Add option to keep models only in memory for backtest

config_examples/config_freqai.example.json

@@ -56,6 +56,7 @@
         "purge_old_models": true,
         "train_period_days": 15,
         "backtest_period_days": 7,
+        "backtest_save_model": true,
         "live_retrain_hours": 0,
         "identifier": "uniqe-id",
         "feature_parameters": {
@@ -94,4 +95,4 @@
     "internals": {
         "process_throttle_secs": 5
     }
-}
+}

docs/freqai.md

@@ -93,6 +93,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `purge_old_models` | Delete obsolete models (otherwise, all historic models will remain on disk). <br> **Datatype:** Boolean. Default: `False`.
 | `train_period_days` | **Required.** <br> Number of days to use for the training data (width of the sliding window). <br> **Datatype:** Positive integer.
 | `backtest_period_days` | **Required.** <br> Number of days to inference from the trained model before sliding the window defined above, and retraining the model. This can be fractional days, but beware that the user-provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest. <br> **Datatype:** Float.
+| `backtest_save_model` | Saves models to disk when running backtesting. <br> **Datatype:** Boolean. Default: `True`.
 | `identifier` | **Required.** <br> A unique name for the current model. This can be reused to reload pre-trained models/data. <br> **Datatype:** String.
 | `live_retrain_hours` | Frequency of retraining during dry/live runs. <br> Default set to 0, which means the model will retrain as often as possible. <br> **Datatype:** Float > 0.
 | `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old. <br> Defaults set to 0, which means models never expire. <br> **Datatype:** Positive integer.

freqtrade/freqai/freqai_interface.py

@@ -71,6 +71,7 @@ class IFreqaiModel(ABC):
         self.first = True
         self.set_full_path()
         self.follow_mode: bool = self.freqai_info.get("follow_mode", False)
+        self.backtest_save_model: bool = self.freqai_info.get("backtest_save_model", True)
         self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
         self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
         self.scanning = False
@@ -246,7 +247,8 @@ class IFreqaiModel(ABC):
                     self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int(
                         trained_timestamp.stopts)
                     dk.set_new_model_names(metadata["pair"], trained_timestamp)
-                    self.dd.save_data(self.model, metadata["pair"], dk)
+                    if self.backtest_save_model:
+                        self.dd.save_data(self.model, metadata["pair"], dk)
                 else:
                     self.model = self.dd.load_data(metadata["pair"], dk)