# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/LSv3.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 pd.options.mode.chained_assignment = None 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 from freqtrade.strategy import DecimalParameter, IntParameter, CategoricalParameter class Github_remiotore_freqtrade__LSv3__20260111_210550(IStrategy): can_short = True buy_long_trend_above_senkou_level = IntParameter(1, 8, default=4, space = "buy") buy_long_trend_bullish_level = IntParameter(1, 8, default=4, space = "buy") buy_long_fan_magnitude_shift_value = IntParameter(1, 6, default=3, space = "buy") buy_long_min_fan_magnitude_gain = DecimalParameter(1.000, 1.500, default=1.025, space = "buy") buy_short_trend_below_senkou_level = IntParameter(1, 8, default=4, space = "buy") buy_short_trend_bearish_level = IntParameter(1, 8, default=4, space = "buy") buy_short_fan_magnitude_shift_value = IntParameter(1, 5, default=3, space = "buy") buy_short_min_fan_magnitude_gain = DecimalParameter(0.500, 1.000, default=0.975, space = "buy") exit_long_params = CategoricalParameter(["trend_close_1h", "trend_close_2h"], default= "trend_close_2h", space = "sell") exit_short_params = CategoricalParameter(["trend_close_1h", "trend_close_2h"], default= "trend_close_2h", space = "sell") minimal_roi = { "0": 0.08, "10": 0.059, "25": 0.02, "40": 0.015, "60": 0 } stoploss = -0.125 timeframe = '15m' startup_candle_count = 96 process_only_new_candles = False trailing_stop = True trailing_stop_positive = 0.05 trailing_stop_positive_offset = 0.1 trailing_only_offset_is_reached = False 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['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_2h'] / 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_long = [] conditions_short = [] if self.buy_long_trend_above_senkou_level.value >= 1: conditions_long.append(dataframe['trend_close_5m'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_5m'] > dataframe['senkou_b']) if self.buy_long_trend_above_senkou_level.value >= 2: conditions_long.append(dataframe['trend_close_15m'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_15m'] > dataframe['senkou_b']) if self.buy_long_trend_above_senkou_level.value >= 3: conditions_long.append(dataframe['trend_close_30m'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_30m'] > dataframe['senkou_b']) if self.buy_long_trend_above_senkou_level.value >= 4: conditions_long.append(dataframe['trend_close_1h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_1h'] > dataframe['senkou_b']) if self.buy_long_trend_above_senkou_level.value >= 5: conditions_long.append(dataframe['trend_close_2h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_2h'] > dataframe['senkou_b']) if self.buy_long_trend_above_senkou_level.value >= 6: conditions_long.append(dataframe['trend_close_4h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_4h'] > dataframe['senkou_b']) if self.buy_long_trend_above_senkou_level.value >= 7: conditions_long.append(dataframe['trend_close_6h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_6h'] > dataframe['senkou_b']) if self.buy_long_trend_above_senkou_level.value >= 8: conditions_long.append(dataframe['trend_close_8h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_8h'] > dataframe['senkou_b']) if self.buy_long_trend_bullish_level.value >= 1: conditions_long.append(dataframe['trend_close_5m'] > dataframe['trend_open_5m']) if self.buy_long_trend_bullish_level.value >= 2: conditions_long.append(dataframe['trend_close_15m'] > dataframe['trend_open_15m']) if self.buy_long_trend_bullish_level.value >= 3: conditions_long.append(dataframe['trend_close_30m'] > dataframe['trend_open_30m']) if self.buy_long_trend_bullish_level.value >= 4: conditions_long.append(dataframe['trend_close_1h'] > dataframe['trend_open_1h']) if self.buy_long_trend_bullish_level.value >= 5: conditions_long.append(dataframe['trend_close_2h'] > dataframe['trend_open_2h']) if self.buy_long_trend_bullish_level.value >= 6: conditions_long.append(dataframe['trend_close_4h'] > dataframe['trend_open_4h']) if self.buy_long_trend_bullish_level.value >= 7: conditions_long.append(dataframe['trend_close_6h'] > dataframe['trend_open_6h']) if self.buy_long_trend_bullish_level.value >= 8: conditions_long.append(dataframe['trend_close_8h'] > dataframe['trend_open_8h']) conditions_long.append(dataframe['fan_magnitude_gain'] >= self.buy_long_min_fan_magnitude_gain.value) conditions_long.append(dataframe['fan_magnitude'] > 1) for x in range(self.buy_long_fan_magnitude_shift_value.value): conditions_long.append(dataframe['fan_magnitude'].shift(x+1) < dataframe['fan_magnitude']) if self.buy_short_trend_below_senkou_level.value >= 1: conditions_short.append(dataframe['trend_close_5m'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_5m'] < dataframe['senkou_b']) if self.buy_short_trend_below_senkou_level.value >= 2: conditions_short.append(dataframe['trend_close_15m'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_15m'] < dataframe['senkou_b']) if self.buy_short_trend_below_senkou_level.value >= 3: conditions_short.append(dataframe['trend_close_30m'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_30m'] < dataframe['senkou_b']) if self.buy_short_trend_below_senkou_level.value >= 4: conditions_short.append(dataframe['trend_close_1h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_1h'] < dataframe['senkou_b']) if self.buy_short_trend_below_senkou_level.value >= 5: conditions_short.append(dataframe['trend_close_2h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_2h'] < dataframe['senkou_b']) if self.buy_short_trend_below_senkou_level.value >= 6: conditions_short.append(dataframe['trend_close_4h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_4h'] < dataframe['senkou_b']) if self.buy_short_trend_below_senkou_level.value >= 7: conditions_short.append(dataframe['trend_close_6h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_6h'] < dataframe['senkou_b']) if self.buy_short_trend_below_senkou_level.value >= 8: conditions_short.append(dataframe['trend_close_8h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_8h'] < dataframe['senkou_b']) if self.buy_short_trend_bearish_level.value >= 1: conditions_short.append(dataframe['trend_close_5m'] < dataframe['trend_open_5m']) if self.buy_short_trend_bearish_level.value >= 2: conditions_short.append(dataframe['trend_close_15m'] < dataframe['trend_open_15m']) if self.buy_short_trend_bearish_level.value >= 3: conditions_short.append(dataframe['trend_close_30m'] < dataframe['trend_open_30m']) if self.buy_short_trend_bearish_level.value >= 4: conditions_short.append(dataframe['trend_close_1h'] < dataframe['trend_open_1h']) if self.buy_short_trend_bearish_level.value >= 5: conditions_short.append(dataframe['trend_close_2h'] < dataframe['trend_open_2h']) if self.buy_short_trend_bearish_level.value >= 6: conditions_short.append(dataframe['trend_close_4h'] < dataframe['trend_open_4h']) if self.buy_short_trend_bearish_level.value >= 7: conditions_short.append(dataframe['trend_close_6h'] < dataframe['trend_open_6h']) if self.buy_short_trend_bearish_level.value >= 8: conditions_short.append(dataframe['trend_close_8h'] < dataframe['trend_open_8h']) conditions_short.append(dataframe['fan_magnitude_gain'] <= self.buy_short_min_fan_magnitude_gain.value) conditions_short.append(dataframe['fan_magnitude'] < 1) for x in range(self.buy_short_fan_magnitude_shift_value.value): conditions_short.append(dataframe['fan_magnitude'].shift(x+1) > dataframe['fan_magnitude']) if conditions_long: dataframe.loc[ reduce(lambda x, y: x & y, conditions_long), 'enter_long'] = 1 if conditions_short: dataframe.loc[ reduce(lambda x, y: x & y, conditions_short), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions_long = [] conditions_short = [] conditions_long.append(qtpylib.crossed_below(dataframe['trend_close_5m'], dataframe[self.exit_long_params.value])) conditions_short.append(qtpylib.crossed_above(dataframe['trend_close_5m'], dataframe[self.exit_short_params.value])) if conditions_long: dataframe.loc[ reduce(lambda x, y: x & y, conditions_long), 'exit_long'] = 1 if conditions_short: dataframe.loc[ reduce(lambda x, y: x & y, conditions_short), 'exit_short'] = 1 return dataframe