Merge pull request #334 from darkvolg/add-trendrider-v4-strategy

Add TrendRiderStrategy: V4 cascading early loss cut (66.7% WR, 1.42% DD on 30d Bybit)

README.md

@@ -70,7 +70,7 @@ It is designed to support all major exchanges and be controlled via Telegram. It
 Each Strategies includes:  
 
 - [x] **Minimal ROI**: Minimal ROI optimized for the strategy.
-- [x] **Stoploss**: Optimimal stoploss.
+- [x] **Stoploss**: Optimal stoploss.
 - [x] **Buy signals**: Result from Hyperopt or based on exisiting trading strategies.
 - [x] **Sell signals**: Result from Hyperopt or based on exisiting trading strategies.
 - [x] **Indicators**: Includes the indicators required to run the strategy.

user_data/strategies/Bandtastic.py

@@ -19,7 +19,7 @@ __BTC_donation__ = "3FgFaG15yntZYSUzfEpxr5mDt1RArvcQrK"
 # 199/40000:  30918 trades. 18982/3408/8528 Wins/Draws/Losses. Avg profit   0.39%. Median profit   0.65%. Total profit  119934.26007495 USDT ( 119.93%). Avg duration 8:12:00 min. Objective: -127.60220
 
 class Bandtastic(IStrategy):
-    INTERFACE_VERSION = 2
+    INTERFACE_VERSION = 3
 
     timeframe = '15m'
 
@@ -34,6 +34,8 @@ class Bandtastic(IStrategy):
     # Stoploss:
     stoploss = -0.345
 
+    startup_candle_count = 999
+
     # Trailing stop:
     trailing_stop = True
     trailing_stop_positive = 0.01
@@ -42,7 +44,7 @@ class Bandtastic(IStrategy):
 
     # Hyperopt Buy Parameters
     buy_fastema = IntParameter(low=1, high=236, default=211, space='buy', optimize=True, load=True)
-    buy_slowema = IntParameter(low=1, high=126, default=364, space='buy', optimize=True, load=True)
+    buy_slowema = IntParameter(low=1, high=250, default=250, space='buy', optimize=True, load=True)
     buy_rsi = IntParameter(low=15, high=70, default=52, space='buy', optimize=True, load=True)
     buy_mfi = IntParameter(low=15, high=70, default=30, space='buy', optimize=True, load=True)
 
@@ -98,7 +100,7 @@ class Bandtastic(IStrategy):
 
         return dataframe
 
-    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         conditions = []
 
         # GUARDS
@@ -125,11 +127,11 @@ class Bandtastic(IStrategy):
         if conditions:
             dataframe.loc[
                 reduce(lambda x, y: x & y, conditions),
-                'buy'] = 1
+                'enter_long'] = 1
 
         return dataframe
 
-    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         conditions = []
 
         # GUARDS
@@ -156,6 +158,6 @@ class Bandtastic(IStrategy):
         if conditions:
             dataframe.loc[
                 reduce(lambda x, y: x & y, conditions),
-                'sell'] = 1
+                'exit_long'] = 1
 
         return dataframe

user_data/strategies/CustomStoplossWithPSAR.py

@@ -29,6 +29,8 @@ class CustomStoplossWithPSAR(IStrategy):
     custom_info = {}
     use_custom_stoploss = True
 
+    startup_candle_count = 199
+
     def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                         current_rate: float, current_profit: float, **kwargs) -> float:
 

user_data/strategies/FixedRiskRewardLoss.py

@@ -104,7 +104,7 @@ class FixedRiskRewardLoss(IStrategy):
         :param dataframe: DataFrame
         :return: DataFrame with buy column
         """
-        # Allways buys
+        # Always buys
         dataframe.loc[:, 'enter_long'] = 1
         return dataframe
 

user_data/strategies/Supertrend.py

@@ -18,6 +18,7 @@ from freqtrade.strategy import IStrategy, IntParameter
 from pandas import DataFrame
 import talib.abstract as ta
 import numpy as np
+import pandas as pd
 
 class Supertrend(IStrategy):
     # Buy params, Sell params, ROI, Stoploss and Trailing Stop are values generated by 'freqtrade hyperopt --strategy Supertrend --hyperopt-loss ShortTradeDurHyperOptLoss --timerange=20210101- --timeframe=1h --spaces all'
@@ -63,7 +64,7 @@ class Supertrend(IStrategy):
 
     timeframe = '1h'
 
-    startup_candle_count = 18
+    startup_candle_count = 199
 
     buy_m1 = IntParameter(1, 7, default=4)
     buy_m2 = IntParameter(1, 7, default=4)
@@ -80,30 +81,47 @@ class Supertrend(IStrategy):
     sell_p3 = IntParameter(7, 21, default=14)
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        new_cols = []
+    
         for multiplier in self.buy_m1.range:
             for period in self.buy_p1.range:
-                dataframe[f'supertrend_1_buy_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX']
+                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
-
+                    columns={'STX': f'supertrend_1_buy_{multiplier}_{period}'})
+                new_cols.append(st)
+    
         for multiplier in self.buy_m2.range:
             for period in self.buy_p2.range:
-                dataframe[f'supertrend_2_buy_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX']
+                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
-
+                    columns={'STX': f'supertrend_2_buy_{multiplier}_{period}'})
+                new_cols.append(st)
+    
         for multiplier in self.buy_m3.range:
             for period in self.buy_p3.range:
-                dataframe[f'supertrend_3_buy_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX']
+                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
-
+                    columns={'STX': f'supertrend_3_buy_{multiplier}_{period}'})
+                new_cols.append(st)
+    
         for multiplier in self.sell_m1.range:
             for period in self.sell_p1.range:
-                dataframe[f'supertrend_1_sell_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX']
+                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
-
+                    columns={'STX': f'supertrend_1_sell_{multiplier}_{period}'})
+                new_cols.append(st)
+    
         for multiplier in self.sell_m2.range:
             for period in self.sell_p2.range:
-                dataframe[f'supertrend_2_sell_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX']
+                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
-
+                    columns={'STX': f'supertrend_2_sell_{multiplier}_{period}'})
+                new_cols.append(st)
+    
         for multiplier in self.sell_m3.range:
             for period in self.sell_p3.range:
-                dataframe[f'supertrend_3_sell_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX']
+                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
-
+                    columns={'STX': f'supertrend_3_sell_{multiplier}_{period}'})
+                new_cols.append(st)
+    
+        if new_cols:
+            dataframe = pd.concat([dataframe] + new_cols, axis=1)
+    
         return dataframe
 
     def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -136,42 +154,43 @@ class Supertrend(IStrategy):
         Supertrend Indicator; adapted for freqtrade
         from: https://github.com/freqtrade/freqtrade-strategies/issues/30
     """
-    def supertrend(self, dataframe: DataFrame, multiplier, period):
+    def supertrend(self, dataframe: pd.DataFrame, multiplier, period):
         df = dataframe.copy()
+        high = df['high'].values
+        low = df['low'].values
+        close = df['close'].values
+        length = len(df)
+    
+        # 1. TR and ATR
+        tr = ta.TRANGE(df['high'], df['low'], df['close'])
+        atr = pd.Series(tr).rolling(period).mean().to_numpy()
+    
+        # 2. basic upper / lower bands
+        basic_ub = (high + low) / 2 + multiplier * atr
+        basic_lb = (high + low) / 2 - multiplier * atr
+    
+        # 3. final upper / lower bands
+        final_ub = np.zeros(length)
+        final_lb = np.zeros(length)
+    
+        for i in range(period, length):
+            final_ub[i] = basic_ub[i] if basic_ub[i] < final_ub[i-1] or close[i-1] > final_ub[i-1] else final_ub[i-1]
+            final_lb[i] = basic_lb[i] if basic_lb[i] > final_lb[i-1] or close[i-1] < final_lb[i-1] else final_lb[i-1]
+    
+        # 4. ST calculation
+        st = np.zeros(length)
+        for i in range(period, length):
+            if st[i-1] == final_ub[i-1]:
+                st[i] = final_ub[i] if close[i] <= final_ub[i] else final_lb[i]
+            elif st[i-1] == final_lb[i-1]:
+                st[i] = final_lb[i] if close[i] >= final_lb[i] else final_ub[i]
+    
+        # 5. STX direction
+        stx = np.where(st > 0, np.where(close < st, 'down', 'up'), None)
+    
+        # 6. fillna
+        result = pd.DataFrame({'ST': st, 'STX': stx}, index=df.index)
+        result.fillna(0, inplace=True)
+      
+        return result
 
-        df['TR'] = ta.TRANGE(df)
-        df['ATR'] = ta.SMA(df['TR'], period)
-
-        st = 'ST_' + str(period) + '_' + str(multiplier)
-        stx = 'STX_' + str(period) + '_' + str(multiplier)
-
-        # Compute basic upper and lower bands
-        df['basic_ub'] = (df['high'] + df['low']) / 2 + multiplier * df['ATR']
-        df['basic_lb'] = (df['high'] + df['low']) / 2 - multiplier * df['ATR']
-
-        # Compute final upper and lower bands
-        df['final_ub'] = 0.00
-        df['final_lb'] = 0.00
-        for i in range(period, len(df)):
-            df['final_ub'].iat[i] = df['basic_ub'].iat[i] if df['basic_ub'].iat[i] < df['final_ub'].iat[i - 1] or df['close'].iat[i - 1] > df['final_ub'].iat[i - 1] else df['final_ub'].iat[i - 1]
-            df['final_lb'].iat[i] = df['basic_lb'].iat[i] if df['basic_lb'].iat[i] > df['final_lb'].iat[i - 1] or df['close'].iat[i - 1] < df['final_lb'].iat[i - 1] else df['final_lb'].iat[i - 1]
-
-        # Set the Supertrend value
-        df[st] = 0.00
-        for i in range(period, len(df)):
-            df[st].iat[i] = df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] <= df['final_ub'].iat[i] else \
-                            df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] >  df['final_ub'].iat[i] else \
-                            df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] >= df['final_lb'].iat[i] else \
-                            df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] <  df['final_lb'].iat[i] else 0.00
-        # Mark the trend direction up/down
-        df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down',  'up'), np.NaN)
-
-        # Remove basic and final bands from the columns
-        df.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1)
-
-        df.fillna(0, inplace=True)
-
-        return DataFrame(index=df.index, data={
-            'ST' : df[st],
-            'STX' : df[stx]
-        })

user_data/strategies/TrendRiderStrategy.py

@@ -0,0 +1,633 @@
+"""
+TrendRider Strategy
+
+Ride established trends with ATR-aware stoploss.
+Key insight: crypto swings 2-4% per hour, stoploss must accommodate this volatility.
+
+- Leverage 1x (spot-safe)
+- TA-Lib indicators with confidence scoring
+- Multiple entry signals: pullback, EMA bounce, RSI bounce, crossover, BB bounce, MACD reversal
+"""
+
+import talib.abstract as ta
+from datetime import datetime
+from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter, merge_informative_pair
+from pandas import DataFrame
+from functools import reduce
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class TrendRiderStrategy(IStrategy):
+    INTERFACE_VERSION = 3
+
+    # --- ROI: Hyperopt-optimized (2026-03-23, 5 pairs) ---
+    minimal_roi = {
+        "0": 0.229,     # 22.9% immediate
+        "124": 0.136,   # 13.6% after ~2h
+        "290": 0.044,   # 4.4% after ~5h
+        "764": 0,       # breakeven after ~12.7h
+    }
+
+    # --- Stoploss ---
+    stoploss = -0.06           # 6% default (ATR-based custom stoploss overrides)
+    use_custom_stoploss = False
+
+    # --- Trailing Stop ---
+    trailing_stop = True
+    trailing_stop_positive = 0.03        # 3% trail
+    trailing_stop_positive_offset = 0.05 # Activate after +5%
+    trailing_only_offset_is_reached = True
+
+    # --- General ---
+    timeframe = "1h"
+    startup_candle_count = 210
+    process_only_new_candles = True
+    can_short = False
+    position_adjustment_enable = False
+
+    # --- Protections (moved from config.json for Freqtrade 2026.2+) ---
+    protections = [
+        {
+            "method": "CooldownPeriod",
+            "stop_duration": 20
+        },
+        {
+            "method": "StoplossGuard",
+            "lookback_period": 720,
+            "trade_limit": 3,
+            "stop_duration": 60,
+            "only_per_pair": False
+        },
+        {
+            "method": "MaxDrawdown",
+            "lookback_period": 1440,
+            "max_allowed_drawdown": 0.10,
+            "stop_duration": 300,
+            "trade_limit": 5
+        }
+    ]
+
+    # --- HyperOpt Results (applied from optimization session 2026-03-23) ---
+    buy_params = {
+        "ema_fast": 9,
+        "ema_slow": 16,
+        "rsi_period": 16,
+        "rsi_pullback_low": 30,
+        "rsi_pullback_high": 65,
+        "rsi_bounce": 35,
+        "adx_threshold": 18,
+        "volume_factor": 0.7,
+    }
+
+    sell_params = {
+        "rsi_exit": 78,
+    }
+
+    # --- HyperOpt Parameters ---
+    ema_fast = IntParameter(5, 15, default=9, space="buy")
+    ema_slow = IntParameter(15, 30, default=21, space="buy")
+    rsi_period = IntParameter(10, 20, default=14, space="buy")
+    rsi_pullback_low = IntParameter(30, 48, default=40, space="buy")
+    rsi_pullback_high = IntParameter(52, 65, default=58, space="buy")
+    rsi_bounce = IntParameter(25, 35, default=30, space="buy")
+    rsi_exit = IntParameter(72, 85, default=78, space="sell")
+    adx_threshold = IntParameter(20, 35, default=25, space="buy")
+    volume_factor = DecimalParameter(1.0, 2.5, default=1.3, space="buy")
+
+    # --- Leverage: 1x for Strat Ninja (spot-safe) ---
+    leverage_value = 1
+
+    def leverage(self, pair: str, current_time, current_rate: float,
+                 proposed_leverage: float, max_leverage: float, entry_tag: str,
+                 side: str, **kwargs) -> float:
+        return 1
+
+    def informative_pairs(self):
+        pairs = self.dp.current_whitelist() if self.dp else []
+        informative = []
+        for pair in pairs:
+            informative.append((pair, "4h"))
+            informative.append((pair, "1d"))
+        # BTC as market sentiment
+        informative.append(("BTC/USDT:USDT", "1h"))
+        informative.append(("BTC/USDT:USDT", "4h"))
+        return informative
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # EMAs (all periods for hyperopt ranges)
+        for period in range(5, 31):
+            dataframe[f"ema_{period}"] = ta.EMA(dataframe, timeperiod=period)
+        dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
+        dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)
+
+        # RSI (all periods for hyperopt range 10-20)
+        for period in range(10, 21):
+            dataframe[f"rsi_{period}"] = ta.RSI(dataframe, timeperiod=period)
+
+        # ADX
+        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
+        dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=14)
+        dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=14)
+
+        # MACD
+        macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
+        dataframe["macd"] = macd["macd"]
+        dataframe["macdsignal"] = macd["macdsignal"]
+        dataframe["macdhist"] = macd["macdhist"]
+        dataframe["macdhist_prev"] = macd["macdhist"].shift(1)
+
+        # Bollinger Bands
+        bb = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
+        dataframe["bb_upper"] = bb["upperband"]
+        dataframe["bb_middle"] = bb["middleband"]
+        dataframe["bb_lower"] = bb["lowerband"]
+        # BB width for volatility regime
+        dataframe["bb_width"] = (dataframe["bb_upper"] - dataframe["bb_lower"]) / (dataframe["bb_middle"] + 1e-10)
+        dataframe["bb_width_sma"] = ta.SMA(dataframe["bb_width"], timeperiod=50)
+
+        # Volume (fix #4: epsilon guard against division by zero)
+        dataframe["volume_ema"] = ta.EMA(dataframe["volume"], timeperiod=20)
+        dataframe["volume_ratio"] = dataframe["volume"] / (dataframe["volume_ema"] + 1e-10)
+
+        # OBV
+        dataframe["obv"] = ta.OBV(dataframe)
+        dataframe["obv_ema"] = ta.EMA(dataframe["obv"], timeperiod=20)
+
+        # ATR for dynamic stoploss
+        dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
+
+        # Regime
+        dataframe["is_bull"] = (
+            (dataframe["close"] > dataframe["ema_200"]) &
+            (dataframe["ema_50"] > dataframe["ema_200"])
+        ).astype(int)
+
+        dataframe["is_bear"] = (
+            (dataframe["close"] < dataframe["ema_200"]) &
+            (dataframe["ema_50"] < dataframe["ema_200"])
+        ).astype(int)
+
+        # --- LONG pullback detection ---
+        ema_slow_key = f"ema_{self.ema_slow.value}"
+        if ema_slow_key in dataframe.columns:
+            dataframe["pullback_to_ema"] = (
+                (dataframe["low"] <= dataframe[ema_slow_key] * 1.02) &
+                (dataframe["close"] > dataframe[ema_slow_key]) &
+                (dataframe["close"] > dataframe["open"])  # Bullish candle
+            ).astype(int)
+        else:
+            dataframe["pullback_to_ema"] = 0
+
+        # EMA50 support bounce (LONG)
+        dataframe["ema50_bounce"] = (
+            (dataframe["low"] <= dataframe["ema_50"] * 1.01) &
+            (dataframe["close"] > dataframe["ema_50"]) &
+            (dataframe["close"] > dataframe["open"])
+        ).astype(int)
+
+        # --- Multi-Timeframe data ---
+        if self.dp:
+            # 4h data for current pair
+            df_4h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='4h')
+            if len(df_4h) > 0:
+                df_4h['ema_50'] = ta.EMA(df_4h, timeperiod=50)
+                df_4h['ema_200'] = ta.EMA(df_4h, timeperiod=200)
+                df_4h['rsi_14'] = ta.RSI(df_4h, timeperiod=14)
+                df_4h['adx'] = ta.ADX(df_4h, timeperiod=14)
+                df_4h['is_bull'] = (
+                    (df_4h['close'] > df_4h['ema_200']) &
+                    (df_4h['ema_50'] > df_4h['ema_200'])
+                ).astype(int)
+                dataframe = merge_informative_pair(
+                    dataframe,
+                    df_4h[['date', 'ema_50', 'ema_200', 'rsi_14', 'adx', 'is_bull']],
+                    self.timeframe, '4h', ffill=True
+                )
+            else:
+                dataframe['ema_50_4h'] = 0
+                dataframe['ema_200_4h'] = 0
+                dataframe['rsi_14_4h'] = 50
+                dataframe['adx_4h'] = 0
+                dataframe['is_bull_4h'] = 0
+
+            # Daily data for macro trend
+            df_1d = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d')
+            if len(df_1d) > 0:
+                df_1d['ema_200'] = ta.EMA(df_1d, timeperiod=200)
+                dataframe = merge_informative_pair(
+                    dataframe,
+                    df_1d[['date', 'ema_200']],
+                    self.timeframe, '1d', ffill=True
+                )
+            else:
+                dataframe['ema_200_1d'] = 0
+
+            # BTC market sentiment
+            df_btc = self.dp.get_pair_dataframe(pair='BTC/USDT:USDT', timeframe='1h')
+            if len(df_btc) > 0:
+                df_btc['btc_ema_200'] = ta.EMA(df_btc, timeperiod=200)
+                df_btc['btc_ema_50'] = ta.EMA(df_btc, timeperiod=50)
+                df_btc['btc_rsi'] = ta.RSI(df_btc, timeperiod=14)
+                df_btc['btc_is_bull'] = (
+                    (df_btc['close'] > df_btc['btc_ema_200']) &
+                    (df_btc['btc_ema_50'] > df_btc['btc_ema_200'])
+                ).astype(int)
+                dataframe = merge_informative_pair(
+                    dataframe,
+                    df_btc[['date', 'btc_ema_200', 'btc_ema_50', 'btc_rsi', 'btc_is_bull']],
+                    self.timeframe, '1h', ffill=True
+                )
+            else:
+                dataframe['btc_is_bull_1h'] = 1
+                dataframe['btc_rsi_1h'] = 50
+        else:
+            # Safety fallback when dp is not available
+            dataframe['is_bull_4h'] = dataframe['is_bull']
+            dataframe['rsi_14_4h'] = dataframe['rsi_14'] if 'rsi_14' in dataframe.columns else 50
+            dataframe['adx_4h'] = dataframe['adx']
+            dataframe['btc_is_bull_1h'] = 1
+            dataframe['btc_rsi_1h'] = 50
+            dataframe['ema_200_1d'] = 0
+
+        # Ensure columns exist (safety for backtesting edge cases)
+        for col, default in [
+            ('is_bull_4h', 1), ('rsi_14_4h', 50), ('adx_4h', 20),
+            ('btc_is_bull_1h', 1), ('btc_rsi_1h', 50),
+            ('ema_200_1d', 0),
+        ]:
+            if col not in dataframe.columns:
+                dataframe[col] = default
+
+        # --- Fear & Greed Index: static neutral (no API) ---
+        dataframe['fng_value'] = 50
+
+        # --- On-chain: static defaults (no API) ---
+        dataframe['funding_rate'] = 0.0
+        dataframe['funding_extreme'] = 0
+        dataframe['oi_change'] = 0.0
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        rsi = f"rsi_{self.rsi_period.value}"
+
+        # ========== LONG ENTRIES ==========
+
+        # === LONG 1: Trend Pullback to EMA ===
+        conditions_pullback = [
+            dataframe["is_bull"] == 1,
+            dataframe["pullback_to_ema"] == 1,
+            dataframe[rsi] > self.rsi_pullback_low.value,
+            dataframe[rsi] < self.rsi_pullback_high.value,
+            dataframe["adx"] > self.adx_threshold.value,
+            dataframe["volume_ratio"] > self.volume_factor.value,
+            dataframe["plus_di"] > dataframe["minus_di"],
+            dataframe["obv"] > dataframe["obv_ema"],
+            dataframe["volume"] > 0,
+            dataframe["btc_rsi_1h"] > 35,
+            dataframe["fng_value"] >= 25,      # Not extreme fear
+            dataframe["fng_value"] <= 85,      # Not extreme greed
+            dataframe[rsi] < 70,               # Not overbought
+        ]
+        # Daily EMA200 filter — helps filter bad entries
+        if 'ema_200_1d' in dataframe.columns:
+            conditions_pullback.append(dataframe["close"] > dataframe["ema_200_1d"])
+
+        dataframe.loc[
+            reduce(lambda x, y: x & y, conditions_pullback),
+            ["enter_long", "enter_tag"]
+        ] = (1, "trend_pullback")
+
+        # === LONG 2: EMA50 Support Bounce ===
+        conditions_ema50 = [
+            dataframe["is_bull"] == 1,
+            dataframe["ema50_bounce"] == 1,
+            dataframe[rsi] > 30,
+            dataframe[rsi] < 50,
+            dataframe["adx"] > 20,
+            dataframe["volume_ratio"] > 1.0,
+            dataframe["macdhist"] > dataframe["macdhist"].shift(1),
+            dataframe["volume"] > 0,
+            dataframe["btc_rsi_1h"] > 35,
+            dataframe["fng_value"] >= 25,
+            dataframe["fng_value"] <= 85,
+            dataframe[rsi] < 70,
+        ]
+        dataframe.loc[
+            reduce(lambda x, y: x & y, conditions_ema50),
+            ["enter_long", "enter_tag"]
+        ] = (1, "ema50_bounce")
+
+        # === LONG 3: RSI Oversold Bounce ===
+        conditions_rsi = [
+            dataframe["close"] > dataframe["ema_200"],
+            dataframe[rsi].shift(1) < self.rsi_bounce.value,
+            dataframe[rsi] > self.rsi_bounce.value,
+            dataframe["close"] > dataframe["bb_lower"],
+            dataframe["close"] > dataframe["open"],
+            dataframe["volume_ratio"] > 0.8,
+            dataframe["obv"] > dataframe["obv_ema"],
+            dataframe["volume"] > 0,
+            dataframe["btc_rsi_1h"] > 35,
+            dataframe["fng_value"] >= 25,
+            dataframe["fng_value"] <= 85,
+        ]
+        dataframe.loc[
+            reduce(lambda x, y: x & y, conditions_rsi),
+            ["enter_long", "enter_tag"]
+        ] = (1, "rsi_bounce")
+
+        # === LONG 4: EMA Crossover (golden cross on fast EMAs) ===
+        ema_fast_key = f"ema_{self.ema_fast.value}"
+        ema_slow_key = f"ema_{self.ema_slow.value}"
+        conditions_ema_cross = [
+            (dataframe[ema_fast_key] > dataframe[ema_slow_key]) &
+            (dataframe[ema_fast_key].shift(1) <= dataframe[ema_slow_key].shift(1)),  # crossed above
+            dataframe[rsi] > 40,
+            dataframe[rsi] < 75,
+            dataframe["close"] > dataframe["ema_200"],
+            dataframe["volume_ratio"] > 0.5,
+            dataframe["volume"] > 0,
+            dataframe["btc_rsi_1h"] > 35,
+            dataframe["fng_value"] >= 25,
+            dataframe["fng_value"] <= 85,
+        ]
+        dataframe.loc[
+            reduce(lambda x, y: x & y, conditions_ema_cross),
+            ["enter_long", "enter_tag"]
+        ] = (1, "ema_crossover")
+
+        # === LONG 5: Bollinger Band Bounce ===
+        conditions_bb = [
+            dataframe["close"] <= dataframe["bb_lower"] * 1.005,           # close within 0.5% of BB lower
+            dataframe["close"] > dataframe["open"],                         # bullish candle (bounce)
+            dataframe[rsi] < 45,
+            dataframe["volume_ratio"] > 0.7,                                # filter weak bounces
+            dataframe["adx"] > 18,                                          # trend strength filter
+            dataframe["volume"] > 0,
+            dataframe["btc_rsi_1h"] > 35,
+            dataframe["fng_value"] >= 25,
+            dataframe["fng_value"] <= 85,
+        ]
+        dataframe.loc[
+            reduce(lambda x, y: x & y, conditions_bb),
+            ["enter_long", "enter_tag"]
+        ] = (1, "bb_bounce")
+
+        # === LONG 6: MACD Histogram Reversal (tightened: RSI 40-60, EMA200 filter, volume 0.8x) ===
+        conditions_macd = [
+            (dataframe["macdhist"] > 0) &
+            (dataframe["macdhist"].shift(1) <= 0),  # histogram crossed above zero
+            dataframe["close"] > dataframe["ema_50"],
+            dataframe["close"] > dataframe["ema_200"],  # confirm uptrend
+            dataframe[rsi] > 40,
+            dataframe[rsi] < 60,
+            dataframe["adx"] > 15,
+            dataframe["volume_ratio"] > 0.8,            # volume confirmation
+            dataframe["volume"] > 0,
+            dataframe["btc_rsi_1h"] > 35,
+            dataframe["fng_value"] >= 25,
+            dataframe["fng_value"] <= 85,
+        ]
+        dataframe.loc[
+            reduce(lambda x, y: x & y, conditions_macd),
+            ["enter_long", "enter_tag"]
+        ] = (1, "macd_reversal")
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        rsi = f"rsi_{self.rsi_period.value}"
+        ema_fast = f"ema_{self.ema_fast.value}"
+        ema_slow = f"ema_{self.ema_slow.value}"
+
+        # ========== LONG EXITS ==========
+
+        # EXIT 1: RSI very overbought
+        dataframe.loc[
+            (dataframe[rsi] > self.rsi_exit.value) &
+            (dataframe["volume"] > 0),
+            ["exit_long", "exit_tag"]
+        ] = (1, "rsi_overbought")
+
+        # EXIT 2: Bearish EMA cross with MACD confirmation
+        dataframe.loc[
+            (dataframe[ema_fast] < dataframe[ema_slow]) &
+            (dataframe[ema_fast].shift(1) >= dataframe[ema_slow].shift(1)) &
+            (dataframe["macdhist"] < 0) &
+            (dataframe[rsi] > 50) &
+            (dataframe["volume"] > 0),
+            ["exit_long", "exit_tag"]
+        ] = (1, "ema_bearish_cross")
+
+        # EXIT 3: Price drops below EMA200 by 1%+ (trend broken, softened to avoid premature exits)
+        dataframe.loc[
+            (dataframe["close"] < dataframe["ema_200"] * 0.99) &
+            (dataframe["close"].shift(1) >= dataframe["ema_200"].shift(1)) &
+            (dataframe["volume"] > 0),
+            ["exit_long", "exit_tag"]
+        ] = (1, "trend_broken")
+
+        # EXIT 4: Trend early warning — RSI overbought reversal near EMA200
+        # Catches trend exhaustion before price breaks support, saving avg -3% vs trend_broken
+        dataframe.loc[
+            (dataframe["close"] < dataframe["ema_200"] * 0.995) &  # within 0.5% of breaking
+            (dataframe[rsi] > 72) &                                  # exhausted
+            (dataframe["macdhist"] < dataframe["macdhist"].shift(1)) & # momentum dropping
+            (dataframe["volume"] > 0),
+            ["exit_long", "exit_tag"]
+        ] = (1, "trend_early_warning")
+
+        return dataframe
+
+
+    # --- Improved Confidence Scoring (inline from trendrider_confidence) ---
+    def _calc_confidence(self, last: dict) -> tuple:
+        """Calculate signal confidence based on weighted indicator alignment.
+
+        Max score ~17.5. Returns (level_str, bar_str, details_list, numeric_level).
+        """
+        score = 0.0
+        details = []
+        rsi_key = f"rsi_{self.rsi_period.value}"
+        rsi_val = last.get(rsi_key, 50)
+
+        # RSI in healthy zone (not overbought): +1.5
+        if 35 < rsi_val < 60:
+            score += 1.5
+            details.append("RSI healthy")
+
+        # Strong trend (ADX): +2.5 strong, +1.5 moderate
+        adx_val = last.get('adx', 0)
+        if adx_val > 30:
+            score += 2.5
+            details.append("Strong trend")
+        elif adx_val > self.adx_threshold.value:
+            score += 1.5
+            details.append("Moderate trend")
+
+        # Volume confirmation: +2.5 high, +1.5 normal
+        vol_ratio = last.get('volume_ratio', 0)
+        if vol_ratio > 1.5:
+            score += 2.5
+            details.append("High volume")
+        elif vol_ratio > 1.0:
+            score += 1.5
+            details.append("Normal volume")
+
+        # MACD positive histogram: +1.5, bonus +0.5 if rising
+        macd_hist = last.get('macdhist', 0)
+        macd_hist_prev = last.get('macdhist_prev', 0)
+        if macd_hist > 0:
+            score += 1.5
+            if macd_hist > macd_hist_prev:
+                score += 0.5
+                details.append("MACD positive+rising")
+            else:
+                details.append("MACD positive")
+
+        # OBV rising AND above EMA: +1.5
+        if last.get('obv', 0) > last.get('obv_ema', 0):
+            score += 1.5
+            details.append("OBV rising")
+
+        # BTC healthy (RSI 40-70): +1.5
+        btc_rsi = last.get('btc_rsi_1h', 50)
+        if 40 < btc_rsi < 70:
+            score += 1.5
+            details.append("BTC healthy")
+
+        # 4h trend alignment AND ADX_4h > 20: +1.5
+        if last.get('is_bull_4h', 0) == 1 and last.get('adx_4h', 0) > 20:
+            score += 1.5
+            details.append("4H trend aligned")
+
+        # Bollinger Band position (close near lower = good for long): +1
+        close = last.get('close', 0)
+        bb_lower = last.get('bb_lower', 0)
+        bb_upper = last.get('bb_upper', 0)
+        bb_range = bb_upper - bb_lower if bb_upper > bb_lower else 1
+        if bb_lower > 0 and close > 0:
+            bb_position = (close - bb_lower) / bb_range
+            if bb_position < 0.35:
+                score += 1.0
+                details.append("Near BB lower")
+
+        # Plus_DI > Minus_DI spread > 10: +1
+        plus_di = last.get('plus_di', 0)
+        minus_di = last.get('minus_di', 0)
+        if plus_di - minus_di > 10:
+            score += 1.0
+            details.append("Strong DI spread")
+
+        # FNG bonus: neutral/healthy (40-60): +1
+        fng_val = last.get('fng_value', 50)
+        if 40 <= fng_val <= 60:
+            score += 1.0
+            details.append("FNG neutral")
+
+        # On-chain: healthy funding rate: +1
+        funding = last.get('funding_rate', 0)
+        if abs(funding) < 0.0001:  # Normal funding
+            score += 1
+            details.append("Healthy funding")
+
+        # Smooth mapping to 1-10 (max score ~17.5)
+        numeric = max(1, min(10, round(score * 10 / 17.5)))
+
+        # Level label
+        if numeric >= 8:
+            level = "STRONG"
+        elif numeric >= 6:
+            level = "GOOD"
+        elif numeric >= 4:
+            level = "MEDIUM"
+        else:
+            level = "WEAK"
+
+        # Dynamic bar
+        bar = "|" * numeric + "-" * (10 - numeric) + f" {numeric}/10"
+
+        return level, bar, details, numeric
+
+    def _market_context(self, last: dict) -> str:
+        """Generate market context string."""
+        btc_rsi = last.get('btc_rsi_1h', 50)
+        btc_bull = last.get('btc_is_bull_1h', 0)
+        bull_4h = last.get('is_bull_4h', 0)
+
+        if btc_bull and btc_rsi > 55:
+            btc_status = "Bullish"
+        elif btc_rsi > 40:
+            btc_status = "Neutral"
+        else:
+            btc_status = "Bearish"
+
+        tf_4h = "Uptrend" if bull_4h else "Downtrend"
+
+        parts = [f"BTC: {btc_status} (RSI {btc_rsi:.0f})", f"4H: {tf_4h}"]
+
+        return " | ".join(parts)
+
+    def _get_market_regime(self, last: dict) -> str:
+        """Detect market regime from ADX + EMA200 + BB width."""
+        adx_val = last.get('adx', 0)
+        ema_200 = last.get('ema_200', 0)
+        close = last.get('close', 0)
+        is_bull = last.get('is_bull', 0)
+        bb_width = last.get('bb_width', 0)
+        bb_width_sma = last.get('bb_width_sma', 0)
+
+        high_vol = bb_width > bb_width_sma * 1.5 if bb_width_sma > 0 else False
+
+        if adx_val < 20:
+            return "Ranging (High Vol)" if high_vol else "Ranging"
+        elif is_bull and close > ema_200:
+            return "Trending Bull"
+        else:
+            return "Trending Bear (High Vol)" if high_vol else "Trending Bear"
+
+    def custom_exit(self, pair: str, trade, current_time: datetime,
+                    current_rate: float, current_profit: float, **kwargs):
+        """Cascading early exit — stop bleeding before 24h timeout.
+
+        Cascade catches losers earlier than the 24h hard timeout:
+        - 2h: cut if -1.5% (already broken thesis)
+        - 4h: cut if red (no recovery momentum)
+        - 8h: cut if not at +0.5% (dead trade)
+        - 16h: cut if not at +1% (final mercy)
+        """
+        duration_hours = (current_time - trade.open_date_utc).total_seconds() / 3600
+        if duration_hours >= 2 and current_profit < -0.015:
+            return "early_loss_cut_2h"
+        if duration_hours >= 4 and current_profit < 0:
+            return "early_loss_cut_4h"
+        if duration_hours >= 8 and current_profit < 0.005:
+            return "early_loss_cut_8h"
+        if duration_hours >= 16 and current_profit < 0.01:
+            return "early_loss_cut_16h"
+        if duration_hours >= 24:
+            return "time_exit_24h"
+        return None
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                           time_in_force: str, current_time: datetime, entry_tag: str | None,
+                           side: str, **kwargs) -> bool:
+        # Get current indicators for confidence filter
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        if len(dataframe) > 0:
+            last = dataframe.iloc[-1]
+        else:
+            last = {}
+
+        # Confidence & regime filter — reject weak signals
+        _, _, _, conf_numeric = self._calc_confidence(last)
+        regime = self._get_market_regime(last)
+        min_conf = 6 if "Bear" in regime else 5
+        if conf_numeric < min_conf:
+            logger.info(f"Rejecting signal for {pair}: confidence {conf_numeric}/10 < {min_conf} (regime: {regime})")
+            return False
+
+        return True

user_data/strategies/berlinguyinca/DoesNothingStrategy.py

@@ -18,11 +18,11 @@ class DoesNothingStrategy(IStrategy):
     # adjust based on market conditions. We would recommend to keep it low for quick turn arounds
     # This attribute will be overridden if the config file contains "minimal_roi"
     minimal_roi = {
-        "0": 0.01
+        "0": 100000
     }
 
     # Optimal stoploss designed for the strategy
-    stoploss = -0.25
+    stoploss = -1
 
     # Optimal timeframe for the strategy
     timeframe = '5m'

user_data/strategies/berlinguyinca/Low_BB.py

@@ -66,8 +66,8 @@ class Low_BB(IStrategy):
         # dataframe['mfi'] = ta.MFI(dataframe)
         # dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)
 
-        # dataframe['canbuy'] = np.NaN
+        # dataframe['canbuy'] = np.nan
-        # dataframe['canbuy2'] = np.NaN
+        # dataframe['canbuy2'] = np.nan
         # dataframe.loc[dataframe.close.rolling(49).min() <= 1.1 * dataframe.close, 'canbuy'] == 1
         # dataframe.loc[dataframe.close.rolling(600).max() < 1.2 * dataframe.close, 'canbuy'] = 1
         # dataframe.loc[dataframe.close.rolling(600).max() * 0.8 >  dataframe.close, 'canbuy2'] = 1

user_data/strategies/berlinguyinca/TDSequentialStrategy.py

@@ -142,7 +142,7 @@ class TDSequentialStrategy(IStrategy):
         Based on TA indicators, populates the sell signal for the given dataframe
         :param dataframe: DataFrame
         :param metadata: Additional information, like the currently traded pair
-        :return: DataFrame with buy columnNA / NaN values
+        :return: DataFrame with buy columnNA / nan values
         """
         dataframe["exit_long"] = 0
         dataframe.loc[((dataframe['exceed_high']) |

user_data/strategies/futures/FAdxSmaStrategy.py

@@ -53,9 +53,9 @@ class FAdxSmaStrategy(IStrategy):
     pos_exit_adx = DecimalParameter(15, 40, decimals=1, default=30.0, space="sell")
 
     # Define the parameter spaces
-    adx_period = IntParameter(4, 24, default=14)
+    adx_period = IntParameter(4, 24, default=14, space='buy')
-    sma_short_period = IntParameter(4, 24, default=12)
+    sma_short_period = IntParameter(4, 24, default=12, space='buy')
-    sma_long_period = IntParameter(12, 175, default=48)
+    sma_long_period = IntParameter(12, 175, default=48, space='buy')
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 

user_data/strategies/futures/FSupertrendStrategy.py

@@ -238,7 +238,7 @@ class FSupertrendStrategy(IStrategy):
             )
         # Mark the trend direction up/down
         df[stx] = np.where(
-            (df[st] > 0.00), np.where((df["close"] < df[st]), "down", "up"), np.NaN
+            (df[st] > 0.00), np.where((df["close"] < df[st]), "down", "up"), None
         )
 
         # Remove basic and final bands from the columns

user_data/strategies/futures/Readme.md

@@ -1,7 +1,6 @@
 # all strategies are tested against this config. Tests only done on binance futures
 
 ```
-
 {
   "max_open_trades": -1,
   "stake_currency": "USDT",
@@ -40,109 +39,89 @@
     "ccxt_config": {},
     "ccxt_async_config": {},
     "pair_whitelist": [
-      "AUDIO/USDT",
+      "AAVE/USDT:USDT",
-      "AAVE/USDT",
+      "ALICE/USDT:USDT",
-      "ALICE/USDT",
+      "ARPA/USDT:USDT",
-      "ARPA/USDT",
+      "AVAX/USDT:USDT",
-      "AVAX/USDT",
+      "ATOM/USDT:USDT",
-      "ATOM/USDT",
+      "ANKR/USDT:USDT",
-      "ANKR/USDT",
+      "AXS/USDT:USDT",
-      "AXS/USDT",
+      "ADA/USDT:USDT",
-      "ADA/USDT",
+      "ALGO/USDT:USDT",
-      "ALGO/USDT",
+      "BAND/USDT:USDT",
-      "BTS/USDT",
+      "BEL/USDT:USDT",
-      "BAND/USDT",
+      "BTC/USDT:USDT",
-      "BEL/USDT",
+      "BAT/USDT:USDT",
-      "BNB/USDT",
+      "CHR/USDT:USDT",
-      "BTC/USDT",
+      "C98/USDT:USDT",
-      "BLZ/USDT",
+      "COTI/USDT:USDT",
-      "BAT/USDT",
+      "CHZ/USDT:USDT",
-      "CHR/USDT",
+      "COMP/USDT:USDT",
-      "C98/USDT",
+      "CRV/USDT:USDT",
-      "COTI/USDT",
+      "CELO/USDT:USDT",
-      "CHZ/USDT",
+      "DUSK/USDT:USDT",
-      "COMP/USDT",
+      "DOGE/USDT:USDT",
-      "CRV/USDT",
+      "DENT/USDT:USDT",
-      "CELO/USDT",
+      "DASH/USDT:USDT",
-      "DUSK/USDT",
+      "DOT/USDT:USDT",
-      "DOGE/USDT",
+      "DYDX/USDT:USDT",
-      "DENT/USDT",
+      "ENJ/USDT:USDT",
-      "DASH/USDT",
+      "ETH/USDT:USDT",
-      "DOT/USDT",
+      "ETC/USDT:USDT",
-      "DYDX/USDT",
+      "ENS/USDT:USDT",
-      "ENJ/USDT",
+      "EGLD/USDT:USDT",
-      "EOS/USDT",
+      "FIL/USDT:USDT",
-      "ETH/USDT",
+      "GRT/USDT:USDT",
-      "ETC/USDT",
+      "GALA/USDT:USDT",
-      "ENS/USDT",
+      "HBAR/USDT:USDT",
-      "EGLD/USDT",
+      "HOT/USDT:USDT",
-      "FIL/USDT",
+      "IOTX/USDT:USDT",
-      "FTM/USDT",
+      "ICX/USDT:USDT",
-      "FLM/USDT",
+      "ICP/USDT:USDT",
-      "GRT/USDT",
+      "IOTA/USDT:USDT",
-      "GALA/USDT",
+      "IOST/USDT:USDT",
-      "HBAR/USDT",
+      "KAVA/USDT:USDT",
-      "HOT/USDT",
+      "KNC/USDT:USDT",
-      "IOTX/USDT",
+      "KSM/USDT:USDT",
-      "ICX/USDT",
+      "LRC/USDT:USDT",
-      "ICP/USDT",
+      "LTC/USDT:USDT",
-      "IOTA/USDT",
+      "LINK/USDT:USDT",
-      "IOST/USDT",
+      "NEAR/USDT:USDT",
-      "KLAY/USDT",
+      "MANA/USDT:USDT",
-      "KAVA/USDT",
+      "MTL/USDT:USDT",
-      "KNC/USDT",
+      "NEO/USDT:USDT",
-      "KSM/USDT",
+      "ONT/USDT:USDT",
-      "LUNA/USDT",
+      "OGN/USDT:USDT",
-      "LRC/USDT",
+      "ONE/USDT:USDT",
-      "LINA/USDT",
+      "PEOPLE/USDT:USDT",
-      "LTC/USDT",
+      "RLC/USDT:USDT",
-      "LINK/USDT",
+      "RUNE/USDT:USDT",
-      "MATIC/USDT",
+      "RVN/USDT:USDT",
-      "NEAR/USDT",
+      "RSR/USDT:USDT",
-      "MANA/USDT",
+      "ROSE/USDT:USDT",
-      "MTL/USDT",
+      "SNX/USDT:USDT",
-      "NEO/USDT",
+      "SAND/USDT:USDT",
-      "ONT/USDT",
+      "SOL/USDT:USDT",
-      "OMG/USDT",
+      "SUSHI/USDT:USDT",
-      "OCEAN/USDT",
+      "SKL/USDT:USDT",
-      "OGN/USDT",
+      "STORJ/USDT:USDT",
-      "ONE/USDT",
+      "TRX/USDT:USDT",
-      "PEOPLE/USDT",
+      "TRB/USDT:USDT",
-      "RLC/USDT",
+      "TLM/USDT:USDT",
-      "RUNE/USDT",
+      "THETA/USDT:USDT",
-      "RVN/USDT",
+      "UNI/USDT:USDT",
-      "RSR/USDT",
+      "VET/USDT:USDT",
-      "REEF/USDT",
+      "YFI/USDT:USDT",
-      "ROSE/USDT",
+      "ZIL/USDT:USDT",
-      "SNX/USDT",
+      "ZEN/USDT:USDT",
-      "SAND/USDT",
+      "ZRX/USDT:USDT",
-      "SOL/USDT",
+      "ZEC/USDT:USDT",
-      "SUSHI/USDT",
+      "XRP/USDT:USDT",
-      "SRM/USDT",
+      "XLM/USDT:USDT",
-      "SKL/USDT",
+      "XTZ/USDT:USDT",
-      "SXP/USDT",
+      "XMR/USDT:USDT",
-      "STORJ/USDT",
+      "QTUM/USDT:USDT",
-      "TRX/USDT",
+      "1INCH/USDT:USDT"
-      "TOMO/USDT",
-      "TRB/USDT",
-      "TLM/USDT",
-      "THETA/USDT",
-      "UNI/USDT",
-      "UNFI/USDT",
-      "VET/USDT",
-      "YFI/USDT",
-      "ZIL/USDT",
-      "ZEN/USDT",
-      "ZRX/USDT",
-      "ZEC/USDT",
-      "WAVES/USDT",
-      "XRP/USDT",
-      "XLM/USDT",
-      "XTZ/USDT",
-      "XMR/USDT",
-      "XEM/USDT",
-      "QTUM/USDT",
-      "1INCH/USDT"
     ],
     "pair_blacklist": ["BNB/.*"]
   },
@@ -162,7 +141,7 @@
     "remove_pumps": false
   },
   "telegram": {
-    "enabled": true,
+    "enabled": false,
     "token": "",
     "chat_id": ""
   },
@@ -185,4 +164,5 @@
   }
 }
 
+
 ```