add doc for single precision, dont allow half precision, add test

docs/freqai-parameter-table.md

@@ -18,6 +18,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer.
 | `follow_mode` | Use a `follower` that will look for models associated with a specific `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. <br> Default: `False`.
 | `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). <br> **Datatype:** Boolean. <br> Default: `False`.
+| `convert_df_to_float32` | Recast all numeric columns to float32, with the objective of reducing ram/disk usage and decreasing train/inference timing. <br> **Datatype:** Boolean. <br> Default: `False`.
 |  |  **Feature parameters**
 | `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md). <br> **Datatype:** Dictionary.
 | `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).

freqtrade/freqai/data_kitchen.py

@@ -1357,21 +1357,10 @@ class FreqaiDataKitchen:
 
         for col in df.columns[1:]:
             col_type = df[col].dtype
-
             if col_type != object:
-                c_min = df[col].min()
-                c_max = df[col].max()
                 if str(col_type)[:3] == "int":
-                    if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
-                        df[col] = df[col].astype(np.int8)
-                    elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
-                        df[col] = df[col].astype(np.int16)
-                    elif c_min > np.iinfo(np.int32).min:
                     df[col] = df[col].astype(np.int32)
                 else:
-                    if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
-                        df[col] = df[col].astype(np.float16)
-                    elif c_min > np.finfo(np.float32).min:
                     df[col] = df[col].astype(np.float32)
 
         end_mem = df.memory_usage().sum() / 1024**2

tests/freqai/test_freqai_interface.py

@@ -27,13 +27,13 @@ def is_mac() -> bool:
     return "Darwin" in machine
 
 
-@pytest.mark.parametrize('model, pca, dbscan', [
+@pytest.mark.parametrize('model, pca, dbscan, float32', [
-    ('LightGBMRegressor', True, False),
+    ('LightGBMRegressor', True, False, True),
-    ('XGBoostRegressor', False, True),
+    ('XGBoostRegressor', False, True, False),
-    ('XGBoostRFRegressor', False, False),
+    ('XGBoostRFRegressor', False, False, False),
-    ('CatboostRegressor', False, False),
+    ('CatboostRegressor', False, False, False),
     ])
-def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, dbscan):
+def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, dbscan, float32):
     if is_arm() and model == 'CatboostRegressor':
         pytest.skip("CatBoost is not supported on ARM")
 
@@ -43,6 +43,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
     freqai_conf.update({"strategy": "freqai_test_strat"})
     freqai_conf['freqai']['feature_parameters'].update({"principal_component_analysis": pca})
     freqai_conf['freqai']['feature_parameters'].update({"use_DBSCAN_to_remove_outliers": dbscan})
+    freqai_conf['freqai'].update({"convert_df_to_float32": float32})
 
     strategy = get_patched_freqai_strategy(mocker, freqai_conf)
     exchange = get_patched_exchange(mocker, freqai_conf)