# source: https://raw.githubusercontent.com/webclinic017/Estrategias/10c676aac2e2f55d54e418edd899a50624421fc7/Obelisk_Ichimoku_Slow_v1.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy 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 pd.options.mode.chained_assignment = None # default='warn' # Github_webclinic017_Estrategias__Obelisk_Ichimoku_Slow_v1__20210408_010818 - 2021-04-02 # # by Obelisk # https://twitter.com/brookmiles # # The point of this strategy is to buy and hold an up trend as long as possible. # If you are tempted to add ROI or trailing stops, you might be better off using Obelisk_TradePro_Ichi instead. # # This strat will buy into ongoing trends, so pay attention to timing when you launch it. # If the market is peaking then you may end up buying into trends that are just about to end. # # Contributions: # JimmyNixx - SSL Channel confirmation # # Backtested with pairlist generated with: # "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}, # {"method": "SpreadFilter", "max_spread_ratio": 0.002}, # { # "method": "RangeStabilityFilter", # "lookback_days": 3, # "min_rate_of_change": 0.2, # "refresh_period": 1440 # }, # { # "method": "VolumePairList", # "number_assets": 25, # "sort_key": "quoteVolume", # }, # ], 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_webclinic017_Estrategias__Obelisk_Ichimoku_Slow_v1__20210408_010818(IStrategy): # Optimal timeframe for the strategy 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 # 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, } # Stoploss: stoploss = -0.99 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 populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # # Standard Settings # displacement = 26 # ichimoku = ftt.ichimoku(dataframe, # conversion_line_period=9, # base_line_periods=26, # laggin_span=52, # displacement=displacement # ) # Crypto Settings displacement = 30 ichimoku = ftt.ichimoku(dataframe, conversion_line_period=20, base_line_periods=60, laggin_span=120, 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['buy_criteria'] = ( (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['open'] < dataframe['ssl_up']) & (dataframe['close'] < dataframe['ssl_up']) ).astype('int') * 2 dataframe['sell_criteria'] = ( (dataframe['ssl_high'] == 0) ).astype('int') * 1 dataframe.loc[ (dataframe['buy_criteria'] > 0), 'go_long'] = 3 dataframe.loc[ (dataframe['sell_criteria'] > 0) , 'go_long'] = 0 dataframe['go_long'].fillna(method='ffill', inplace=True) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ dataframe['go_long'] > 0 , 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ dataframe['go_long'] == 0 , 'sell'] = 1 return dataframe