# source: https://raw.githubusercontent.com/rkholoniuk/crypto-lab/88bb8781f70d01afd11db71f638b1e12c10f9155/freqtrade-docker-compose-template/template_dir/BinMfiBTCv4.py # --- Do not remove these libs --- from datetime import datetime from datetime import timedelta import numpy as np # -------------------------------- import talib.abstract as ta from pandas import DataFrame from freqtrade.persistence import Trade from freqtrade.strategy import CategoricalParameter from freqtrade.strategy import DecimalParameter from freqtrade.strategy import IntParameter from freqtrade.strategy import merge_informative_pair from freqtrade.strategy.interface import IStrategy # -------------------------------------------------------------------------------- # Author: rextea 2021/05/21 Version: 4.0 # -------------------------------------------------------------------------------- # Strategy based on the legendary BinHV45: # https://github.com/freqtrade/freqtrade-strategies # # # 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 def bollinger_bands(stock_price, window_size, num_of_std): rolling_mean = stock_price.rolling(window=window_size).mean() rolling_std = stock_price.rolling(window=window_size).std() lower_band = rolling_mean - (rolling_std * num_of_std) return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band) class github_rkholoniuk_crypto_lab__BinMfiBTCv4__20210706_193924(IStrategy): timeframe = '5m' btc_timeframe = '5m' btc_pair = 'BTC/USDT' stoploss = -0.095 use_custom_stoploss = True minimal_roi = { "0": 0.08, "1": 0.015, "10": 0.02, "90": 0.005 } use_sell_signal = False trailing_stop = True trailing_stop_positive = 0.0075 trailing_stop_positive_offset = 0.02 trailing_only_offset_is_reached = True process_only_new_candles = True startup_candle_count: int = 15 # Strict mode buy params: strict_bbdelta_close = DecimalParameter(0.0, 0.045, default=0.03344, space='buy', optimize=False, load=True) strict_closedelta_close = DecimalParameter(-0.015, 0.045, default=0.00681, space='buy', optimize=False, load=True) strict_tail_bbdelta = DecimalParameter(0.0, 2.0, default=1.73588, space='buy', optimize=False, load=True) strict_mfi_limit = IntParameter(-15, 70, default=25, space='buy', optimize=False, load=True) # Loose mode buy params: loose_bbdelta_close = DecimalParameter(0.0, 0.045, default=0.03344, space='buy', optimize=False, load=True) loose_closedelta_close = DecimalParameter(-0.015, 0.045, default=0.00681, space='buy', optimize=False, load=True) loose_tail_bbdelta = DecimalParameter(0.0, 2.0, default=1.73588, space='buy', optimize=False, load=True) loose_mfi_limit = IntParameter(-15, 100, default=25, space='buy', optimize=False, load=True) ewo_low = DecimalParameter(-20.0, -8.0, default=-12.0, space='buy', optimize=False) ewo_high = DecimalParameter(2.0, 12.0, default=6.0, space='buy', optimize=False) rsi_buy = IntParameter(30, 70, default=55, space='buy', optimize=False, load=True) btc_bail_roc = DecimalParameter(-20.0, -2.0, default=-5.0, space='sell', optimize=False, load=True) bail_after_period = CategoricalParameter([60, 120, 240, 280, 340, 400, 480, 600, 800, 1200], default=480, space='sell', optimize=True) bail_ng_profit = CategoricalParameter([0, -0.01, -0.02, -0.03, -0.05, -0.06], default=-0.03, space='sell', optimize=True) bail_ng_profit_2 = CategoricalParameter([-0.05, -0.06, -0.07, -0.08, -0.09], default=-0.07, space='sell', optimize=True) bail_ewo = CategoricalParameter([0, -0.5, -1, -1.5, -2, -2.5], default=-1, space='sell', optimize=True) bail_ewo_2 = CategoricalParameter([0, -0.5, -1, -1.5, -2, -2.5], default=0, space='sell', optimize=True) ignore_roi_ewo = DecimalParameter(0.0, 10.0, default=3.0, space='sell', optimize=True) ignore_roi_rsi = IntParameter(30, 90, default=50, space='sell', optimize=True) buy_params = { 'loose_bbdelta_close': 0.03, 'loose_closedelta_close': 0.019, 'loose_mfi_limit': 43, 'loose_tail_bbdelta': 1.739, 'strict_bbdelta_close': 0.042, 'strict_closedelta_close': 0.008, 'strict_mfi_limit': 59, 'strict_tail_bbdelta': 0.788 } sell_params = { "bail_after_period": 480, "bail_ewo": -2, "bail_ewo_2": -2.5, "bail_ng_profit": -0.05, "bail_ng_profit_2": -0.07, "ignore_roi_ewo": 3.636, "ignore_roi_rsi": 31, "btc_bail_roc": -5.0, # value loaded from strategy } def informative_pairs(self): return [(self.btc_pair, self.btc_timeframe)] 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_candle['btc-roc'] < self.btc_bail_roc.value: return 0.01 if current_profit < 0: if current_profit > self.bail_ng_profit.value: if (current_time - timedelta(minutes=int(self.bail_after_period.value)) > trade.open_date_utc) & ( current_candle['EWO'] < self.bail_ewo.value): return 0.01 elif current_profit < self.bail_ng_profit_2.value: if current_candle['EWO'] < self.bail_ewo_2.value: return 0.01 return 0.99 def populate_btc_indicators(self, dataframe): informative = self.dp.get_pair_dataframe(self.btc_pair, self.btc_timeframe) informative['sma25'] = ta.SMA(informative, timeperiod=25) informative['sma100'] = ta.SMA(informative, timeperiod=100) informative['btc_sma_delta'] = (((informative['sma25'] - informative['sma100']) / informative['sma25']) * 100) informative['bull_100'] = informative['sma100'].gt(informative['sma100'].shift(10)) informative['bear_100'] = informative['sma100'].lt(informative['sma100'].shift(10)) informative['btc-roc'] = ta.ROC(informative, timeperiod=6) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.btc_timeframe, ffill=True) skip_columns = [(s + "_" + self.btc_timeframe) for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.rename( columns=lambda s: s.replace("_{}".format(self.btc_timeframe), "") if (not s in skip_columns) else s, inplace=True) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = self.populate_btc_indicators(dataframe.copy()) dataframe['hl2'] = (dataframe["high"] + dataframe["low"]) / 2 mid, lower = bollinger_bands(dataframe['hl2'], window_size=16, num_of_std=2) dataframe['lower'] = lower dataframe['bbdelta'] = (mid - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['EWO'] = EWO(dataframe, 5, 35) dataframe['3close'] = dataframe['close'].rolling(window=3).mean() return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # loose params bbdelta = self.loose_bbdelta_close.value closedelta = self.loose_closedelta_close.value tail_bbdelta = self.loose_tail_bbdelta.value mfi = self.loose_mfi_limit.value # strict params strict_bbdelta = self.strict_bbdelta_close.value strict_closedelta = self.strict_closedelta_close.value strict_tail_bbdelta = self.strict_tail_bbdelta.value strict_mfi = self.strict_mfi_limit.value dataframe.loc[ ( ( (dataframe['bull_100']) & (dataframe['mfi'] <= mfi) & ( dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * bbdelta) & dataframe['closedelta'].gt(dataframe['close'] * closedelta) & dataframe['tail'].lt(dataframe['bbdelta'] * tail_bbdelta) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) ) | (dataframe['bear_100']) & (dataframe['mfi'] <= strict_mfi) & ( dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * strict_bbdelta) & dataframe['closedelta'].gt(dataframe['close'] * strict_closedelta) & dataframe['tail'].lt(dataframe['bbdelta'] * strict_tail_bbdelta) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) ) ) ) | # Elliot wave trend: buy when it's really low -> usually goes up from here: (dataframe['EWO'] < self.ewo_low.value) | # Buy when Elliot wave trend is up, btc trend is up, and rsi not too high: ( (dataframe['bull_100']) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) ) , 'buy' ] = 1 # Avoid buying when BTC dumping dataframe.loc[(dataframe['btc-roc'] < self.btc_bail_roc.value) & dataframe['btc-roc'] < dataframe['btc-roc'].shift(), 'buy'] = 0 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: 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: # Ignore ROI if seems to go up: if sell_reason == 'roi': dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if last_candle['rsi'] > self.ignore_roi_rsi.value: return False if last_candle['EWO'] > self.ignore_roi_ewo.value: return False if last_candle['close'] > last_candle['3close']: return False return True return True