# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/ichiV1_3.py import logging from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import (CategoricalParameter, DecimalParameter, IntParameter) 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 from freqtrade.strategy import stoploss_from_open from typing import Dict, Optional, Union logger = logging.getLogger(__name__) class Github_remiotore_freqtrade__ichiV1_3__20260111_210550(IStrategy): INTERFACE_VERSION = 3 def version(self) -> str: return "v0.0.001" custom_leverage = 1.0 buy_trend_above_senkou_level = IntParameter(1,8, default=5, space="buy") buy_trend_bullish_level = IntParameter(1,8, default=6, space="buy") buy_fan_magnitude_shift_value = IntParameter(1,8, default=5, space="buy") buy_min_fan_magnitude_gain = DecimalParameter(0.980, 1.020, default=1.002, space="buy") sell_trend_indicator = CategoricalParameter(["trend_close_5m", "trend_close_15m", "trend_close_30m", "trend_close_1h", "trend_close_2h", "trend_close_4h", "trend_close_6h", "trend_close_8h"], default="trend_close_15m", space="sell") minimal_roi = { "0": 0.061 * custom_leverage, "3": 0.023 * custom_leverage, "6": 0.008 * custom_leverage, "19": 0 * custom_leverage } stoploss = -0.217 * custom_leverage timeframe = '1m' startup_candle_count = 130 process_only_new_candles = False trailing_stop = False use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False optimize = 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 bot_start(self, **kwargs) -> None: """ Called only once after bot instantiation. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. """ self.optimize = self.config['runmode'].value in ('live', 'dry_run') self.custom_leverage = self.config['custom_leverage'] logger.info(f"--> runmode: {self.config['runmode'].value} | custom_leverage: {self.custom_leverage} ") def bot_loop_start(self, current_time: datetime, **kwargs) -> None: """ Called at the start of the bot iteration (one loop). Might be used to perform pair-independent tasks (e.g. gather some remote resource for comparison) :param current_time: datetime object, containing the current datetime :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. """ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: logger.info(f"START populate_indicators : {metadata['pair']}") heikinashi = qtpylib.heikinashi(dataframe) dataframe_open = heikinashi['open'] dataframe_close = dataframe['close'] moltiplicatore = 1 #dipende dal timeframe if moltiplicatore > 1: dataframe['trend_close_5m'] = ta.EMA(dataframe_close, timeperiod=1*moltiplicatore) else: dataframe['trend_close_5m'] = dataframe_close if not self.optimize or (self.buy_trend_above_senkou_level.value >= 2 or self.buy_trend_bullish_level.value >= 2): dataframe['trend_close_15m'] = ta.EMA(dataframe_close, timeperiod=3*moltiplicatore) if not self.optimize or (self.buy_trend_above_senkou_level.value >= 3 or self.buy_trend_bullish_level.value >= 3): dataframe['trend_close_30m'] = ta.EMA(dataframe_close, timeperiod=6*moltiplicatore) dataframe['trend_close_1h'] = ta.EMA(dataframe_close, timeperiod=12*moltiplicatore) dataframe['trend_close_2h'] = ta.EMA(dataframe_close, timeperiod=24*moltiplicatore) if not self.optimize or (self.buy_trend_above_senkou_level.value >= 6 or self.buy_trend_bullish_level.value >= 6): dataframe['trend_close_4h'] = ta.EMA(dataframe_close, timeperiod=48*moltiplicatore) if not self.optimize or (self.buy_trend_above_senkou_level.value >= 7 or self.buy_trend_bullish_level.value >= 7): dataframe['trend_close_6h'] = ta.EMA(dataframe_close, timeperiod=72*moltiplicatore) dataframe['trend_close_8h'] = ta.EMA(dataframe_close, timeperiod=96*moltiplicatore) if not self.optimize or (self.buy_trend_bullish_level.value >= 1): if moltiplicatore > 1: dataframe['trend_open_5m'] = ta.EMA(dataframe_open, timepriod=1*moltiplicatore) else: dataframe['trend_open_5m'] = dataframe_open if not self.optimize or (self.buy_trend_bullish_level.value >= 2): dataframe['trend_open_15m'] = ta.EMA(dataframe_open, timeperiod=3*moltiplicatore) if not self.optimize or (self.buy_trend_bullish_level.value >= 3): dataframe['trend_open_30m'] = ta.EMA(dataframe_open, timeperiod=6*moltiplicatore) if not self.optimize or (self.buy_trend_bullish_level.value >= 4): dataframe['trend_open_1h'] = ta.EMA(dataframe_open, timeperiod=12*moltiplicatore) if not self.optimize or (self.buy_trend_bullish_level.value >= 5): dataframe['trend_open_2h'] = ta.EMA(dataframe_open, timeperiod=24*moltiplicatore) if not self.optimize or (self.buy_trend_bullish_level.value >= 6): dataframe['trend_open_4h'] = ta.EMA(dataframe_open, timeperiod=48*moltiplicatore) if not self.optimize or (self.buy_trend_bullish_level.value >= 7): dataframe['trend_open_6h'] = ta.EMA(dataframe_open, timeperiod=72*moltiplicatore) if not self.optimize or (self.buy_trend_bullish_level.value >= 8): dataframe['trend_open_8h'] = ta.EMA(dataframe_open, timeperiod=96*moltiplicatore) 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['senkou_a'] = ichimoku['senkou_span_a'] dataframe['senkou_b'] = ichimoku['senkou_span_b'] logger.info(f"END populate_indicators : {metadata['pair']}") return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: logger.info(f"START populate_entry_trend : {metadata['pair']}") conditions = [] if self.buy_trend_above_senkou_level.value >= 1: conditions.append(dataframe['trend_close_5m'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_5m'] > dataframe['senkou_b']) if self.buy_trend_above_senkou_level.value >= 2: conditions.append(dataframe['trend_close_15m'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_15m'] > dataframe['senkou_b']) if self.buy_trend_above_senkou_level.value >= 3: conditions.append(dataframe['trend_close_30m'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_30m'] > dataframe['senkou_b']) if self.buy_trend_above_senkou_level.value >= 4: conditions.append(dataframe['trend_close_1h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_1h'] > dataframe['senkou_b']) if self.buy_trend_above_senkou_level.value >= 5: conditions.append(dataframe['trend_close_2h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_2h'] > dataframe['senkou_b']) if self.buy_trend_above_senkou_level.value >= 6: conditions.append(dataframe['trend_close_4h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_4h'] > dataframe['senkou_b']) if self.buy_trend_above_senkou_level.value >= 7: conditions.append(dataframe['trend_close_6h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_6h'] > dataframe['senkou_b']) if self.buy_trend_above_senkou_level.value >= 8: conditions.append(dataframe['trend_close_8h'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_8h'] > dataframe['senkou_b']) if self.buy_trend_bullish_level.value >= 1: conditions.append(dataframe['trend_close_5m'] > dataframe['trend_open_5m']) if self.buy_trend_bullish_level.value >= 2: conditions.append(dataframe['trend_close_15m'] > dataframe['trend_open_15m']) if self.buy_trend_bullish_level.value >= 3: conditions.append(dataframe['trend_close_30m'] > dataframe['trend_open_30m']) if self.buy_trend_bullish_level.value >= 4: conditions.append(dataframe['trend_close_1h'] > dataframe['trend_open_1h']) if self.buy_trend_bullish_level.value >= 5: conditions.append(dataframe['trend_close_2h'] > dataframe['trend_open_2h']) if self.buy_trend_bullish_level.value >= 6: conditions.append(dataframe['trend_close_4h'] > dataframe['trend_open_4h']) if self.buy_trend_bullish_level.value >= 7: conditions.append(dataframe['trend_close_6h'] > dataframe['trend_open_6h']) if self.buy_trend_bullish_level.value >= 8: conditions.append(dataframe['trend_close_8h'] > dataframe['trend_open_8h']) conditions.append(dataframe['fan_magnitude_gain'] >= self.buy_min_fan_magnitude_gain.value) conditions.append(dataframe['fan_magnitude'] > 1) for x in range(self.buy_fan_magnitude_shift_value.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 logger.info(f"END populate_entry_trend : {metadata['pair']}") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append(qtpylib.crossed_below(dataframe['trend_close_5m'], dataframe[self.sell_trend_indicator.value])) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1 return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """ Customize leverage for each new trade. This method is only called in futures mode. :param pair: Pair that's currently analyzed :param current_time: datetime object, containing the current datetime :param current_rate: Rate, calculated based on pricing settings in exit_pricing. :param proposed_leverage: A leverage proposed by the bot. :param max_leverage: Max leverage allowed on this pair :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal. :param side: 'long' or 'short' - indicating the direction of the proposed trade :return: A leverage amount, which is between 1.0 and max_leverage. """ return self.custom_leverage