# source: https://raw.githubusercontent.com/DerSalvador/nateemma-strategies/32f714b24b69c53504cd605ec21e3fd0728b1594/archived/BuyDips.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 # -------------------------------- from freqtrade.strategy.hyper import CategoricalParameter, DecimalParameter, IntParameter import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy # noqa from user_data.strategies import Config class Github_DerSalvador_nateemma_strategies__BuyDips__20240413_232736(IStrategy): """ Simple strategy that buys on dips and holds until ROI target is met How to use it? > python3 ./freqtrade/main.py -s Github_DerSalvador_nateemma_strategies__BuyDips__20240413_232736 """ # Buy hyperspace params: buy_params = { "buy_bb_gain": 0.07, "buy_bb_gain_enabled": True, "buy_mfi": 54.0, "buy_mfi_enabled": True, "buy_neg_macd_enabled": False, } # MFI (Money Flow Indicator) buy limit buy_mfi = DecimalParameter(10, 100, decimals=0, default=54, space="buy") # Bollinger Band 'gain' (% difference between current price and upper band). # Since we are looking for potential swings of >2%, we look for potential of more than that buy_bb_gain = DecimalParameter(0.01, 0.10, decimals=2, default=0.07, space="buy") # Categorical parameters that control whether a trend/check is used or not buy_mfi_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_bb_gain_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_neg_macd_enabled = CategoricalParameter([True, False], default=False, space="buy") sell_fisher = DecimalParameter(-1, 1, decimals=2, default=0.3, space="sell") sell_fisher_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_sar_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_hold_enabled = CategoricalParameter([True, False], default=True, space="sell") # set the startup candles count to the longest average used (EMA, EMA etc) startup_candle_count = 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. """ # MFI dataframe['mfi'] = ta.MFI(dataframe) # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # 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) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_upperband'] = bollinger['upper'] dataframe["bb_gain"] = ((dataframe["bb_upperband"] - dataframe["close"]) / dataframe["close"]) # EMA - Exponential Moving Average 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) # SAR Parabol dataframe['sar'] = ta.SAR(dataframe) # SMA - Simple Moving Average 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 """ conditions = [] # GUARDS AND TRENDS if self.buy_mfi_enabled.value: conditions.append(dataframe['mfi'] >= self.buy_mfi.value) if self.buy_neg_macd_enabled.value: conditions.append(dataframe['macd'] < 0.0) if self.buy_bb_gain_enabled.value: conditions.append(dataframe['bb_gain'] >= self.buy_bb_gain.value) # Triggers conditions.append(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) # check that volume is not 0 conditions.append(dataframe['volume'] > 0) # build the dataframe using the conditions 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 """ conditions = [] # if hold, then don't set a sell signal if self.sell_hold_enabled.value: dataframe.loc[(dataframe['close'].notnull() ), 'sell'] = 0 else: if self.sell_sar_enabled.value: conditions.append(dataframe['sar'] > dataframe['close']) if self.sell_fisher_enabled.value: conditions.append(dataframe['fisher_rsi'] > self.sell_fisher.value) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe