# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/KeltnerChannels_3.py 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 import CategoricalParameter, DecimalParameter, IntParameter import Config class Github_remiotore_freqtrade__KeltnerChannels_3__20260111_210550(IStrategy): """ Simple strategy based on Keltner Channel Breakout How to use it? > python3 ./freqtrade/main.py -s KeltnerChannels """ buy_params = { "buy_adx": 47.0, "buy_adx_enabled": True, "buy_dm_enabled": True, "buy_ema_enabled": False, "buy_fisher": 0.95, "buy_fisher_enabled": False, "buy_macd_enabled": False, "buy_mfi": 44.0, "buy_mfi_enabled": True, "buy_sar_enabled": True, "buy_sma_enabled": False, } buy_adx = DecimalParameter(1, 99, decimals=0, default=30, space="buy") buy_mfi = DecimalParameter(1, 99, decimals=0, default=50, space="buy") buy_fisher = DecimalParameter(-1.0, 1.0, decimals=2, default=0.81, space="buy") buy_adx_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_dm_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_mfi_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_sma_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_ema_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_sar_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_macd_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_fisher_enabled = CategoricalParameter([True, False], default=True, space="buy") sell_hold = CategoricalParameter([True, False], default=True, space="sell") startup_candle_count = 50 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_exit_signal = Config.use_exit_signal exit_profit_only = Config.exit_profit_only ignore_roi_if_entry_signal = Config.ignore_roi_if_entry_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. """ 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'] dataframe['mfi'] = ta.MFI(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] keltner = qtpylib.keltner_channel(dataframe) dataframe["kc_upperband"] = keltner["upper"] dataframe["kc_lowerband"] = keltner["lower"] dataframe["kc_middleband"] = keltner["mid"] dataframe["kc_percent"] = ( (dataframe["close"] - dataframe["kc_lowerband"]) / (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) ) dataframe["kc_width"] = ( (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"] ) dataframe['kc_diff'] = (dataframe['kc_upperband'] - dataframe['close']) stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] dataframe['rsi'] = ta.RSI(dataframe) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) dataframe['sar'] = ta.SAR(dataframe) dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column long position when close is above 200 SMA and close is above upper band """ conditions = [] if self.buy_sar_enabled.value: conditions.append(dataframe['sar'].notnull()) conditions.append(dataframe['close'] < dataframe['sar']) if self.buy_sma_enabled.value: conditions.append(dataframe['sma'].notnull()) conditions.append(dataframe['close'] > dataframe['sma']) if self.buy_ema_enabled.value: conditions.append(dataframe['ema50'].notnull()) conditions.append(dataframe['close'] > dataframe['ema50']) if self.buy_mfi_enabled.value: conditions.append(dataframe['mfi'].notnull()) conditions.append(dataframe['mfi'] >= self.buy_mfi.value) 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_macd_enabled.value: conditions.append(dataframe['macd'] > dataframe['macdsignal']) if self.buy_fisher_enabled.value: conditions.append(dataframe['fisher_rsi'] < self.buy_fisher.value) conditions.append( (dataframe['close'] >= dataframe['kc_upperband']) & (dataframe['close'].shift(1) < dataframe['kc_upperband'].shift(1)) ) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe def populate_exit_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 close if price is below Keltner lower band """ if self.sell_hold.value: dataframe.loc[(dataframe['close'].notnull() ), 'sell'] = 0 return dataframe dataframe.loc[ ( (dataframe['close'] < dataframe['kc_lowerband']) ), 'sell'] = 1 return dataframe