80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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from pandas import DataFrame
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from freqtrade.strategy import IStrategy, informative, merge_informative_pair
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class InformativeDecoratorTest(IStrategy):
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"""
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Strategy used by tests freqtrade bot.
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Please do not modify this strategy, it's intended for internal use only.
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Please look at the SampleStrategy in the user_data/strategy directory
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or strategy repository https://github.com/freqtrade/freqtrade-strategies
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for samples and inspiration.
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"""
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INTERFACE_VERSION = 2
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stoploss = -0.10
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timeframe = '5m'
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startup_candle_count: int = 20
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def informative_pairs(self):
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# Intentionally return 2 tuples, must be converted to 3 in compatibility code
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return [
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('NEO/USDT', '5m'),
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('NEO/USDT', '15m', ''),
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('NEO/USDT', '2h', 'futures'),
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]
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['buy'] = 0
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['sell'] = 0
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return dataframe
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# Decorator stacking test.
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@informative('30m')
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@informative('1h')
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def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['rsi'] = 14
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return dataframe
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# Simple informative test.
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@informative('1h', 'NEO/{stake}')
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def populate_indicators_neo_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['rsi'] = 14
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return dataframe
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# Quote currency different from stake currency test.
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@informative('1h', 'ETH/BTC', candle_type='spot')
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def populate_indicators_eth_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['rsi'] = 14
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return dataframe
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# Formatting test.
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@informative('30m', 'NEO/{stake}', '{column}_{BASE}_{QUOTE}_{base}_{quote}_{asset}_{timeframe}')
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def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['rsi'] = 14
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return dataframe
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# Custom formatter test
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@informative('30m', 'ETH/{stake}', fmt=lambda column, **kwargs: column + '_from_callable')
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def populate_indicators_eth_30m(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['rsi'] = 14
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# Strategy timeframe indicators for current pair.
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dataframe['rsi'] = 14
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# Informative pairs are available in this method.
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dataframe['rsi_less'] = dataframe['rsi'] < dataframe['rsi_1h']
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# Mixing manual informative pairs with decorators.
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informative = self.dp.get_pair_dataframe('NEO/USDT', '5m', '')
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informative['rsi'] = 14
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dataframe = merge_informative_pair(dataframe, informative, self.timeframe, '5m', ffill=True)
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return dataframe
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