# source: https://raw.githubusercontent.com/Ilovecodinghhh/Astock/5f12aa0e26f7a2eb60e44059fed6de29e2681dae/crypto_freqtrade/strategies/BreakoutTrendStrategy.py from functools import reduce from datetime import datetime import pandas as pd import talib.abstract as ta from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair from pandas import DataFrame class Github_Ilovecodinghhh_Astock__BreakoutTrendStrategy__20260613_081719(IStrategy): """ Long-only trend breakout strategy for liquid Binance spot pairs. Signals use shifted rolling highs/lows so the current candle is never part of its own breakout threshold. """ INTERFACE_VERSION = 3 timeframe = "4h" startup_candle_count = 500 can_short = False minimal_roi = { "0": 0.40, "120": 0.20, "360": 0.08, "720": 0 } stoploss = -0.12 trailing_stop = True trailing_stop_positive = 0.035 trailing_stop_positive_offset = 0.12 trailing_only_offset_is_reached = True breakout_window = IntParameter(18, 72, default=18, space="buy", optimize=True) exit_window = IntParameter(8, 36, default=8, space="sell", optimize=True) buy_adx = IntParameter(14, 34, default=20, space="buy", optimize=True) buy_atr_min = DecimalParameter(0.008, 0.04, default=0.012, decimals=3, space="buy", optimize=True) buy_atr_max = DecimalParameter(0.04, 0.14, default=0.10, decimals=3, space="buy", optimize=True) btc_pair = "BTC/USDT" eth_pair = "ETH/USDT" informative_timeframe = "1d" relative_strength_top_n = 8 target_trade_volatility = 0.065 min_stake_fraction = 0.50 max_stake_fraction = 1.0 use_btc_regime_filter = False btc_regime_mode = "strict" use_eth_regime_filter = False use_pair_daily_filter = True pair_daily_mode = "strict" use_relative_strength_filter = False use_volatility_stake = True use_chandelier_exit = False chandelier_atr_mult = 3.0 exit_rsi = 42 @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 2, }, { "method": "StoplossGuard", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 12, "required_profit": 0.0, "only_per_pair": False, }, { "method": "MaxDrawdown", "lookback_period_candles": 90, "trade_limit": 20, "stop_duration_candles": 18, "max_allowed_drawdown": 0.16, "calculation_mode": "equity", }, ] def informative_pairs(self): pairs = set() if self.use_btc_regime_filter: pairs.add((self.btc_pair, self.informative_timeframe)) if self.use_eth_regime_filter: pairs.add((self.eth_pair, self.informative_timeframe)) if self.use_pair_daily_filter and self.dp: for pair in self.dp.current_whitelist(): pairs.add((pair, self.informative_timeframe)) return sorted(pairs) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_100"] = ta.EMA(dataframe, timeperiod=100) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) dataframe["atr_pct"] = dataframe["atr"] / dataframe["close"] dataframe["volume_mean_12"] = dataframe["volume"].rolling(12, min_periods=12).mean() dataframe["momentum_14"] = dataframe["close"] / dataframe["close"].shift(42) - 1.0 dataframe["momentum_30"] = dataframe["close"] / dataframe["close"].shift(180) - 1.0 dataframe["volatility_14"] = dataframe["close"].pct_change().rolling(42, min_periods=42).std() dataframe["risk_adj_momentum"] = dataframe["momentum_14"] / dataframe["volatility_14"].replace(0, pd.NA) dataframe["chandelier_long"] = ( dataframe["high"].rolling(22, min_periods=22).max().shift(1) - dataframe["atr"] * self.chandelier_atr_mult ) for window in self.breakout_window.range: dataframe[f"breakout_high_{window}"] = dataframe["high"].rolling(window, min_periods=window).max().shift(1) for window in self.exit_window.range: dataframe[f"exit_low_{window}"] = dataframe["low"].rolling(window, min_periods=window).min().shift(1) dataframe["ema_100_slope"] = dataframe["ema_100"] / dataframe["ema_100"].shift(12) - 1.0 if self.use_btc_regime_filter and self.dp: btc_daily = self.dp.get_pair_dataframe(pair=self.btc_pair, timeframe=self.informative_timeframe) btc_daily = self._daily_regime_indicators(btc_daily, "btc") btc_daily["btc_regime_ok"] = self._market_regime_ok(btc_daily, "btc", self.btc_regime_mode).astype(int) dataframe = merge_informative_pair( dataframe, btc_daily, self.timeframe, self.informative_timeframe, ffill=True, ) else: dataframe["btc_regime_ok_1d"] = 1 if self.use_eth_regime_filter and self.dp: eth_daily = self.dp.get_pair_dataframe(pair=self.eth_pair, timeframe=self.informative_timeframe) eth_daily = self._daily_regime_indicators(eth_daily, "eth") eth_daily["eth_regime_ok"] = self._market_regime_ok(eth_daily, "eth", "strict").astype(int) dataframe = merge_informative_pair( dataframe, eth_daily, self.timeframe, self.informative_timeframe, ffill=True, ) else: dataframe["eth_regime_ok_1d"] = 1 if self.use_pair_daily_filter and self.dp: pair_daily = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.informative_timeframe) pair_daily = self._daily_regime_indicators(pair_daily, "pair") pair_daily["pair_daily_regime_ok"] = self._pair_daily_regime_ok( pair_daily, self.pair_daily_mode, ).astype(int) pair_daily = pair_daily[["date", "pair_daily_regime_ok"]] dataframe = merge_informative_pair( dataframe, pair_daily, self.timeframe, self.informative_timeframe, ffill=True, ) else: dataframe["pair_daily_regime_ok_1d"] = 1 if self.use_relative_strength_filter: dataframe["relative_strength_rank"] = self._relative_strength_rank(dataframe, metadata["pair"]) else: dataframe["relative_strength_rank"] = 1.0 return dataframe @staticmethod def _daily_regime_indicators(dataframe: DataFrame, prefix: str) -> DataFrame: dataframe[f"{prefix}_ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe[f"{prefix}_ema_200"] = ta.EMA(dataframe, timeperiod=200) dataframe[f"{prefix}_momentum_30"] = dataframe["close"] / dataframe["close"].shift(30) - 1.0 dataframe[f"{prefix}_momentum_90"] = dataframe["close"] / dataframe["close"].shift(90) - 1.0 dataframe[f"{prefix}_volatility_30"] = dataframe["close"].pct_change().rolling(30, min_periods=30).std() return dataframe @staticmethod def _market_regime_ok(dataframe: DataFrame, prefix: str, mode: str) -> pd.Series: if mode == "strict": return ( (dataframe["close"] > dataframe[f"{prefix}_ema_200"]) & (dataframe[f"{prefix}_ema_50"] > dataframe[f"{prefix}_ema_200"]) & (dataframe[f"{prefix}_momentum_30"] > 0) & (dataframe[f"{prefix}_momentum_90"] > -0.05) & (dataframe[f"{prefix}_volatility_30"] < 0.055) ) return ( ( (dataframe["close"] > dataframe[f"{prefix}_ema_200"]) | ( (dataframe["close"] > dataframe[f"{prefix}_ema_50"]) & (dataframe[f"{prefix}_momentum_30"] > -0.02) ) | (dataframe[f"{prefix}_momentum_90"] > 0.08) ) & (dataframe[f"{prefix}_volatility_30"] < 0.09) ) @staticmethod def _pair_daily_regime_ok(dataframe: DataFrame, mode: str) -> pd.Series: if mode == "strict": return ( (dataframe["close"] > dataframe["pair_ema_50"]) & (dataframe["close"] > dataframe["pair_ema_200"]) & (dataframe["pair_ema_50"] > dataframe["pair_ema_200"]) & (dataframe["pair_momentum_30"] > 0) ) return ( (dataframe["close"] > dataframe["pair_ema_200"]) & (dataframe["pair_ema_50"] > dataframe["pair_ema_200"]) & (dataframe["pair_momentum_30"] > -0.05) ) def _relative_strength_rank(self, dataframe: DataFrame, pair: str) -> pd.Series: if not self.dp: return pd.Series(1.0, index=dataframe.index) try: whitelist = [candidate for candidate in self.dp.current_whitelist() if candidate != self.btc_pair] except Exception: return pd.Series(1.0, index=dataframe.index) if pair == self.btc_pair: whitelist = [self.btc_pair] + whitelist strength = pd.DataFrame(index=dataframe["date"]) strength[pair] = dataframe.set_index("date")["risk_adj_momentum"] for candidate in whitelist: if candidate == pair: continue candidate_df = self.dp.get_pair_dataframe(candidate, self.timeframe) if candidate_df.empty: continue candidate_strength = ( candidate_df["close"] / candidate_df["close"].shift(42) - 1.0 ) / candidate_df["close"].pct_change().rolling(42, min_periods=42).std().replace(0, pd.NA) strength[candidate] = candidate_strength.set_axis(candidate_df["date"]).reindex(strength.index) return strength.rank(axis=1, ascending=False, method="first")[pair].to_numpy() def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: window = self.breakout_window.value conditions = [ dataframe["volume"] > dataframe["volume_mean_12"], dataframe["close"] > dataframe[f"breakout_high_{window}"], dataframe["close"] > dataframe["ema_200"], dataframe["ema_50"] > dataframe["ema_100"], dataframe["ema_100"] > dataframe["ema_200"], dataframe["ema_100_slope"] > 0, dataframe["adx"] > self.buy_adx.value, dataframe["rsi"].between(52, 78), dataframe["atr_pct"] > self.buy_atr_min.value, dataframe["atr_pct"] < self.buy_atr_max.value, ] if self.use_btc_regime_filter: conditions.append(dataframe["btc_regime_ok_1d"] == 1) if self.use_eth_regime_filter: conditions.append(dataframe["eth_regime_ok_1d"] == 1) if self.use_pair_daily_filter: conditions.append(dataframe["pair_daily_regime_ok_1d"] == 1) if self.use_relative_strength_filter: conditions.append(dataframe["relative_strength_rank"] <= self.relative_strength_top_n) dataframe.loc[reduce(lambda left, right: left & right, conditions), ["enter_long", "enter_tag"]] = ( 1, "breakout_trend", ) return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: window = self.exit_window.value trend_break = ( (dataframe["close"] < dataframe[f"exit_low_{window}"]) | (dataframe["ema_50"] < dataframe["ema_100"]) | (dataframe["rsi"] < self.exit_rsi) ) if self.use_chandelier_exit: trend_break = trend_break | (dataframe["close"] < dataframe["chandelier_long"]) conditions = [ dataframe["volume"] > 0, trend_break, ] dataframe.loc[reduce(lambda left, right: left & right, conditions), ["exit_long", "exit_tag"]] = ( 1, "trend_break", ) return dataframe def custom_stake_amount( self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: float | None, max_stake: float, leverage: float, entry_tag: str | None, side: str, **kwargs, ) -> float: if not self.use_volatility_stake or not self.dp: return proposed_stake dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if dataframe.empty or "atr_pct" not in dataframe: return proposed_stake atr_pct = dataframe.iloc[-1]["atr_pct"] if pd.isna(atr_pct) or atr_pct <= 0: return proposed_stake fraction = self.target_trade_volatility / float(atr_pct) fraction = min(self.max_stake_fraction, max(self.min_stake_fraction, fraction)) stake = proposed_stake * fraction if min_stake: stake = max(min_stake, stake) return min(max_stake, stake) class BreakoutBaselineStrategy(Github_Ilovecodinghhh_Astock__BreakoutTrendStrategy__20260613_081719): use_btc_regime_filter = False use_relative_strength_filter = False use_volatility_stake = False class BreakoutBtcRegimeStrategy(BreakoutBaselineStrategy): use_btc_regime_filter = True class BreakoutStrictBtcRegimeStrategy(BreakoutBtcRegimeStrategy): btc_regime_mode = "strict" class BreakoutEthRegimeStrategy(BreakoutBaselineStrategy): use_eth_regime_filter = True class BreakoutBtcEthRegimeStrategy(BreakoutStrictBtcRegimeStrategy): use_eth_regime_filter = True class BreakoutPairDailyTrendStrategy(BreakoutBaselineStrategy): use_pair_daily_filter = True class BreakoutStrictPairDailyTrendStrategy(BreakoutPairDailyTrendStrategy): pair_daily_mode = "strict" class BreakoutBtcPairDailyTrendStrategy(BreakoutStrictBtcRegimeStrategy): use_pair_daily_filter = True class BreakoutBtcStrictPairDailyTrendStrategy(BreakoutBtcPairDailyTrendStrategy): pair_daily_mode = "strict" class BreakoutRelativeStrengthStrategy(BreakoutBaselineStrategy): use_relative_strength_filter = True class BreakoutTop4RelativeStrengthStrategy(BreakoutRelativeStrengthStrategy): relative_strength_top_n = 4 class BreakoutVolatilityStakeStrategy(BreakoutBaselineStrategy): use_volatility_stake = True class BreakoutChandelierExitStrategy(BreakoutBaselineStrategy): use_chandelier_exit = True class BreakoutFastRsiExitStrategy(BreakoutBaselineStrategy): exit_rsi = 48 class BreakoutStrictDefensiveStrategy(Github_Ilovecodinghhh_Astock__BreakoutTrendStrategy__20260613_081719): btc_regime_mode = "strict" relative_strength_top_n = 4 use_chandelier_exit = True