Merge branch 'develop' into fix/multioutput-bug

config_examples/config_freqai.example.json

@@ -79,8 +79,7 @@
             "test_size": 0.33,
             "random_state": 1
         },
-        "model_training_parameters": {
+        "model_training_parameters": {}
-        }
     },
     "bot_name": "",
     "force_entry_enable": true,

docs/freqai-configuration.md

@@ -26,10 +26,7 @@ FreqAI is configured through the typical [Freqtrade config file](configuration.m
         },
         "data_split_parameters" : {
             "test_size": 0.25
-        },
+        }
-        "model_training_parameters" : {
-            "n_estimators": 100
-        },
     }
 ```
 
@@ -118,7 +115,7 @@ The FreqAI strategy requires including the following lines of code in the standa
 
 ```
 
-Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/targets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`. 
+Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/targets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
 
 Notice also the location of the labels under `if set_generalized_indicators:` at the bottom of the example. This is where single features and labels/targets should be added to the feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`.
 
@@ -182,7 +179,7 @@ The `startup_candle_count` in the FreqAI strategy needs to be set up in the same
 
 ## Creating a dynamic target threshold
 
-Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. FreqAI allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and  `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out. 
+Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. FreqAI allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and  `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out.
 
 ```python
 dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
@@ -230,7 +227,7 @@ If you want to predict multiple targets, you need to define multiple labels usin
 
 #### Classifiers
 
-If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set 
+If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set
 
 ```python
 df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')

freqtrade/configuration/config_validation.py

@@ -355,6 +355,13 @@ def _validate_freqai_include_timeframes(conf: Dict[str, Any]) -> None:
                 f"Main timeframe of {main_tf} must be smaller or equal to FreqAI "
                 f"`include_timeframes`.Offending include-timeframes: {', '.join(offending_lines)}")
 
+        # Ensure that the base timeframe is included in the include_timeframes list
+        if main_tf not in freqai_include_timeframes:
+            feature_parameters = conf.get('freqai', {}).get('feature_parameters', {})
+            include_timeframes = [main_tf] + freqai_include_timeframes
+            conf.get('freqai', {}).get('feature_parameters', {}) \
+                .update({**feature_parameters, 'include_timeframes': include_timeframes})
+
 
 def _validate_freqai_backtest(conf: Dict[str, Any]) -> None:
     if conf.get('runmode', RunMode.OTHER) == RunMode.BACKTEST:

freqtrade/constants.py

@@ -608,9 +608,8 @@ CONF_SCHEMA = {
                 "backtest_period_days",
                 "identifier",
                 "feature_parameters",
-                "data_split_parameters",
+                "data_split_parameters"
-                "model_training_parameters"
+            ]
-                ]
         },
     },
 }

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -61,7 +61,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
                                     tensorboard_log=Path(
                                         dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
-                                    **self.freqai_info['model_training_parameters']
+                                    **self.freqai_info.get('model_training_parameters', {})
                                     )
         else:
             logger.info('Continual training activated - starting training from previously '

tests/test_configuration.py

@@ -1046,8 +1046,13 @@ def test__validate_freqai_include_timeframes(default_conf, caplog) -> None:
     # Validation pass
     conf.update({'timeframe': '1m'})
     validate_config_consistency(conf)
-    conf.update({'analyze_per_epoch': True})
 
+    # Ensure base timeframe is in include_timeframes
+    conf['freqai']['feature_parameters']['include_timeframes'] = ["5m", "15m"]
+    validate_config_consistency(conf)
+    assert conf['freqai']['feature_parameters']['include_timeframes'] == ["1m", "5m", "15m"]
+
+    conf.update({'analyze_per_epoch': True})
     with pytest.raises(OperationalException,
                        match=r"Using analyze-per-epoch .* not supported with a FreqAI strategy."):
         validate_config_consistency(conf)