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ruff format: template directory
This commit is contained in:
Matthias
parent
439b8a0320
commit
5783a44c86
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@ -61,27 +61,28 @@ class FreqaiExampleHybridStrategy(IStrategy):
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"""
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minimal_roi = {
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# "120": 0.0, # exit after 120 minutes at break even
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"60": 0.01,
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"30": 0.02,
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"0": 0.04
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}
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plot_config = {
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'main_plot': {
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'tema': {},
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"main_plot": {
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"tema": {},
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},
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'subplots': {
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"subplots": {
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"MACD": {
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'macd': {'color': 'blue'},
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'macdsignal': {'color': 'orange'},
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"macd": {"color": "blue"},
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"macdsignal": {"color": "orange"},
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},
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"RSI": {
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'rsi': {'color': 'red'},
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"rsi": {"color": "red"},
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},
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"Up_or_down": {
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'&s-up_or_down': {'color': 'green'},
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}
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}
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"&s-up_or_down": {"color": "green"},
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},
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},
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}
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process_only_new_candles = True
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@ -91,13 +92,14 @@ class FreqaiExampleHybridStrategy(IStrategy):
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can_short = True
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# Hyperoptable parameters
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buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
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sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)
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short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
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exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
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buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
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sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
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short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True)
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exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
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def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
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metadata: Dict, **kwargs) -> DataFrame:
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def feature_engineering_expand_all(
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self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs
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) -> DataFrame:
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"""
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*Only functional with FreqAI enabled strategies*
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This function will automatically expand the defined features on the config defined
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@ -136,12 +138,9 @@ class FreqaiExampleHybridStrategy(IStrategy):
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dataframe["bb_upperband-period"] = bollinger["upper"]
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dataframe["%-bb_width-period"] = (
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dataframe["bb_upperband-period"]
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- dataframe["bb_lowerband-period"]
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dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"]
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) / dataframe["bb_middleband-period"]
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dataframe["%-close-bb_lower-period"] = (
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dataframe["close"] / dataframe["bb_lowerband-period"]
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)
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dataframe["%-close-bb_lower-period"] = dataframe["close"] / dataframe["bb_lowerband-period"]
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dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
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@ -152,7 +151,8 @@ class FreqaiExampleHybridStrategy(IStrategy):
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return dataframe
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def feature_engineering_expand_basic(
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self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame:
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self, dataframe: DataFrame, metadata: Dict, **kwargs
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) -> DataFrame:
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"""
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*Only functional with FreqAI enabled strategies*
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This function will automatically expand the defined features on the config defined
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@ -185,7 +185,8 @@ class FreqaiExampleHybridStrategy(IStrategy):
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return dataframe
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def feature_engineering_standard(
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self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame:
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self, dataframe: DataFrame, metadata: Dict, **kwargs
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) -> DataFrame:
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"""
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*Only functional with FreqAI enabled strategies*
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This optional function will be called once with the dataframe of the base timeframe.
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@ -226,13 +227,13 @@ class FreqaiExampleHybridStrategy(IStrategy):
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usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
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"""
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self.freqai.class_names = ["down", "up"]
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dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-50) >
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dataframe["close"], 'up', 'down')
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dataframe["&s-up_or_down"] = np.where(
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dataframe["close"].shift(-50) > dataframe["close"], "up", "down"
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)
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # noqa: C901
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# User creates their own custom strat here. Present example is a supertrend
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# based strategy.
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@ -240,78 +241,81 @@ class FreqaiExampleHybridStrategy(IStrategy):
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# TA indicators to combine with the Freqai targets
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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dataframe["rsi"] = ta.RSI(dataframe)
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# Bollinger Bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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dataframe["bb_percent"] = (
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(dataframe["close"] - dataframe["bb_lowerband"]) /
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
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)
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dataframe["bb_width"] = (
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
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dataframe["bb_lowerband"] = bollinger["lower"]
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dataframe["bb_middleband"] = bollinger["mid"]
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dataframe["bb_upperband"] = bollinger["upper"]
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dataframe["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
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dataframe["bb_upperband"] - dataframe["bb_lowerband"]
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)
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dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[
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"bb_middleband"
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]
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# TEMA - Triple Exponential Moving Average
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
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return dataframe
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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df.loc[
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(
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# Signal: RSI crosses above 30
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(qtpylib.crossed_above(df['rsi'], self.buy_rsi.value)) &
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(df['tema'] <= df['bb_middleband']) & # Guard: tema below BB middle
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(df['tema'] > df['tema'].shift(1)) & # Guard: tema is raising
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(df['volume'] > 0) & # Make sure Volume is not 0
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(df['do_predict'] == 1) & # Make sure Freqai is confident in the prediction
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(qtpylib.crossed_above(df["rsi"], self.buy_rsi.value))
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& (df["tema"] <= df["bb_middleband"]) # Guard: tema below BB middle
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& (df["tema"] > df["tema"].shift(1)) # Guard: tema is raising
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& (df["volume"] > 0) # Make sure Volume is not 0
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& (df["do_predict"] == 1) # Make sure Freqai is confident in the prediction
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&
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# Only enter trade if Freqai thinks the trend is in this direction
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(df['&s-up_or_down'] == 'up')
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(df["&s-up_or_down"] == "up")
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),
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'enter_long'] = 1
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"enter_long",
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] = 1
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df.loc[
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(
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# Signal: RSI crosses above 70
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(qtpylib.crossed_above(df['rsi'], self.short_rsi.value)) &
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(df['tema'] > df['bb_middleband']) & # Guard: tema above BB middle
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(df['tema'] < df['tema'].shift(1)) & # Guard: tema is falling
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(df['volume'] > 0) & # Make sure Volume is not 0
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(df['do_predict'] == 1) & # Make sure Freqai is confident in the prediction
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(qtpylib.crossed_above(df["rsi"], self.short_rsi.value))
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& (df["tema"] > df["bb_middleband"]) # Guard: tema above BB middle
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& (df["tema"] < df["tema"].shift(1)) # Guard: tema is falling
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& (df["volume"] > 0) # Make sure Volume is not 0
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& (df["do_predict"] == 1) # Make sure Freqai is confident in the prediction
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&
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# Only enter trade if Freqai thinks the trend is in this direction
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(df['&s-up_or_down'] == 'down')
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(df["&s-up_or_down"] == "down")
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),
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'enter_short'] = 1
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"enter_short",
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] = 1
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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df.loc[
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(
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# Signal: RSI crosses above 70
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(qtpylib.crossed_above(df['rsi'], self.sell_rsi.value)) &
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(df['tema'] > df['bb_middleband']) & # Guard: tema above BB middle
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(df['tema'] < df['tema'].shift(1)) & # Guard: tema is falling
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(df['volume'] > 0) # Make sure Volume is not 0
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(qtpylib.crossed_above(df["rsi"], self.sell_rsi.value))
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& (df["tema"] > df["bb_middleband"]) # Guard: tema above BB middle
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& (df["tema"] < df["tema"].shift(1)) # Guard: tema is falling
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& (df["volume"] > 0) # Make sure Volume is not 0
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),
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'exit_long'] = 1
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"exit_long",
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] = 1
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df.loc[
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(
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# Signal: RSI crosses above 30
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(qtpylib.crossed_above(df['rsi'], self.exit_short_rsi.value)) &
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(qtpylib.crossed_above(df["rsi"], self.exit_short_rsi.value))
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&
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# Guard: tema below BB middle
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(df['tema'] <= df['bb_middleband']) &
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(df['tema'] > df['tema'].shift(1)) & # Guard: tema is raising
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(df['volume'] > 0) # Make sure Volume is not 0
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(df["tema"] <= df["bb_middleband"])
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& (df["tema"] > df["tema"].shift(1)) # Guard: tema is raising
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& (df["volume"] > 0) # Make sure Volume is not 0
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),
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'exit_short'] = 1
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"exit_short",
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] = 1
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return df
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@ -45,8 +45,9 @@ class FreqaiExampleStrategy(IStrategy):
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startup_candle_count: int = 40
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can_short = True
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def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
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metadata: Dict, **kwargs) -> DataFrame:
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def feature_engineering_expand_all(
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self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs
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) -> DataFrame:
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"""
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*Only functional with FreqAI enabled strategies*
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This function will automatically expand the defined features on the config defined
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@ -89,12 +90,9 @@ class FreqaiExampleStrategy(IStrategy):
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dataframe["bb_upperband-period"] = bollinger["upper"]
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dataframe["%-bb_width-period"] = (
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dataframe["bb_upperband-period"]
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- dataframe["bb_lowerband-period"]
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dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"]
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) / dataframe["bb_middleband-period"]
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dataframe["%-close-bb_lower-period"] = (
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dataframe["close"] / dataframe["bb_lowerband-period"]
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)
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dataframe["%-close-bb_lower-period"] = dataframe["close"] / dataframe["bb_lowerband-period"]
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dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
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@ -105,7 +103,8 @@ class FreqaiExampleStrategy(IStrategy):
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return dataframe
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def feature_engineering_expand_basic(
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self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame:
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self, dataframe: DataFrame, metadata: Dict, **kwargs
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) -> DataFrame:
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"""
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*Only functional with FreqAI enabled strategies*
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This function will automatically expand the defined features on the config defined
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@ -142,7 +141,8 @@ class FreqaiExampleStrategy(IStrategy):
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return dataframe
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def feature_engineering_standard(
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self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame:
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self, dataframe: DataFrame, metadata: Dict, **kwargs
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) -> DataFrame:
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"""
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*Only functional with FreqAI enabled strategies*
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This optional function will be called once with the dataframe of the base timeframe.
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@ -197,7 +197,7 @@ class FreqaiExampleStrategy(IStrategy):
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.mean()
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/ dataframe["close"]
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- 1
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)
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)
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# Classifiers are typically set up with strings as targets:
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# df['&s-up_or_down'] = np.where( df["close"].shift(-100) >
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@ -224,7 +224,6 @@ class FreqaiExampleStrategy(IStrategy):
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# All indicators must be populated by feature_engineering_*() functions
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# the model will return all labels created by user in `set_freqai_targets()`
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@ -237,11 +236,10 @@ class FreqaiExampleStrategy(IStrategy):
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return dataframe
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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enter_long_conditions = [
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df["do_predict"] == 1,
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df["&-s_close"] > 0.01,
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]
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]
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if enter_long_conditions:
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df.loc[
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@ -251,7 +249,7 @@ class FreqaiExampleStrategy(IStrategy):
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enter_short_conditions = [
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df["do_predict"] == 1,
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df["&-s_close"] < -0.01,
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]
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]
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if enter_short_conditions:
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df.loc[
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@ -261,17 +259,11 @@ class FreqaiExampleStrategy(IStrategy):
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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exit_long_conditions = [
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df["do_predict"] == 1,
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df["&-s_close"] < 0
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]
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exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < 0]
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if exit_long_conditions:
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df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
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exit_short_conditions = [
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df["do_predict"] == 1,
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df["&-s_close"] > 0
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]
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exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > 0]
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if exit_short_conditions:
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df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
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@ -289,7 +281,6 @@ class FreqaiExampleStrategy(IStrategy):
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side: str,
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**kwargs,
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) -> bool:
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df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = df.iloc[-1].squeeze()
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|
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@ -35,17 +35,23 @@ class SampleHyperOptLoss(IHyperOptLoss):
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"""
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@staticmethod
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def hyperopt_loss_function(results: DataFrame, trade_count: int,
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min_date: datetime, max_date: datetime,
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config: Config, processed: Dict[str, DataFrame],
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*args, **kwargs) -> float:
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def hyperopt_loss_function(
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results: DataFrame,
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trade_count: int,
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min_date: datetime,
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max_date: datetime,
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config: Config,
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processed: Dict[str, DataFrame],
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*args,
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**kwargs,
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) -> float:
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"""
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Objective function, returns smaller number for better results
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"""
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total_profit = results['profit_ratio'].sum()
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trade_duration = results['trade_duration'].mean()
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total_profit = results["profit_ratio"].sum()
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trade_duration = results["trade_duration"].mean()
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trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
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trade_loss = 1 - 0.25 * exp(-((trade_count - TARGET_TRADES) ** 2) / 10**5.8)
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profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
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duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
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result = trade_loss + profit_loss + duration_loss
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|
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@ -7,8 +7,13 @@ import pandas as pd # noqa
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from pandas import DataFrame
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from typing import Optional, Union
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from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
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IStrategy, IntParameter)
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from freqtrade.strategy import (
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BooleanParameter,
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CategoricalParameter,
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DecimalParameter,
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IStrategy,
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IntParameter,
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)
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# --------------------------------
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# Add your lib to import here
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@ -34,6 +39,7 @@ class SampleStrategy(IStrategy):
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You should keep:
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- timeframe, minimal_roi, stoploss, trailing_*
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"""
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# Strategy interface version - allow new iterations of the strategy interface.
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# Check the documentation or the Sample strategy to get the latest version.
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INTERFACE_VERSION = 3
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@ -44,6 +50,7 @@ class SampleStrategy(IStrategy):
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# Minimal ROI designed for the strategy.
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# This attribute will be overridden if the config file contains "minimal_roi".
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minimal_roi = {
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# "120": 0.0, # exit after 120 minutes at break even
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"60": 0.01,
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"30": 0.02,
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"0": 0.04
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@ -60,7 +67,7 @@ class SampleStrategy(IStrategy):
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# trailing_stop_positive_offset = 0.0 # Disabled / not configured
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|
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# Optimal timeframe for the strategy.
|
||||
timeframe = '5m'
|
||||
timeframe = "5m"
|
||||
|
||||
# Run "populate_indicators()" only for new candle.
|
||||
process_only_new_candles = True
|
||||
|
|
@ -71,42 +78,39 @@ class SampleStrategy(IStrategy):
|
|||
ignore_roi_if_entry_signal = False
|
||||
|
||||
# Hyperoptable parameters
|
||||
buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
|
||||
sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)
|
||||
short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
|
||||
exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
|
||||
buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
|
||||
sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
|
||||
short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True)
|
||||
exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
|
||||
|
||||
# Number of candles the strategy requires before producing valid signals
|
||||
startup_candle_count: int = 200
|
||||
|
||||
# Optional order type mapping.
|
||||
order_types = {
|
||||
'entry': 'limit',
|
||||
'exit': 'limit',
|
||||
'stoploss': 'market',
|
||||
'stoploss_on_exchange': False
|
||||
"entry": "limit",
|
||||
"exit": "limit",
|
||||
"stoploss": "market",
|
||||
"stoploss_on_exchange": False,
|
||||
}
|
||||
|
||||
# Optional order time in force.
|
||||
order_time_in_force = {
|
||||
'entry': 'GTC',
|
||||
'exit': 'GTC'
|
||||
}
|
||||
order_time_in_force = {"entry": "GTC", "exit": "GTC"}
|
||||
|
||||
plot_config = {
|
||||
'main_plot': {
|
||||
'tema': {},
|
||||
'sar': {'color': 'white'},
|
||||
"main_plot": {
|
||||
"tema": {},
|
||||
"sar": {"color": "white"},
|
||||
},
|
||||
'subplots': {
|
||||
"subplots": {
|
||||
"MACD": {
|
||||
'macd': {'color': 'blue'},
|
||||
'macdsignal': {'color': 'orange'},
|
||||
"macd": {"color": "blue"},
|
||||
"macdsignal": {"color": "orange"},
|
||||
},
|
||||
"RSI": {
|
||||
'rsi': {'color': 'red'},
|
||||
}
|
||||
}
|
||||
"rsi": {"color": "red"},
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
def informative_pairs(self):
|
||||
|
|
@ -138,7 +142,7 @@ class SampleStrategy(IStrategy):
|
|||
# ------------------------------------
|
||||
|
||||
# ADX
|
||||
dataframe['adx'] = ta.ADX(dataframe)
|
||||
dataframe["adx"] = ta.ADX(dataframe)
|
||||
|
||||
# # Plus Directional Indicator / Movement
|
||||
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
|
||||
|
|
@ -177,7 +181,7 @@ class SampleStrategy(IStrategy):
|
|||
# dataframe['cci'] = ta.CCI(dataframe)
|
||||
|
||||
# RSI
|
||||
dataframe['rsi'] = ta.RSI(dataframe)
|
||||
dataframe["rsi"] = ta.RSI(dataframe)
|
||||
|
||||
# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
|
||||
# rsi = 0.1 * (dataframe['rsi'] - 50)
|
||||
|
|
@ -193,8 +197,8 @@ class SampleStrategy(IStrategy):
|
|||
|
||||
# Stochastic 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"]
|
||||
|
||||
# # Stochastic RSI
|
||||
# Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
|
||||
|
|
@ -205,12 +209,12 @@ class SampleStrategy(IStrategy):
|
|||
|
||||
# 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"]
|
||||
|
||||
# MFI
|
||||
dataframe['mfi'] = ta.MFI(dataframe)
|
||||
dataframe["mfi"] = ta.MFI(dataframe)
|
||||
|
||||
# # ROC
|
||||
# dataframe['roc'] = ta.ROC(dataframe)
|
||||
|
|
@ -220,16 +224,15 @@ class SampleStrategy(IStrategy):
|
|||
|
||||
# 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_percent"] = (
|
||||
(dataframe["close"] - dataframe["bb_lowerband"]) /
|
||||
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
|
||||
)
|
||||
dataframe["bb_width"] = (
|
||||
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
|
||||
dataframe["bb_lowerband"] = bollinger["lower"]
|
||||
dataframe["bb_middleband"] = bollinger["mid"]
|
||||
dataframe["bb_upperband"] = bollinger["upper"]
|
||||
dataframe["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
|
||||
dataframe["bb_upperband"] - dataframe["bb_lowerband"]
|
||||
)
|
||||
dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[
|
||||
"bb_middleband"
|
||||
]
|
||||
|
||||
# Bollinger Bands - Weighted (EMA based instead of SMA)
|
||||
# weighted_bollinger = qtpylib.weighted_bollinger_bands(
|
||||
|
|
@ -264,17 +267,17 @@ class SampleStrategy(IStrategy):
|
|||
# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
|
||||
|
||||
# Parabolic SAR
|
||||
dataframe['sar'] = ta.SAR(dataframe)
|
||||
dataframe["sar"] = ta.SAR(dataframe)
|
||||
|
||||
# TEMA - Triple Exponential Moving Average
|
||||
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
|
||||
dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
|
||||
|
||||
# Cycle Indicator
|
||||
# ------------------------------------
|
||||
# Hilbert Transform Indicator - SineWave
|
||||
hilbert = ta.HT_SINE(dataframe)
|
||||
dataframe['htsine'] = hilbert['sine']
|
||||
dataframe['htleadsine'] = hilbert['leadsine']
|
||||
dataframe["htsine"] = hilbert["sine"]
|
||||
dataframe["htleadsine"] = hilbert["leadsine"]
|
||||
|
||||
# Pattern Recognition - Bullish candlestick patterns
|
||||
# ------------------------------------
|
||||
|
|
@ -353,22 +356,24 @@ class SampleStrategy(IStrategy):
|
|||
dataframe.loc[
|
||||
(
|
||||
# Signal: RSI crosses above 30
|
||||
(qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) &
|
||||
(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard: tema below BB middle
|
||||
(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising
|
||||
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||||
(qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value))
|
||||
& (dataframe["tema"] <= dataframe["bb_middleband"]) # Guard: tema below BB middle
|
||||
& (dataframe["tema"] > dataframe["tema"].shift(1)) # Guard: tema is raising
|
||||
& (dataframe["volume"] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
'enter_long'] = 1
|
||||
"enter_long",
|
||||
] = 1
|
||||
|
||||
dataframe.loc[
|
||||
(
|
||||
# Signal: RSI crosses above 70
|
||||
(qtpylib.crossed_above(dataframe['rsi'], self.short_rsi.value)) &
|
||||
(dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle
|
||||
(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling
|
||||
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||||
(qtpylib.crossed_above(dataframe["rsi"], self.short_rsi.value))
|
||||
& (dataframe["tema"] > dataframe["bb_middleband"]) # Guard: tema above BB middle
|
||||
& (dataframe["tema"] < dataframe["tema"].shift(1)) # Guard: tema is falling
|
||||
& (dataframe["volume"] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
'enter_short'] = 1
|
||||
"enter_short",
|
||||
] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
|
|
@ -382,23 +387,25 @@ class SampleStrategy(IStrategy):
|
|||
dataframe.loc[
|
||||
(
|
||||
# Signal: RSI crosses above 70
|
||||
(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) &
|
||||
(dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle
|
||||
(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling
|
||||
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||||
(qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value))
|
||||
& (dataframe["tema"] > dataframe["bb_middleband"]) # Guard: tema above BB middle
|
||||
& (dataframe["tema"] < dataframe["tema"].shift(1)) # Guard: tema is falling
|
||||
& (dataframe["volume"] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
|
||||
'exit_long'] = 1
|
||||
"exit_long",
|
||||
] = 1
|
||||
|
||||
dataframe.loc[
|
||||
(
|
||||
# Signal: RSI crosses above 30
|
||||
(qtpylib.crossed_above(dataframe['rsi'], self.exit_short_rsi.value)) &
|
||||
(qtpylib.crossed_above(dataframe["rsi"], self.exit_short_rsi.value))
|
||||
&
|
||||
# Guard: tema below BB middle
|
||||
(dataframe['tema'] <= dataframe['bb_middleband']) &
|
||||
(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising
|
||||
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||||
(dataframe["tema"] <= dataframe["bb_middleband"])
|
||||
& (dataframe["tema"] > dataframe["tema"].shift(1)) # Guard: tema is raising
|
||||
& (dataframe["volume"] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
'exit_short'] = 1
|
||||
"exit_short",
|
||||
] = 1
|
||||
|
||||
return dataframe
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user