# --- 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 # noqa pd.options.mode.chained_assignment = None # default='warn' import technical.indicators as ftt from functools import reduce from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair import numpy as np from freqtrade.strategy import stoploss_from_open class ichiV3_1(IStrategy): """ This is the next version of ichiVx, and the previous version was ichiV2_5. ============================== Summary of changes from ichiV2_5: 1. Does not alter the original OHLC data. The Heikin Ashi data is now only for calculating signals. 2. "buy_min_fan_magnitude_gain" is now 1.002, to potentially catch a trend better. 3. The sell parameter is now when the close price is lower than the senkou a. """ INTERFACE_VERSION = 2 minimal_roi = { "300": 0.01, "60": 0.03, "30": 0.05, "0": 0.07, # "420" : 0.005, # "300" : 0.007, # "240" : 0.009, # "0": 0.018 # "0": 0.007 } # NOTE: settings as of the 25th july 21 # Buy hyperspace params: buy_params = { "buy_trend_above_senkou_level": 5, "buy_trend_bullish_level": 6, "buy_fan_magnitude_shift_value": 3, "buy_min_fan_magnitude_gain": 1.002 # "buy_min_fan_magnitude_gain": 1.0013 # NOTE: Good value (Win% ~70%), alot of trades #"buy_min_fan_magnitude_gain": 1.008 # NOTE: Very save value (Win% ~90%), only the biggest moves 1.008, } # Sell hyperspace params: # NOTE: was 15m but kept bailing out in dryrun sell_params = { "sell_trend_indicator": "senkou_a", } # # ROI table: # minimal_roi = { # "0": 0.3 # } # Stoploss: stoploss = -0.5 # Optimal timeframe for the strategy timeframe = '5m' startup_candle_count = 96 process_only_new_candles = False trailing_stop = True trailing_stop_positive = 0.007 trailing_stop_positive_offset = 0.015 # Disabled / not configured trailing_only_offset_is_reached = True use_sell_signal = True sell_profit_only = False # ignore_roi_if_buy_signal = True plot_config = { 'main_plot': { # fill area between senkou_a and senkou_b 'senkou_a': { 'color': 'green', #optional 'fill_to': 'senkou_b', 'fill_label': 'Ichimoku Cloud', #optional 'fill_color': 'rgba(255,76,46,0.2)', #optional }, # plot senkou_b, too. Not only the area to it. 'senkou_b': {}, 'trend_close_5m': {'color': '#FF5733'}, 'trend_close_15m': {'color': '#FF8333'}, 'trend_close_30m': {'color': '#FFB533'}, 'trend_close_1h': {'color': '#FFE633'}, 'trend_close_2h': {'color': '#E3FF33'}, 'trend_close_4h': {'color': '#C4FF33'}, 'trend_close_6h': {'color': '#61FF33'}, 'trend_close_8h': {'color': '#33FF7D'}, 'trend_open_5m': {'color': '#4D7BF5'}, 'trend_open_15m': {'color': '#1E8C38'}, 'trend_open_30m': {'color': '#ABA6D0'}, 'trend_open_1h': {'color': '#631B69'}, 'trend_open_2h': {'color': '#A553FF'}, 'trend_open_4h': {'color': '#B10C78'}, 'trend_open_6h': {'color': '#389E03'}, 'trend_open_8h': {'color': '#571E60'} }, 'subplots': { 'fan_magnitude': { 'fan_magnitude': {} }, 'fan_magnitude_gain': { 'fan_magnitude_gain': {} } } } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Populate Heikin Ashi candles heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] # dataframe['close'] = heikinashi['close'] # dataframe['ha_high'] = heikinashi['high'] # dataframe['ha_low'] = heikinashi['low'] dataframe['trend_close_5m'] = dataframe['close'] dataframe['trend_close_15m'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=3) dataframe['trend_close_30m'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=6) dataframe['trend_close_1h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=12) dataframe['trend_close_1.5h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=18) dataframe['trend_close_2h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=24) dataframe['trend_close_4h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=48) dataframe['trend_close_6h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=72) dataframe['trend_close_8h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=96) # Use Heikin Ashi open as the open trend tracker: it is the average of # the open and close of the last period dataframe['trend_open_5m'] = dataframe['ha_open'] dataframe['trend_open_15m'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=3) dataframe['trend_open_30m'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=6) dataframe['trend_open_1h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=12) dataframe['trend_open_2h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=24) dataframe['trend_open_4h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=48) dataframe['trend_open_6h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=72) dataframe['trend_open_8h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=96) dataframe['fan_magnitude'] = (dataframe['trend_close_1h'] / dataframe['trend_close_8h']) dataframe['fan_magnitude_gain'] = dataframe['fan_magnitude'] / dataframe['fan_magnitude'].shift(1) ichimoku = ftt.ichimoku(dataframe, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30) dataframe['chikou_span'] = ichimoku['chikou_span'] # do not use this in live, it has lookahead bias dataframe['tenkan_sen'] = ichimoku['tenkan_sen'] dataframe['kijun_sen'] = ichimoku['kijun_sen'] 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'] dataframe['cloud_red'] = ichimoku['cloud_red'] dataframe['atr'] = ta.ATR(dataframe) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # Trending market if self.buy_params['buy_trend_above_senkou_level'] >= 1: conditions.append(dataframe['trend_close_5m'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_5m'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 2: conditions.append(dataframe['trend_close_15m'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_15m'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 3: conditions.append(dataframe['trend_close_30m'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_30m'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 4: conditions.append(dataframe['trend_close_1h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_1h'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 5: conditions.append(dataframe['trend_close_2h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_2h'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 6: conditions.append(dataframe['trend_close_4h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_4h'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 7: conditions.append(dataframe['trend_close_6h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_6h'] > dataframe['senkou_b']) if self.buy_params['buy_trend_above_senkou_level'] >= 8: conditions.append(dataframe['trend_close_8h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_8h'] > dataframe['senkou_b']) # Trends bullish if self.buy_params['buy_trend_bullish_level'] >= 1: conditions.append(dataframe['trend_close_5m'] > dataframe['trend_open_5m']) if self.buy_params['buy_trend_bullish_level'] >= 2: conditions.append(dataframe['trend_close_15m'] > dataframe['trend_open_15m']) if self.buy_params['buy_trend_bullish_level'] >= 3: conditions.append(dataframe['trend_close_30m'] > dataframe['trend_open_30m']) if self.buy_params['buy_trend_bullish_level'] >= 4: conditions.append(dataframe['trend_close_1h'] > dataframe['trend_open_1h']) if self.buy_params['buy_trend_bullish_level'] >= 5: conditions.append(dataframe['trend_close_2h'] > dataframe['trend_open_2h']) if self.buy_params['buy_trend_bullish_level'] >= 6: conditions.append(dataframe['trend_close_4h'] > dataframe['trend_open_4h']) if self.buy_params['buy_trend_bullish_level'] >= 7: conditions.append(dataframe['trend_close_6h'] > dataframe['trend_open_6h']) if self.buy_params['buy_trend_bullish_level'] >= 8: conditions.append(dataframe['trend_close_8h'] > dataframe['trend_open_8h']) # Trends magnitude conditions.append(dataframe['fan_magnitude_gain'] >= self.buy_params['buy_min_fan_magnitude_gain']) conditions.append(dataframe['fan_magnitude'] > 1) for x in range(self.buy_params['buy_fan_magnitude_shift_value']): conditions.append(dataframe['fan_magnitude'].shift(x+1) < dataframe['fan_magnitude']) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( qtpylib.crossed_below( dataframe['trend_close_5m'], dataframe[self.sell_params['sell_trend_indicator']] ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe