Merge branch 'develop' into list-models

freqtrade/freqai/prediction_models/CatboostClassifier.py

@@ -30,6 +30,14 @@ class CatboostClassifier(BaseClassifierModel):
             label=data_dictionary["train_labels"],
             weight=data_dictionary["train_weights"],
         )
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
+            test_data = None
+        else:
+            test_data = Pool(
+                data=data_dictionary["test_features"],
+                label=data_dictionary["test_labels"],
+                weight=data_dictionary["test_weights"],
+            )
 
         cbr = CatBoostClassifier(
             allow_writing_files=True,
@@ -40,6 +48,6 @@ class CatboostClassifier(BaseClassifierModel):
 
         init_model = self.get_init_model(dk.pair)
 
-        cbr.fit(train_data, init_model=init_model)
+        cbr.fit(X=train_data, eval_set=test_data, init_model=init_model)
 
         return cbr

freqtrade/freqai/prediction_models/XGBoostRFClassifier.py

@@ -0,0 +1,85 @@
+import logging
+from typing import Any, Dict, Tuple
+
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+from pandas import DataFrame
+from pandas.api.types import is_integer_dtype
+from sklearn.preprocessing import LabelEncoder
+from xgboost import XGBRFClassifier
+
+from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class XGBoostRFClassifier(BaseClassifierModel):
+    """
+    User created prediction model. The class needs to override three necessary
+    functions, predict(), train(), fit(). The class inherits ModelHandler which
+    has its own DataHandler where data is held, saved, loaded, and managed.
+    """
+
+    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :params:
+        :data_dictionary: the dictionary constructed by DataHandler to hold
+        all the training and test data/labels.
+        """
+
+        X = data_dictionary["train_features"].to_numpy()
+        y = data_dictionary["train_labels"].to_numpy()[:, 0]
+
+        le = LabelEncoder()
+        if not is_integer_dtype(y):
+            y = pd.Series(le.fit_transform(y), dtype="int64")
+
+        if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
+            eval_set = None
+        else:
+            test_features = data_dictionary["test_features"].to_numpy()
+            test_labels = data_dictionary["test_labels"].to_numpy()[:, 0]
+
+            if not is_integer_dtype(test_labels):
+                test_labels = pd.Series(le.transform(test_labels), dtype="int64")
+
+            eval_set = [(test_features, test_labels)]
+
+        train_weights = data_dictionary["train_weights"]
+
+        init_model = self.get_init_model(dk.pair)
+
+        model = XGBRFClassifier(**self.model_training_parameters)
+
+        model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
+                  xgb_model=init_model)
+
+        return model
+
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param: unfiltered_df: Full dataframe for the current backtest period.
+        :return:
+        :pred_df: dataframe containing the predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        """
+
+        (pred_df, dk.do_predict) = super().predict(unfiltered_df, dk, **kwargs)
+
+        le = LabelEncoder()
+        label = dk.label_list[0]
+        labels_before = list(dk.data['labels_std'].keys())
+        labels_after = le.fit_transform(labels_before).tolist()
+        pred_df[label] = le.inverse_transform(pred_df[label])
+        pred_df = pred_df.rename(
+            columns={labels_after[i]: labels_before[i] for i in range(len(labels_before))})
+
+        return (pred_df, dk.do_predict)

freqtrade/freqai/prediction_models/XGBoostRFRegressor.py

@@ -0,0 +1,45 @@
+import logging
+from typing import Any, Dict
+
+from xgboost import XGBRFRegressor
+
+from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class XGBoostRFRegressor(BaseRegressionModel):
+    """
+    User created prediction model. The class needs to override three necessary
+    functions, predict(), train(), fit(). The class inherits ModelHandler which
+    has its own DataHandler where data is held, saved, loaded, and managed.
+    """
+
+    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
+        """
+
+        X = data_dictionary["train_features"]
+        y = data_dictionary["train_labels"]
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
+            eval_set = None
+        else:
+            eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
+            eval_weights = [data_dictionary['test_weights']]
+
+        sample_weight = data_dictionary["train_weights"]
+
+        xgb_model = self.get_init_model(dk.pair)
+
+        model = XGBRFRegressor(**self.model_training_parameters)
+
+        model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set,
+                  sample_weight_eval_set=eval_weights, xgb_model=xgb_model)
+
+        return model

freqtrade/optimize/backtesting.py

@@ -919,30 +919,23 @@ class Backtesting:
         return trade
 
     def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
-                         data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
+                         data: Dict[str, List[Tuple]]) -> None:
         """
         Handling of left open trades at the end of backtesting
         """
-        trades = []
         for pair in open_trades.keys():
-            if len(open_trades[pair]) > 0:
+            for trade in open_trades[pair]:
-                for trade in open_trades[pair]:
+                if trade.open_order_id and trade.nr_of_successful_entries == 0:
-                    if trade.open_order_id and trade.nr_of_successful_entries == 0:
+                    # Ignore trade if entry-order did not fill yet
-                        # Ignore trade if entry-order did not fill yet
+                    continue
-                        continue
+                exit_row = data[pair][-1]
-                    exit_row = data[pair][-1]
+                self._exit_trade(trade, exit_row, exit_row[OPEN_IDX], trade.amount)
-                    self._exit_trade(trade, exit_row, exit_row[OPEN_IDX], trade.amount)
+                trade.orders[-1].close_bt_order(exit_row[DATE_IDX].to_pydatetime(), trade)
-                    trade.orders[-1].close_bt_order(exit_row[DATE_IDX].to_pydatetime(), trade)
 
-                    trade.close_date = exit_row[DATE_IDX].to_pydatetime()
+                trade.close_date = exit_row[DATE_IDX].to_pydatetime()
-                    trade.exit_reason = ExitType.FORCE_EXIT.value
+                trade.exit_reason = ExitType.FORCE_EXIT.value
-                    trade.close(exit_row[OPEN_IDX], show_msg=False)
+                trade.close(exit_row[OPEN_IDX], show_msg=False)
-                    LocalTrade.close_bt_trade(trade)
+                LocalTrade.close_bt_trade(trade)
-                    # Deepcopy object to have wallets update correctly
-                    trade1 = deepcopy(trade)
-                    trade1.is_open = True
-                    trades.append(trade1)
-        return trades
 
     def trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool:
         # Always allow trades when max_open_trades is enabled.
@@ -1094,7 +1087,6 @@ class Backtesting:
         :param enable_protections: Should protections be enabled?
         :return: DataFrame with trades (results of backtesting)
         """
-        trades: List[LocalTrade] = []
         self.prepare_backtest(enable_protections)
         # Ensure wallets are uptodate (important for --strategy-list)
         self.wallets.update()
@@ -1188,7 +1180,6 @@ class Backtesting:
                             open_trade_count -= 1
                             open_trades[pair].remove(trade)
                             LocalTrade.close_bt_trade(trade)
-                            trades.append(trade)
                         self.wallets.update()
                         self.run_protections(
                             enable_protections, pair, current_time, trade.trade_direction)
@@ -1197,10 +1188,10 @@ class Backtesting:
             self.progress.increment()
             current_time += timedelta(minutes=self.timeframe_min)
 
-        trades += self.handle_left_open(open_trades, data=data)
+        self.handle_left_open(open_trades, data=data)
         self.wallets.update()
 
-        results = trade_list_to_dataframe(trades)
+        results = trade_list_to_dataframe(LocalTrade.trades)
         return {
             'results': results,
             'config': self.strategy.config,

freqtrade/optimize/optimize_reports.py

@@ -408,10 +408,10 @@ def generate_strategy_stats(pairlist: List[str],
 
     exit_reason_stats = generate_exit_reason_stats(max_open_trades=max_open_trades,
                                                    results=results)
-    left_open_results = generate_pair_metrics(pairlist, stake_currency=stake_currency,
+    left_open_results = generate_pair_metrics(
-                                              starting_balance=start_balance,
+        pairlist, stake_currency=stake_currency, starting_balance=start_balance,
-                                              results=results.loc[results['is_open']],
+        results=results.loc[results['exit_reason'] == 'force_exit'], skip_nan=True)
-                                              skip_nan=True)
+
     daily_stats = generate_daily_stats(results)
     trade_stats = generate_trading_stats(results)
     best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],

tests/freqai/test_freqai_interface.py

@@ -30,6 +30,7 @@ def is_mac() -> bool:
 @pytest.mark.parametrize('model', [
     'LightGBMRegressor',
     'XGBoostRegressor',
+    'XGBoostRFRegressor',
     'CatboostRegressor',
     ])
 def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
@@ -55,6 +56,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
 
     data_load_timerange = TimeRange.parse_timerange("20180125-20180130")
     new_timerange = TimeRange.parse_timerange("20180127-20180130")
+    freqai.dk.set_paths('ADA/BTC', None)
 
     freqai.extract_data_and_train_model(
         new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
@@ -93,6 +95,7 @@ def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
 
     data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
     new_timerange = TimeRange.parse_timerange("20180120-20180130")
+    freqai.dk.set_paths('ADA/BTC', None)
 
     freqai.extract_data_and_train_model(
         new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
@@ -111,6 +114,7 @@ def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
     'LightGBMClassifier',
     'CatboostClassifier',
     'XGBoostClassifier',
+    'XGBoostRFClassifier',
     ])
 def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
     if is_arm() and model == 'CatboostClassifier':
@@ -134,6 +138,7 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
 
     data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
     new_timerange = TimeRange.parse_timerange("20180120-20180130")
+    freqai.dk.set_paths('ADA/BTC', None)
 
     freqai.extract_data_and_train_model(new_timerange, "ADA/BTC",
                                         strategy, freqai.dk, data_load_timerange)