# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/MACDCross.py from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame from freqtrade.strategy.hyper import CategoricalParameter, DecimalParameter, IntParameter import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy # noqa import Config class Github_remiotore_freqtrade__MACDCross__20260111_210550(IStrategy): """ This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/ You can: :return: a Dataframe with all mandatory indicators for the strategies - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your strategy - Add any lib you need to build your strategy You must keep: - the lib in the section "Do not remove these libs" - the methods: populate_indicators, populate_buy_trend, populate_sell_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ buy_params = Config.strategyParameters["Github_remiotore_freqtrade__MACDCross__20260111_210550"] buy_mfi = DecimalParameter(10, 100, decimals=0, default=79, space="buy") buy_adx = DecimalParameter(1, 99, decimals=0, default=1, space="buy") buy_fisher = DecimalParameter(-1, 1, decimals=2, default=0.18, space="buy") buy_period = IntParameter(3, 20, default=16, space="buy") buy_bb_gain = DecimalParameter(0.01, 0.10, decimals=2, default=0.04, space="buy") buy_bb_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_neg_macd_enabled = CategoricalParameter([True, False], default=True, 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_sar_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") sell_pos_macd_enabled = CategoricalParameter([True, False], default=True, space="sell") startup_candle_count = max(buy_period.value, 20) 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. :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 """ dataframe['adx'] = ta.ADX(dataframe) dataframe['dm_plus'] = ta.PLUS_DM(dataframe) dataframe['di_plus'] = ta.PLUS_DI(dataframe) dataframe['dm_minus'] = ta.MINUS_DM(dataframe) dataframe['di_minus'] = ta.MINUS_DI(dataframe) dataframe['dm_delta'] = dataframe['dm_plus'] - dataframe['dm_minus'] dataframe['di_delta'] = dataframe['di_plus'] - dataframe['di_minus'] dataframe['rsi'] = ta.RSI(dataframe) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] dataframe['mfi'] = ta.MFI(dataframe) 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'] dataframe["bb_percent"] = ( (dataframe["close"] - dataframe["bb_lowerband"]) / (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) ) dataframe["bb_width"] = ( (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"] ) dataframe["bb_gain"] = ((dataframe["bb_upperband"] - dataframe["close"]) / dataframe["close"]) dataframe['ema7'] = ta.EMA(dataframe, timeperiod=7) dataframe['ema25'] = ta.EMA(dataframe, timeperiod=25) dataframe['sar'] = ta.SAR(dataframe) dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) 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 = [] 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) if self.buy_sar_enabled.value: conditions.append(dataframe['close'] < dataframe['sar']) if self.buy_fisher_enabled.value: conditions.append(dataframe['fisher_rsi'] < self.buy_fisher.value) if self.buy_neg_macd_enabled.value: conditions.append(dataframe['macd'] < 0.0) if self.buy_bb_enabled.value: conditions.append(dataframe['bb_gain'] >= self.buy_bb_gain.value) conditions.append(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) 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 buy column """ conditions = [] if self.sell_hold.value: dataframe.loc[(dataframe['close'].notnull() ), 'sell'] = 0 else: if self.sell_pos_macd_enabled: conditions.append((dataframe['macd'] > 0.0)) conditions.append(qtpylib.crossed_below(dataframe['macd'], dataframe['macdsignal'])) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe