# source: https://raw.githubusercontent.com/mrgolfprat/ft_sample/d24c79b0d852e13757c6d523524b1660b19ec6e6/user_data/strategies/Obelisk_TradePro_Ichi_v2_2.py # --- Do not remove these libs --- from freqtrade.strategy import IStrategy, merge_informative_pair from pandas import DataFrame import talib.abstract as ta 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 # Obelisk_TradePro_Ichi v2.2 - 2021-04-04 # # by Obelisk # https://github.com/brookmiles/ # # WARNING # # While this strategy is designed to be run at 1h, it should be backtested at 5m (or 1m). # This is done to avoid misleading results produced using trailing stops and roi values at longer timeframes. # # When running at 5m, an informative pair at 1h will be used to generate signals equivalent to running at 1h. # # live / dryrun: use 1h # backtest / hyperopt: use 5m or 1m # # WARNING see note on stoploss_on_exchange below # # Contributions: # # JimmyNixx # - SSL Channel confirmation # - ROCR & RMI confirmations # # # Backtested with pairlist generated from: # "pairlists": [ # { # "method": "VolumePairList", # "number_assets": 50, # "sort_key": "quoteVolume", # "refresh_period": 1800 # }, # {"method": "AgeFilter", "min_days_listed": 10}, # {"method": "PrecisionFilter"}, # {"method": "PriceFilter", # "low_price_ratio": 0.001, # "max_price": 20, # }, # {"method": "SpreadFilter", "max_spread_ratio": 0.002}, # { # "method": "RangeStabilityFilter", # "lookback_days": 3, # "min_rate_of_change": 0.1, # "refresh_period": 1440 # }, # ], 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_mrgolfprat_ft_sample__Obelisk_TradePro_Ichi_v2_2__20211117_150019(IStrategy): # Do not change the timeframe values in this file # Use the config file or command line options to select the appropriate timeframe: # 5m - BACKTEST or HYPEROPT # 1h - LIVE or DRYRUN # Backtest or hyperopt at this timeframe timeframe = '1h' # Generate signals from the 1h timeframe # Live or Dry-run at this timeframe informative_timeframe = '1h' # 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 = 180 # 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 # ROI table: # minimal_roi = { # "0": 0.16, # "40": 0.1, # "90": 0.035, # "210": 0 # } minimal_roi = { "0": 0.15, } # I haven't been able to determine a good default stoploss. # Select or hyperopt an stoploss that you're happy with, and backtest the result. # # Do not use stoploss_on_exchange if you leave the stoploss at the default value # or the bot may trigger emergencysell when it fails to place the stoploss. # # Stoploss: stoploss = -0.99 use_sell_signal = True sell_profit_only = True plot_config = { # Main plot indicators (Moving averages, ...) 'main_plot': { 'senkou_a': { 'color': 'green', 'fill_to': 'senkou_b', 'fill_label': 'Ichimoku Cloud', 'fill_color': 'rgba(0,0,0,0.2)', }, # plot senkou_b, too. Not only the area to it. 'senkou_b': { 'color': 'red', }, 'tenkan_sen': {'color': 'orange'}, 'kijun_sen': {'color': 'blue'}, 'chikou_span': {'color': 'lightgreen'}, # 'ssl_up': { 'color': 'green' }, # 'ssl_down': { 'color': 'red' }, }, 'subplots': { "Signals": { 'go_long': {'color': 'blue'}, 'buy_criteria': {'color': 'green'}, 'sell_criteria': {'color': 'red'}, }, } } def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs def do_indicators(self, dataframe: DataFrame, metadata: dict): # # Standard Settings # displacement = 26 # ichimoku = ftt.ichimoku(dataframe, # conversion_line_period=9, # base_line_periods=26, # laggin_span=52, # displacement=displacement # ) # Crypto Settings displacement = 26 ichimoku = ftt.ichimoku(dataframe, conversion_line_period=9, base_line_periods=26, laggin_span=52, displacement=displacement ) dataframe['chikou_span'] = ichimoku['chikou_span'] # cross indicators dataframe['tenkan_sen'] = ichimoku['tenkan_sen'] dataframe['kijun_sen'] = ichimoku['kijun_sen'] # cloud, green a > b, red a < b dataframe['senkou_a'] = ichimoku['senkou_span_a'] dataframe['senkou_b'] = ichimoku['senkou_span_b'] dataframe['leading_senkou_span_a'] = ichimoku['leading_senkou_span_a'] dataframe['leading_senkou_span_b'] = ichimoku['leading_senkou_span_b'] dataframe['cloud_green'] = ichimoku['cloud_green'] * 1 dataframe['cloud_red'] = ichimoku['cloud_red'] * -1 # DANGER ZONE START # NOTE: Not actually the future, present data that is normally shifted forward for display as the cloud dataframe['future_green'] = ( dataframe['leading_senkou_span_a'] > dataframe['leading_senkou_span_b']).astype('int') * 2 # The chikou_span is shifted into the past, so we need to be careful not to read the # current value. But if we shift it forward again by displacement it should be safe to use. # We're effectively "looking back" at where it normally appears on the chart. dataframe['chikou_high'] = ( (dataframe['chikou_span'] > dataframe['senkou_a']) & (dataframe['chikou_span'] > dataframe['senkou_b']) ).shift(displacement).fillna(0).astype('int') # DANGER ZONE END ssl_down, ssl_up = SSLChannels(dataframe, 10) dataframe['ssl_down'] = ssl_down dataframe['ssl_up'] = ssl_up dataframe['ssl_high'] = (ssl_up > ssl_down).astype('int') * 3 dataframe['rocr'] = ta.ROCR(dataframe, timeperiod=28) dataframe['rmi-fast'] = ftt.RMI(dataframe, length=9, mom=3) dataframe['go_long'] = ( (dataframe['tenkan_sen'] > dataframe['kijun_sen']) & (dataframe['close'] > dataframe['senkou_a']) & (dataframe['close'] > dataframe['senkou_b']) & (dataframe['future_green'] > 0) & (dataframe['chikou_high'] > 0) & (dataframe['ssl_high'] > 0) & (dataframe['rocr'] > dataframe['rocr'].shift()) & (dataframe['rmi-fast'] > dataframe['rmi-fast'].shift(2)) ).astype('int') * 4 return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # if self.config['runmode'].value in ('backtest', 'hyperopt'): # assert (timeframe_to_minutes(self.timeframe) <= # 5), "Backtest this strategy in 5m or 1m timeframe. Read comments for details." if self.timeframe == self.informative_timeframe: dataframe = self.do_indicators(dataframe, metadata) else: if not self.dp: return dataframe informative = self.dp.get_pair_dataframe( pair=metadata['pair'], timeframe=self.informative_timeframe) informative = self.do_indicators(informative.copy(), metadata) dataframe = merge_informative_pair( dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True) # don't overwrite the base dataframe's HLCV 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) # SAR Parabol dataframe['sar'] = ta.SAR(dataframe) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ qtpylib.crossed_above(dataframe['go_long'], 0) & (dataframe['sar'] < dataframe['close']), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (dataframe['ssl_high'] == 0) & ( (dataframe['tenkan_sen'] < dataframe['kijun_sen']) | (dataframe['close'] < dataframe['kijun_sen']) ), 'sell'] = 1 return dataframe