# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/ichiV2_5.py 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_remiotore_ccxt_freqtrade__ichiV2_5__20260111_210550(IStrategy): """ This is the next version of ichiVx, and the previous version was ichiV1. ============================== Summary of changes from ichiV2: 1. Buy parameter change: buy_fan_magnitude_shift_value is now 2 2. In the config file: all order types are now of type "market", because the transaction costs are the same, and missed orders are less likely. 3. The trailing stoploss now activates at 40% above original price, and the trailing stop positive is now 0.03. 4. The stoploss is now 0.06. 5. The "buy_min_fan_magnitude_gain" is now 1.0007. This catches smaller waves, and is safe enough to minimise false signals. 6. The maximum open trades is now 4. """ INTERFACE_VERSION = 2 buy_params = { "buy_trend_above_senkou_level": 1, "buy_trend_bullish_level": 4, "buy_fan_magnitude_shift_value": 2, "buy_min_fan_magnitude_gain": 1.0007 } sell_params = { "sell_trend_indicator": "trend_close_1.5h", } stoploss = -0.06 timeframe = '5m' startup_candle_count = 96 process_only_new_candles = False trailing_stop = True trailing_stop_positive = 0.03 trailing_stop_positive_offset = 0.4 trailing_only_offset_is_reached = True use_sell_signal = True sell_profit_only = False plot_config = { 'main_plot': { 'senkou_a': { 'color': 'green', #optional 'fill_to': 'senkou_b', 'fill_label': 'Ichimoku Cloud', #optional 'fill_color': 'rgba(255,76,46,0.2)', #optional }, '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['high'] = heikinashi['high'] dataframe['low'] = heikinashi['low'] dataframe['trend_close_5m'] = dataframe['close'].shift(1) 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) dataframe['trend_open_5m'] = dataframe['open'].shift(1) 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.shift(1), 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 = [] 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']) 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']) 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