# source: https://raw.githubusercontent.com/meir/freqtrade/31418bdee9fab734e15f2da787aba5c425b0f1b4/user_data/strategies/ichiV1.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_meir_freqtrade__ichiV1__20250407_144529(IStrategy): INTERFACE_VERSION = 3 def version(self) -> str: return "v0.0.001" # Futures custom_leverage = 1.0 # NOTE: settings as of the 25th july 21 # Buy hyperspace params: # "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, 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 hyperspace params: # NOTE: was 15m but kept bailing out in dryrun 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", ) # ROI table: minimal_roi = { "0": 0.061 * custom_leverage, "3": 0.023 * custom_leverage, "6": 0.008 * custom_leverage, "19": 0 * custom_leverage, } # Stoploss: stoploss = -0.217 * custom_leverage # Optimal timeframe for the strategy timeframe = "1m" startup_candle_count = 130 # startup_candle_count = 96 # process_only_new_candles = True 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 # calcola solo gli indicatori strettamente necessari se True optimize = 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 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. """ # logger.info(" bot_loop_start ") 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_open = dataframe['open'] # dataframe_close = heikinashi['close'] dataframe_close = dataframe["close"] # dataframe['hk_close'] = heikinashi['close'] # dataframe['hk_high'] = heikinashi['high'] # dataframe['hk_low'] = heikinashi['low'] 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 ) # if not self.optimize or (self.buy_trend_above_senkou_level.value >= 4 or self.buy_trend_bullish_level.value >= 4): dataframe["trend_close_1h"] = ta.EMA( dataframe_close, timeperiod=12 * moltiplicatore ) # if not self.optimize or (self.buy_trend_above_senkou_level.value >= 5 or self.buy_trend_bullish_level.value >= 5): 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 ) # if not self.optimize or (self.buy_trend_above_senkou_level.value >= 8 or self.buy_trend_bullish_level.value >= 8): 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['chikou_span'] = ichimoku['chikou_span'] NON UTILIZZARE: ha 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) 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 = [] # 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), "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