# source: https://raw.githubusercontent.com/Danson77/Freqtrade_AIO_UI/f0bf29851d0577f244c17c0aa7d593ba3a7f3551/user_data/backtest_reports/Registerd_with_100USDT/ichiv2/ichiV2.py # directory_url: https://github.com/Danson77/Freqtrade_AIO_UI/blob/main/user_data/backtest_reports/Registerd_with_100USDT/ichiv2/ # User: Danson77 # Repository: Freqtrade_AIO_UI # --------------------# --- Do not remove these libs --- 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 class Github_Danson77_Freqtrade_AIO_UI__ichiV2__20260520_191933(IStrategy): """ Trend + pullback + reclaim Ichimoku strategy. Goal: - Stop buying late into extension - Buy only when trend is valid AND a pullback has happened - Enter when price reclaims short-term strength - Exit on confirmed weakness, not every small wobble """ INTERFACE_VERSION = 3 buy_params = { "buy_trend_above_senkou_level": 1, "buy_trend_bullish_level": 4, "buy_fan_magnitude_shift_value": 2, "buy_min_fan_magnitude_gain": 1.001, "buy_max_rsi": 68, "buy_max_close_to_trend": 1.03, } sell_params = { "sell_trend_indicator": "trend_close_1h", "sell_fan_magnitude_gain": 0.995, "sell_min_rsi": 43, } minimal_roi = { "0": 0.059, "10": 0.037, "41": 0.012, "114": 0 } stoploss = -0.07 timeframe = '5m' startup_candle_count = 120 process_only_new_candles = True trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.02 trailing_only_offset_is_reached = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = 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'}, 'ema_entry': {'color': '#00D1FF'}, 'ema_trend': {'color': '#B388FF'}, }, 'subplots': { 'fan_magnitude': { 'fan_magnitude': {} }, 'fan_magnitude_gain': { 'fan_magnitude_gain': {} }, 'rsi': { 'rsi': {}, 'rsi_fast': {} } } } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if dataframe.empty: return dataframe heikinashi = qtpylib.heikinashi(dataframe) # Keep real close; smooth the rest a bit dataframe['open'] = heikinashi['open'] dataframe['high'] = heikinashi['high'] dataframe['low'] = heikinashi['low'] # Trend stack 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) # Fan magnitude dataframe['fan_magnitude'] = dataframe['trend_close_1h'] / dataframe['trend_close_8h'] dataframe['fan_magnitude_gain'] = dataframe['fan_magnitude'] / dataframe['fan_magnitude'].shift(1) # Ichimoku 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'] # Helpers dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=7) dataframe['ema_entry'] = ta.EMA(dataframe['close'], timeperiod=9) dataframe['ema_fast'] = ta.EMA(dataframe['close'], timeperiod=12) dataframe['ema_trend'] = ta.EMA(dataframe['close'], timeperiod=21) dataframe['ema_slow'] = ta.EMA(dataframe['close'], timeperiod=26) dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean() # Pullback happened recently near short EMA dataframe['pullback_ok'] = ( (dataframe['low'].shift(1) <= dataframe['ema_entry'].shift(1)) | (dataframe['close'].shift(1) <= dataframe['ema_entry'].shift(1) * 1.002) | (dataframe['rsi_fast'].shift(1) < 52) ) # Reclaim / bounce after pullback dataframe['reclaim_ema'] = qtpylib.crossed_above(dataframe['close'], dataframe['ema_entry']) # Small recovery candle after pullback dataframe['rebound_ok'] = ( (dataframe['close'] > dataframe['open']) & (dataframe['close'] > dataframe['close'].shift(1)) & (dataframe['rsi_fast'] > dataframe['rsi_fast'].shift(1)) ) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if dataframe.empty: return dataframe conditions = [] # Trend above cloud 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']) # Bullish structure, but less stacked than before 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']) # Trend still needs to be strengthening, but not extreme conditions.append(dataframe['fan_magnitude'] > 1.0) conditions.append(dataframe['fan_magnitude_gain'] >= self.buy_params['buy_min_fan_magnitude_gain']) for x in range(self.buy_params['buy_fan_magnitude_shift_value']): conditions.append(dataframe['fan_magnitude'].shift(x + 1) < dataframe['fan_magnitude']) # Anti-chase / pullback logic conditions.append(dataframe['ema_fast'] > dataframe['ema_slow']) conditions.append(dataframe['close'] > dataframe['ema_trend']) conditions.append(dataframe['close'] <= dataframe['ema_trend'] * self.buy_params['buy_max_close_to_trend']) conditions.append(dataframe['rsi'] < self.buy_params['buy_max_rsi']) conditions.append(dataframe['rsi_fast'] > 45) conditions.append(dataframe['rsi_fast'] < 68) conditions.append(dataframe['volume'] > 0) conditions.append(dataframe['volume'] > dataframe['volume_mean_24'] * 0.25) # Key fix: require recent pullback, then reclaim conditions.append(dataframe['pullback_ok']) conditions.append(dataframe['reclaim_ema'] | dataframe['rebound_ok']) if conditions: entry_cond = reduce(lambda x, y: x & y, conditions).fillna(False) dataframe.loc[entry_cond, ['enter_long', 'enter_tag']] = (1, 'ichi_pullback_reclaim') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if dataframe.empty: return dataframe trend_break = qtpylib.crossed_below( dataframe['trend_close_5m'], dataframe[self.sell_params['sell_trend_indicator']] ) fan_rollover = dataframe['fan_magnitude_gain'] < self.sell_params['sell_fan_magnitude_gain'] rsi_loss = ( (dataframe['rsi'] < self.sell_params['sell_min_rsi']) & (dataframe['rsi'] < dataframe['rsi'].shift(1)) ) cloud_loss = ( (dataframe['close'] < dataframe['senkou_a']) & (dataframe['close'] < dataframe['senkou_b']) ) ema_weakness = ( (dataframe['ema_fast'] < dataframe['ema_trend']) & (dataframe['close'] < dataframe['ema_entry']) ) # Need real weakness confirmation exit_cond = ( (trend_break & rsi_loss) | (cloud_loss & fan_rollover & rsi_loss) | (ema_weakness & fan_rollover & rsi_loss) ).fillna(False) dataframe.loc[exit_cond, ['exit_long', 'exit_tag']] = (1, 'trend_rollover') return dataframe