# source: https://raw.githubusercontent.com/webclinic017/test-1/a1afda90d5d3c09579a671697b5e3f48b6b4e63c/tesla.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 # Buy hyperspace params: buy_params = { "low_offset": 0.981, "base_nb_candles_buy": 8, # value loaded from strategy "ewo_high": 3.553, # value loaded from strategy "ewo_high_2": -5.585, # value loaded from strategy "ewo_low": -14.378, # value loaded from strategy "lookback_candles": 32, # value loaded from strategy "low_offset_2": 0.942, # value loaded from strategy "profit_threshold": 1.037, # value loaded from strategy "rsi_buy": 78, # value loaded from strategy "rsi_fast_buy": 37, # value loaded from strategy } # Sell hyperspace params: sell_params = { "sell_custom_dec_profit_1": 0.002, "sell_custom_dec_profit_2": 0.162, "sell_custom_profit_0": 0.041, "sell_custom_profit_1": 0.02, "sell_custom_profit_2": 0.084, "sell_custom_profit_3": 0.176, "sell_custom_profit_4": 0.511, "sell_custom_profit_under_rel_1": 0.015, "sell_custom_profit_under_rsi_diff_1": 1.337, "sell_custom_rsi_0": 31.342, "sell_custom_rsi_1": 36.57, "sell_custom_rsi_2": 49.69, "sell_custom_rsi_3": 47.78, "sell_custom_rsi_4": 42.64, "sell_custom_stoploss_under_rel_1": 0.015, "sell_custom_stoploss_under_rsi_diff_1": 12.341, "sell_custom_under_profit_1": 0.081, "sell_custom_under_profit_2": 0.078, "sell_custom_under_profit_3": 0.045, "sell_custom_under_rsi_1": 45.8, "sell_custom_under_rsi_2": 62.8, "sell_custom_under_rsi_3": 51.6, "sell_trail_down_1": 0.114, "sell_trail_down_2": 0.146, "sell_trail_down_3": 0.015, "sell_trail_profit_max_1": 0.23, "sell_trail_profit_max_2": 0.22, "sell_trail_profit_max_3": 0.11, "sell_trail_profit_min_1": 0.249, "sell_trail_profit_min_2": 0.088, "sell_trail_profit_min_3": 0.021, "base_nb_candles_sell": 16, # value loaded from strategy "high_offset": 1.097, # value loaded from strategy "high_offset_2": 1.472, # 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_webclinic017_test_1__tesla__20210905_191817(IStrategy): INTERFACE_VERSION = 2 # ROI table: minimal_roi = { # "0": 0.283, # "40": 0.086, # "99": 0.036, "0": 10, } # 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_custom_profit_0 = DecimalParameter(0.001, 0.1, default=sell_params['sell_custom_profit_0'], space='sell', decimals=3, optimize=True, load=True) sell_custom_rsi_0 = DecimalParameter(30.0, 40.0, default=sell_params['sell_custom_rsi_0'], space='sell', decimals=3, optimize=True, load=True) sell_custom_profit_1 = DecimalParameter(0.005, 0.1, default=sell_params['sell_custom_profit_1'], space='sell', decimals=3, optimize=True, load=True) sell_custom_rsi_1 = DecimalParameter(30.0, 50.0, default=sell_params['sell_custom_rsi_1'], space='sell', decimals=2, optimize=True, load=True) sell_custom_profit_2 = DecimalParameter(0.007, 0.1, default=sell_params['sell_custom_profit_2'], space='sell', decimals=3, optimize=True, load=True) sell_custom_rsi_2 = DecimalParameter(34.0, 50.0, default=sell_params['sell_custom_rsi_2'], space='sell', decimals=2, optimize=True, load=True) sell_custom_profit_3 = DecimalParameter(0.009, 0.30, default=sell_params['sell_custom_profit_3'], space='sell', decimals=3, optimize=True, load=True) sell_custom_rsi_3 = DecimalParameter(38.0, 55.0, default=sell_params['sell_custom_rsi_3'], space='sell', decimals=2, optimize=True, load=True) sell_custom_profit_4 = DecimalParameter(0.01, 0.6, default=sell_params['sell_custom_profit_4'], space='sell', decimals=3, optimize=True, load=True) sell_custom_rsi_4 = DecimalParameter(40.0, 58.0, default=sell_params['sell_custom_under_profit_1'], space='sell', decimals=2, optimize=True, load=True) sell_custom_under_profit_1 = DecimalParameter(0.001, 0.10, default=sell_params['sell_custom_under_profit_1'], space='sell', decimals=3, optimize=True, load=True) sell_custom_under_rsi_1 = DecimalParameter(36.0, 60.0, default=sell_params['sell_custom_under_rsi_1'], space='sell', decimals=1, optimize=True, load=True) sell_custom_under_profit_2 = DecimalParameter(0.001, 0.10, default=sell_params['sell_custom_under_profit_2'], space='sell', decimals=3, optimize=True, load=True) sell_custom_under_rsi_2 = DecimalParameter(46.0, 66.0, default=sell_params['sell_custom_under_rsi_2'], space='sell', decimals=1, optimize=True, load=True) sell_custom_under_profit_3 = DecimalParameter(0.001, 0.10, default=sell_params['sell_custom_under_profit_3'], space='sell', decimals=3, optimize=True, load=True) sell_custom_under_rsi_3 = DecimalParameter(50.0, 68.0, default=sell_params['sell_custom_under_rsi_3'], space='sell', decimals=1, optimize=True, load=True) sell_custom_dec_profit_1 = DecimalParameter(0.001, 0.10, default=sell_params['sell_custom_dec_profit_1'], space='sell', decimals=3, optimize=True, load=True) sell_custom_dec_profit_2 = DecimalParameter(0.05, 0.2, default=sell_params['sell_custom_dec_profit_2'], space='sell', decimals=3, optimize=True, load=True) sell_trail_profit_min_1 = DecimalParameter(0.001, 0.25, default=sell_params['sell_trail_profit_min_1'], space='sell', decimals=3, optimize=True, load=True) sell_trail_profit_max_1 = DecimalParameter(0.03, 0.5, default=sell_params['sell_trail_profit_max_1'], space='sell', decimals=2, optimize=True, load=True) sell_trail_down_1 = DecimalParameter(0.04, 0.2, default=sell_params['sell_trail_down_1'], space='sell', decimals=3, optimize=True, load=True) sell_trail_profit_min_2 = DecimalParameter(0.004, 0.1, default=sell_params['sell_trail_profit_min_2'], space='sell', decimals=3, optimize=True, load=True) sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=sell_params['sell_trail_profit_max_2'], space='sell', decimals=2, optimize=True, load=True) sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=sell_params['sell_trail_down_2'], space='sell', decimals=3, optimize=True, load=True) sell_trail_profit_min_3 = DecimalParameter(0.006, 0.1, default=sell_params['sell_trail_profit_min_3'], space='sell', decimals=3, optimize=True, load=True) sell_trail_profit_max_3 = DecimalParameter(0.08, 0.16, default=sell_params['sell_trail_profit_max_3'], space='sell', decimals=2, optimize=True, load=True) sell_trail_down_3 = DecimalParameter(0.01, 0.04, default=sell_params['sell_trail_down_3'], space='sell', decimals=3, optimize=True, load=True) sell_custom_profit_under_rel_1 = DecimalParameter(0.01, 0.04, default=sell_params['sell_custom_profit_under_rel_1'], space='sell', optimize=True, load=True) sell_custom_profit_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=sell_params['sell_custom_profit_under_rsi_diff_1'], space='sell', optimize=True, load=True) sell_custom_stoploss_under_rel_1 = DecimalParameter(0.001, 0.02, default=sell_params['sell_custom_stoploss_under_rel_1'], space='sell', optimize=True, load=True) sell_custom_stoploss_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=sell_params['sell_custom_stoploss_under_rsi_diff_1'], space='sell', optimize=True, 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 = False trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.016 trailing_only_offset_is_reached = True # Sell signal use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.01 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 = True 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 custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: if (current_profit > 0.3): return 0.05 elif (current_profit > 0.1): return 0.03 elif (current_profit > 0.06): return 0.02 elif (current_profit > 0.04): return 0.01 elif (current_profit > 0.025): return 0.005 elif (current_profit > 0.018): return 0.005 return 0.15 def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate) if(current_time < trade.open_date_utc + timedelta(minutes=360)) : return None if (last_candle is not None): if (current_profit > self.sell_custom_profit_4.value) & (last_candle['rsi'] < self.sell_custom_rsi_4.value): return 'signal_profit_4' elif (current_profit > self.sell_custom_profit_3.value) & (last_candle['rsi'] < self.sell_custom_rsi_3.value): return 'signal_profit_3' elif (current_profit > self.sell_custom_profit_2.value) & (last_candle['rsi'] < self.sell_custom_rsi_2.value): return 'signal_profit_2' elif (current_profit > self.sell_custom_profit_1.value) & (last_candle['rsi'] < self.sell_custom_rsi_1.value): return 'signal_profit_1' elif (current_profit > self.sell_custom_profit_0.value) & (last_candle['rsi'] < self.sell_custom_rsi_0.value): return 'signal_profit_0' elif (current_profit > self.sell_custom_under_profit_1.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_1.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_1' elif (current_profit > self.sell_custom_under_profit_2.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_2.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_2' elif (current_profit > self.sell_custom_under_profit_3.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_3.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_3' elif (current_profit > self.sell_custom_dec_profit_1.value) & (last_candle['sma_200_dec']): return 'signal_profit_d_1' elif (current_profit > self.sell_custom_dec_profit_2.value) & (last_candle['close'] < last_candle['ema_100']): return 'signal_profit_d_2' elif (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (max_profit > (current_profit + self.sell_trail_down_1.value)): return 'signal_profit_t_1' elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & (max_profit > (current_profit + self.sell_trail_down_2.value)): return 'signal_profit_t_2' elif (last_candle['close'] < last_candle['ema_200']) & (current_profit > self.sell_trail_profit_min_3.value) & (current_profit < self.sell_trail_profit_max_3.value) & (max_profit > (current_profit + self.sell_trail_down_3.value)): return 'signal_profit_u_t_1' return None 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 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) # 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) # 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['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) 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['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['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['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') 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