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chore: update tests to modern typing syntax

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This commit is contained in:
Matthias
2024-10-04 07:09:51 +02:00
parent 628983d123
commit 2e0a597ee4
10 changed files with 32 additions and 40 deletions
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5
tests/exchange_online/conftest.py
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@@ -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_WS_FIXTURE_TYPE = Tuple[Exchange, str, str]
EXCHANGE_FIXTURE_TYPE = tuple[Exchange, str]
EXCHANGE_WS_FIXTURE_TYPE = tuple[Exchange, str, str]
# Exchanges that should be tested online

4
tests/freqai/conftest.py
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@@ -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": {

9
tests/freqtradebot/test_freqtradebot.py
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@@ -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)

6
tests/optimize/__init__.py
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@@ -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]
trades: List[BTrade]
roi: dict[str, float]
trades: list[BTrade]
profit_perc: float
trailing_stop: bool = False
trailing_only_offset_is_reached: bool = False

3
tests/optimize/test_hyperopt_tools.py
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@@ -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}]

9
tests/strategy/strats/freqai_rl_test_strat.py
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@@ -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

9
tests/strategy/strats/freqai_test_classifier.py
View File

@@ -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"

9
tests/strategy/strats/freqai_test_multimodel_classifier_strat.py
View File

@@ -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"
)

9
tests/strategy/strats/freqai_test_multimodel_strat.py
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@@ -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"])

9
tests/strategy/strats/freqai_test_strat.py
View File

@@ -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"])