# source: https://raw.githubusercontent.com/Willemstijn/user_data/758a23f80ce7a5dca176e939c4f3f8562d11a304/strategies/2023_downloads_freqtrade_discord_%23strategy-collection/ewt2.py from datetime import datetime from datetime import timedelta import talib.abstract as ta from finta import TA as fta from pandas import DataFrame from freqtrade.persistence import Trade from freqtrade.strategy import DecimalParameter from freqtrade.strategy import IStrategy, merge_informative_pair # -------------------------------------------------------------------------------- # Author: rextea 2021/04/28 Version: 0.01 # -------------------------------------------------------------------------------- # Simple strategy based on Elliot Wave Theory # https://www.tradingview.com/script/uculwCTj-Indicator-ElliotWave-Oscillator-EWO/ # # # Posted on Freqtrade discord channel: https://discord.gg/Xr4wUYc6 def EWO(dataframe, sma_length=5, sma2_length=35): df = dataframe.copy() sma1 = ta.SMA(df, timeperiod=sma_length) sma2 = ta.SMA(df, timeperiod=sma2_length) smadif = (sma1 - sma2) / df['close'] * 100 return smadif class github_Willemstijn_user_data__ewt2__20230822_152729(IStrategy): minimal_roi = { "0": 0.5 } stoploss = -0.1 timeframe = '5m' informative_timeframe = '15m' sell_profit_only = False process_only_new_candles = True startup_candle_count: int = 100 # trailing_stop = True # trailing_stop_positive = 0.02 # trailing_stop_positive_offset = 0.075 # trailing_only_offset_is_reached = True buy_EWO_val = DecimalParameter(0, 3.00, default=0.75, space='buy', optimize=True, load=True) sell_EWO_val = DecimalParameter(-3.00, 0, default=-2, space='sell', optimize=True, load=True) use_custom_stoploss = False custom_trade_info = {} def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: if self.config['runmode'].value in ('live', 'dry_run'): dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) EWO_BUY = dataframe['EWO_BUY'].iat[-1] EWO_SELL = dataframe['EWO_SELL'].iat[-1] # If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!) else: EWO_BUY = self.custom_trade_info[trade.pair]['EWO_BUY'].loc[current_time]['EWO_BUY'] EWO_SELL = self.custom_trade_info[trade.pair]['EWO_SELL'].loc[current_time]['EWO_SELL'] if (current_time - timedelta(minutes=600) > trade.open_date_utc) & (current_profit < 0): if EWO_BUY < 0: return 0.01 return 0.5 def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] informative_pairs.append(('BTC/USDT', self.informative_timeframe)) return informative_pairs @staticmethod def coock_indicators(dataframe: DataFrame, metadata: dict): dataframe['ema32'] = fta.EMA(dataframe, period=32) dataframe['EWO_BUY'] = EWO(dataframe, 5, 35) dataframe['EWO_SELL'] = EWO(dataframe, 5, 35) dataframe['mfi'] = fta.MFI(dataframe) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if not metadata['pair'] in self.custom_trade_info: self.custom_trade_info[metadata['pair']] = {} if self.timeframe == self.informative_timeframe: dataframe = self.coock_indicators(dataframe, metadata) else: assert self.dp, "DataProvider is required for multiple timeframes." informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe) informative = self.coock_indicators(informative.copy(), metadata) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True) # don't overwrite the base dataframe's OHLCV information skip_columns = [(s + "_" + self.informative_timeframe) for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.rename(columns=lambda s: s.replace("_{}".format(self.informative_timeframe), "") if ( not s in skip_columns) else s, inplace=True) informative_btc = self.dp.get_pair_dataframe(pair='BTC/USDT', timeframe=self.informative_timeframe) informative_btc['EWO_BTC'] = EWO(informative_btc, 10, 40) informative_btc['ema32_btc'] = fta.EMA(informative_btc, period=32) informative_btc['btc_open'] = informative_btc['open'] dataframe = merge_informative_pair(dataframe, informative_btc, self.timeframe, self.informative_timeframe, ffill=True) # don't overwrite the base dataframe's OHLCV information skip_columns = [(s + "_" + self.informative_timeframe) for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.rename(columns=lambda s: s.replace("_{}".format(self.informative_timeframe), "") if ( not s in skip_columns) else s, inplace=True) dataframe['4h_high'] = dataframe['close'].rolling(48).max() dataframe['8h_high'] = dataframe['close'].rolling(96).max() if self.dp.runmode.value in ('backtest', 'hyperopt'): self.custom_trade_info[metadata['pair']]['EWO_BUY'] = dataframe[['date', 'EWO_BUY']].copy().set_index( 'date') self.custom_trade_info[metadata['pair']]['EWO_SELL'] = dataframe[['date', 'EWO_SELL']].copy().set_index( 'date') return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( ( # (dataframe['8h_high'] > dataframe['close']) & ( (dataframe['EWO_BUY'].shift(1) < 0.00) | (dataframe['EWO_BUY'].shift(2) < 0.00) | (dataframe['EWO_BUY'].shift(3) < 0.00) ) & (dataframe['EWO_BUY'] > self.buy_EWO_val.value) # & (dataframe['EWO_BTC'] > 0.00) & (dataframe['volume'] > 0) & (dataframe['open'] > dataframe['ema32']) # & (dataframe['btc_open'] > dataframe['ema32_btc']) ) | (dataframe['EWO_BUY'] < -7.00) ), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['EWO_SELL'] <= self.sell_EWO_val.value) # & (dataframe['EWO_SELL'] > dataframe['EWO_SELL'].shift(1)) & # (dataframe['EWO_SELL'].shift(1) > dataframe['EWO_SELL'].shift(2)) & & (dataframe['volume'] > 0) ), 'sell' ] = 1 # dataframe.loc[:, 'sell'] = 0 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, **kwargs) -> bool: # activate sell signal only when profit is above 1.5% and below -1.5% if sell_reason == 'sell_signal': if 0.02 > trade.calc_profit_ratio(rate): return False else: return True return True