# source: https://raw.githubusercontent.com/cahangeorge/freq_coolab/11fa14b4db274e7b5dd4c95d2db7e384a18d7ce9/user_data/strategies/ichiV1.py # --- Do not remove these libs --- from typing import Optional from freqtrade.strategy.parameters import DecimalParameter, IntParameter 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_cahangeorge_freq_coolab__ichiV1__20250630_184025(IStrategy): buy_trend_above_senkou_level = IntParameter(1, 8, default=1, space="buy") buy_trend_bullish_level = IntParameter(1, 8, default=6, space="buy") buy_fan_magnitude_shift_value = IntParameter(1, 10, default=3, space="buy") buy_min_fan_magnitude_gain = DecimalParameter(1.001, 1.010, decimals=3, default=1.002, space="buy") # NOTE: settings as of the 25th july 21 # Buy hyperspace params: buy_params = { "buy_trend_above_senkou_level": 1, "buy_trend_bullish_level": 6, "buy_fan_magnitude_shift_value": 3, "buy_min_fan_magnitude_gain": 1.002 # 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": "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 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'} }, 'subplots': { 'fan_magnitude': { 'fan_magnitude': {} }, 'fan_magnitude_gain': { 'fan_magnitude_gain': {} } } } 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. """ entry_tag = '' max_leverage = 2 return max_leverage 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.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_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # Trending market 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']) # Trends bullish 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']) # Trends magnitude 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), 'buy'] = 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.sell_params['sell_trend_indicator']])) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe