Column names are defined by the download script. Note that future and kline data have the same column names when loaded from API. Future columns get prefix "f_".
The columns added to df are hard-coded by functions in module common.feature_generation.py
- klines columns: function
generate_features(). They are listed inApp.config.features_kline - futur columns: function
generate_features_futur(). They are listed inApp.config.features_futur - depth columns: function
generate_features_depth(). They are listed inApp.config.features_depth
The columns added to df are hard-coded by functions in module common.label_generation_highlow.py. These columns are similar to derived features but describe future values. Note that labels are derived from kline data only (at least if we trade on spot market).
Currently, we generate labels using generate_labels_thresholds() which is a long list in App.config.class_labels_all. Only a subset of these labels is really used listed in App.config.labels.
Predicting columns are generated using the following dimensions:
- target label (like
high_15): we generate predictions for each chosen label. Note that labels themselves may have structure like threshold level (10, 15, 20 etc.) but here we ignore this and assume that all labels are independent - input features (
k,f): currently we use either klines or future - algorithm (
gb,nn,lc): we do the same predictions using different algorithms. We assume that algorithm name includes also its hyper-parameters and maybe even history length. - history length: how much historic data was used to train the model. Currently we do not use this parameter. We could also assume that it is part of the algorithm or its hyper-parameters.
Accordingly, the generated (predicted) column names have the following structure:
<target_label>_<input_data>_<algorithm>
For example: high_15_k_nn means label high_15 for kline derived features and nn prediction model.