTBusiness
2026-03-28 11:26:37 +01:00
parent b39f2b7e46
commit 44eeb01ec4
8 changed files with 26 additions and 37 deletions
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patches/qlib_experiment_prompts.yaml
+9 -12
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@@ -76,24 +76,21 @@ qlib_factor_strategy: |-
qlib_factor_output_format: |-
Your output should be a pandas dataframe similar to the following example information:
<class 'pandas.core.frame.DataFrame'>
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: <ignore>
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 <br> Valid: 2015-01-01 to 2016-12-31 <br> Test &nbsp;: 2017-01-01 to 2020-08-01 |
| EURUSD | LGBModel | Alpha158 Plus | Train: 2022-01-01 to 2024-06-30 <br> Valid: 2024-07-01 to 2024-12-31 <br> Test &nbsp;: 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 <br> Valid: 2015-01-01 to 2016-12-31 <br> Test &nbsp;: 2017-01-01 to 2020-08-01 |
| EURUSD | RDAgent-dev | 20 factors (Alpha158) | Train: 2022-01-01 to 2024-06-30 <br> Valid: 2024-07-01 to 2024-12-31 <br> Test &nbsp;: 2025-01-01 to 2026-03-20 |