# source: https://raw.githubusercontent.com/gksdud6330/crytpo-auto-trading-bot/e754e3c873b4845c56f7fb752748330c48cd6369/freqtrade-bot/user_data/strategies/TripleConfirmScalper.py from freqtrade.strategy import IStrategy, merge_informative_pair from pandas import DataFrame import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib class Github_gksdud6330_crytpo_auto_trading_bot__TripleConfirmScalper__20260118_144545(IStrategy): """ Triple-Confirmation Scalper 1m timeframe + 5m trend + 1h direction confirmation Focused on PROFIT MAXIMIZATION, not stability """ INTERFACE_VERSION = 3 timeframe = '1m' startup_candle_count = 200 # Aggressive ROI - quick profits minimal_roi = { "0": 0.015, # 1.5% immediate profit target "3": 0.01, # 1% after 3 minutes "5": 0.005, # 0.5% after 5 minutes "15": 0 # Break even after 15 minutes } # Tight stoploss - but not too tight stoploss = -0.015 # Trailing stop for big moves trailing_stop = True trailing_stop_positive = 0.005 trailing_stop_positive_offset = 0.008 trailing_only_offset_is_reached = True def informative_pairs(self): whitelist = [] if self.dp: whitelist = self.dp.current_whitelist() if not whitelist: whitelist = self.config.get('exchange', {}).get('pair_whitelist', []) return [ (pair, '5m') for pair in whitelist ] + [ (pair, '1h') for pair in whitelist ] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # === 1m Indicators (Entry) === # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=7) # EMA for quick trend dataframe['ema_9'] = ta.EMA(dataframe, timeperiod=9) dataframe['ema_21'] = ta.EMA(dataframe, timeperiod=21) # MACD for momentum macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # Bollinger Bands for volatility bollinger = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0) dataframe['bb_lower'] = bollinger['lowerband'] dataframe['bb_middle'] = bollinger['middleband'] dataframe['bb_upper'] = bollinger['upperband'] dataframe['bb_width'] = (dataframe['bb_upper'] - dataframe['bb_lower']) / dataframe['bb_middle'] # Volume dataframe['volume_mean'] = dataframe['volume'].rolling(window=20).mean() dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_mean'] # Stochastic for oversold/overbought stoch = ta.STOCH(dataframe) dataframe['stoch_k'] = stoch['slowk'] dataframe['stoch_d'] = stoch['slowd'] # === Merge 5m and 1h data === if self.dp: # 5m timeframe informative_5m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='5m') informative_5m['ema_21_5m'] = ta.EMA(informative_5m, timeperiod=21) dataframe = merge_informative_pair(dataframe, informative_5m, '1m', '5m', ffill=True) # 1h timeframe informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h') informative_1h['ema_21_1h'] = ta.EMA(informative_1h, timeperiod=21) dataframe = merge_informative_pair(dataframe, informative_1h, '1m', '1h', ffill=True) # Fallback if merge failed if 'ema_21_5m' not in dataframe: dataframe['ema_21_5m'] = dataframe['ema_21'] if 'ema_21_1h' not in dataframe: dataframe['ema_21_1h'] = dataframe['ema_21'] # Fill NaN dataframe['ema_21_5m'] = dataframe['ema_21_5m'].fillna(dataframe['ema_21']) dataframe['ema_21_1h'] = dataframe['ema_21_1h'].fillna(dataframe['ema_21']) # === Multi-timeframe Confirmation === # 5m trend: price above/below 5m EMA dataframe['trend_5m'] = dataframe['close'] > dataframe['ema_21_5m'] # 1h trend: price above/below 1h EMA dataframe['trend_1h'] = dataframe['close'] > dataframe['ema_21_1h'] # 1m quick trend dataframe['trend_1m'] = dataframe['ema_9'] > dataframe['ema_21'] # Strong uptrend: all timeframes aligned dataframe['strong_uptrend'] = ( dataframe['trend_1h'] & dataframe['trend_5m'] & dataframe['trend_1m'] ) # Strong downtrend: all timeframes aligned dataframe['strong_downtrend'] = ( (~dataframe['trend_1h']) & (~dataframe['trend_5m']) & (~dataframe['trend_1m']) ) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # === Aggressive Long Entries === # Condition 1: Strong uptrend confirmation (all timeframes) cond_trend = dataframe['strong_uptrend'] # Condition 2: RSI oversold or recovering cond_rsi = ( (dataframe['rsi'] < 40) | # Oversold (qtpylib.crossed_above(dataframe['rsi_fast'], dataframe['rsi'])) # RSI cross up ) # Condition 3: MACD turning bullish cond_macd = ( (qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) | # MACD cross (dataframe['macdhist'] > 0) & (dataframe['macdhist'].shift(1) <= 0) # MACD turning positive ) # Condition 4: Price near or below lower Bollinger Band (bounce opportunity) cond_bb = dataframe['close'] < dataframe['bb_middle'] # Condition 5: Volume confirmation cond_volume = dataframe['volume'] > dataframe['volume_mean'] * 0.8 # Combined entry signal dataframe.loc[ cond_trend & cond_rsi & cond_macd & cond_bb & cond_volume, 'enter_long' ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # === Exit Conditions === # Exit 1: RSI overbought cond_rsi_overbought = ( (dataframe['rsi'] > 70) | (qtpylib.crossed_above(dataframe['rsi'], dataframe['rsi_fast'])) # RSI cross down ) # Exit 2: MACD turning bearish cond_macd_bearish = ( (qtpylib.crossed_below(dataframe['macd'], dataframe['macdsignal'])) | (dataframe['macdhist'] < 0) & (dataframe['macdhist'].shift(1) >= 0) ) # Exit 3: Trend reversal on any timeframe cond_trend_reversal = ( (~dataframe['trend_1m']) | # 1m trend broken (~dataframe['trend_5m']) # 5m trend broken ) # Exit 4: Price hits upper Bollinger Band cond_bb_upper = dataframe['close'] > dataframe['bb_upper'] * 0.99 # Combined exit signal dataframe.loc[ (cond_rsi_overbought | cond_macd_bearish | cond_trend_reversal | cond_bb_upper) & (dataframe['volume'] > 0), 'exit_long' ] = 1 return dataframe