ruff format: Update test strategies

tests/strategy/strats/broken_strats/broken_futures_strategies.py

@@ -12,7 +12,6 @@ from freqtrade.strategy.interface import IStrategy
 
 
 class TestStrategyNoImplements(IStrategy):
-
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         return super().populate_indicators(dataframe, metadata)
 
@@ -26,9 +25,15 @@ class TestStrategyImplementCustomSell(TestStrategyNoImplementSell):
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         return super().populate_exit_trend(dataframe, metadata)
 
-    def custom_sell(self, pair: str, trade, current_time: datetime,
-                    current_rate: float, current_profit: float,
-                    **kwargs):
+    def custom_sell(
+        self,
+        pair: str,
+        trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        **kwargs,
+    ):
         return False
 
 
@@ -36,8 +41,9 @@ class TestStrategyImplementBuyTimeout(TestStrategyNoImplementSell):
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         return super().populate_exit_trend(dataframe, metadata)
 
-    def check_buy_timeout(self, pair: str, trade, order: Order,
-                          current_time: datetime, **kwargs) -> bool:
+    def check_buy_timeout(
+        self, pair: str, trade, order: Order, current_time: datetime, **kwargs
+    ) -> bool:
         return False
 
 
@@ -45,6 +51,7 @@ class TestStrategyImplementSellTimeout(TestStrategyNoImplementSell):
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         return super().populate_exit_trend(dataframe, metadata)
 
-    def check_sell_timeout(self, pair: str, trade, order: Order,
-                           current_time: datetime, **kwargs) -> bool:
+    def check_sell_timeout(
+        self, pair: str, trade, order: Order, current_time: datetime, **kwargs
+    ) -> bool:
         return False

tests/strategy/strats/broken_strats/legacy_strategy_v1.py

@@ -6,25 +6,16 @@ from freqtrade.strategy import IStrategy
 
 # Dummy strategy - no longer loads but raises an exception.
 class TestStrategyLegacyV1(IStrategy):
-
-    minimal_roi = {
-        "40": 0.0,
-        "30": 0.01,
-        "20": 0.02,
-        "0": 0.04
-    }
+    minimal_roi = {"40": 0.0, "30": 0.01, "20": 0.02, "0": 0.04}
     stoploss = -0.10
 
-    timeframe = '5m'
+    timeframe = "5m"
 
     def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
-
         return dataframe
 
     def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
-
         return dataframe
 
     def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
-
         return dataframe

tests/strategy/strats/freqai_rl_test_strat.py

@@ -25,22 +25,20 @@ class freqai_rl_test_strat(IStrategy):
     startup_candle_count: int = 300
     can_short = False
 
-    def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
-                                       metadata: Dict, **kwargs):
-
+    def feature_engineering_expand_all(
+        self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs
+    ):
         dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
 
         return dataframe
 
     def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
-
         dataframe["%-pct-change"] = dataframe["close"].pct_change()
         dataframe["%-raw_volume"] = dataframe["volume"]
 
         return dataframe
 
     def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
-
         dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
         dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
 
@@ -52,19 +50,16 @@ class freqai_rl_test_strat(IStrategy):
         return dataframe
 
     def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
-
         dataframe["&-action"] = 0
 
         return dataframe
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
         dataframe = self.freqai.start(dataframe, metadata, self)
 
         return dataframe
 
     def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
-
         enter_long_conditions = [df["do_predict"] == 1, df["&-action"] == 1]
 
         if enter_long_conditions:

tests/strategy/strats/freqai_test_classifier.py

@@ -57,9 +57,9 @@ class freqai_test_classifier(IStrategy):
                 informative_pairs.append((pair, tf))
         return informative_pairs
 
-    def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
-                                       metadata: Dict, **kwargs):
-
+    def feature_engineering_expand_all(
+        self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs
+    ):
         dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
         dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
         dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
@@ -67,7 +67,6 @@ class freqai_test_classifier(IStrategy):
         return dataframe
 
     def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
-
         dataframe["%-pct-change"] = dataframe["close"].pct_change()
         dataframe["%-raw_volume"] = dataframe["volume"]
         dataframe["%-raw_price"] = dataframe["close"]
@@ -75,7 +74,6 @@ class freqai_test_classifier(IStrategy):
         return dataframe
 
     def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
-
         dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
         dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
 
@@ -83,13 +81,13 @@ class freqai_test_classifier(IStrategy):
 
     def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
         self.freqai.class_names = ["down", "up"]
-        dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-100) >
-                                              dataframe["close"], 'up', 'down')
+        dataframe["&s-up_or_down"] = np.where(
+            dataframe["close"].shift(-100) > dataframe["close"], "up", "down"
+        )
 
         return dataframe
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
         self.freqai_info = self.config["freqai"]
 
         dataframe = self.freqai.start(dataframe, metadata, self)
@@ -97,15 +95,14 @@ class freqai_test_classifier(IStrategy):
         return dataframe
 
     def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
-
-        enter_long_conditions = [df['&s-up_or_down'] == 'up']
+        enter_long_conditions = [df["&s-up_or_down"] == "up"]
 
         if enter_long_conditions:
             df.loc[
                 reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
             ] = (1, "long")
 
-        enter_short_conditions = [df['&s-up_or_down'] == 'down']
+        enter_short_conditions = [df["&s-up_or_down"] == "down"]
 
         if enter_short_conditions:
             df.loc[
@@ -115,5 +112,4 @@ class freqai_test_classifier(IStrategy):
         return df
 
     def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
-
         return df

tests/strategy/strats/freqai_test_multimodel_classifier_strat.py

@@ -44,9 +44,9 @@ class freqai_test_multimodel_classifier_strat(IStrategy):
     )
     max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
 
-    def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
-                                       metadata: Dict, **kwargs):
-
+    def feature_engineering_expand_all(
+        self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs
+    ):
         dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
         dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
         dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
@@ -54,7 +54,6 @@ class freqai_test_multimodel_classifier_strat(IStrategy):
         return dataframe
 
     def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
-
         dataframe["%-pct-change"] = dataframe["close"].pct_change()
         dataframe["%-raw_volume"] = dataframe["volume"]
         dataframe["%-raw_price"] = dataframe["close"]
@@ -62,24 +61,23 @@ class freqai_test_multimodel_classifier_strat(IStrategy):
         return dataframe
 
     def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
-
         dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
         dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
 
         return dataframe
 
     def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
+        dataframe["&s-up_or_down"] = np.where(
+            dataframe["close"].shift(-50) > dataframe["close"], "up", "down"
+        )
 
-        dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-50) >
-                                              dataframe["close"], 'up', 'down')
-
-        dataframe['&s-up_or_down2'] = np.where(dataframe["close"].shift(-50) >
-                                               dataframe["close"], 'up2', 'down2')
+        dataframe["&s-up_or_down2"] = np.where(
+            dataframe["close"].shift(-50) > dataframe["close"], "up2", "down2"
+        )
 
         return dataframe
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
         self.freqai_info = self.config["freqai"]
 
         dataframe = self.freqai.start(dataframe, metadata, self)
@@ -89,7 +87,6 @@ class freqai_test_multimodel_classifier_strat(IStrategy):
         return dataframe
 
     def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
-
         enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
 
         if enter_long_conditions:

tests/strategy/strats/freqai_test_multimodel_strat.py

@@ -43,9 +43,9 @@ class freqai_test_multimodel_strat(IStrategy):
     )
     max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
 
-    def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
-                                       metadata: Dict, **kwargs):
-
+    def feature_engineering_expand_all(
+        self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs
+    ):
         dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
         dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
         dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
@@ -53,7 +53,6 @@ class freqai_test_multimodel_strat(IStrategy):
         return dataframe
 
     def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
-
         dataframe["%-pct-change"] = dataframe["close"].pct_change()
         dataframe["%-raw_volume"] = dataframe["volume"]
         dataframe["%-raw_price"] = dataframe["close"]
@@ -61,14 +60,12 @@ class freqai_test_multimodel_strat(IStrategy):
         return dataframe
 
     def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
-
         dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
         dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
 
         return dataframe
 
     def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
-
         dataframe["&-s_close"] = (
             dataframe["close"]
             .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
@@ -76,15 +73,14 @@ class freqai_test_multimodel_strat(IStrategy):
             .mean()
             / dataframe["close"]
             - 1
-            )
+        )
 
         dataframe["&-s_range"] = (
             dataframe["close"]
             .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
             .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
             .max()
-            -
-            dataframe["close"]
+            - dataframe["close"]
             .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
             .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
             .min()
@@ -93,7 +89,6 @@ class freqai_test_multimodel_strat(IStrategy):
         return dataframe
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
         self.freqai_info = self.config["freqai"]
 
         dataframe = self.freqai.start(dataframe, metadata, self)
@@ -103,7 +98,6 @@ class freqai_test_multimodel_strat(IStrategy):
         return dataframe
 
     def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
-
         enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
 
         if enter_long_conditions:

tests/strategy/strats/freqai_test_strat.py

@@ -43,9 +43,9 @@ class freqai_test_strat(IStrategy):
     )
     max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
 
-    def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
-                                       metadata: Dict, **kwargs):
-
+    def feature_engineering_expand_all(
+        self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs
+    ):
         dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
         dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
         dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
@@ -53,7 +53,6 @@ class freqai_test_strat(IStrategy):
         return dataframe
 
     def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
-
         dataframe["%-pct-change"] = dataframe["close"].pct_change()
         dataframe["%-raw_volume"] = dataframe["volume"]
         dataframe["%-raw_price"] = dataframe["close"]
@@ -61,14 +60,12 @@ class freqai_test_strat(IStrategy):
         return dataframe
 
     def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
-
         dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
         dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
 
         return dataframe
 
     def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
-
         dataframe["&-s_close"] = (
             dataframe["close"]
             .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
@@ -76,12 +73,11 @@ class freqai_test_strat(IStrategy):
             .mean()
             / dataframe["close"]
             - 1
-            )
+        )
 
         return dataframe
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
         self.freqai_info = self.config["freqai"]
 
         dataframe = self.freqai.start(dataframe, metadata, self)
@@ -91,7 +87,6 @@ class freqai_test_strat(IStrategy):
         return dataframe
 
     def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
-
         enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
 
         if enter_long_conditions:

tests/strategy/strats/hyperoptable_strategy.py

@@ -17,20 +17,18 @@ class HyperoptableStrategy(StrategyTestV3):
     """
 
     buy_params = {
-        'buy_rsi': 35,
+        "buy_rsi": 35,
         # Intentionally not specified, so "default" is tested
         # 'buy_plusdi': 0.4
     }
 
-    sell_params = {
-        'sell_rsi': 74,
-        'sell_minusdi': 0.4
-    }
+    sell_params = {"sell_rsi": 74, "sell_minusdi": 0.4}
 
-    buy_plusdi = RealParameter(low=0, high=1, default=0.5, space='buy')
-    sell_rsi = IntParameter(low=50, high=100, default=70, space='sell')
-    sell_minusdi = DecimalParameter(low=0, high=1, default=0.5001, decimals=3, space='sell',
-                                    load=False)
+    buy_plusdi = RealParameter(low=0, high=1, default=0.5, space="buy")
+    sell_rsi = IntParameter(low=50, high=100, default=70, space="sell")
+    sell_minusdi = DecimalParameter(
+        low=0, high=1, default=0.5001, decimals=3, space="sell", load=False
+    )
     protection_enabled = BooleanParameter(default=True)
     protection_cooldown_lookback = IntParameter([0, 50], default=30)
 
@@ -43,10 +41,12 @@ class HyperoptableStrategy(StrategyTestV3):
     def protections(self):
         prot = []
         if self.protection_enabled.value:
-            prot.append({
-                "method": "CooldownPeriod",
-                "stop_duration_candles": self.protection_cooldown_lookback.value
-            })
+            prot.append(
+                {
+                    "method": "CooldownPeriod",
+                    "stop_duration_candles": self.protection_cooldown_lookback.value,
+                }
+            )
         return prot
 
     bot_loop_started = False
@@ -60,7 +60,7 @@ class HyperoptableStrategy(StrategyTestV3):
         Parameters can also be defined here ...
         """
         self.bot_started = True
-        self.buy_rsi = IntParameter([0, 50], default=30, space='buy')
+        self.buy_rsi = IntParameter([0, 50], default=30, space="buy")
 
     def informative_pairs(self):
         """
@@ -84,16 +84,14 @@ class HyperoptableStrategy(StrategyTestV3):
         """
         dataframe.loc[
             (
-                (dataframe['rsi'] < self.buy_rsi.value) &
-                (dataframe['fastd'] < 35) &
-                (dataframe['adx'] > 30) &
-                (dataframe['plus_di'] > self.buy_plusdi.value)
-            ) |
-            (
-                (dataframe['adx'] > 65) &
-                (dataframe['plus_di'] > self.buy_plusdi.value)
-            ),
-            'buy'] = 1
+                (dataframe["rsi"] < self.buy_rsi.value)
+                & (dataframe["fastd"] < 35)
+                & (dataframe["adx"] > 30)
+                & (dataframe["plus_di"] > self.buy_plusdi.value)
+            )
+            | ((dataframe["adx"] > 65) & (dataframe["plus_di"] > self.buy_plusdi.value)),
+            "buy",
+        ] = 1
 
         return dataframe
 
@@ -107,15 +105,13 @@ class HyperoptableStrategy(StrategyTestV3):
         dataframe.loc[
             (
                 (
-                    (qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) |
-                    (qtpylib.crossed_above(dataframe['fastd'], 70))
-                ) &
-                (dataframe['adx'] > 10) &
-                (dataframe['minus_di'] > 0)
-            ) |
-            (
-                (dataframe['adx'] > 70) &
-                (dataframe['minus_di'] > self.sell_minusdi.value)
-            ),
-            'sell'] = 1
+                    (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value))
+                    | (qtpylib.crossed_above(dataframe["fastd"], 70))
+                )
+                & (dataframe["adx"] > 10)
+                & (dataframe["minus_di"] > 0)
+            )
+            | ((dataframe["adx"] > 70) & (dataframe["minus_di"] > self.sell_minusdi.value)),
+            "sell",
+        ] = 1
         return dataframe

tests/strategy/strats/hyperoptable_strategy_v2.py

@@ -15,20 +15,22 @@ class HyperoptableStrategyV2(StrategyTestV2):
     """
 
     buy_params = {
-        'buy_rsi': 35,
+        "buy_rsi": 35,
         # Intentionally not specified, so "default" is tested
         # 'buy_plusdi': 0.4
     }
 
     sell_params = {
-        'sell_rsi': 74,
-        'sell_minusdi': 0.4
+        # Sell parameters
+        "sell_rsi": 74,
+        "sell_minusdi": 0.4,
     }
 
-    buy_plusdi = RealParameter(low=0, high=1, default=0.5, space='buy')
-    sell_rsi = IntParameter(low=50, high=100, default=70, space='sell')
-    sell_minusdi = DecimalParameter(low=0, high=1, default=0.5001, decimals=3, space='sell',
-                                    load=False)
+    buy_plusdi = RealParameter(low=0, high=1, default=0.5, space="buy")
+    sell_rsi = IntParameter(low=50, high=100, default=70, space="sell")
+    sell_minusdi = DecimalParameter(
+        low=0, high=1, default=0.5001, decimals=3, space="sell", load=False
+    )
     protection_enabled = BooleanParameter(default=True)
     protection_cooldown_lookback = IntParameter([0, 50], default=30)
 
@@ -36,10 +38,12 @@ class HyperoptableStrategyV2(StrategyTestV2):
     def protections(self):
         prot = []
         if self.protection_enabled.value:
-            prot.append({
-                "method": "CooldownPeriod",
-                "stop_duration_candles": self.protection_cooldown_lookback.value
-            })
+            prot.append(
+                {
+                    "method": "CooldownPeriod",
+                    "stop_duration_candles": self.protection_cooldown_lookback.value,
+                }
+            )
         return prot
 
     bot_loop_started = False
@@ -51,4 +55,4 @@ class HyperoptableStrategyV2(StrategyTestV2):
         """
         Parameters can also be defined here ...
         """
-        self.buy_rsi = IntParameter([0, 50], default=30, space='buy')
+        self.buy_rsi = IntParameter([0, 50], default=30, space="buy")

tests/strategy/strats/informative_decorator_strategy.py

@@ -13,72 +13,73 @@ class InformativeDecoratorTest(IStrategy):
     or strategy repository https://github.com/freqtrade/freqtrade-strategies
     for samples and inspiration.
     """
+
     INTERFACE_VERSION = 2
     stoploss = -0.10
-    timeframe = '5m'
+    timeframe = "5m"
     startup_candle_count: int = 20
 
     def informative_pairs(self):
         # Intentionally return 2 tuples, must be converted to 3 in compatibility code
         return [
-            ('NEO/USDT', '5m'),
-            ('NEO/USDT', '15m', ''),
-            ('NEO/USDT', '2h', 'futures'),
-            ]
+            ("NEO/USDT", "5m"),
+            ("NEO/USDT", "15m", ""),
+            ("NEO/USDT", "2h", "futures"),
+        ]
 
     def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        dataframe['buy'] = 0
+        dataframe["buy"] = 0
         return dataframe
 
     def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        dataframe['sell'] = 0
+        dataframe["sell"] = 0
         return dataframe
 
     # Decorator stacking test.
-    @informative('30m')
-    @informative('1h')
+    @informative("30m")
+    @informative("1h")
     def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        dataframe['rsi'] = 14
+        dataframe["rsi"] = 14
         return dataframe
 
     # Simple informative test.
-    @informative('1h', 'NEO/{stake}')
+    @informative("1h", "NEO/{stake}")
     def populate_indicators_neo_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        dataframe['rsi'] = 14
+        dataframe["rsi"] = 14
         return dataframe
 
-    @informative('1h', '{base}/BTC')
+    @informative("1h", "{base}/BTC")
     def populate_indicators_base_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        dataframe['rsi'] = 14
+        dataframe["rsi"] = 14
         return dataframe
 
     # Quote currency different from stake currency test.
-    @informative('1h', 'ETH/BTC', candle_type='spot')
+    @informative("1h", "ETH/BTC", candle_type="spot")
     def populate_indicators_eth_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        dataframe['rsi'] = 14
+        dataframe["rsi"] = 14
         return dataframe
 
     # Formatting test.
-    @informative('30m', 'NEO/{stake}', '{column}_{BASE}_{QUOTE}_{base}_{quote}_{asset}_{timeframe}')
+    @informative("30m", "NEO/{stake}", "{column}_{BASE}_{QUOTE}_{base}_{quote}_{asset}_{timeframe}")
     def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        dataframe['rsi'] = 14
+        dataframe["rsi"] = 14
         return dataframe
 
     # Custom formatter test
-    @informative('30m', 'ETH/{stake}', fmt=lambda column, **kwargs: column + '_from_callable')
+    @informative("30m", "ETH/{stake}", fmt=lambda column, **kwargs: column + "_from_callable")
     def populate_indicators_eth_30m(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        dataframe['rsi'] = 14
+        dataframe["rsi"] = 14
         return dataframe
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         # Strategy timeframe indicators for current pair.
-        dataframe['rsi'] = 14
+        dataframe["rsi"] = 14
         # Informative pairs are available in this method.
-        dataframe['rsi_less'] = dataframe['rsi'] < dataframe['rsi_1h']
+        dataframe["rsi_less"] = dataframe["rsi"] < dataframe["rsi_1h"]
 
         # Mixing manual informative pairs with decorators.
-        informative = self.dp.get_pair_dataframe('NEO/USDT', '5m', '')
-        informative['rsi'] = 14
-        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, '5m', ffill=True)
+        informative = self.dp.get_pair_dataframe("NEO/USDT", "5m", "")
+        informative["rsi"] = 14
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "5m", ffill=True)
 
         return dataframe

tests/strategy/strats/lookahead_bias/strategy_test_v3_with_lookahead_bias.py

@@ -10,49 +10,44 @@ class strategy_test_v3_with_lookahead_bias(IStrategy):
     INTERFACE_VERSION = 3
 
     # Minimal ROI designed for the strategy
-    minimal_roi = {
-        "40": 0.0,
-        "30": 0.01,
-        "20": 0.02,
-        "0": 0.04
-    }
+    minimal_roi = {"40": 0.0, "30": 0.01, "20": 0.02, "0": 0.04}
 
     # Optimal stoploss designed for the strategy
     stoploss = -0.10
 
     # Optimal timeframe for the strategy
-    timeframe = '5m'
-    scenario = CategoricalParameter(['no_bias', 'bias1'], default='bias1', space="buy")
+    timeframe = "5m"
+    scenario = CategoricalParameter(["no_bias", "bias1"], default="bias1", space="buy")
 
     # Number of candles the strategy requires before producing valid signals
     startup_candle_count: int = 20
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         # bias is introduced here
-        if self.scenario.value != 'no_bias':
-            ichi = ichimoku(dataframe,
-                            conversion_line_period=20,
-                            base_line_periods=60,
-                            laggin_span=120,
-                            displacement=30)
-            dataframe['chikou_span'] = ichi['chikou_span']
+        if self.scenario.value != "no_bias":
+            ichi = ichimoku(
+                dataframe,
+                conversion_line_period=20,
+                base_line_periods=60,
+                laggin_span=120,
+                displacement=30,
+            )
+            dataframe["chikou_span"] = ichi["chikou_span"]
 
         return dataframe
 
     def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        if self.scenario.value == 'no_bias':
-            dataframe.loc[dataframe['close'].shift(10) < dataframe['close'], 'enter_long'] = 1
+        if self.scenario.value == "no_bias":
+            dataframe.loc[dataframe["close"].shift(10) < dataframe["close"], "enter_long"] = 1
         else:
-            dataframe.loc[dataframe['close'].shift(-10) > dataframe['close'], 'enter_long'] = 1
+            dataframe.loc[dataframe["close"].shift(-10) > dataframe["close"], "enter_long"] = 1
 
         return dataframe
 
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        if self.scenario.value == 'no_bias':
-            dataframe.loc[
-                dataframe['close'].shift(10) < dataframe['close'], 'exit'] = 1
+        if self.scenario.value == "no_bias":
+            dataframe.loc[dataframe["close"].shift(10) < dataframe["close"], "exit"] = 1
         else:
-            dataframe.loc[
-                dataframe['close'].shift(-10) > dataframe['close'], 'exit'] = 1
+            dataframe.loc[dataframe["close"].shift(-10) > dataframe["close"], "exit"] = 1
 
         return dataframe

tests/strategy/strats/strategy_test_v2.py

@@ -15,28 +15,24 @@ class StrategyTestV2(IStrategy):
     or strategy repository https://github.com/freqtrade/freqtrade-strategies
     for samples and inspiration.
     """
+
     INTERFACE_VERSION = 2
 
     # Minimal ROI designed for the strategy
-    minimal_roi = {
-        "40": 0.0,
-        "30": 0.01,
-        "20": 0.02,
-        "0": 0.04
-    }
+    minimal_roi = {"40": 0.0, "30": 0.01, "20": 0.02, "0": 0.04}
 
     # Optimal stoploss designed for the strategy
     stoploss = -0.10
 
     # Optimal timeframe for the strategy
-    timeframe = '5m'
+    timeframe = "5m"
 
     # Optional order type mapping
     order_types = {
-        'entry': 'limit',
-        'exit': 'limit',
-        'stoploss': 'limit',
-        'stoploss_on_exchange': False
+        "entry": "limit",
+        "exit": "limit",
+        "stoploss": "limit",
+        "stoploss_on_exchange": False,
     }
 
     # Number of candles the strategy requires before producing valid signals
@@ -44,8 +40,8 @@ class StrategyTestV2(IStrategy):
 
     # Optional time in force for orders
     order_time_in_force = {
-        'entry': 'gtc',
-        'exit': 'gtc',
+        "entry": "gtc",
+        "exit": "gtc",
     }
     # Test legacy use_sell_signal definition
     use_sell_signal = False
@@ -69,36 +65,36 @@ class StrategyTestV2(IStrategy):
         # ------------------------------------
 
         # ADX
-        dataframe['adx'] = ta.ADX(dataframe)
+        dataframe["adx"] = ta.ADX(dataframe)
 
         # MACD
         macd = ta.MACD(dataframe)
-        dataframe['macd'] = macd['macd']
-        dataframe['macdsignal'] = macd['macdsignal']
-        dataframe['macdhist'] = macd['macdhist']
+        dataframe["macd"] = macd["macd"]
+        dataframe["macdsignal"] = macd["macdsignal"]
+        dataframe["macdhist"] = macd["macdhist"]
 
         # Minus Directional Indicator / Movement
-        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+        dataframe["minus_di"] = ta.MINUS_DI(dataframe)
 
         # Plus Directional Indicator / Movement
-        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+        dataframe["plus_di"] = ta.PLUS_DI(dataframe)
 
         # RSI
-        dataframe['rsi'] = ta.RSI(dataframe)
+        dataframe["rsi"] = ta.RSI(dataframe)
 
         # Stoch fast
         stoch_fast = ta.STOCHF(dataframe)
-        dataframe['fastd'] = stoch_fast['fastd']
-        dataframe['fastk'] = stoch_fast['fastk']
+        dataframe["fastd"] = stoch_fast["fastd"]
+        dataframe["fastk"] = stoch_fast["fastk"]
 
         # Bollinger bands
         bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
-        dataframe['bb_lowerband'] = bollinger['lower']
-        dataframe['bb_middleband'] = bollinger['mid']
-        dataframe['bb_upperband'] = bollinger['upper']
+        dataframe["bb_lowerband"] = bollinger["lower"]
+        dataframe["bb_middleband"] = bollinger["mid"]
+        dataframe["bb_upperband"] = bollinger["upper"]
 
         # EMA - Exponential Moving Average
-        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+        dataframe["ema10"] = ta.EMA(dataframe, timeperiod=10)
 
         return dataframe
 
@@ -111,16 +107,14 @@ class StrategyTestV2(IStrategy):
         """
         dataframe.loc[
             (
-                (dataframe['rsi'] < 35) &
-                (dataframe['fastd'] < 35) &
-                (dataframe['adx'] > 30) &
-                (dataframe['plus_di'] > 0.5)
-            ) |
-            (
-                (dataframe['adx'] > 65) &
-                (dataframe['plus_di'] > 0.5)
-            ),
-            'buy'] = 1
+                (dataframe["rsi"] < 35)
+                & (dataframe["fastd"] < 35)
+                & (dataframe["adx"] > 30)
+                & (dataframe["plus_di"] > 0.5)
+            )
+            | ((dataframe["adx"] > 65) & (dataframe["plus_di"] > 0.5)),
+            "buy",
+        ] = 1
 
         return dataframe
 
@@ -134,15 +128,13 @@ class StrategyTestV2(IStrategy):
         dataframe.loc[
             (
                 (
-                    (qtpylib.crossed_above(dataframe['rsi'], 70)) |
-                    (qtpylib.crossed_above(dataframe['fastd'], 70))
-                ) &
-                (dataframe['adx'] > 10) &
-                (dataframe['minus_di'] > 0)
-            ) |
-            (
-                (dataframe['adx'] > 70) &
-                (dataframe['minus_di'] > 0.5)
-            ),
-            'sell'] = 1
+                    (qtpylib.crossed_above(dataframe["rsi"], 70))
+                    | (qtpylib.crossed_above(dataframe["fastd"], 70))
+                )
+                & (dataframe["adx"] > 10)
+                & (dataframe["minus_di"] > 0)
+            )
+            | ((dataframe["adx"] > 70) & (dataframe["minus_di"] > 0.5)),
+            "sell",
+        ] = 1
         return dataframe

tests/strategy/strats/strategy_test_v3.py

@@ -25,15 +25,11 @@ class StrategyTestV3(IStrategy):
     or strategy repository https://github.com/freqtrade/freqtrade-strategies
     for samples and inspiration.
     """
+
     INTERFACE_VERSION = 3
 
     # Minimal ROI designed for the strategy
-    minimal_roi = {
-        "40": 0.0,
-        "30": 0.01,
-        "20": 0.02,
-        "0": 0.04
-    }
+    minimal_roi = {"40": 0.0, "30": 0.01, "20": 0.02, "0": 0.04}
 
     # Optimal max_open_trades for the strategy
     max_open_trades = -1
@@ -42,14 +38,14 @@ class StrategyTestV3(IStrategy):
     stoploss = -0.10
 
     # Optimal timeframe for the strategy
-    timeframe = '5m'
+    timeframe = "5m"
 
     # Optional order type mapping
     order_types = {
-        'entry': 'limit',
-        'exit': 'limit',
-        'stoploss': 'limit',
-        'stoploss_on_exchange': False
+        "entry": "limit",
+        "exit": "limit",
+        "stoploss": "limit",
+        "stoploss_on_exchange": False,
     }
 
     # Number of candles the strategy requires before producing valid signals
@@ -57,26 +53,24 @@ class StrategyTestV3(IStrategy):
 
     # Optional time in force for orders
     order_time_in_force = {
-        'entry': 'gtc',
-        'exit': 'gtc',
+        "entry": "gtc",
+        "exit": "gtc",
     }
 
     buy_params = {
-        'buy_rsi': 35,
+        "buy_rsi": 35,
         # Intentionally not specified, so "default" is tested
         # 'buy_plusdi': 0.4
     }
 
-    sell_params = {
-        'sell_rsi': 74,
-        'sell_minusdi': 0.4
-    }
+    sell_params = {"sell_rsi": 74, "sell_minusdi": 0.4}
 
-    buy_rsi = IntParameter([0, 50], default=30, space='buy')
-    buy_plusdi = RealParameter(low=0, high=1, default=0.5, space='buy')
-    sell_rsi = IntParameter(low=50, high=100, default=70, space='sell')
-    sell_minusdi = DecimalParameter(low=0, high=1, default=0.5001, decimals=3, space='sell',
-                                    load=False)
+    buy_rsi = IntParameter([0, 50], default=30, space="buy")
+    buy_plusdi = RealParameter(low=0, high=1, default=0.5, space="buy")
+    sell_rsi = IntParameter(low=50, high=100, default=70, space="sell")
+    sell_minusdi = DecimalParameter(
+        low=0, high=1, default=0.5001, decimals=3, space="sell", load=False
+    )
     protection_enabled = BooleanParameter(default=True)
     protection_cooldown_lookback = IntParameter([0, 50], default=30)
 
@@ -97,67 +91,61 @@ class StrategyTestV3(IStrategy):
         self.bot_started = True
 
     def informative_pairs(self):
-
         return []
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
         # Momentum Indicator
         # ------------------------------------
 
         # ADX
-        dataframe['adx'] = ta.ADX(dataframe)
+        dataframe["adx"] = ta.ADX(dataframe)
 
         # MACD
         macd = ta.MACD(dataframe)
-        dataframe['macd'] = macd['macd']
-        dataframe['macdsignal'] = macd['macdsignal']
-        dataframe['macdhist'] = macd['macdhist']
+        dataframe["macd"] = macd["macd"]
+        dataframe["macdsignal"] = macd["macdsignal"]
+        dataframe["macdhist"] = macd["macdhist"]
 
         # Minus Directional Indicator / Movement
-        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+        dataframe["minus_di"] = ta.MINUS_DI(dataframe)
 
         # Plus Directional Indicator / Movement
-        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+        dataframe["plus_di"] = ta.PLUS_DI(dataframe)
 
         # RSI
-        dataframe['rsi'] = ta.RSI(dataframe)
+        dataframe["rsi"] = ta.RSI(dataframe)
 
         # Stoch fast
         stoch_fast = ta.STOCHF(dataframe)
-        dataframe['fastd'] = stoch_fast['fastd']
-        dataframe['fastk'] = stoch_fast['fastk']
+        dataframe["fastd"] = stoch_fast["fastd"]
+        dataframe["fastk"] = stoch_fast["fastk"]
 
         # Bollinger bands
         bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
-        dataframe['bb_lowerband'] = bollinger['lower']
-        dataframe['bb_middleband'] = bollinger['mid']
-        dataframe['bb_upperband'] = bollinger['upper']
+        dataframe["bb_lowerband"] = bollinger["lower"]
+        dataframe["bb_middleband"] = bollinger["mid"]
+        dataframe["bb_upperband"] = bollinger["upper"]
 
         # EMA - Exponential Moving Average
-        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+        dataframe["ema10"] = ta.EMA(dataframe, timeperiod=10)
 
         return dataframe
 
     def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
         dataframe.loc[
             (
-                (dataframe['rsi'] < self.buy_rsi.value) &
-                (dataframe['fastd'] < 35) &
-                (dataframe['adx'] > 30) &
-                (dataframe['plus_di'] > self.buy_plusdi.value)
-            ) |
-            (
-                (dataframe['adx'] > 65) &
-                (dataframe['plus_di'] > self.buy_plusdi.value)
-            ),
-            'enter_long'] = 1
+                (dataframe["rsi"] < self.buy_rsi.value)
+                & (dataframe["fastd"] < 35)
+                & (dataframe["adx"] > 30)
+                & (dataframe["plus_di"] > self.buy_plusdi.value)
+            )
+            | ((dataframe["adx"] > 65) & (dataframe["plus_di"] > self.buy_plusdi.value)),
+            "enter_long",
+        ] = 1
         dataframe.loc[
-            (
-                qtpylib.crossed_below(dataframe['rsi'], self.sell_rsi.value)
-            ),
-            ('enter_short', 'enter_tag')] = (1, 'short_Tag')
+            (qtpylib.crossed_below(dataframe["rsi"], self.sell_rsi.value)),
+            ("enter_short", "enter_tag"),
+        ] = (1, "short_Tag")
 
         return dataframe
 
@@ -165,41 +153,53 @@ class StrategyTestV3(IStrategy):
         dataframe.loc[
             (
                 (
-                    (qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) |
-                    (qtpylib.crossed_above(dataframe['fastd'], 70))
-                ) &
-                (dataframe['adx'] > 10) &
-                (dataframe['minus_di'] > 0)
-            ) |
-            (
-                (dataframe['adx'] > 70) &
-                (dataframe['minus_di'] > self.sell_minusdi.value)
-            ),
-            'exit_long'] = 1
+                    (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value))
+                    | (qtpylib.crossed_above(dataframe["fastd"], 70))
+                )
+                & (dataframe["adx"] > 10)
+                & (dataframe["minus_di"] > 0)
+            )
+            | ((dataframe["adx"] > 70) & (dataframe["minus_di"] > self.sell_minusdi.value)),
+            "exit_long",
+        ] = 1
 
         dataframe.loc[
-            (
-                qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)
-            ),
-            ('exit_short', 'exit_tag')] = (1, 'short_Tag')
+            (qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value)),
+            ("exit_short", "exit_tag"),
+        ] = (1, "short_Tag")
 
         return dataframe
 
-    def leverage(self, pair: str, current_time: datetime, current_rate: float,
-                 proposed_leverage: float, max_leverage: float, entry_tag: Optional[str],
-                 side: str, **kwargs) -> float:
+    def leverage(
+        self,
+        pair: str,
+        current_time: datetime,
+        current_rate: float,
+        proposed_leverage: float,
+        max_leverage: float,
+        entry_tag: Optional[str],
+        side: str,
+        **kwargs,
+    ) -> float:
         # Return 3.0 in all cases.
         # Bot-logic must make sure it's an allowed leverage and eventually adjust accordingly.
 
         return 3.0
 
-    def adjust_trade_position(self, trade: Trade, current_time: datetime,
-                              current_rate: float, current_profit: float,
-                              min_stake: Optional[float], max_stake: float,
-                              current_entry_rate: float, current_exit_rate: float,
-                              current_entry_profit: float, current_exit_profit: float,
-                              **kwargs) -> Optional[float]:
-
+    def adjust_trade_position(
+        self,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        min_stake: Optional[float],
+        max_stake: float,
+        current_entry_rate: float,
+        current_exit_rate: float,
+        current_entry_profit: float,
+        current_exit_profit: float,
+        **kwargs,
+    ) -> Optional[float]:
         if current_profit < -0.0075:
             orders = trade.select_filled_orders(trade.entry_side)
             return round(orders[0].stake_amount, 0)

tests/strategy/strats/strategy_test_v3_custom_entry_price.py

@@ -17,24 +17,28 @@ class StrategyTestV3CustomEntryPrice(StrategyTestV3):
     or strategy repository https://github.com/freqtrade/freqtrade-strategies
     for samples and inspiration.
     """
+
     new_entry_price: float = 0.001
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         return dataframe
 
     def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
-        dataframe.loc[
-            dataframe['volume'] > 0,
-            'enter_long'] = 1
+        dataframe.loc[dataframe["volume"] > 0, "enter_long"] = 1
 
         return dataframe
 
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         return dataframe
 
-    def custom_entry_price(self, pair: str, trade: Optional[Trade], current_time: datetime,
-                           proposed_rate: float,
-                           entry_tag: Optional[str], side: str, **kwargs) -> float:
-
+    def custom_entry_price(
+        self,
+        pair: str,
+        trade: Optional[Trade],
+        current_time: datetime,
+        proposed_rate: float,
+        entry_tag: Optional[str],
+        side: str,
+        **kwargs,
+    ) -> float:
         return self.new_entry_price

tests/strategy/strats/strategy_test_v3_recursive_issue.py

@@ -10,37 +10,33 @@ class strategy_test_v3_recursive_issue(IStrategy):
     INTERFACE_VERSION = 3
 
     # Minimal ROI designed for the strategy
-    minimal_roi = {
-        "0": 0.04
-    }
+    minimal_roi = {"0": 0.04}
 
     # Optimal stoploss designed for the strategy
     stoploss = -0.10
 
     # Optimal timeframe for the strategy
-    timeframe = '5m'
-    scenario = CategoricalParameter(['no_bias', 'bias1', 'bias2'], default='bias1', space="buy")
+    timeframe = "5m"
+    scenario = CategoricalParameter(["no_bias", "bias1", "bias2"], default="bias1", space="buy")
 
     # Number of candles the strategy requires before producing valid signals
     startup_candle_count: int = 100
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         # bias is introduced here
-        if self.scenario.value == 'no_bias':
-            dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+        if self.scenario.value == "no_bias":
+            dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
         else:
-            dataframe['rsi'] = ta.RSI(dataframe, timeperiod=50)
+            dataframe["rsi"] = ta.RSI(dataframe, timeperiod=50)
 
-        if self.scenario.value == 'bias2':
+        if self.scenario.value == "bias2":
             # Has both bias1 and bias2
-            dataframe['rsi_lookahead'] = ta.RSI(dataframe, timeperiod=50).shift(-1)
+            dataframe["rsi_lookahead"] = ta.RSI(dataframe, timeperiod=50).shift(-1)
 
         return dataframe
 
     def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
         return dataframe
 
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-
         return dataframe