# source: https://raw.githubusercontent.com/nmenescardi/some_freqtrade_stuff/211b174a61b9a787b9f03994e96f7843656060e4/strategies/08_NostalgiaForInfinityV2_OPT.py import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import DecimalParameter, IntParameter from pandas import DataFrame from freqtrade.strategy import merge_informative_pair from freqtrade.persistence import Trade ########################################################################################################### ## NostalgiaForInfinityV2 by iterativ ## ## ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ## ## ########################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ## ## A pairlist with 20 to 60 pairs. Volume pairlist works well. ## ## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ## ## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ## ## Ensure that you don't override any variables in you config.json. Especially ## ## the timeframe (must be 5m). ## ## ## ########################################################################################################### # [Epoch 1000 of 1000 (100%)] || | [Time: 4:13:05, Elapsed Time: 4:13:05] #2021-06-01 22:07:28,157 - freqtrade.optimize.hyperopt - INFO - 1000 epochs saved to '/freqtrade/user_data/hyperopt_results/#strategy_NostalgiaForInfinityV2_2021-06-01_17-52-30.fthypt'. # #Best result: # # 577/1000: 137 trades. 95/0/42 Wins/Draws/Losses. Avg profit 4.08%. Median profit 3.63%. Total profit 653645.48997451 USDT ( 1307.29%). Avg duration 4:42:00 min. Objective: -15.48610 # SSL Channels def SSLChannels(dataframe, length = 7): df = dataframe.copy() df['ATR'] = ta.ATR(df, timeperiod=14) df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR'] df['smaLow'] = df['low'].rolling(length).mean() - df['ATR'] df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN)) df['hlv'] = df['hlv'].ffill() df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow']) df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh']) return df['sslDown'], df['sslUp'] class github_nmenescardi_some_freqtrade_stuff__08_NostalgiaForInfinityV2_OPT__20210702_093024(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 10 } stoploss = -1.0 timeframe = '5m' inf_1h = '1h' custom_info = {} # Sell signal use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit. ignore_roi_if_buy_signal = True # Trailing stoploss trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.04 # Custom stoploss use_custom_stoploss = False # Run "populate_indicators()" only for new candle. process_only_new_candles = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } buy_bb40_bbdelta_close = DecimalParameter(0.005, 0.05, default=0.041, space='buy', optimize=True, load=True) buy_bb40_closedelta_close = DecimalParameter(0.01, 0.03, default=0.021, space='buy', optimize=True, load=True) buy_bb40_tail_bbdelta = DecimalParameter(0.15, 0.45, default=0.287, space='buy', optimize=True, load=True) buy_bb20_close_bblowerband = DecimalParameter(0.8, 1.1, default=0.924, space='buy', optimize=True, load=True) buy_bb20_volume = IntParameter(18, 34, default=21, space='buy', optimize=True, load=True) buy_rsi_diff = DecimalParameter(36.0, 54.0, default=50.648, space='buy', optimize=True, load=True) sell_rsi_bb = DecimalParameter(60.0, 80.0, default=60.397, space='sell', optimize=True, load=True) sell_rsi_main = DecimalParameter(72.0, 90.0, default=89.568, space='sell', optimize=True, load=True) sell_rsi_2 = DecimalParameter(72.0, 90.0, default=87.786, space='sell', optimize=True, load=True) sell_ema_relative = DecimalParameter(0.005, 0.1, default=0.03, space='sell', optimize=False, load=True) sell_rsi_diff = DecimalParameter(0.0, 5.0, default=1.488, space='sell', optimize=True, load=True) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) # EMA informative_1h['ema_20'] = ta.EMA(informative_1h, timeperiod=20) informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # RSI informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) # SSL Channels ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20) informative_1h['ssl_down'] = ssl_down_1h informative_1h['ssl_up'] = ssl_up_1h return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2) dataframe['lower'] = bb_40['lower'] dataframe['mid'] = bb_40['mid'] dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() 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['ema_slow'] = ta.EMA(dataframe, timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() # EMA dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) # SMA dataframe['sma_5'] = ta.SMA(dataframe, timeperiod=5) dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # The indicators for the 1h informative timeframe informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['close'] < dataframe['sma_9']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * self.buy_bb40_bbdelta_close.value) & dataframe['closedelta'].gt(dataframe['close'] * self.buy_bb40_closedelta_close.value) & dataframe['tail'].lt(dataframe['bbdelta'] * self.buy_bb40_tail_bbdelta.value) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['volume'] > 0) ) | ( (dataframe['close'] < dataframe['sma_9']) & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < self.buy_bb20_close_bblowerband.value * dataframe['bb_lowerband']) & (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * self.buy_bb20_volume.value)) ) | ( (dataframe['close'] < dataframe['sma_5']) & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ssl_up_1h'] > dataframe['ssl_down_1h']) & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['rsi'] < dataframe['rsi_1h'] - self.buy_rsi_diff.value) & (dataframe['volume'] > 0) ) , 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['rsi'] > self.sell_rsi_bb.value) & (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) & (dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) & (dataframe['volume'] > 0) ) | ( (dataframe['rsi'] > self.sell_rsi_main.value) & (dataframe['volume'] > 0) ) | ( (dataframe['close'] < dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_50']) & (dataframe['rsi'] > self.sell_rsi_2.value) & (dataframe['volume'] > 0) ) | ( (dataframe['close'] < dataframe['ema_200']) & (((dataframe['ema_200'] - dataframe['close']) / dataframe['close']) < self.sell_ema_relative.value) & (dataframe['rsi'] > dataframe['rsi_1h'] + self.sell_rsi_diff.value) & (dataframe['volume'] > 0) ) , 'sell' ] = 1 return dataframe