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freqtrade_strs/binance/E0V1E.py /
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| 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 E0V1E(IStrategy): | |
| minimal_roi = { | |
| "0": 10 | |
| } | |
| # Optimal timeframe for the strategy | |
| timeframe = '5m' | |
| # Run "populate_indicators()" only for new candle. | |
| process_only_new_candles = True | |
| startup_candle_count = 20 | |
| 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 | |
| } | |
| # Disabled | |
| stoploss = -0.99 | |
| # Custom stoploss | |
| use_custom_stoploss = True | |
| is_optimize_ewo = True | |
| buy_rsi_fast = IntParameter(35, 50, default=45, space='buy', optimize=is_optimize_ewo) | |
| buy_rsi = IntParameter(15, 35, default=35, space='buy', optimize=is_optimize_ewo) | |
| buy_ewo = DecimalParameter(-6.0, 5, default=-5.585, space='buy', optimize=is_optimize_ewo) | |
| buy_ema_low = DecimalParameter(0.9, 0.99, default=0.942, space='buy', optimize=is_optimize_ewo) | |
| buy_ema_high = DecimalParameter(0.95, 1.2, default=1.084, space='buy', optimize=is_optimize_ewo) | |
| is_optimize_32 = True | |
| buy_rsi_fast_32 = IntParameter(20, 70, default=46, space='buy', optimize=is_optimize_32) | |
| buy_rsi_32 = IntParameter(15, 50, default=19, space='buy', optimize=is_optimize_32) | |
| buy_sma15_32 = DecimalParameter(0.900, 1, default=0.942, decimals=3, space='buy', optimize=is_optimize_32) | |
| buy_cti_32 = DecimalParameter(-1, 0, default=-0.86, 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) | |
| delay_time = IntParameter(90, 1440, default=300, space='sell', optimize=True) | |
| fask_trailing = DecimalParameter(0.001, 0.02, default=0.001, space='sell', optimize=True) | |
| def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: | |
| # buy_1 indicators | |
| 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) | |
| # ewo indicators | |
| dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) | |
| dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) | |
| dataframe['EWO'] = ewo(dataframe, 50, 200) | |
| # profit sell indicators | |
| stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) | |
| dataframe['fastd'] = stoch_fast['fastd'] | |
| dataframe['fastk'] = stoch_fast['fastk'] | |
| # loss sell indicators | |
| 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 = [] | |
| 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 | |
| return dataframe | |
| def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, | |
| current_profit: float, **kwargs) -> float: | |
| dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) | |
| current_candle = dataframe.iloc[-1].squeeze() | |
| if current_time - timedelta(minutes=int(self.delay_time.value)) > trade.open_date_utc: | |
| if current_profit >= -0.01: | |
| return -0.003 | |
| if current_time - timedelta(minutes=int(self.delay_time.value) * 2) > trade.open_date_utc: | |
| if current_profit >= -0.02: | |
| return -0.006 | |
| # if hold > 1 day.sell in stoploss -0.05 | |
| if current_time - timedelta(days=1) > trade.open_date_utc: | |
| if 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 -0.015 | |
| if current_profit > 0: | |
| if current_candle["fastk"] > self.sell_fastx.value: | |
| return self.fask_trailing.value | |
| 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]]: | |
| dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) | |
| current_candle = dataframe.iloc[-1].squeeze() | |
| # stoploss - deadfish | |
| 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: | |
| dataframe.loc[(), ['exit_long', 'exit_tag']] = (0, 'long_out') | |
| return dataframe |