# source: https://raw.githubusercontent.com/webclinic017/test-1/a1afda90d5d3c09579a671697b5e3f48b6b4e63c/tesla4.py # --- Do not remove these libs --- # --- Do not remove these libs --- from logging import FATAL from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame # -------------------------------- import talib.abstract as ta import numpy as np import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime from technical.util import resample_to_interval, resampled_merge from datetime import datetime, timedelta from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter import technical.indicators as ftt # @Rallipanos # @pluxury # with help from @stash86 and @Perkmeister # Buy hyperspace params: buy_params = { "base_nb_candles_buy": 8, "ewo_high": 2.675, "ewo_high_2": 4.516, "ewo_low": -9.263, "lookback_candles": 22, "low_offset": 0.988, "low_offset_2": 0.915, "profit_threshold": 1.0408, "rsi_buy": 57, "rsi_fast_buy": 49 } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 24, "high_offset": 0.998, "high_offset_2": 1, "sell_rsi_main": 87.43 } def zlema2(dataframe, fast): df = dataframe.copy() zema1=ta.EMA(df['close'], fast) zema2=ta.EMA(zema1, fast) d1=zema1-zema2 df['zlema2']=zema1+d1 return df['zlema2'] 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_webclinic017_test_1__tesla4__20210905_191817(IStrategy): INTERFACE_VERSION = 2 # ROI table: minimal_roi = { "0": 0.215, "40": 0.032, "64": 0.03, "87": 0.016, "201": 0 } # Stoploss: stoploss = -0.15 # SMAOffset base_nb_candles_buy = IntParameter( 2, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False) base_nb_candles_sell = IntParameter( 2, 25, 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) sell_rsi_main = DecimalParameter(72.0, 90.0, default=sell_params['sell_rsi_main'], space='sell', decimals=2, optimize=False, load=True) # Protection fast_ewo = 50 slow_ewo = 200 lookback_candles = IntParameter( 1, 36, default=buy_params['lookback_candles'], space='buy', optimize=False) profit_threshold = DecimalParameter(0.99, 1.05, default=buy_params['profit_threshold'], space='buy', optimize=False) 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(10, 80, default=buy_params['rsi_buy'], space='buy', optimize=False) rsi_fast_buy = IntParameter( 10, 50, default=buy_params['rsi_fast_buy'], space='buy', optimize=False) # Trailing stop: trailing_stop = True trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.010 trailing_only_offset_is_reached = True # Sell signal use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.001 ignore_roi_if_buy_signal = False # Optional order time in force. order_time_in_force = { 'buy': 'gtc', 'sell': 'ioc' } # Optimal timeframe for the strategy timeframe = '5m' inf_15m = '15m' inf_1h = '1h' process_only_new_candles = True startup_candle_count = 200 use_custom_stoploss = False plot_config = { 'main_plot': { 'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}, }, 'subplots': { 'rsi': { 'rsi': {'color': 'orange'}, 'rsi_fast': {'color': 'red'}, 'rsi_slow': {'color': 'green'}, }, 'ewo': { 'EWO': {'color': 'orange'} }, } } slippage_protection = { 'retries': 3, 'max_slippage': -0.02 } def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] current_profit = trade.calc_profit_ratio(rate) if 'bb_bull' in trade.buy_tag and current_profit > 0.01: return True if (last_candle is not None): if (sell_reason in ['sell_signal']): if (last_candle['hma_50'] > last_candle['ema_100']) and (last_candle['rsi'] < 45): #*1.2 return False 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 if (trade.buy_tag == 'bb_bull'): if (sell_reason in ['sell_signal'])or (sell_reason in ['roi']): 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 informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '15m') for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) # EMA # informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) # informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # # RSI # informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) # bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) # informative_1h['bb_lowerband'] = bollinger['lower'] # informative_1h['bb_middleband'] = bollinger['mid'] # informative_1h['bb_upperband'] = bollinger['upper'] return informative_1h def informative_15m_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_15m) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) # EMA # informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) # informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # # RSI # informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) # bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) # informative_1h['bb_lowerband'] = bollinger['lower'] # informative_1h['bb_middleband'] = bollinger['mid'] # informative_1h['bb_upperband'] = bollinger['upper'] return informative_15m def normal_tf_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['ema_10'] = zlema2(dataframe, 10) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) 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=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2) dataframe['lower'] = bb_40['lower'] dataframe['mid'] = bb_40['mid'] dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() # strategy ClucMay72018 bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() dataframe['vol_7_max'] = dataframe['volume'].rolling(window=20).max() dataframe['vol_14_max'] = dataframe['volume'].rolling(window=14).max() dataframe['vol_7_min'] = dataframe['volume'].rolling(window=20).min() dataframe['vol_14_min'] = dataframe['volume'].rolling(window=14).min() dataframe['roll_7'] = 100*((dataframe['volume']-dataframe['vol_7_max'])/(dataframe['vol_7_max']-dataframe['vol_7_min'])) dataframe['vol_base']=ta.SMA(dataframe['roll_7'], timeperiod=5) dataframe['vol_ma_26'] = ta.EMA(dataframe['volume'], timeperiod=26) dataframe['vol_ma_200'] = ta.EMA(dataframe['volume'], timeperiod=200) dataframe['vol_ma_26_front'] = ((ta.EMA(dataframe['volume'], timeperiod=26).max())-(ta.EMA(dataframe['volume'], timeperiod=26).min()))/2 return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # informative_1h = self.informative_1h_indicators(dataframe, metadata) informative_15m = self.informative_15m_indicators(dataframe, metadata) dataframe = merge_informative_pair( dataframe, informative_15m, self.timeframe, self.inf_15m, ffill=True) # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dont_buy_conditions = [] dont_buy_conditions.append( ( # don't buy if there isn't 3% profit to be made (dataframe['close_15m'].rolling(self.lookback_candles.value).max() < (dataframe['close'] * self.profit_threshold.value)) ) ) dataframe.loc[ ( (dataframe['vol_base']<-80) & (dataframe['rsi_fast'] < self.rsi_fast_buy.value) & (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['vol_base']<-80) & (dataframe['rsi_fast'] < self.rsi_fast_buy.value) & (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'] < 25) ), ['buy', 'buy_tag']] = (1, 'ewo2') dataframe.loc[ ( (dataframe['vol_base']>-96)& (dataframe['vol_base']> -20)& (dataframe['rsi_fast'] < self.rsi_fast_buy.value) & (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, 'ewo3') dataframe.loc[ ( (dataframe['vol_base']<-80) & (dataframe['rsi_fast'] < self.rsi_fast_buy.value) & (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') # buy in bull market dataframe.loc[ ( (dataframe['vol_base']<-80) & (dataframe['ema_10'].rolling(10).mean() > dataframe['ema_100'].rolling(10).mean()) & (dataframe['lower'].shift().gt(0)) & (dataframe['bbdelta'].gt(dataframe['close'] * 0.031)) & (dataframe['closedelta'].gt(dataframe['close'] * 0.018)) & (dataframe['tail'].lt(dataframe['bbdelta'] * 0.233)) & (dataframe['close'].lt(dataframe['lower'].shift())) & (dataframe['close'].le(dataframe['close'].shift())) & (dataframe['volume'] > 0) ) | ( (dataframe['vol_base']<-80) & (dataframe['ema_10'].rolling(10).mean() > dataframe['ema_100'].rolling(10).mean()) & (dataframe['close'] > dataframe['ema_100']) & (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.993 * dataframe['bb_lowerband']) & (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 21)) & (dataframe['volume'] > 0) ), ['buy', 'buy_tag']] = (1, 'bb_bull') if dont_buy_conditions: for condition in dont_buy_conditions: dataframe.loc[condition, 'buy'] = 0 return dataframe def populate_sell_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