# source: https://raw.githubusercontent.com/wanderindev/crypto-bot/6381fd64061c5364383e542c484baa7ea687db8a/user_data/strategies/bbrsi_strategy_1h_naive.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from functools import reduce from freqtrade.strategy import IStrategy from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib # Strategy based on Bollinger Bands (BB) and Relative Strength Index (RSI) class github_wanderindev_crypto_bot__bbrsi_strategy_1h_naive__20210622_221428(IStrategy): INTERFACE_VERSION = 2 # Minimal ROI designed for the strategy. minimal_roi = { "0": 0.319, "116": 0.121, "285": 0.051, "483": 0 } # Optimal stoploss designed for the strategy. stoploss = -0.207 # Trailing stoploss trailing_stop = True trailing_stop_positive = 0.038 trailing_stop_positive_offset = 0.138 trailing_only_offset_is_reached = True # Hyperoptable parameters buy_rsi = IntParameter(low=10, high=50, default=30, space='buy', optimize=True, load=True) buy_rsi_enabled = CategoricalParameter([True, False], default=True, space='buy') buy_trigger = CategoricalParameter(['bb_lower_1', 'bb_lower_2', 'bb_lower_3'], default='bb_lower_2', space='buy') sell_rsi = IntParameter(low=50, high=90, default=70, space='sell', optimize=True, load=True) sell_rsi_enabled = CategoricalParameter([True, False], default=True, space='sell') sell_trigger = CategoricalParameter(['bb_middle_1', 'bb_upper_1', 'bb_upper_2', 'bb_upper_3',], default='bb_upper_2', space='sell') # Run "populate_indicators()" only for new candle. process_only_new_candles = False # These values can be overridden in the "ask_strategy" section in the config. use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 30 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': True, 'stoploss_on_exchange_limit_ratio': 0.99 } # Optional order time in force. order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } plot_config = { 'main_plot': { 'tema': {}, 'sar': {'color': 'white'}, }, 'subplots': { "MACD": { 'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}, }, "RSI": { 'rsi': {'color': 'red'}, } } } 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 :param dataframe: Dataframe with data from the exchange :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ # RSI dataframe['rsi'] = ta.RSI(dataframe) # Bollinger bands 1 standard deviation bb_1std = ta.BBANDS(dataframe, timeperiod=20, nbdevup=1.0, nbdevdn=1.0) dataframe['bb_lowerband_1'] = bb_1std['lowerband'] dataframe['bb_middleband_1'] = bb_1std['middleband'] dataframe['bb_upperband_1'] = bb_1std['upperband'] # Bollinger bands 2 standard deviations bb_2std = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0) dataframe['bb_lowerband_2'] = bb_2std['lowerband'] dataframe['bb_upperband_2'] = bb_2std['upperband'] # Bollinger bands 3 standard deviations bb_3std = ta.BBANDS(dataframe, timeperiod=20, nbdevup=3.0, nbdevdn=3.0) dataframe['bb_lowerband_3'] = bb_3std['lowerband'] dataframe['bb_upperband_3'] = bb_3std['upperband'] 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 populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ conditions = [] # Guards and trends if self.buy_rsi_enabled.value: conditions.append(dataframe['rsi'] <= self.buy_rsi.value) # Triggers if self.buy_trigger.value == 'bb_lower_1': conditions.append(dataframe['close'] < dataframe['bb_lowerband_1']) elif self.buy_trigger.value == 'bb_lower_2': conditions.append(dataframe['close'] < dataframe['bb_lowerband_2']) elif self.buy_trigger.value == 'bb_lower_3': conditions.append(dataframe['close'] < dataframe['bb_lowerband_3']) conditions.append(dataframe['volume'] > 0) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), '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 populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with sell column """ conditions = [] # Guards and trends if self.sell_rsi_enabled.value: conditions.append(dataframe['rsi'] > self.sell_rsi.value) # Triggers if self.sell_trigger.value == 'bb_middle_1': conditions.append(dataframe['close'] > dataframe['bb_middleband_1']) elif self.sell_trigger.value == 'bb_upper_1': conditions.append(dataframe['close'] > dataframe['bb_upperband_1']) elif self.sell_trigger.value == 'bb_upper_2': conditions.append(dataframe['close'] > dataframe['bb_upperband_2']) elif self.sell_trigger.value == 'bb_upper_3': conditions.append(dataframe['close'] > dataframe['bb_upperband_3']) conditions.append(dataframe['volume'] > 0) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe