# source: https://raw.githubusercontent.com/nateemma/strategies/0d370dbbfd815ee5492f4da7a2b2fcb35561ab46/archived/BBKCBounce.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame # -------------------------------- import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy # noqa from freqtrade.strategy.hyper import CategoricalParameter, DecimalParameter, IntParameter from user_data.strategies import Config class github_nateemma_strategies__BBKCBounce__20220511_044916(IStrategy): """ Strategy based on prices bouncing between the Bollinger and Keltner Channel bounds How to use it? > python3 ./freqtrade/main.py -s github_nateemma_strategies__BBKCBounce__20220511_044916 """ # Buy hyperspace params: buy_params = { "buy_adx": 43.0, "buy_adx_enabled": False, "buy_dm_enabled": False, "buy_fisher": -0.41, "buy_fisher_enabled": True, "buy_kc_gain": 0.05, "buy_macd_enabled": False, "buy_mfi": 6.0, "buy_mfi_enabled": True, "buy_period": 16, "buy_sar_enabled": True, } buy_period = IntParameter(3, 20, default=16, space="buy") buy_kc_gain = DecimalParameter(0.01, 0.10, decimals=2, default=0.05, space="buy") buy_adx = DecimalParameter(1, 99, decimals=0, default=43, space="buy") buy_mfi = DecimalParameter(1, 99, decimals=0, default=6, space="buy") buy_fisher = DecimalParameter(-1, 1, decimals=2, default=-0.41, space="buy") buy_adx_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_dm_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_macd_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_mfi_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_sar_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_fisher_enabled = CategoricalParameter([True, False], default=True, space="buy") sell_hold = CategoricalParameter([True, False], default=True, space="sell") sell_upper_band = CategoricalParameter([True, False], default=False, space="sell") # set the startup candles count to the longest average used (SMA, EMA etc) startup_candle_count = max(buy_period.value, 20) # set common parameters minimal_roi = Config.minimal_roi trailing_stop = Config.trailing_stop trailing_stop_positive = Config.trailing_stop_positive trailing_stop_positive_offset = Config.trailing_stop_positive_offset trailing_only_offset_is_reached = Config.trailing_only_offset_is_reached stoploss = Config.stoploss timeframe = Config.timeframe process_only_new_candles = Config.process_only_new_candles use_sell_signal = Config.use_sell_signal sell_profit_only = Config.sell_profit_only ignore_roi_if_buy_signal = Config.ignore_roi_if_buy_signal order_types = Config.order_types 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. """ # SMA - Simple Moving Average dataframe['sma'] = ta.SMA(dataframe, timeperiod=self.buy_period.value) dataframe['ema'] = ta.EMA(dataframe, timeperiod=self.buy_period.value) dataframe['tema'] = ta.TEMA(dataframe, timeperiod=self.buy_period.value) dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # MFI dataframe['mfi'] = ta.MFI(dataframe) # ADX dataframe['adx'] = ta.ADX(dataframe) dataframe['dm_plus'] = ta.PLUS_DM(dataframe) dataframe['dm_minus'] = ta.MINUS_DM(dataframe) dataframe['dm_delta'] = dataframe['dm_plus'] - dataframe['dm_minus'] # SAR Parabolic dataframe['sar'] = ta.SAR(dataframe) # RSI 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) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) # Bollinger Bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) #bollinger = qtpylib.weighted_bollinger_bands(qtpylib.typical_price(dataframe), window=self.buy_period.value, stds=2) dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_mid'] = bollinger['mid'] dataframe['bb_lowerband'] = bollinger['lower'] # Keltner Channel keltner = qtpylib.keltner_channel(dataframe) dataframe["kc_upper"] = keltner["upper"] dataframe["kc_lower"] = keltner["lower"] dataframe["kc_middle"] = keltner["mid"] dataframe["kc_gain"] = ((dataframe["kc_upper"] - dataframe["close"]) / dataframe["close"]) # # Donchian Channels # dataframe['dc_upper'] = ta.MAX(dataframe['high'], timeperiod=self.buy_period.value) # dataframe['dc_lower'] = ta.MIN(dataframe['low'], timeperiod=self.buy_period.value) # dataframe['dc_mid'] = ta.TEMA(((dataframe['dc_upper'] + dataframe['dc_lower']) / 2), # timeperiod=self.buy_period.value) # # Fibonacci Levels (of Donchian Channel) # dataframe['dc_dist'] = (dataframe['dc_upper'] - dataframe['dc_lower']) # dataframe['dc_hf'] = dataframe['dc_upper'] - dataframe['dc_dist'] * 0.236 # Highest Fib # dataframe['dc_chf'] = dataframe['dc_upper'] - dataframe['dc_dist'] * 0.382 # Centre High Fib # dataframe['dc_clf'] = dataframe['dc_upper'] - dataframe['dc_dist'] * 0.618 # Centre Low Fib # dataframe['dc_lf'] = dataframe['dc_upper'] - dataframe['dc_dist'] * 0.764 # Low Fib # # # Squeeze Indicators. # # 'on' means Bollinger Band lies completely within the Keltner Channel # # 'off' means Keltner Channel lies completely within the Bollinger Band # # Booleans are funky with dataframes, so just do an intermediate calculation # dataframe['sqz_upper'] = (dataframe['bb_upperband'] - dataframe["kc_upper"]) # dataframe['sqz_lower'] = (dataframe['bb_lowerband'] - dataframe["kc_lower"]) # dataframe['sqz_on'] = ((dataframe['sqz_upper'] < 0) & (dataframe['sqz_lower'] > 0)) # dataframe['sqz_off'] = ((dataframe['sqz_upper'] > 0) & (dataframe['sqz_lower'] < 0)) # # # Momentum # # value is: Close - Moving Average( (Donchian midline + EMA) / 2 ) # # # get momentum value by running linear regression on delta # dataframe['sqz_ave'] = ta.TEMA(((dataframe['dc_mid'] + dataframe['tema']) / 2), # timeperiod=self.buy_period.value) # dataframe['sqz_delta'] = ta.TEMA((dataframe['close'] - dataframe['sqz_ave']), # timeperiod=30) # # timeperiod = self.buy_period.value) # dataframe['sqz_val'] = ta.LINEARREG(dataframe['sqz_delta'], timeperiod=self.buy_period.value) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # GUARDS AND TRENDS if self.buy_adx_enabled.value: conditions.append(dataframe['adx'] >= self.buy_adx.value) if self.buy_dm_enabled.value: conditions.append(dataframe['dm_delta'] > 0) if self.buy_mfi_enabled.value: conditions.append(dataframe['mfi'] > self.buy_mfi.value) # only buy if close is below SAR if self.buy_sar_enabled.value: conditions.append(dataframe['close'] < dataframe['sar']) if self.buy_macd_enabled.value: conditions.append(dataframe['macd'] > dataframe['macdsignal']) if self.buy_fisher_enabled.value: conditions.append(dataframe['fisher_rsi'] < self.buy_fisher.value) # green candle conditions.append(dataframe['close'] > dataframe['open']) # Potential gain greater than goal conditions.append(dataframe['kc_gain'] >= self.buy_kc_gain.value) # TRIGGERS # candle crossed either BB or KC lower band and ended up above both conditions.append( ( (qtpylib.crossed_above(dataframe['close'], dataframe['bb_lowerband'])) | (qtpylib.crossed_above(dataframe['close'], dataframe['kc_lower'])) ) & ( (dataframe['close'] >= dataframe['bb_lowerband']) & (dataframe['close'] >= dataframe['kc_lower']) ) ) # build the dataframe using the conditions 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 :return: DataFrame with buy column """ conditions = [] if self.sell_hold.value: dataframe.loc[(dataframe['close'].notnull() ), 'sell'] = 0 return dataframe # sell if price crosses above BB and KC upper band if self.sell_upper_band.value: conditions.append( ( # candle crossed either BB or KC lower band and ended up above both ( (qtpylib.crossed_above(dataframe['close'], dataframe['bb_upperband'])) | (qtpylib.crossed_above(dataframe['close'], dataframe['kc_upper'])) ) & ( (dataframe['close'] >= dataframe['bb_upperband']) & (dataframe['close'] >= dataframe['kc_upper']) ) ) # ) | # ( # # safeguard: if prices drops below both lower bounds, sell # ( # (qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband'])) & # (qtpylib.crossed_below(dataframe['close'], dataframe['kc_lower'])) # ) & # ( # (dataframe['close'] <= dataframe['bb_upperband']) & # (dataframe['close'] <= dataframe['kc_upper']) # ) # ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe