# source: https://raw.githubusercontent.com/zrowan1/CryptoBuddy/95337605e36ea45da681ce5626a68713a85f7eb4/user_data/strategies/MomentumRSI.py from freqtrade.strategy import IStrategy from pandas import DataFrame import talib.abstract as ta import numpy as np class Github_zrowan1_CryptoBuddy__MomentumRSI__20260406_183845(IStrategy): """ Github_zrowan1_CryptoBuddy__MomentumRSI__20260406_183845 V9 (Final Edition) — Bybit Spot USDT Orthogonale indicator-matrix: 1. StochRSI (momentum timing) 2. Bollinger Bands (volatiliteits-uitrekking) 3. ADX (directionele trendsterkte) Trendfilters: - 1h EMA 200 (lokaal regime) - BTC/USDT EMA 200 "Remora" macro-filter (informative pair) Risicobeheer: - Dynamische ATR-stoploss (Chandelier Exit) via custom_stoploss - MaxDrawdown + StoplossGuard protections """ INTERFACE_VERSION = 3 # ROI tabel minimal_roi = { "0": 0.15, "120": 0.08, "240": 0.04, "480": 0.02, } # Vangnet stoploss — custom_stoploss overschrijft dit dynamisch stoploss = -0.10 # Oude trailing stop verwijderd; Chandelier Exit via custom_stoploss trailing_stop = False # Activeer dynamische ATR-stoploss use_custom_stoploss = True timeframe = "1h" startup_candle_count = 200 # ATR multiplier voor Chandelier Exit atr_multiplier = 3.0 # --- Informative Pairs: BTC macro-filter ("Remora") --- def informative_pairs(self): return [("BTC/USDT", self.timeframe)] # --- Protections: Cash-Out logica --- @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 # Normaliseer naar 0-1 dataframe["stochrsi_d"] = stochrsi_d / 100.0 # Bollinger Bands (volatiliteits-uitrekking) 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 (directionele trendsterkte) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) # EMA 200 (1h trendfilter) dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200) # ATR 14 (voor custom_stoploss — pre-computed) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) # BTC Remora filter via informative pair merge # Freqtrade merget BTC/USDT data automatisch met prefix "BTC/USDT_" # maar we moeten de kolommen zelf berekenen als ze niet bestaan if "BTC/USDT_close" not in dataframe.columns: # Wanneer het paar BTC/USDT zelf is, gebruik eigen data if metadata.get("pair") == "BTC/USDT": dataframe["btc_close"] = dataframe["close"] dataframe["btc_ema200"] = dataframe["ema200"] else: # Informative pair data ophalen via dataprovider 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) # Merge op date kolom 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: # Freqtrade auto-merge format dataframe["btc_close"] = dataframe["BTC/USDT_close"] dataframe["btc_ema200"] = ta.EMA( dataframe["BTC/USDT_close"], timeperiod=200 ) return dataframe # --- Entry --- def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = ( # 1. Bollinger Band: prijs onder lower band (vorige gesloten kaars) (dataframe["close"].shift(1) < dataframe["bb_lower"].shift(1)) # 2. StochRSI: oversold zone + kruist opwaarts & (dataframe["stochrsi_k"].shift(1) < 0.20) & (dataframe["stochrsi_k"] > dataframe["stochrsi_d"]) # 3. ADX: sterke trend & (dataframe["adx"].shift(1) > 25) # 4. EMA 200: macro-uptrend op 1h & (dataframe["close"].shift(1) > dataframe["ema200"].shift(1)) # 5. Remora: BTC in uptrend & (dataframe["btc_close"].shift(1) > dataframe["btc_ema200"].shift(1)) # Basis volume check & (dataframe["volume"] > 0) ) dataframe.loc[conditions, "enter_long"] = 1 return dataframe # --- Exit --- 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 Stake Amount: dynamisch risicopercentage op basis van equity --- 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 stake = equity * risk_pct min_viable = max(min_stake or 1, 5.0) # Bybit minimum + buffer return max(min(stake, max_stake), min_viable) # --- Custom Stoploss: Chandelier Exit (ATR-based trailing) --- def custom_stoploss( self, pair, trade, current_time, current_rate, current_profit, **kwargs ): 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 # Hoogste close since trade entry (echte Chandelier Exit) 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 # Stop level = hoogste_close - (ATR * multiplier) chandelier_stop = highest_close - (atr * self.atr_multiplier) # Omzetten naar relatieve stoploss t.o.v. huidige koers (negatief getal) stoploss = (chandelier_stop / current_rate) - 1.0 # Nooit verder openen dan de harde stoploss return max(stoploss, self.stoploss)