# source: https://raw.githubusercontent.com/rmallarapu-bc/brahma/9287745fc036c2f4c00c586e598290885c2883b9/archive/Karna1.py # --- Do not remove these libs --- from typing import Optional from pandas import DataFrame import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from datetime import datetime from freqtrade.persistence import Trade from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy from functools import reduce # -------------------------------- # Buy hyperspace params: buy_params = { "base_nb_candles_buy": 8, "ewo_high": 4.179, "ewo_low": -16.917, "ewo_high_2": -2.609, # value loaded from strategy "low_offset": 0.986, # value loaded from strategy "low_offset_2": 0.944, # value loaded from strategy "rsi_buy": 58, # value loaded from strategy } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 16, "high_offset": 1.054, "high_offset_2": 1.018, # value loaded from strategy } 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_rmallarapu_bc_brahma__Karna1__20240229_213751(IStrategy): INTERFACE_VERSION: int = 3 minimal_roi = {"0": 0.1} can_short = True # SMAOffset base_nb_candles_buy = IntParameter( 2, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False) base_nb_candles_sell = IntParameter( 10, 40, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False) low_offset = DecimalParameter( 0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=False) low_offset_2 = DecimalParameter( 0.9, 0.99, default=buy_params['low_offset_2'], space='buy', optimize=False) high_offset = DecimalParameter( 0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=False) high_offset_2 = DecimalParameter( 0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=False) # Protection fast_ewo = 50 slow_ewo = 200 ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=False) ewo_high = DecimalParameter( 2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=False) ewo_high_2 = DecimalParameter( -6.0, 12.0, default=buy_params['ewo_high_2'], space='buy', optimize=False) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True) # Trailing stop: # trailing_stop = True # trailing_stop_positive = 0.0025 # trailing_stop_positive_offset = 0.01 # trailing_only_offset_is_reached = True # Sell signal use_exit_signal = True # exit_profit_only = False # exit_profit_offset = 0.01 # ignore_roi_if_entry_signal = False # Optional order time in force. order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' } # # Trailing stoploss # trailing_stop = True # trailing_stop_positive = 0.005 # trailing_stop_positive_offset = 0.01 # trailing_only_offset_is_reached = True # # Optimal stoploss designed for the strategy stoploss = -0.20 # # use_exit_signal = False # ignore_roi_if_entry_signal = False # Optimal timeframe for the strategy # timeframe = '1h' timeframe = '5m' informative_timeframe = '15m' # Strategy parameters rsi_length = IntParameter(10, 40, default=14, space="buy") bb_window = IntParameter(10, 40, default=17, space="buy") bb_std = IntParameter(1, 6, default=1, space="buy") buy_rsi = IntParameter(10, 40, default=42, space="buy") sell_rsi = IntParameter(60, 90, default=76, space="sell") slippage_protection = { 'retries': 3, 'max_slippage': -0.01 } buy_signals = {} # timeframe = '5m' # informative_timeframe = '1h' process_only_new_candles = False startup_candle_count = 200 use_custom_stoploss = False def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs # def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, # rate: float, time_in_force: str, sell_reason: str, # current_time: datetime, **kwargs) -> bool: # dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # last_candle = dataframe.iloc[-1] # if (last_candle is not None): # if (sell_reason in ['sell_signal']): # if (last_candle['hma_50'] * 1.149 > last_candle['ema_100']) and ( # last_candle['close'] < last_candle['ema_100'] * 0.951): # *1.2 # return False # # slippage # try: # state = self.slippage_protection['__pair_retries'] # except KeyError: # state = self.slippage_protection['__pair_retries'] = {} # candle = dataframe.iloc[-1].squeeze() # slippage = (rate / candle['close']) - 1 # if slippage < self.slippage_protection['max_slippage']: # pair_retries = state.get(pair, 0) # if pair_retries < self.slippage_protection['retries']: # state[pair] = pair_retries + 1 # return False # state[pair] = 0 # return True def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi'] = ta.RSI(dataframe, timeperiod=self.rsi_length.value) # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=self.bb_window.value, stds=self.bb_std.value) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['rsi'] < self.buy_rsi.value) & (dataframe['date'].dt.minute.isin([0, 1, 2, 3, 4, 5])) & (dataframe['close'] < dataframe['bb_lowerband']) ), 'enter_long'] = 1 dataframe.loc[ ( (dataframe['rsi'] > self.sell_rsi.value) & (dataframe['date'].dt.minute.isin([0, 1, 2, 3, 4, 5])) & (dataframe['close'] > dataframe['bb_upperband']) ), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['rsi'] > self.sell_rsi.value) & (dataframe['close'] > dataframe['bb_upperband']) ), 'exit_long'] = 1 dataframe.loc[ ( (dataframe['rsi'] < self.buy_rsi.value) & (dataframe['close'] < dataframe['bb_lowerband']) ), 'exit_short'] = 1 return dataframe # def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # # Calculate all ma_buy values # for val in self.base_nb_candles_buy.range: # dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) # # Calculate all ma_sell values # for val in self.base_nb_candles_sell.range: # dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) # dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) # dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) # dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) # # Elliot # dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) # # RSI # dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=20) # dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=50) # return dataframe # def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # dataframe.loc[ # ( # (dataframe['rsi_fast'] < 35) & # (dataframe['close'] < ( # dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & # (dataframe['EWO'] > self.ewo_high.value) & # (dataframe['rsi'] < self.rsi_buy.value) & # (dataframe['volume'] > 0) & # (dataframe['close'] < ( # dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) # ), # ['buy', 'buy_tag']] = (1, 'ewo1') # dataframe.loc[ # ( # (dataframe['rsi_fast'] < 35) & # (dataframe['close'] < ( # dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) & # (dataframe['EWO'] > self.ewo_high_2.value) & # (dataframe['rsi'] < self.rsi_buy.value) & # (dataframe['volume'] > 0) & # (dataframe['close'] < ( # dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & # (dataframe['rsi'] < self.buy_rsi.value) # ), # ['buy', 'buy_tag']] = (1, 'ewo2') # dataframe.loc[ # ( # (dataframe['rsi_fast'] < 35) & # (dataframe['close'] < ( # dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & # (dataframe['EWO'] < self.ewo_low.value) & # (dataframe['volume'] > 0) & # (dataframe['close'] < ( # dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) # ), # ['buy', 'buy_tag']] = (1, 'ewolow') # return dataframe # def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # conditions = [] # conditions.append( # ((dataframe['close'] > dataframe['sma_9']) & # (dataframe['close'] > ( # dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) & # (dataframe['rsi'] > 50) & # (dataframe['volume'] > 0) & # (dataframe['rsi_fast'] > dataframe['rsi_slow']) # ) # | # ( # (dataframe['close'] < dataframe['hma_50']) & # (dataframe['close'] > ( # dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & # (dataframe['volume'] > 0) & # (dataframe['rsi_fast'] > dataframe['rsi_slow']) # ) # ) # if conditions: # dataframe.loc[ # reduce(lambda x, y: x | y, conditions), # 'sell' # ] = 1 # return dataframe