From 464d99808fbcb6e4d22a782ab662daecdfaa5460 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 14 Aug 2022 18:22:01 +0200 Subject: [PATCH] Update doc table formatting --- docs/freqai.md | 2 +- freqtrade/freqai/freqai_interface.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/freqai.md b/docs/freqai.md index 6631c271e..cde3ea0f7 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -133,7 +133,7 @@ Here are the values the user can expect to include/use inside the typical strate | `df['&*_std/mean']` | The standard deviation and mean values of the user defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand rarity of prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training (or historically with `fit_live_predictions_candles`)
**Datatype:** float. | `df['do_predict']` | An indication of an outlier, this return value is integer between -1 and 2 which lets the user understand if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the [Dissimilarity Index](#removing-outliers-with-the-dissimilarity-index) is above the user defined threshold, it will subtract 1 from `do_predict`. If `use_SVM_to_remove_outliers()` is active, then the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract one from `do_predict`. A particular case is when `do_predict == 2`, it means that the model has expired due to `expired_hours`.
**Datatype:** integer between -1 and 2. | `df['DI_values']` | The raw Dissimilarity Index values to give the user a sense of confidence in the prediction. Lower DI means the data point is closer to the trained parameter space.
**Datatype:** float. -| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature inside FreqAI. For example, the user can include the rsi in the training feature set (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#building-the-feature-set). Note: since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table.) these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, you can prepend it with `%%`.
**Datatype:** depends on the output of the model. +| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature inside FreqAI. For example, the user can include the rsi in the training feature set (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#building-the-feature-set).
**Note**: since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table.) these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, you can prepend it with `%%`.
**Datatype:** depends on the output of the model. ### Example config file diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 4c9cf1c05..e48de1fb8 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -500,7 +500,7 @@ class IFreqaiModel(ABC): dk.set_new_model_names(pair, new_trained_timerange) self.dd.pair_dict[pair]["first"] = False if self.dd.pair_dict[pair]["priority"] == 1 and self.scanning: - with self.lock: + with dk.analysis_lock: self.dd.pair_to_end_of_training_queue(pair) self.dd.save_data(model, pair, dk)