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improve migration doc

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robcaulk
2023-06-10 13:00:59 +02:00
parent f8d7c2e21d
commit d9bdd879ab
1 changed files with 7 additions and 5 deletions
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docs/strategy_migration.md
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@@ -736,7 +736,7 @@ If you have created your own custom `IFreqaiModel` with a custom `train()`/`pred
The conversion involves first removing `data_cleaning_train/predict()` and replacing them with a `define_data_pipeline()` and `define_label_pipeline()` function to your `IFreqaiModel` class: The conversion involves first removing `data_cleaning_train/predict()` and replacing them with a `define_data_pipeline()` and `define_label_pipeline()` function to your `IFreqaiModel` class:
```python linenums="1" hl_lines="10-13 41-42 48-49" ```python linenums="1" hl_lines="11-14 43-44 51-52"
class MyCoolFreqaiModel(BaseRegressionModel): class MyCoolFreqaiModel(BaseRegressionModel):
""" """
Some cool custom IFreqaiModel you made before Freqtrade version 2023.6 Some cool custom IFreqaiModel you made before Freqtrade version 2023.6
@@ -751,6 +751,7 @@ class MyCoolFreqaiModel(BaseRegressionModel):
# data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered) # data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
# self.data_cleaning_train(dk) # self.data_cleaning_train(dk)
# data_dictionary = dk.normalize_data(data_dictionary) # data_dictionary = dk.normalize_data(data_dictionary)
# (1)
# Add these lines. Now we control the pipeline fit/transform ourselves # Add these lines. Now we control the pipeline fit/transform ourselves
dd = dk.make_train_test_datasets(features_filtered, labels_filtered) dd = dk.make_train_test_datasets(features_filtered, labels_filtered)
@@ -780,6 +781,7 @@ class MyCoolFreqaiModel(BaseRegressionModel):
# Remove these lines: # Remove these lines:
# self.data_cleaning_predict(dk) # self.data_cleaning_predict(dk)
# (2)
# Add these lines: # Add these lines:
dk.data_dictionary["prediction_features"], outliers, _ = dk.feature_pipeline.transform( dk.data_dictionary["prediction_features"], outliers, _ = dk.feature_pipeline.transform(
@@ -787,6 +789,7 @@ class MyCoolFreqaiModel(BaseRegressionModel):
# Remove this line # Remove this line
# pred_df = dk.denormalize_labels_from_metadata(pred_df) # pred_df = dk.denormalize_labels_from_metadata(pred_df)
# (3)
# Replace with these lines # Replace with these lines
pred_df, _, _ = dk.label_pipeline.inverse_transform(pred_df) pred_df, _, _ = dk.label_pipeline.inverse_transform(pred_df)
@@ -798,7 +801,6 @@ class MyCoolFreqaiModel(BaseRegressionModel):
``` ```
1. Features - Move to `feature_engineering_expand_all` 1. Data normalization and cleaning is now homogenized with the new pipeline definition. This is created in the new `define_data_pipeline()` and `define_label_pipeline()` functions. The `data_cleaning_train()` and `data_cleaning_predict()` functions are no longer used. You can override `define_data_pipeline()` to create your own custom pipeline if you wish.
2. Basic features, not expanded across `include_periods_candles` - move to`feature_engineering_expand_basic()`. 2. Data normalization and cleaning is now homogenized with the new pipeline definition. This is created in the new `define_data_pipeline()` and `define_label_pipeline()` functions. The `data_cleaning_train()` and `data_cleaning_predict()` functions are no longer used. You can override `define_data_pipeline()` to create your own custom pipeline if you wish.
3. Standard features which should not be expanded - move to `feature_engineering_standard()`. 3. Data denormalization is done with the new pipeline. Replace this with the lines below.
4. Targets - Move this part to `set_freqai_targets()`.