chore: update tests to modern typing syntax

tests/exchange_online/conftest.py

@@ -1,6 +1,5 @@
 from copy import deepcopy
 from pathlib import Path
-from typing import Tuple
 
 import pytest
 
@@ -10,8 +9,8 @@ from freqtrade.resolvers.exchange_resolver import ExchangeResolver
 from tests.conftest import EXMS, get_default_conf_usdt
 
 
-EXCHANGE_FIXTURE_TYPE = Tuple[Exchange, str]
+EXCHANGE_FIXTURE_TYPE = tuple[Exchange, str]
-EXCHANGE_WS_FIXTURE_TYPE = Tuple[Exchange, str, str]
+EXCHANGE_WS_FIXTURE_TYPE = tuple[Exchange, str, str]
 
 
 # Exchanges that should be tested online

tests/freqai/conftest.py

@@ -2,7 +2,7 @@ import platform
 import sys
 from copy import deepcopy
 from pathlib import Path
-from typing import Any, Dict
+from typing import Any
 from unittest.mock import MagicMock
 
 import pytest
@@ -112,7 +112,7 @@ def make_rl_config(conf):
     return conf
 
 
-def mock_pytorch_mlp_model_training_parameters() -> Dict[str, Any]:
+def mock_pytorch_mlp_model_training_parameters() -> dict[str, Any]:
     return {
         "learning_rate": 3e-4,
         "trainer_kwargs": {

tests/freqtradebot/test_freqtradebot.py

@@ -5,7 +5,6 @@ import logging
 import time
 from copy import deepcopy
 from datetime import timedelta
-from typing import List
 from unittest.mock import ANY, MagicMock, PropertyMock, patch
 
 import pytest
@@ -5442,7 +5441,7 @@ def test_position_adjust(mocker, default_conf_usdt, fee) -> None:
     assert trade.amount == 10
     assert trade.stake_amount == 110
     assert not trade.fee_updated("buy")
-    trades: List[Trade] = Trade.get_open_trades_without_assigned_fees()
+    trades: list[Trade] = Trade.get_open_trades_without_assigned_fees()
     assert len(trades) == 1
     assert trade.is_open
     assert not trade.fee_updated("buy")
@@ -5468,7 +5467,7 @@ def test_position_adjust(mocker, default_conf_usdt, fee) -> None:
     assert orders
     assert len(orders) == 2
     # Assert that the trade is found as open and without fees
-    trades: List[Trade] = Trade.get_open_trades_without_assigned_fees()
+    trades: list[Trade] = Trade.get_open_trades_without_assigned_fees()
     assert len(trades) == 1
     # Assert trade is as expected
     trade = Trade.session.scalars(select(Trade)).first()
@@ -5525,7 +5524,7 @@ def test_position_adjust(mocker, default_conf_usdt, fee) -> None:
     assert order.order_id == "651"
 
     # Assert that the trade is not found as open and without fees
-    trades: List[Trade] = Trade.get_open_trades_without_assigned_fees()
+    trades: list[Trade] = Trade.get_open_trades_without_assigned_fees()
     assert len(trades) == 1
 
     # Add a second DCA
@@ -5725,7 +5724,7 @@ def test_position_adjust2(mocker, default_conf_usdt, fee) -> None:
         exit_check=ExitCheckTuple(exit_type=ExitType.PARTIAL_EXIT),
         sub_trade_amt=amount,
     )
-    trades: List[Trade] = trade.get_open_trades_without_assigned_fees()
+    trades: list[Trade] = trade.get_open_trades_without_assigned_fees()
     assert len(trades) == 1
     # Assert trade is as expected (averaged dca)
 

tests/optimize/__init__.py

@@ -29,10 +29,10 @@ class BTContainer(NamedTuple):
     Minimal BacktestContainer defining Backtest inputs and results.
     """
 
-    data: List[List[float]]
+    data: list[list[float]]
     stop_loss: float
-    roi: Dict[str, float]
+    roi: dict[str, float]
-    trades: List[BTrade]
+    trades: list[BTrade]
     profit_perc: float
     trailing_stop: bool = False
     trailing_only_offset_is_reached: bool = False

tests/optimize/test_hyperopt_tools.py

@@ -1,7 +1,6 @@
 import logging
 import re
 from pathlib import Path
-from typing import Dict, List
 
 import numpy as np
 import pytest
@@ -14,7 +13,7 @@ from tests.conftest import CURRENT_TEST_STRATEGY, log_has, log_has_re
 
 
 # Functions for recurrent object patching
-def create_results() -> List[Dict]:
+def create_results() -> list[dict]:
     return [{"loss": 1, "result": "foo", "params": {}, "is_best": True}]
 
 

tests/strategy/strats/freqai_rl_test_strat.py

@@ -1,6 +1,5 @@
 import logging
 from functools import reduce
-from typing import Dict
 
 import talib.abstract as ta
 from pandas import DataFrame
@@ -26,19 +25,19 @@ class freqai_rl_test_strat(IStrategy):
     can_short = False
 
     def feature_engineering_expand_all(
-        self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs
+        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):
+    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):
+    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
 
@@ -49,7 +48,7 @@ class freqai_rl_test_strat(IStrategy):
 
         return dataframe
 
-    def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
+    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs):
         dataframe["&-action"] = 0
 
         return dataframe

tests/strategy/strats/freqai_test_classifier.py

@@ -1,6 +1,5 @@
 import logging
 from functools import reduce
-from typing import Dict
 
 import numpy as np
 import talib.abstract as ta
@@ -58,7 +57,7 @@ class freqai_test_classifier(IStrategy):
         return informative_pairs
 
     def feature_engineering_expand_all(
-        self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs
+        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
     ):
         dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
         dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
@@ -66,20 +65,20 @@ class freqai_test_classifier(IStrategy):
 
         return dataframe
 
-    def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
+    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"]
 
         return dataframe
 
-    def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
+    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):
+    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"

tests/strategy/strats/freqai_test_multimodel_classifier_strat.py

@@ -1,6 +1,5 @@
 import logging
 from functools import reduce
-from typing import Dict
 
 import numpy as np
 import talib.abstract as ta
@@ -45,7 +44,7 @@ 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
+        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
     ):
         dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
         dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
@@ -53,20 +52,20 @@ class freqai_test_multimodel_classifier_strat(IStrategy):
 
         return dataframe
 
-    def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
+    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"]
 
         return dataframe
 
-    def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
+    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):
+    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"
         )

tests/strategy/strats/freqai_test_multimodel_strat.py

@@ -1,6 +1,5 @@
 import logging
 from functools import reduce
-from typing import Dict
 
 import talib.abstract as ta
 from pandas import DataFrame
@@ -44,7 +43,7 @@ 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
+        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
     ):
         dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
         dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
@@ -52,20 +51,20 @@ class freqai_test_multimodel_strat(IStrategy):
 
         return dataframe
 
-    def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
+    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"]
 
         return dataframe
 
-    def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
+    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):
+    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs):
         dataframe["&-s_close"] = (
             dataframe["close"]
             .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])

tests/strategy/strats/freqai_test_strat.py

@@ -1,6 +1,5 @@
 import logging
 from functools import reduce
-from typing import Dict
 
 import talib.abstract as ta
 from pandas import DataFrame
@@ -44,7 +43,7 @@ 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
+        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
     ):
         dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
         dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
@@ -52,20 +51,20 @@ class freqai_test_strat(IStrategy):
 
         return dataframe
 
-    def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
+    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"]
 
         return dataframe
 
-    def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
+    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):
+    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs):
         dataframe["&-s_close"] = (
             dataframe["close"]
             .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])