From 72d33070d40323d579e1cc75ba3bfe40f0fb1c96 Mon Sep 17 00:00:00 2001 From: Matthias Date: Tue, 28 May 2024 06:37:54 +0200 Subject: [PATCH] Fix a few codespell typos --- docs/freqai-feature-engineering.md | 2 +- freqtrade/freqai/data_kitchen.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/freqai-feature-engineering.md b/docs/freqai-feature-engineering.md index f603baca3..d25051291 100644 --- a/docs/freqai-feature-engineering.md +++ b/docs/freqai-feature-engineering.md @@ -224,7 +224,7 @@ where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. B ## Building the data pipeline -By default, FreqAI builds a dynamic pipeline based on user congfiguration settings. The default settings are robust and designed to work with a variety of methods. These two steps are a `MinMaxScaler(-1,1)` and a `VarianceThreshold` which removes any column that has 0 variance. Users can activate other steps with more configuration parameters. For example if users add `use_SVM_to_remove_outliers: true` to the `freqai` config, then FreqAI will automatically add the [`SVMOutlierExtractor`](#identifying-outliers-using-a-support-vector-machine-svm) to the pipeline. Likewise, users can add `principal_component_analysis: true` to the `freqai` config to activate PCA. The [DissimilarityIndex](#identifying-outliers-with-the-dissimilarity-index-di) is activated with `DI_threshold: 1`. Finally, noise can also be added to the data with `noise_standard_deviation: 0.1`. Finally, users can add [DBSCAN](#identifying-outliers-with-dbscan) outlier removal with `use_DBSCAN_to_remove_outliers: true`. +By default, FreqAI builds a dynamic pipeline based on user configuration settings. The default settings are robust and designed to work with a variety of methods. These two steps are a `MinMaxScaler(-1,1)` and a `VarianceThreshold` which removes any column that has 0 variance. Users can activate other steps with more configuration parameters. For example if users add `use_SVM_to_remove_outliers: true` to the `freqai` config, then FreqAI will automatically add the [`SVMOutlierExtractor`](#identifying-outliers-using-a-support-vector-machine-svm) to the pipeline. Likewise, users can add `principal_component_analysis: true` to the `freqai` config to activate PCA. The [DissimilarityIndex](#identifying-outliers-with-the-dissimilarity-index-di) is activated with `DI_threshold: 1`. Finally, noise can also be added to the data with `noise_standard_deviation: 0.1`. Finally, users can add [DBSCAN](#identifying-outliers-with-dbscan) outlier removal with `use_DBSCAN_to_remove_outliers: true`. !!! note "More information available" Please review the [parameter table](freqai-parameter-table.md) for more information on these parameters. diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index b17fffe0a..d43f569d8 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -960,7 +960,7 @@ class FreqaiDataKitchen: """ Remove all special characters from feature strings (:) :param dataframe: the dataframe that just finished indicator population. (unfiltered) - :return: dataframe with cleaned featrue names + :return: dataframe with cleaned feature names """ spec_chars = [":"]