add exception for not passing timerange. Remove hard coded arguments for CatboostPredictionModels. Update docs

docs/freqai.md

@@ -83,7 +83,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `backtest_period_days` | **Required.** Number of days to inference into the trained model before sliding the window and retraining. 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.
 | `live_retrain_hours` | Frequency of retraining during dry/live runs. Default set to 0, which means it will retrain as often as possible. **Datatype:** Float > 0.
 | `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models. `False` by default. <br> **Datatype:** boolean.
-| `live_trained_timestamp` | Useful if user wants to start from models trained during a *backtest*. The timestamp can be located in the `user_data/models` backtesting folder. This is not a commonly used parameter, leave undefined for most applications. <br> **Datatype:** positive integer.
+| `startup_candles` | Number of candles needed for *backtesting only* to ensure all indicators are non NaNs at the start of the first train period. <br> **Datatype:** positive integer.
 | `fit_live_predictions_candles` | Computes target (label) statistics from prediction data, instead of from the training data set. Number of candles is the number of historical candles it uses to generate the statistics. <br> **Datatype:** positive integer.
 | `purge_old_models` | Tell FreqAI to delete obsolete models. Otherwise, all historic models will remain on disk. Defaults to `False`. <br> **Datatype:** boolean.
 | `expiration_hours` | Ask FreqAI to avoid making predictions if a model is more than `expiration_hours` old. Defaults to 0 which means models never expire. <br> **Datatype:** positive integer.

freqtrade/freqai/data_kitchen.py

@@ -76,6 +76,9 @@ class FreqaiDataKitchen:
         self.keras = self.freqai_config.get("keras", False)
         self.set_all_pairs()
         if not self.live:
+            if not self.config["timerange"]:
+                raise OperationalException(
+                    'Please pass --timerange if you intend to use FreqAI for backtesting.')
             self.full_timerange = self.create_fulltimerange(
                 self.config["timerange"], self.freqai_config.get("train_period_days")
             )

freqtrade/freqai/prediction_models/CatboostPredictionModel.py

@@ -38,8 +38,6 @@ class CatboostPredictionModel(BaseRegressionModel):
 
         model = CatBoostRegressor(
             allow_writing_files=False,
-            verbose=100,
-            early_stopping_rounds=400,
             **self.model_training_parameters,
         )
         model.fit(X=train_data, eval_set=test_data)

freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py

@@ -27,9 +27,6 @@ class CatboostPredictionMultiModel(BaseRegressionModel):
 
         cbr = CatBoostRegressor(
             allow_writing_files=False,
-            gpu_ram_part=0.5,
-            verbose=100,
-            early_stopping_rounds=400,
             **self.model_training_parameters,
         )