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ruff format: Update test strategies

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Matthias
2024-05-12 15:41:07 +02:00
parent 099b1fc8c4
commit 8c7d80b78e
23 changed files with 420 additions and 462 deletions
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41
tests/strategy/strats/lookahead_bias/strategy_test_v3_with_lookahead_bias.py
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@@ -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