# source: https://raw.githubusercontent.com/flessner/freqtrade/414f55eef74e0cb5a65a1ae22aac9b41dc591b5f/user_data/strategies/ichit.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 import technical.indicators as ftt from functools import reduce from freqtrade.strategy import ( CategoricalParameter, DecimalParameter, IStrategy, IntParameter, ) pd.options.mode.chained_assignment = None # ichiV1 - f(ast) variant of the original ichi strategy # source: https://github.com/PeetCrypto/freqtrade-stuff/blob/main/IchisV1.py leverage = 10 class Github_flessner_freqtrade__ichit__20250310_104636(IStrategy): # Buy hyperspace params: buy_params = { "buy_fan_magnitude_shift_value": 4, "buy_min_fan_magnitude_gain": 1.00581, "buy_trend_above_senkou_level": 2, "buy_trend_bullish_level": 1, } # ROI table: minimal_roi = { "0": 0.04 * leverage, "10": 0.025 * leverage, "25": 0.01 * leverage, "50": 0, } # Stoploss: stoploss = -0.014 * leverage # 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 # more variables use_exit_signal = False exit_profit_only = False ignore_roi_if_entry_signal = False # hyperoptable parameters buy_trend_above_senkou_level = IntParameter( 1, 8, default=buy_params["buy_trend_above_senkou_level"], space="buy", optimize=True, ) buy_trend_bullish_level = IntParameter( 1, 8, default=buy_params["buy_trend_bullish_level"], space="buy", optimize=True ) buy_fan_magnitude_shift_value = IntParameter( 1, 5, default=buy_params["buy_fan_magnitude_shift_value"], space="buy", optimize=True, ) buy_min_fan_magnitude_gain = DecimalParameter( 1, 1.05, default=buy_params["buy_min_fan_magnitude_gain"], decimals=5, space="buy", optimize=True, ) def leverage(self, **kwargs) -> float: return 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_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: return dataframe