# source: https://raw.githubusercontent.com/thaodo2512/lstm_cnn/41abaaa52a63ac2274275f4f9147957c742276aa/freqtrade_user_data/strategies/ichi_v1.py from __future__ import annotations # Ichimoku + FreqAI hybrid strategy (long-only) # - Moves indicators into FreqAI feature hooks # - Uses FreqAI predictions to gate entries # - Trend filters via Ichimoku cloud and EMA fans # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from pandas import DataFrame import talib.abstract as ta # type: ignore 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 # type: ignore from functools import reduce from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair # type: ignore import numpy as np class Github_thaodo2512_lstm_cnn__ichi_v1__20251004_131124(IStrategy): """Ichimoku + FreqAI hybrid strategy. Notes - Subclasses IStrategy (FreqAI integrates via self.freqai.start()). - Predictions column name follows the label name set in set_freqai_targets (here: '&-s_close'). - If FreqAI does not provide '&-s_close_mean'/'&-s_close_std', a rolling mean/std fallback is used. """ # Buy hyperspace params buy_params = { "buy_trend_above_senkou_level": 1, "buy_trend_bullish_level": 6, "buy_fan_magnitude_shift_value": 3, # NOTE: Good value (Win% ~70%), a lot of trades "buy_min_fan_magnitude_gain": 1.002, # "buy_min_fan_magnitude_gain": 1.008, # very safe (Win% ~90%), fewer trades } # Sell hyperspace params sell_params = { # Use EMA trend cross for exits "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 trend_period_map = { "trend_close_5m": 1, "trend_close_15m": 3, "trend_close_30m": 6, "trend_close_1h": 12, "trend_close_2h": 24, "trend_close_4h": 48, "trend_close_6h": 72, "trend_close_8h": 96, } freqai_info = { "model_per_pair": True, "feature_parameters": { "include_timeframes": ["5m", "15m", "30m", "1h", "2h", "4h"], "include_shifted_candles": 3, "include_corr_pairlist": [], "indicator_periods_candles": [1, 3, 6, 12, 24, 48, 72, 96], "label_period_candles": 24, }, "data_split_parameters": { "test_size": 0.25, }, "fit_live_predictions_candles": 300, } 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_period_1": {"color": "#FF5733"}, "%%-trend_close_period_3": {"color": "#FF8333"}, "%%-trend_close_period_6": {"color": "#FFB533"}, "%%-trend_close_period_12": {"color": "#FFE633"}, "%%-trend_close_period_24": {"color": "#E3FF33"}, "%%-trend_close_period_48": {"color": "#C4FF33"}, "%%-trend_close_period_72": {"color": "#61FF33"}, "%%-trend_close_period_96": {"color": "#33FF7D"}, }, "subplots": { "fan_magnitude": {"%%-fan_magnitude": {}}, "fan_magnitude_gain": {"%%-fan_magnitude_gain": {}}, }, } # -------------------- FreqAI feature hooks -------------------- def feature_engineering_expand_all( self, dataframe: DataFrame, period: int, metadata: dict, **kwargs ) -> DataFrame: # Write explicit period-suffixed columns to avoid naming ambiguity across periods dataframe[f"%%-trend_close_period_{period}"] = ta.EMA( dataframe["close"], timeperiod=period ) dataframe[f"%%-trend_open_period_{period}"] = ta.EMA( dataframe["open"], timeperiod=period ) dataframe[f"%%-atr_period_{period}"] = ta.ATR(dataframe, timeperiod=period) return dataframe def feature_engineering_expand_basic( self, dataframe: DataFrame, metadata: dict, **kwargs ) -> DataFrame: dataframe["%-pct-change"] = dataframe["close"].pct_change() dataframe["%-raw_volume"] = dataframe["volume"] dataframe["%-raw_price"] = dataframe["close"] return dataframe def feature_engineering_standard( self, dataframe: DataFrame, metadata: dict, **kwargs ) -> DataFrame: if "date" in dataframe.columns: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7 dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25 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"] # Ensure required EMA fan columns exist even if expand_all hasn't populated them yet def _ema(series: pd.Series, n: int) -> pd.Series: try: return ta.EMA(series, timeperiod=n) except Exception: return series.ewm(span=max(1, n), adjust=False).mean() for p in [1, 3, 6, 12, 24, 48, 72, 96]: # Prefer underscore naming; also support legacy hyphen names by aliasing ccol = f"%%-trend_close_period_{p}" ocol = f"%%-trend_open_period_{p}" if ccol not in dataframe.columns: legacy = f"%%-trend_close-period_{p}" if legacy in dataframe.columns: dataframe[ccol] = dataframe[legacy] else: dataframe[ccol] = _ema(dataframe["close"], p) if ocol not in dataframe.columns: legacy = f"%%-trend_open-period_{p}" if legacy in dataframe.columns: dataframe[ocol] = dataframe[legacy] else: dataframe[ocol] = _ema(dataframe["open"], p) # Fan magnitude and acceleration (safe if columns were just created) dataframe["%%-fan_magnitude"] = ( dataframe["%%-trend_close_period_12"] / dataframe["%%-trend_close_period_96"] ) dataframe["%%-fan_magnitude_gain"] = ( dataframe["%%-fan_magnitude"] / dataframe["%%-fan_magnitude"].shift(1) ) return dataframe def set_freqai_targets( self, dataframe: DataFrame, metadata: dict, **kwargs ) -> DataFrame: # Predict forward return over the label_period (mean-close ratio - 1) lp = int(self.freqai_info["feature_parameters"]["label_period_candles"]) dataframe["&-s_close"] = ( dataframe["close"].shift(-lp).rolling(lp).mean() / dataframe["close"] - 1 ) return dataframe # -------------------- Strategy hooks -------------------- 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 = self.freqai.start(dataframe, metadata, self) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: df = dataframe.copy() df["enter_long"] = 0 conditions = [] # Integrate FreqAI prediction if "do_predict" in df.columns: conditions.append(df["do_predict"] == 1) # Fallback for target statistics if not provided by FreqAI if "&-s_close_mean" not in df.columns or "&-s_close_std" not in df.columns: pred = df.get("&-s_close", pd.Series(np.nan, index=df.index)) roll = pred.rolling(100, min_periods=10) df["&-s_close_mean"] = roll.mean() df["&-s_close_std"] = roll.std(ddof=0) # Thresholded target df["target_roi"] = df["&-s_close_mean"] + df["&-s_close_std"] * 1.5 if "&-s_close" in df.columns: conditions.append(df["&-s_close"] > df["target_roi"]) # Trending market above cloud level = int(self.buy_params["buy_trend_above_senkou_level"]) if level >= 1: conditions.append(df["%%-trend_close_period_1"] > df["%%-senkou_a"]) conditions.append(df["%%-trend_close_period_1"] > df["%%-senkou_b"]) if level >= 2: conditions.append(df["%%-trend_close_period_3"] > df["%%-senkou_a"]) conditions.append(df["%%-trend_close_period_3"] > df["%%-senkou_b"]) if level >= 3: conditions.append(df["%%-trend_close_period_6"] > df["%%-senkou_a"]) conditions.append(df["%%-trend_close_period_6"] > df["%%-senkou_b"]) if level >= 4: conditions.append(df["%%-trend_close_period_12"] > df["%%-senkou_a"]) conditions.append(df["%%-trend_close_period_12"] > df["%%-senkou_b"]) if level >= 5: conditions.append(df["%%-trend_close_period_24"] > df["%%-senkou_a"]) conditions.append(df["%%-trend_close_period_24"] > df["%%-senkou_b"]) if level >= 6: conditions.append(df["%%-trend_close_period_48"] > df["%%-senkou_a"]) conditions.append(df["%%-trend_close_period_48"] > df["%%-senkou_b"]) if level >= 7: conditions.append(df["%%-trend_close_period_72"] > df["%%-senkou_a"]) conditions.append(df["%%-trend_close_period_72"] > df["%%-senkou_b"]) if level >= 8: conditions.append(df["%%-trend_close_period_96"] > df["%%-senkou_a"]) conditions.append(df["%%-trend_close_period_96"] > df["%%-senkou_b"]) # Trends bullish (close EMA above open EMA) bull_level = int(self.buy_params["buy_trend_bullish_level"]) if bull_level >= 1: conditions.append(df["%%-trend_close_period_1"] > df["%%-trend_open_period_1"]) if bull_level >= 2: conditions.append(df["%%-trend_close_period_3"] > df["%%-trend_open_period_3"]) if bull_level >= 3: conditions.append(df["%%-trend_close_period_6"] > df["%%-trend_open_period_6"]) if bull_level >= 4: conditions.append(df["%%-trend_close_period_12"] > df["%%-trend_open_period_12"]) if bull_level >= 5: conditions.append(df["%%-trend_close_period_24"] > df["%%-trend_open_period_24"]) if bull_level >= 6: conditions.append(df["%%-trend_close_period_48"] > df["%%-trend_open_period_48"]) if bull_level >= 7: conditions.append(df["%%-trend_close_period_72"] > df["%%-trend_open_period_72"]) if bull_level >= 8: conditions.append(df["%%-trend_close_period_96"] > df["%%-trend_open_period_96"]) # Fan magnitude acceleration conditions.append(df["%%-fan_magnitude_gain"] >= self.buy_params["buy_min_fan_magnitude_gain"]) conditions.append(df["%%-fan_magnitude"] > 1) for x in range(int(self.buy_params["buy_fan_magnitude_shift_value"])): conditions.append(df["%%-fan_magnitude"].shift(x + 1) < df["%%-fan_magnitude"]) if conditions: df.loc[reduce(lambda x, y: x & y, conditions), "enter_long"] = 1 return df def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: df = dataframe.copy() df["exit_long"] = 0 trend_indicator = self.sell_params["sell_trend_indicator"] period = int(self.trend_period_map[trend_indicator]) cond = qtpylib.crossed_below( df["%%-trend_close_period_1"], df[f"%%-trend_close_period_{period}"] ) df.loc[cond.fillna(False), "exit_long"] = 1 return df