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Merge pull request #7613 from freqtrade/fix_typo_fit_live_predictions_candles

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fix typos - live predictions candles
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Robert Caulk 2022-10-21 16:19:40 +02:00 committed by GitHub
commit 410a744ee9
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4 changed files with 5 additions and 5 deletions
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4
docs/freqai-configuration.md
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@ -192,11 +192,11 @@ dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25 dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
``` ```
To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_prediction_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics. To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_predictions_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics.
```json ```json
"freqai": { "freqai": {
"fit_live_prediction_candles": 300, "fit_live_predictions_candles": 300,
} }
``` ```

2
freqtrade/freqai/base_models/BaseClassifierModel.py
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@ -51,7 +51,7 @@ class BaseClassifierModel(IFreqaiModel):
f"{end_date} --------------------") f"{end_date} --------------------")
# split data into train/test data. # split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered) data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get("fit_live_predictions", 0) or not self.live: if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
dk.fit_labels() dk.fit_labels()
# normalize all data based on train_dataset only # normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary) data_dictionary = dk.normalize_data(data_dictionary)

2
freqtrade/freqai/base_models/BaseRegressionModel.py
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@ -50,7 +50,7 @@ class BaseRegressionModel(IFreqaiModel):
f"{end_date} --------------------") f"{end_date} --------------------")
# split data into train/test data. # split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered) data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get("fit_live_predictions", 0) or not self.live: if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
dk.fit_labels() dk.fit_labels()
# normalize all data based on train_dataset only # normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary) data_dictionary = dk.normalize_data(data_dictionary)

2
freqtrade/freqai/base_models/BaseTensorFlowModel.py
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@ -47,7 +47,7 @@ class BaseTensorFlowModel(IFreqaiModel):
f"{end_date} --------------------") f"{end_date} --------------------")
# split data into train/test data. # split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered) data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get("fit_live_predictions", 0) or not self.live: if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
dk.fit_labels() dk.fit_labels()
# normalize all data based on train_dataset only # normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary) data_dictionary = dk.normalize_data(data_dictionary)