Merge d7b86ee436 into 2fe67edab3

chore(deps-dev): bump types-requests from 2.32.0.20240907 to 2.32.0.20240914 in the types group

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
         additional_dependencies:
           - types-cachetools==5.5.0.20240820
           - types-filelock==3.2.7
-          - types-requests==2.32.0.20240907
+          - types-requests==2.32.0.20240914
           - types-tabulate==0.9.0.20240106
           - types-python-dateutil==2.9.0.20240906
           - SQLAlchemy==2.0.34

freqtrade/exchange/exchange.py

@@ -3594,7 +3594,7 @@ class Exchange:
             Wherein, "+" or "-" depends on whether the contract goes long or short:
             "-" for long, and "+" for short.
 
-         okex: https://www.okex.com/support/hc/en-us/articles/
+         okex: https://www.okx.com/support/hc/en-us/articles/
             360053909592-VI-Introduction-to-the-isolated-mode-of-Single-Multi-currency-Portfolio-margin
 
         :param pair: Pair to calculate liquidation price for

freqtrade/freqai/freqai_interface.py

@@ -70,7 +70,7 @@ class IFreqaiModel(ABC):
         self.retrain = False
         self.first = True
         self.set_full_path()
-        self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True)
+        self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", False)
         if self.save_backtest_models:
             logger.info("Backtesting module configured to save all models.")
 
@@ -258,6 +258,23 @@ class IFreqaiModel(ABC):
                 if self.freqai_info.get("write_metrics_to_disk", False):
                     self.dd.save_metric_tracker_to_disk()
 
+    def _train_model(self, dataframe_train, pair, dk, tr_backtest):
+        try:
+            self.tb_logger = get_tb_logger(
+                self.dd.model_type, dk.data_path, self.activate_tensorboard
+            )
+            model = self.train(dataframe_train, pair, dk)
+            self.tb_logger.close()
+            return model
+        except Exception as msg:
+            logger.warning(
+                f"Training {pair} raised exception {msg.__class__.__name__}. "
+                f"from {tr_backtest.start_fmt} to {tr_backtest.stop_fmt}."
+                f"Message: {msg}, skipping.",
+                exc_info=True,
+            )
+            return None
+
     def start_backtesting(
         self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen, strategy: IStrategy
     ) -> FreqaiDataKitchen:
@@ -352,37 +369,26 @@ class IFreqaiModel(ABC):
                 if not self.model_exists(dk):
                     dk.find_features(dataframe_train)
                     dk.find_labels(dataframe_train)
+                    self.model = self._train_model(dataframe_train, pair, dk, tr_backtest)
 
-                    try:
-                        self.tb_logger = get_tb_logger(
-                            self.dd.model_type, dk.data_path, self.activate_tensorboard
-                        )
-                        self.model = self.train(dataframe_train, pair, dk)
-                        self.tb_logger.close()
-                    except Exception as msg:
-                        logger.warning(
-                            f"Training {pair} raised exception {msg.__class__.__name__}. "
-                            f"Message: {msg}, skipping.",
-                            exc_info=True,
-                        )
-                        self.model = None
-
-                    self.dd.pair_dict[pair]["trained_timestamp"] = int(tr_train.stopts)
-                    if self.plot_features and self.model is not None:
-                        plot_feature_importance(self.model, pair, dk, self.plot_features)
-                    if self.save_backtest_models and self.model is not None:
-                        logger.info("Saving backtest model to disk.")
-                        self.dd.save_data(self.model, pair, dk)
-                    else:
-                        logger.info("Saving metadata to disk.")
-                        self.dd.save_metadata(dk)
+                    if self.model:
+                        self.dd.pair_dict[pair]["trained_timestamp"] = int(tr_train.stopts)
+                        if self.plot_features and self.model is not None:
+                            plot_feature_importance(self.model, pair, dk, self.plot_features)
+                        if self.save_backtest_models and self.model is not None:
+                            logger.info("Saving backtest model to disk.")
+                            self.dd.save_data(self.model, pair, dk)
+                        else:
+                            logger.info("Saving metadata to disk.")
+                            self.dd.save_metadata(dk)
                 else:
                     self.model = self.dd.load_data(pair, dk)
 
-                pred_df, do_preds = self.predict(dataframe_backtest, dk)
-                append_df = dk.get_predictions_to_append(pred_df, do_preds, dataframe_backtest)
-                dk.append_predictions(append_df)
-                dk.save_backtesting_prediction(append_df)
+                if self.model and len(dataframe_backtest):
+                    pred_df, do_preds = self.predict(dataframe_backtest, dk)
+                    append_df = dk.get_predictions_to_append(pred_df, do_preds, dataframe_backtest)
+                    dk.append_predictions(append_df)
+                    dk.save_backtesting_prediction(append_df)
 
         self.backtesting_fit_live_predictions(dk)
         dk.fill_predictions(dataframe)
@@ -829,7 +835,7 @@ class IFreqaiModel(ABC):
         :param pair: current pair
         :return: if the data exists or not
         """
-        if self.config.get("freqai_backtest_live_models", False) and len_dataframe_backtest == 0:
+        if len_dataframe_backtest == 0:
             logger.info(
                 f"No data found for pair {pair} from "
                 f"from {tr_backtest.start_fmt} to {tr_backtest.stop_fmt}. "

requirements-dev.txt

@@ -7,10 +7,10 @@
 -r docs/requirements-docs.txt
 
 coveralls==4.0.1
-ruff==0.6.4
+ruff==0.6.5
 mypy==1.11.2
 pre-commit==3.8.0
-pytest==8.3.2
+pytest==8.3.3
 pytest-asyncio==0.24.0
 pytest-cov==5.0.0
 pytest-mock==3.14.0
@@ -27,6 +27,6 @@ nbconvert==7.16.4
 # mypy types
 types-cachetools==5.5.0.20240820
 types-filelock==3.2.7
-types-requests==2.32.0.20240907
+types-requests==2.32.0.20240914
 types-tabulate==0.9.0.20240106
 types-python-dateutil==2.9.0.20240906

requirements-freqai.txt

@@ -3,7 +3,7 @@
 -r requirements-plot.txt
 
 # Required for freqai
-scikit-learn==1.5.1
+scikit-learn==1.5.2
 joblib==1.4.2
 catboost==1.2.7; 'arm' not in platform_machine
 # Pin Matplotlib - it's depended on by catboost

requirements-hyperopt.txt

@@ -4,6 +4,6 @@
 # Required for hyperopt
 scipy==1.14.1; python_version >= "3.10"
 scipy==1.13.1; python_version < "3.10"
-scikit-learn==1.5.1
+scikit-learn==1.5.2
 ft-scikit-optimize==0.9.2
 filelock==3.16.0

requirements-plot.txt

@@ -1,4 +1,4 @@
 # Include all requirements to run the bot.
 -r requirements.txt
 
-plotly==5.24.0
+plotly==5.24.1

requirements.txt

@@ -4,7 +4,7 @@ bottleneck==1.4.0
 numexpr==2.10.1
 pandas-ta==0.3.14b
 
-ccxt==4.3.98
+ccxt==4.4.3
 cryptography==42.0.8; platform_machine == 'armv7l'
 cryptography==43.0.1; platform_machine != 'armv7l'
 aiohttp==3.10.5
@@ -15,7 +15,7 @@ httpx>=0.24.1
 humanize==4.10.0
 cachetools==5.5.0
 requests==2.32.3
-urllib3==2.2.2
+urllib3==2.2.3
 jsonschema==4.23.0
 TA-Lib==0.4.32
 technical==1.4.4
@@ -26,7 +26,7 @@ jinja2==3.1.4
 tables==3.9.1; python_version < "3.10"
 tables==3.10.1; python_version >= "3.10"
 joblib==1.4.2
-rich==13.8.0
+rich==13.8.1
 pyarrow==17.0.0; platform_machine != 'armv7l'
 
 # find first, C search in arrays
@@ -41,8 +41,8 @@ orjson==3.10.7
 sdnotify==0.3.2
 
 # API Server
-fastapi==0.114.0
-pydantic==2.9.0
+fastapi==0.114.2
+pydantic==2.9.1
 uvicorn==0.30.6
 pyjwt==2.9.0
 aiofiles==24.1.0
@@ -53,7 +53,7 @@ questionary==2.0.1
 prompt-toolkit==3.0.36
 # Extensions to datetime library
 python-dateutil==2.9.0.post0
-pytz==2024.1
+pytz==2024.2
 
 #Futures
 schedule==1.2.2