Forked from hroff-1902/IsNotItTheHolyGrailManStrategy.py
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July 27, 2021 21:59
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IsNotItTheHolyGrailManStrategy
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| # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement | |
| import talib.abstract as ta | |
| from pandas import DataFrame | |
| import freqtrade.vendor.qtpylib.indicators as qtpylib | |
| from freqtrade.strategy.interface import IStrategy | |
| class IsNotItTheHolyGrailManStrategy(IStrategy): | |
| """ | |
| Default Strategy provided by freqtrade bot. | |
| Please do not modify this strategy, it's intended for internal use only. | |
| Please look at the SampleStrategy in the user_data/strategy directory | |
| or strategy repository https://github.com/freqtrade/freqtrade-strategies | |
| for samples and inspiration. | |
| """ | |
| INTERFACE_VERSION = 2 | |
| # Minimal ROI designed for the strategy | |
| minimal_roi = {"0": 0.26457, "11": 0.07775, "33": 0.02212, "47": 0} | |
| # Optimal stoploss designed for the strategy | |
| stoploss = -0.02082 | |
| # Optimal ticker interval for the strategy | |
| ticker_interval = '5m' | |
| # Optional order type mapping | |
| order_types = { | |
| 'buy': 'limit', | |
| 'sell': 'limit', | |
| 'stoploss': 'limit', | |
| 'stoploss_on_exchange': False | |
| } | |
| # Number of candles the strategy requires before producing valid signals | |
| startup_candle_count: int = 0 | |
| # Optional time in force for orders | |
| order_time_in_force = { | |
| 'buy': 'gtc', | |
| 'sell': 'gtc', | |
| } | |
| def informative_pairs(self): | |
| """ | |
| Define additional, informative pair/interval combinations to be cached from the exchange. | |
| These pair/interval combinations are non-tradeable, unless they are part | |
| of the whitelist as well. | |
| For more information, please consult the documentation | |
| :return: List of tuples in the format (pair, interval) | |
| Sample: return [("ETH/USDT", "5m"), | |
| ("BTC/USDT", "15m"), | |
| ] | |
| """ | |
| return [] | |
| def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: | |
| """ | |
| Adds several different TA indicators to the given DataFrame | |
| Performance Note: For the best performance be frugal on the number of indicators | |
| you are using. Let uncomment only the indicator you are using in your strategies | |
| or your hyperopt configuration, otherwise you will waste your memory and CPU usage. | |
| :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe() | |
| :param metadata: Additional information, like the currently traded pair | |
| :return: a Dataframe with all mandatory indicators for the strategies | |
| """ | |
| # MFI | |
| dataframe['mfi'] = ta.MFI(dataframe) | |
| # RSI | |
| dataframe['rsi'] = ta.RSI(dataframe) | |
| # 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'] | |
| return dataframe | |
| def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: | |
| """ | |
| Based on TA indicators, populates the buy signal for the given dataframe | |
| :param dataframe: DataFrame | |
| :param metadata: Additional information, like the currently traded pair | |
| :return: DataFrame with buy column | |
| """ | |
| dataframe.loc[ | |
| ( | |
| (dataframe['mfi'] < 12) & | |
| (dataframe['rsi'] < 35) & | |
| (dataframe['close'] < dataframe['bb_lowerband']) | |
| ), | |
| 'buy'] = 1 | |
| return dataframe | |
| def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: | |
| """ | |
| Based on TA indicators, populates the sell signal for the given dataframe | |
| :param dataframe: DataFrame | |
| :param metadata: Additional information, like the currently traded pair | |
| :return: DataFrame with buy column | |
| """ | |
| dataframe.loc[ | |
| ( | |
| (dataframe['mfi'] > 98) & | |
| (dataframe['rsi'] > 98) & | |
| (dataframe['close'] > dataframe['bb_upperband']) | |
| ), | |
| 'sell'] = 1 | |
| return dataframe |
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