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 import math import logging logger = logging.getLogger(__name__) def EWO(dataframe, ema_length=5, ema2_length=3): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['close'] * 100 return emadif class ETCG(IStrategy):# org :EI3v2_tag_cofi_green_5 INTERFACE_VERSION = 2 minimal_roi = { "0": 0.05, "20": 0.025, "40": 0.015, "87": 0.01, "201": 0.005, "202": 0.002 } buy_params = { "base_nb_candles_buy": 12, "rsi_buy": 58, "ewo_high": 3.001, "ewo_low": -10.289, "low_offset": 0.987, "lambo2_ema_14_factor": 0.981, "lambo2_enabled": True, "lambo2_rsi_14_limit": 39, "lambo2_rsi_4_limit": 44, "buy_adx": 20, "buy_fastd": 20, "buy_fastk": 22, "buy_ema_cofi": 0.98, "buy_ewo_high": 4.179 } sell_params = { "base_nb_candles_sell": 22, "high_offset": 1.014, "high_offset_2": 1.01 } @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 5 }, { "method": "MaxDrawdown", "lookback_period_candles": 48, "trade_limit": 20, "stop_duration_candles": 4, "max_allowed_drawdown": 0.2 }, { "method": "StoplossGuard", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 2, "only_per_pair": False }, { "method": "LowProfitPairs", "lookback_period_candles": 6, "trade_limit": 2, "stop_duration_candles": 60, "required_profit": 0.02 }, { "method": "LowProfitPairs", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 2, "required_profit": 0.01 } ] """ minimal_roi = { "0": 0.99, } """ stoploss = -1 base_nb_candles_buy = IntParameter(8, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False) base_nb_candles_sell = IntParameter(8, 20, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False) low_offset = DecimalParameter(0.985, 0.995, default=buy_params['low_offset'], space='buy', optimize=True) high_offset = DecimalParameter(1.005, 1.015, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter(1.010, 1.020, default=sell_params['high_offset_2'], space='sell', optimize=True) lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=True) lambo2_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=True) lambo2_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=True) fast_ewo = 50 slow_ewo = 200 ewo_low = DecimalParameter(-20.0, -8.0,default=buy_params['ewo_low'], space='buy', optimize=True) ewo_high = DecimalParameter(3.0, 3.4, default=buy_params['ewo_high'], space='buy', optimize=True) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=False) trailing_stop = True trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.012 trailing_only_offset_is_reached = True is_optimize_cofi = False buy_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97 , optimize = is_optimize_cofi) buy_fastk = IntParameter(20, 30, default=20, optimize = is_optimize_cofi) buy_fastd = IntParameter(20, 30, default=20, optimize = is_optimize_cofi) buy_adx = IntParameter(20, 30, default=30, optimize = is_optimize_cofi) buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize = is_optimize_cofi) use_exit_signal = True exit_profit_only = False #default: True ,Set on config? exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False timeframe = '5m' inf_1h = '1h' process_only_new_candles = True startup_candle_count = 400 plot_config = { 'main_plot': { 'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}, }, } btc_info_pair = "BTC/USDT:USDT" def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): if current_profit < -0.04 and (current_time - trade.open_date_utc).days >= 4: return 'unclog' def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']: self.btc_info_pair = f"BTC/{self.config['stake_currency']}" else: self.btc_info_pair = "BTC/USDT" informative_pairs.append((self.btc_info_pair, self.timeframe)) informative_pairs.append((self.btc_info_pair, self.inf_1h)) return informative_pairs def pump_dump_protection(self, dataframe: DataFrame, metadata: dict) -> DataFrame: df36h = dataframe.copy().shift( 432 ) # TODO FIXME: This assumes 5m timeframe df24h = dataframe.copy().shift( 288 ) # TODO FIXME: This assumes 5m timeframe dataframe['volume_mean_short'] = dataframe['volume'].rolling(4).mean() dataframe['volume_mean_long'] = df24h['volume'].rolling(48).mean() dataframe['volume_mean_base'] = df36h['volume'].rolling(288).mean() dataframe['volume_change_percentage'] = (dataframe['volume_mean_long'] / dataframe['volume_mean_base']) dataframe['rsi_mean'] = dataframe['rsi'].rolling(48).mean() dataframe['pnd_volume_warn'] = np.where((dataframe['volume_mean_short'] / dataframe['volume_mean_long'] > 5.0), -1, 0) return dataframe def base_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['price_trend_long'] = (dataframe['close'].rolling(8).mean() / dataframe['close'].shift(8).rolling(144).mean()) ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume'] dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) return dataframe def info_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi_8'] = ta.RSI(dataframe, timeperiod=8) ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume'] dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: btc_info_tf = self.dp.get_pair_dataframe(self.btc_info_pair, self.inf_1h) btc_info_tf = self.info_tf_btc_indicators(btc_info_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_info_tf, self.timeframe, self.inf_1h, ffill=True) drop_columns = [f"{s}_{self.inf_1h}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) btc_base_tf = self.dp.get_pair_dataframe(self.btc_info_pair, self.timeframe) btc_base_tf = self.base_tf_btc_indicators(btc_base_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_base_tf, self.timeframe, self.timeframe, ffill=True) drop_columns = [f"{s}_{self.timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) 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['sma_9'] = ta.SMA(dataframe, timeperiod=9) dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) 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_14'] = ta.EMA(dataframe, timeperiod=14) dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['dema_30'] = ftt.dema(dataframe, period=30) dataframe['dema_200'] = ftt.dema(dataframe, period=200) dataframe['pump_strength'] = (dataframe['dema_30'] - dataframe['dema_200']) / dataframe['dema_30'] stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] dataframe['adx'] = ta.ADX(dataframe) dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe = self.pump_dump_protection(dataframe, metadata) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' lambo2 = ( (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) ) dataframe.loc[lambo2, 'enter_tag'] += 'lambo2_' conditions.append(lambo2) buy1ewo = ( (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)) ) dataframe.loc[buy1ewo, 'enter_tag'] += 'buy1eworsi_' conditions.append(buy1ewo) #有人说效果不好 buy2ewo = ( (dataframe['rsi_fast'] < 35)& (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) ) dataframe.loc[buy2ewo, 'enter_tag'] += 'buy2ewo_' conditions.append(buy2ewo) #有人说效果不好 is_cofi = ( (dataframe['open'] < dataframe['ema_8'] * self.buy_ema_cofi.value) & (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) & (dataframe['fastk'] < self.buy_fastk.value) & (dataframe['fastd'] < self.buy_fastd.value) & (dataframe['adx'] > self.buy_adx.value) & (dataframe['EWO'] > self.buy_ewo_high.value) ) dataframe.loc[is_cofi, 'enter_tag'] += 'cofi_' conditions.append(is_cofi) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'entry_long' ]=1 dont_buy_conditions = [] #放量下跌不买入 dont_buy_conditions.append((dataframe['pnd_volume_warn'] < 0.0)) #BTC行情不好不买入 dont_buy_conditions.append((dataframe['btc_rsi_8_1h'] < 35.0)) if dont_buy_conditions: for condition in dont_buy_conditions: dataframe.loc[condition, 'entry_long'] = 0 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] #可以优化，有一个不会触发 conditions.append( ( (dataframe['close']>dataframe['hma_50'])& (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[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), 'exit_long' ]=1 return dataframe # 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: # trade_info = kwargs.get('trade_info', {}) # trade_info[trade.id] = { # 'sell_reason': sell_reason + "_" + trade.buy_tag # } # return True def pct_change(a, b): return (b - a) / a