# source: https://raw.githubusercontent.com/Zer0phucks/trading-lab/90a6eef67476f5a442f148722ac2f56de8ee2fd1/user_data/strategies/FreqaiBreakoutClassifierV1.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 """ Github_Zer0phucks_trading_lab__FreqaiBreakoutClassifierV1__20260505_075652 Long-only FreqAI classifier strategy for high-volatility breakout regimes. Designed for research, backtesting, hyperopt, and dry-run in a Freqtrade lab. Suggested model: LightGBMClassifier or XGBoostClassifier. Suggested timeframe: 5m. Hyperopt only entry/exit thresholds, ROI, stoploss, trailing, and protections. Do not hyperopt feature_engineering_*() or set_freqai_targets(). """ from __future__ import annotations from functools import reduce import numpy as np import talib.abstract as ta from pandas import DataFrame from freqtrade.strategy import DecimalParameter, IStrategy, IntParameter from freqtrade.vendor.qtpylib import indicators as qtpylib class Github_Zer0phucks_trading_lab__FreqaiBreakoutClassifierV1__20260505_075652(IStrategy): """ Classifies the forward move as down / flat / up. Enters only when the model predicts "up" and deterministic breakout/volume filters confirm expansion. """ INTERFACE_VERSION = 3 timeframe = "5m" can_short = False process_only_new_candles = True startup_candle_count: int = 400 use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False minimal_roi = { "0": 0.075, "60": 0.040, "180": 0.020, "600": 0.0, } stoploss = -0.10 trailing_stop = True trailing_stop_positive = 0.020 trailing_stop_positive_offset = 0.045 trailing_only_offset_is_reached = True # Entry thresholds. buy_di_max = DecimalParameter(0.50, 3.00, decimals=2, default=1.60, space="buy", optimize=True) buy_adx_min = IntParameter(12, 45, default=22, space="buy", optimize=True) buy_volume_ratio_min = DecimalParameter( 1.00, 4.00, decimals=2, default=1.40, space="buy", optimize=True ) buy_atr_pct_min = DecimalParameter(0.002, 0.060, decimals=3, default=0.012, space="buy", optimize=True) buy_rsi_max = IntParameter(55, 88, default=78, space="buy", optimize=True) # Exit thresholds. sell_rsi_min = IntParameter(25, 58, default=42, space="sell", optimize=True) sell_volume_ratio_min = DecimalParameter( 0.30, 2.00, decimals=2, default=0.80, space="sell", optimize=True ) # Protections. protection_cooldown = IntParameter(1, 24, default=8, space="protection", optimize=True) protection_stop_duration = IntParameter(12, 120, default=48, space="protection", optimize=True) @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": self.protection_cooldown.value, }, { "method": "StoplossGuard", "lookback_period_candles": 96, "trade_limit": 2, "stop_duration_candles": self.protection_stop_duration.value, "only_per_pair": False, }, ] def feature_engineering_expand_all( self, dataframe: DataFrame, period: int, metadata: dict, **kwargs ) -> DataFrame: dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period) dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period) dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period) dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period) dataframe["%-atr-period"] = ta.ATR(dataframe, timeperiod=period) dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period) rolling_high = dataframe["high"].rolling(period).max().shift(1) rolling_low = dataframe["low"].rolling(period).min().shift(1) channel_range = (rolling_high - rolling_low).replace(0, np.nan) dataframe["%-donchian_position-period"] = (dataframe["close"] - rolling_low) / channel_range dataframe["%-donchian_width-period"] = channel_range / dataframe["close"].replace(0, np.nan) dataframe["%-relative_volume-period"] = dataframe["volume"] / dataframe[ "volume" ].rolling(period).mean().replace(0, np.nan) 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"] dataframe["%-candle_range_pct"] = (dataframe["high"] - dataframe["low"]) / dataframe[ "close" ].replace(0, np.nan) dataframe["%-candle_body_pct"] = (dataframe["close"] - dataframe["open"]) / dataframe[ "open" ].replace(0, np.nan) return dataframe def feature_engineering_standard( self, dataframe: DataFrame, metadata: dict, **kwargs ) -> DataFrame: dataframe["%%-ema_21"] = ta.EMA(dataframe, timeperiod=21) dataframe["%%-ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["%%-ema_200"] = ta.EMA(dataframe, timeperiod=200) dataframe["%%-rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["%%-adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["%%-atr_pct"] = ta.ATR(dataframe, timeperiod=14) / dataframe["close"].replace( 0, np.nan ) dataframe["%%-volume_ratio_20"] = dataframe["volume"] / dataframe["volume"].rolling( 20 ).mean().replace(0, np.nan) dataframe["%%-donchian_high_20"] = dataframe["high"].rolling(20).max().shift(1) dataframe["%%-donchian_low_20"] = dataframe["low"].rolling(20).min().shift(1) dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7 dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 24 return dataframe def set_freqai_targets( self, dataframe: DataFrame, metadata: dict, **kwargs ) -> DataFrame: """ Classification target. Class threshold is deliberately static. If you change it, create a new freqai.identifier because you changed the target definition. """ self.freqai.class_names = ["down", "flat", "up"] label_period = self.freqai_info["feature_parameters"]["label_period_candles"] class_threshold = 0.010 future_return = dataframe["close"].shift(-label_period) / dataframe["close"] - 1.0 dataframe["&-s_breakout"] = np.select( [future_return < -class_threshold, future_return > class_threshold], ["down", "up"], default="flat", ) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = self.freqai.start(dataframe, metadata, self) if "do_predict" not in dataframe: dataframe["do_predict"] = 0 if "DI_values" not in dataframe: dataframe["DI_values"] = 0.0 if "&-s_breakout" not in dataframe: dataframe["&-s_breakout"] = "flat" return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["enter_long"] = 0 dataframe["enter_tag"] = "" breakout_trigger = qtpylib.crossed_above( dataframe["close"], dataframe["%%-donchian_high_20"] ) conditions = [ dataframe["do_predict"] == 1, dataframe["DI_values"] < self.buy_di_max.value, dataframe["&-s_breakout"] == "up", breakout_trigger, dataframe["%%-ema_50"] > dataframe["%%-ema_200"], dataframe["close"] > dataframe["%%-ema_21"], dataframe["%%-adx"] > self.buy_adx_min.value, dataframe["%%-atr_pct"] > self.buy_atr_pct_min.value, dataframe["%%-volume_ratio_20"] > self.buy_volume_ratio_min.value, dataframe["%%-rsi"] < self.buy_rsi_max.value, dataframe["volume"] > 0, ] dataframe.loc[reduce(lambda x, y: x & y, conditions), ["enter_long", "enter_tag"]] = ( 1, "freqai_breakout_classifier", ) return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["exit_long"] = 0 dataframe["exit_tag"] = "" model_no_longer_up = dataframe["&-s_breakout"].isin(["down", "flat"]) failed_breakout = ( (dataframe["close"] < dataframe["%%-ema_21"]) | (dataframe["close"] < dataframe["%%-donchian_low_20"]) | (dataframe["%%-rsi"] < self.sell_rsi_min.value) ) volume_dried_up = dataframe["%%-volume_ratio_20"] < self.sell_volume_ratio_min.value conditions = [ dataframe["volume"] > 0, model_no_longer_up | failed_breakout | volume_dried_up, ] dataframe.loc[reduce(lambda x, y: x & y, conditions), ["exit_long", "exit_tag"]] = ( 1, "freqai_breakout_exit", ) return dataframe