# source: https://raw.githubusercontent.com/genhack/ft_my/0b33873e7bc41a37463313d5f56070eefb61d0f7/mod.py # --- Do not remove these libs --- from freqtrade.strategy import IStrategy, merge_informative_pair from pandas import DataFrame import talib.abstract as ta import logging import freqtrade.vendor.qtpylib.indicators as qtpylib # -------------------------------- import pandas as pd import numpy as np import technical.indicators as ftt from freqtrade.exchange import timeframe_to_minutes logger = logging.getLogger(__name__) def ssl_atr(dataframe, length=7): df = dataframe.copy() 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'] def create_ichimoku(dataframe, conversion_line_period, displacement, base_line_periods, laggin_span): ichimoku = ftt.ichimoku(dataframe, conversion_line_period=conversion_line_period, base_line_periods=base_line_periods, laggin_span=laggin_span, displacement=displacement ) dataframe[f'tenkan_sen_{conversion_line_period}'] = ichimoku['tenkan_sen'] dataframe[f'kijun_sen_{conversion_line_period}'] = ichimoku['kijun_sen'] dataframe[f'senkou_a_{conversion_line_period}'] = ichimoku['senkou_span_a'] dataframe[f'senkou_b_{conversion_line_period}'] = ichimoku['senkou_span_b'] return dataframe class github_genhack_ft_my__mod__20220213_212105(IStrategy): # Optimal timeframe for the strategy timeframe = '1m' # generate signals from the 5m timeframe informative_timeframe = '1m' # WARNING: ichimoku is a long indicator, if you remove or use a # shorter startup_candle_count your results will be unstable/invalid # for up to a week from the start of your backtest or dry/live run # (180 candles = 7.5 days) startup_candle_count = 444 # MAXIMUM ICHIMOKU # NOTE: this strat only uses candle information, so processing between # new candles is a waste of resources as nothing will change process_only_new_candles = True minimal_roi = { "0": 10, } plot_config = { 'main_plot': { 'pivot_1d': {}, 'rS1_1d': {}, 'ema5': {'color': 'blue'}, 'ema10': {'color': 'pink'}, 'senkou_b_444': {'color': 'grey'}, 'kijun_sen_355_5m,': {'color': 'blue'}, 'kijun_sen_20': {'color': 'yellow'}, 'kijun_sen_9': {'color': 'red'}, 'tenkan_sen_355_5m': {'color': 'red'}, 'tenkan_sen_20': {'color': 'grey'}, 'tenkan_sen_9': {'color': 'black'}, 'senkou_a_100': {'color': 'orange'}, 'senkou_b_100': {'color': 'brown'}, 'senkou_a_20': {'color': 'yellow'}, 'senkou_b_20': {'color': 'pink'}, 'senkou_a_9': {'color': 'black'}, #'tenkan_sen_444': {'color': 'black'}, }, 'subplots': { 'MACD': { 'macd_1h': {'color': 'blue'}, 'macdsignal_1h': {'color': 'orange'}, }, } } # WARNING setting a stoploss for this strategy doesn't make much sense, as it will buy # back into the trend at the next available opportunity, unless the trend has ended, # in which case it would sell anyway. # Stoploss: stoploss = -0.99 def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] if self.dp: informative_pairs += [(pair, "5m") for pair in pairs] return informative_pairs def slow_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe5m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="5m") create_ichimoku(dataframe5m, conversion_line_period=355, displacement=880, base_line_periods=175, laggin_span=175) dataframe = merge_informative_pair(dataframe, dataframe5m, self.timeframe, "5m", ffill=True) create_ichimoku(dataframe, conversion_line_period=20, displacement=88, base_line_periods=88, laggin_span=88) create_ichimoku(dataframe, conversion_line_period=9, displacement=26, base_line_periods=26, laggin_span=52) create_ichimoku(dataframe, conversion_line_period=444, displacement=444, base_line_periods=444, laggin_span=444) create_ichimoku(dataframe, conversion_line_period=100, displacement=88, base_line_periods=440, laggin_span=440) create_ichimoku(dataframe, conversion_line_period=40, displacement=88, base_line_periods=176, laggin_span=176) dataframe['ichimoku_ok'] = ( (dataframe['kijun_sen_355_5m'] >= dataframe['tenkan_sen_355_5m']) & (dataframe['senkou_a_100'] > dataframe['senkou_b_100']) & (dataframe['senkou_a_20'] > dataframe['senkou_b_20']) & (dataframe['kijun_sen_20'] > dataframe['tenkan_sen_444']) & (dataframe['senkou_a_9'] > dataframe['senkou_a_20']) & (dataframe['tenkan_sen_20'] >= dataframe['kijun_sen_20']) & (dataframe['tenkan_sen_9'] >= dataframe['tenkan_sen_20']) & (dataframe['tenkan_sen_9'] >= dataframe['kijun_sen_9']) ).astype('int') * 4 dataframe['trending_over'] = ( (dataframe['senkou_b_444'] > dataframe['close']) ).astype('int') * 1 return dataframe def fast_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # none atm return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.timeframe == self.informative_timeframe: dataframe = self.slow_tf_indicators(dataframe, metadata) else: assert self.dp, "DataProvider is required for multiple timeframes." informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe) informative = self.slow_tf_indicators(informative.copy(), metadata) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True) # don't overwrite the base dataframe's OHLCV information skip_columns = [(s + "_" + self.informative_timeframe) for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.rename(columns=lambda s: s.replace("_{}".format(self.informative_timeframe), "") if ( not s in skip_columns) else s, inplace=True) dataframe = self.fast_tf_indicators(dataframe, metadata) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (dataframe['ichimoku_ok'] > 0) & (dataframe['trending_over'] <= 0) , 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (dataframe['trending_over'] > 0) , 'sell'] = 1 return dataframe