# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-michael-k8s-namespace/IchisV1.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 # 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 Github_DerSalvador_freqtrade_helm_chart__IchisV1__20260115_122204(IStrategy): INTERFACE_VERSION = 3 # NOTE: settings as of the 25th july 21 # Buy hyperspace params: # NOTE: Good value (Win% ~70%), alot of trades #"entry_min_fan_magnitude_gain": 1.008 # NOTE: Very save value (Win% ~90%), only the biggest moves 1.008, entry_params = {'entry_trend_above_senkou_level': 1, 'entry_trend_bullish_level': 6, 'entry_fan_magnitude_shift_value': 3, 'entry_min_fan_magnitude_gain': 1.002} # Sell hyperspace params: # NOTE: was 15m but kept bailing out in dryrun exit_params = {'exit_trend_indicator': 'trend_close_2h'} # ROI table: minimal_roi = {'0': 0.059, '10': 0.037, '41': 0.012, '114': 0} # Stoploss: stoploss = -0.275 # Optimal timeframe for the strategy timeframe = '5m' startup_candle_count = 96 process_only_new_candles = False trailing_stop = False #trailing_stop_positive = 0.002 #trailing_stop_positive_offset = 0.025 #trailing_only_offset_is_reached = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # fill area between senkou_a and senkou_b #optional #optional #optional # plot senkou_b, too. Not only the area to it. plot_config = {'main_plot': {'senkou_a': {'color': 'green', 'fill_to': 'senkou_b', 'fill_label': 'Ichimoku Cloud', 'fill_color': 'rgba(255,76,46,0.2)'}, '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'}}, 'subplots': {'fan_magnitude': {'fan_magnitude': {}}, 'fan_magnitude_gain': {'fan_magnitude_gain': {}}}} def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: heikinashi = qtpylib.heikinashi(dataframe) dataframe['open'] = heikinashi['open'] #dataframe['close'] = heikinashi['close'] dataframe['high'] = heikinashi['high'] dataframe['low'] = heikinashi['low'] dataframe['trend_close_5m'] = dataframe['close'] dataframe['trend_close_15m'] = ta.EMA(dataframe['close'], timeperiod=3) dataframe['trend_close_30m'] = ta.EMA(dataframe['close'], timeperiod=6) dataframe['trend_close_1h'] = ta.EMA(dataframe['close'], timeperiod=12) dataframe['trend_close_2h'] = ta.EMA(dataframe['close'], timeperiod=24) dataframe['trend_close_4h'] = ta.EMA(dataframe['close'], timeperiod=48) dataframe['trend_close_6h'] = ta.EMA(dataframe['close'], timeperiod=72) dataframe['trend_close_8h'] = ta.EMA(dataframe['close'], timeperiod=96) dataframe['trend_open_5m'] = dataframe['open'] dataframe['trend_open_15m'] = ta.EMA(dataframe['open'], timeperiod=3) dataframe['trend_open_30m'] = ta.EMA(dataframe['open'], timeperiod=6) dataframe['trend_open_1h'] = ta.EMA(dataframe['open'], timeperiod=12) dataframe['trend_open_2h'] = ta.EMA(dataframe['open'], timeperiod=24) dataframe['trend_open_4h'] = ta.EMA(dataframe['open'], timeperiod=48) dataframe['trend_open_6h'] = ta.EMA(dataframe['open'], timeperiod=72) dataframe['trend_open_8h'] = ta.EMA(dataframe['open'], 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'] 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_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # Trending market if self.entry_params['entry_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.entry_params['entry_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.entry_params['entry_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.entry_params['entry_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.entry_params['entry_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.entry_params['entry_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.entry_params['entry_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.entry_params['entry_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.entry_params['entry_trend_bullish_level'] >= 1: conditions.append(dataframe['trend_close_5m'] > dataframe['trend_open_5m']) if self.entry_params['entry_trend_bullish_level'] >= 2: conditions.append(dataframe['trend_close_15m'] > dataframe['trend_open_15m']) if self.entry_params['entry_trend_bullish_level'] >= 3: conditions.append(dataframe['trend_close_30m'] > dataframe['trend_open_30m']) if self.entry_params['entry_trend_bullish_level'] >= 4: conditions.append(dataframe['trend_close_1h'] > dataframe['trend_open_1h']) if self.entry_params['entry_trend_bullish_level'] >= 5: conditions.append(dataframe['trend_close_2h'] > dataframe['trend_open_2h']) if self.entry_params['entry_trend_bullish_level'] >= 6: conditions.append(dataframe['trend_close_4h'] > dataframe['trend_open_4h']) if self.entry_params['entry_trend_bullish_level'] >= 7: conditions.append(dataframe['trend_close_6h'] > dataframe['trend_open_6h']) if self.entry_params['entry_trend_bullish_level'] >= 8: conditions.append(dataframe['trend_close_8h'] > dataframe['trend_open_8h']) # Trends magnitude conditions.append(dataframe['fan_magnitude_gain'] >= self.entry_params['entry_min_fan_magnitude_gain']) conditions.append(dataframe['fan_magnitude'] > 1) for x in range(self.entry_params['entry_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), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append(qtpylib.crossed_below(dataframe['trend_close_5m'], dataframe[self.exit_params['exit_trend_indicator']])) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1 return dataframe