# source: https://raw.githubusercontent.com/nateemma/strategies/427c73661cccab17384b3037bdbe1e851cf0d69c/archived/BTCNDrop.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 freqtrade.strategy.strategy_helper import merge_informative_pair import Config class github_nateemma_strategies__BTCNDrop__20230421_080215(IStrategy): """ Simple strategy that looks for N consecutive drops in BTC and then buys This version doesn't issue a sell signal, just holds until ROI or stoploss kicks in How to use it? > python3 ./freqtrade/main.py -s github_nateemma_strategies__BTCNDrop__20230421_080215 """ # Hyperparameters buy_params = Config.strategyParameters["github_nateemma_strategies__BTCNDrop__20230421_080215"] # note that the num_candles and drop params refer to BTC, not the current pair buy_num_candles = IntParameter(2, 9, default=3, space="buy") buy_drop = DecimalParameter(0.01, 0.06, decimals=3, default=0.014, space="buy") buy_fisher = DecimalParameter(-1, 1, decimals=2, default=-0.02, space="buy") buy_mfi = DecimalParameter(10, 40, decimals=0, default=11.0, space="buy") # Categorical parameters that control whether a trend/check is used or not buy_fisher_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_mfi_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_bb_enabled = CategoricalParameter([True, False], default=False, space="buy") 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. """ # NOTE: we are applying this to the BTC/USD dataframe, not the normal dataframe (or in addition to anyway) if not self.dp: # Don't do anything if DataProvider is not available. return dataframe # get BTC dataframe inf_tf = '5m' btc_dataframe = self.dp.get_pair_dataframe(pair=Config.informative_pair, timeframe=inf_tf) # merge into main dataframe. This will create columns with a "_5m" suffix for the BTC data dataframe = merge_informative_pair(dataframe, btc_dataframe, self.timeframe, "5m", ffill=True) # MFI dataframe['mfi'] = ta.MFI(dataframe) # SMA - Simple Moving Average dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) # 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) bollinger = qtpylib.weighted_bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) # A little different than normal - adjust band values based on buy_bb_ratio #dataframe['bb_upperband'] = bollinger['mid'] + (bollinger['upper']-bollinger['mid'])*self.buy_bb_uratio.value #dataframe['bb_middleband'] = bollinger['mid'] #dataframe['bb_lowerband'] = bollinger['mid'] - (bollinger['mid']-bollinger['lower'])*self.buy_bb_lratio.value dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_lowerband'] = bollinger['lower'] 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) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # GUARDS AND TRENDS if self.buy_mfi_enabled.value: conditions.append(dataframe['mfi'] <= self.buy_mfi.value) if self.buy_fisher_enabled.value: conditions.append(dataframe['fisher_rsi'] < self.buy_fisher.value) if self.buy_bb_enabled.value: conditions.append(dataframe['close'] <= dataframe['bb_lowerband']) # TRIGGERS # N red candles in BTC (not the current pair) if self.buy_num_candles.value >= 1: for i in range(self.buy_num_candles.value): conditions.append(dataframe['close_5m'].shift(i) <= dataframe['open_5m'].shift(i)) # big enough drop? conditions.append( (((dataframe['open_5m'].shift(self.buy_num_candles.value-1) - dataframe['close_5m']) / dataframe['open_5m'].shift(self.buy_num_candles.value-1)) >= self.buy_drop.value) ) # 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 """ # Don't sell (have to set something in 'sell' column) dataframe.loc[(dataframe['close'] >= 0), 'sell'] = 0 return dataframe