# source: https://raw.githubusercontent.com/zrowan1/CryptoBuddy/ccb85aad10abf89c941390edc1a137feb7c55b0b/user_data/strategies/AdaptiveMomentumV9.py from freqtrade.strategy import IStrategy from pandas import DataFrame import pandas as pd import talib.abstract as ta import numpy as np class Github_zrowan1_CryptoBuddy__AdaptiveMomentumV9__20260404_205200(IStrategy): """ Github_zrowan1_CryptoBuddy__AdaptiveMomentumV9__20260404_205200 — Regime-adaptieve strategie op basis van MomentumRSI V9 Vier marktregimes: - strong_up: ADX > 28, prijs > EMA200, BTC bullish → volledige V9 momentum-logica - weak_up: ADX 22-28, prijs > EMA200 → V9 met 75% positiegrootte - ranging: ADX < 22, BB squeeze → geen entries (mean-reversion uitgeschakeld) - downtrend: prijs < EMA200, BTC bearish → geen entries Entry-tags: - trend_pullback: strong_up regime (100% positie) - weak_trend_pullback: weak_up regime (75% positie) Exit-logica: - Alle trades: StochRSI overbought + ROI + Chandelier Exit (3× ATR) """ INTERFACE_VERSION = 3 # ROI tabel minimal_roi = { "0": 0.15, "120": 0.08, "240": 0.04, "480": 0.02, } # Vangnet stoploss — custom_stoploss overschrijft dynamisch stoploss = -0.10 trailing_stop = False use_custom_stoploss = True timeframe = "1h" startup_candle_count = 350 # 200 EMA warmup + 100 BB-squeeze venster + 50 buffer # ATR multiplier voor Chandelier Exit (trend trades) atr_multiplier = 3.0 # Strakke stop voor mean-reversion trades MR_STOPLOSS = -0.02 # Regime hysteresis drempels ADX_TRENDING_ENTER = 28 ADX_TRENDING_EXIT = 22 REGIME_SMOOTHING_CANDLES = 4 BB_SQUEEZE_LOOKBACK = 100 # --- Informative Pairs: BTC macro-filter ("Remora") --- def informative_pairs(self): return [("BTC/USDT", self.timeframe)] # --- Protections --- @property def protections(self): return [ { "method": "MaxDrawdown", "lookback_period_candles": 48, "trade_back_period_candles": 48, "max_allowed_drawdown": 0.15, "stop_duration_candles": 24, }, { "method": "StoplossGuard", "lookback_period_candles": 24, "trade_limit": 3, "stop_duration_candles": 12, "only_per_pair": False, }, ] # --- Indicators --- def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # StochRSI (momentum timing) rsi = ta.RSI(dataframe, timeperiod=14) stochrsi_k, stochrsi_d = ta.STOCH( {"high": rsi, "low": rsi, "close": rsi, "open": rsi}, fastk_period=14, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0, ) dataframe["stochrsi_k"] = stochrsi_k / 100.0 dataframe["stochrsi_d"] = stochrsi_d / 100.0 # RSI(14) voor mean-reversion entry dataframe["rsi"] = rsi # Bollinger Bands bb_result = ta.BBANDS( dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0 ) dataframe["bb_upper"] = bb_result["upperband"] dataframe["bb_middle"] = bb_result["middleband"] dataframe["bb_lower"] = bb_result["lowerband"] # ADX dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) # EMA 200 dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200) # ATR 14 (voor Chandelier Exit) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) # BTC Remora filter if "BTC/USDT_close" not in dataframe.columns: if metadata.get("pair") == "BTC/USDT": dataframe["btc_close"] = dataframe["close"] dataframe["btc_ema200"] = dataframe["ema200"] else: btc_dataframe = self.dp.get_pair_dataframe( pair="BTC/USDT", timeframe=self.timeframe ) if len(btc_dataframe) > 0: btc_dataframe["btc_ema200"] = ta.EMA(btc_dataframe, timeperiod=200) btc_dataframe = btc_dataframe[["date", "close", "btc_ema200"]].rename( columns={"close": "btc_close"} ) dataframe = dataframe.merge(btc_dataframe, on="date", how="left") else: dataframe["btc_close"] = np.nan dataframe["btc_ema200"] = np.nan else: dataframe["btc_close"] = dataframe["BTC/USDT_close"] dataframe["btc_ema200"] = ta.EMA( dataframe["BTC/USDT_close"], timeperiod=200 ) # --- Regime-detectie --- # BB-breedte normaliseren (maatstaf voor squeeze) dataframe["bb_width"] = ( (dataframe["bb_upper"] - dataframe["bb_lower"]) / dataframe["bb_middle"] ) # Squeeze-drempel: 25e percentiel van de afgelopen 100 candles dataframe["bb_width_pct25"] = ( dataframe["bb_width"] .rolling(self.BB_SQUEEZE_LOOKBACK) .quantile(0.25) ).bfill() # Ruwe regime-classificatie (vectorized, geen state) conditions = [ # Downtrend: prijs én BTC onder EMA200 (dataframe["close"] < dataframe["ema200"]) & (dataframe["btc_close"] < dataframe["btc_ema200"]), # Sterke uptrend: ADX hoog, prijs én BTC boven EMA200 (dataframe["adx"] > self.ADX_TRENDING_ENTER) & (dataframe["close"] > dataframe["ema200"]) & (dataframe["btc_close"] > dataframe["btc_ema200"]), # Zwakke uptrend: ADX in hysteresis-band, prijs boven EMA200 (dataframe["adx"] >= self.ADX_TRENDING_EXIT) & (dataframe["adx"] <= self.ADX_TRENDING_ENTER) & (dataframe["close"] > dataframe["ema200"]), # Ranging: ADX laag + BB squeeze (dataframe["adx"] < self.ADX_TRENDING_EXIT) & (dataframe["bb_width"] < dataframe["bb_width_pct25"]), ] choices = ["downtrend", "strong_up", "weak_up", "ranging"] dataframe["regime_raw"] = np.select(conditions, choices, default="ranging") # Hysterese-smoothing: regime moet 4 candles aanhouden (whipsaw-preventie) # Rolling mode werkt alleen op numerieke kolommen — map strings naar ints en terug regime_to_int = {"downtrend": 0, "strong_up": 1, "weak_up": 2, "ranging": 3} int_to_regime = {v: k for k, v in regime_to_int.items()} regime_num = dataframe["regime_raw"].map(regime_to_int) regime_smooth = ( regime_num .rolling(self.REGIME_SMOOTHING_CANDLES, min_periods=1) .apply(lambda x: pd.Series(x).mode()[0], raw=True) .fillna(3) .astype(int) ) dataframe["regime"] = regime_smooth.map(int_to_regime) return dataframe # --- Entry --- def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Block A — trend_pullback (strong_up regime) dataframe.loc[ (dataframe["regime"] == "strong_up") & (dataframe["close"].shift(1) < dataframe["bb_lower"].shift(1)) & (dataframe["stochrsi_k"].shift(1) < 0.20) & (dataframe["stochrsi_k"] > dataframe["stochrsi_d"]) & (dataframe["adx"].shift(1) > 25) & (dataframe["volume"] > 0), ["enter_long", "enter_tag"], ] = (1, "trend_pullback") # Block B — weak_trend_pullback (weak_up regime) # Gereduceerde positiegrootte voor zwakkere trends dataframe.loc[ (dataframe["regime"] == "weak_up") & (dataframe["close"].shift(1) < dataframe["bb_lower"].shift(1)) & (dataframe["stochrsi_k"].shift(1) < 0.20) & (dataframe["stochrsi_k"] > dataframe["stochrsi_d"]) & (dataframe["adx"].shift(1) > 25) & (dataframe["volume"] > 0), ["enter_long", "enter_tag"], ] = (1, "weak_trend_pullback") # Block C — ranging/downtrend: geen entries return dataframe # --- Exit (StochRSI voor trend trades) --- def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (dataframe["stochrsi_k"] > 0.80) & (dataframe["stochrsi_k"] < dataframe["stochrsi_d"]), "exit_long", ] = 1 return dataframe # --- Custom Exit: regime-specifieke exits --- def custom_exit(self, pair, trade, current_time, current_rate, current_profit, **kwargs): # Alle exits gebruiken Chandelier Exit via custom_stoploss # of de standaard exit criteria uit populate_exit_trend return None # --- Custom Stake Amount: dynamisch risicopercentage --- def custom_stake_amount(self, pair, current_time, current_rate, proposed_stake, min_stake, max_stake, leverage, entry_tag, side, **kwargs): equity = self.wallets.get_total_stake_amount() if equity < 500: risk_pct = 0.22 elif equity < 750: risk_pct = 0.18 elif equity < 1000: risk_pct = 0.15 elif equity < 1500: risk_pct = 0.12 else: risk_pct = 0.10 # weak_trend_pullback: 75% positie voor gereduceerd risico in zwakke trends position_multiplier = 0.75 if entry_tag == "weak_trend_pullback" else 1.0 stake = equity * risk_pct * position_multiplier min_viable = max(min_stake or 1, 5.0) return max(min(stake, max_stake), min_viable) # --- Custom Stoploss: Chandelier Exit voor alle trend trades --- def custom_stoploss(self, pair, trade, current_time, current_rate, current_profit, **kwargs): # Alle trades gebruiken Chandelier Exit (3× ATR, trailing van hoogste close) dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) < 1: return self.stoploss atr = dataframe["atr"].iat[-1] if atr == 0 or np.isnan(atr): return self.stoploss candles_since_entry = dataframe.loc[dataframe["date"] >= trade.open_date_utc] if len(candles_since_entry) > 0: highest_close = candles_since_entry["close"].max() else: highest_close = current_rate chandelier_stop = highest_close - (atr * self.atr_multiplier) stoploss = (chandelier_stop / current_rate) - 1.0 return max(stoploss, self.stoploss)