# source: https://raw.githubusercontent.com/DeepeshKalura/project-orca/5c6fc6c9c07d52071ba1782e9db82bcc108d3381/bot/user_data/strategies/binance_strategy.py from datetime import datetime, timedelta from typing import Optional, Union import freqtrade.vendor.qtpylib.indicators as qtpylib import talib.abstract as ta import pandas_ta as pta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame from freqtrade.strategy import DecimalParameter, IntParameter from functools import reduce def ewo(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['low'] * 100 return emadif class Github_DeepeshKalura_project_orca__binance_strategy__20250807_111036(IStrategy): minimal_roi = { "0": 10 } timeframe = '5m' process_only_new_candles = True # Corrected startup candle count to match the longest indicator period (EWO uses 200) startup_candle_count = 200 order_types = { 'entry': 'market', 'exit': 'market', 'emergency_exit': 'market', 'force_entry': 'market', 'force_exit': "market", 'stoploss': 'market', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_market_ratio': 0.99 } stoploss = -0.99 use_custom_stoploss = True # Your hyperopt parameters remain unchanged... is_optimize_ewo = True buy_rsi_fast = IntParameter(35, 50, default=50, space='buy', optimize=is_optimize_ewo) buy_rsi = IntParameter(15, 35, default=30, space='buy', optimize=is_optimize_ewo) buy_ewo = DecimalParameter(-6.0, 5, default=-1.238, space='buy', optimize=is_optimize_ewo) buy_ema_low = DecimalParameter(0.9, 0.99, default=0.956, space='buy', optimize=is_optimize_ewo) buy_ema_high = DecimalParameter(0.95, 1.2, default=0.986, space='buy', optimize=is_optimize_ewo) is_optimize_32 = True buy_rsi_fast_32 = IntParameter(20, 70, default=63, space='buy', optimize=is_optimize_32) buy_rsi_32 = IntParameter(15, 50, default=16, space='buy', optimize=is_optimize_32) buy_sma15_32 = DecimalParameter(0.900, 1, default=0.932, decimals=3, space='buy', optimize=is_optimize_32) buy_cti_32 = DecimalParameter(-1, 0, default=-0.8, decimals=2, space='buy', optimize=is_optimize_32) is_optimize_deadfish = True sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, space='sell', optimize=is_optimize_deadfish) sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.05, space='sell', optimize=is_optimize_deadfish) sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0, space='sell', optimize=is_optimize_deadfish) sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0, space='sell', optimize=is_optimize_deadfish) sell_fastx = IntParameter(50, 100, default=75, space='sell', optimize=True) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # --- CRITICAL FIX: Enforce consistent data types for TA-Lib --- # TA-Lib functions are sensitive to input types. We cast to `float` to ensure consistency. dataframe['open'] = dataframe['open'].astype('float') dataframe['high'] = dataframe['high'].astype('float') dataframe['low'] = dataframe['low'].astype('float') dataframe['close'] = dataframe['close'].astype('float') dataframe['volume'] = dataframe['volume'].astype('float') # --- END OF FIX --- dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['cti'] = pta.cti(dataframe["close"], length=20) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['EWO'] = ewo(dataframe, 50, 200) # Corrected STOCHF call stoch_fast = ta.STOCHF(dataframe, fastk_period=5, fastd_period=3, fastd_matype=0) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband2'] = bollinger2['lower'] dataframe['bb_middleband2'] = bollinger2['mid'] dataframe['bb_upperband2'] = bollinger2['upper'] dataframe['bb_width'] = ( (dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']) dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1) dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # --- FIX: Initialize with a non-empty string for consistent data type --- dataframe.loc[:, 'enter_tag'] = ' ' is_ewo = ( (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low.value) & (dataframe['EWO'] > self.buy_ewo.value) & (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high.value) & (dataframe['rsi'] < self.buy_rsi.value) ) buy_1 = ( (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] < self.buy_rsi_fast_32.value) & (dataframe['rsi'] > self.buy_rsi_32.value) & (dataframe['close'] < dataframe['sma_15'] * self.buy_sma15_32.value) & (dataframe['cti'] < self.buy_cti_32.value) ) conditions.append(is_ewo) dataframe.loc[is_ewo, 'enter_tag'] += 'ewo' conditions.append(buy_1) dataframe.loc[buy_1, 'enter_tag'] += 'buy_1' if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1 else: dataframe['enter_long'] = 0 return dataframe def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # ... (Your custom stoploss logic remains unchanged) ... dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) current_candle = dataframe.iloc[-1].squeeze() if current_time - timedelta(minutes=60) > trade.open_date_utc: if (current_candle["fastk"] > self.sell_fastx.value) and (current_profit > -0.01): return -0.001 if current_time - timedelta(days=1) > trade.open_date_utc: if (current_candle["fastk"] > self.sell_fastx.value) and (current_profit > -0.05): return -0.001 enter_tag = '' if hasattr(trade, 'enter_tag') and trade.enter_tag is not None: enter_tag = trade.enter_tag enter_tags = enter_tag.split() if "ewo" in enter_tags: if current_profit >= 0.05: return -0.005 if current_profit > 0: if current_candle["fastk"] > self.sell_fastx.value: return -0.001 return self.stoploss def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> Optional[Union[str, bool]]: # ... (Your custom exit logic remains unchanged) ... dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) current_candle = dataframe.iloc[-1].squeeze() if ((current_profit < self.sell_deadfish_profit.value) and (current_candle['bb_width'] < self.sell_deadfish_bb_width.value) and (current_candle['close'] > current_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value) and (current_candle['volume_mean_12'] < current_candle[ 'volume_mean_24'] * self.sell_deadfish_volume_factor.value)): return "sell_stoploss_deadfish" def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Also ensure the exit column is initialized cleanly dataframe.loc[:, 'exit_long'] = 0 return dataframe