# source: https://raw.githubusercontent.com/TheoBrigitte/freqtrade/82c1c1e1f5f3457fa5376f65124042aff88d7493/strategies/sponsors/BinMfiBTCv5003.py # --- Do not remove these libs --- import random from datetime import datetime from datetime import timedelta from functools import reduce 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 import stoploss_from_open from freqtrade.strategy.interface import IStrategy # -------------------------------------------------------------------------------- # Author: rextea 2021/05/29 Version: 5.0 # -------------------------------------------------------------------------------- # 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_TheoBrigitte_freqtrade__BinMfiBTCv5003__20241203_205711(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 } # protections = [ # { # "method": "MaxDrawdown", # "lookback_period": 280, # "stop_duration": 180, # "max_allowed_drawdown": 0.20 # } # ] trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True process_only_new_candles = True startup_candle_count: int = 100 optimize_bear = True optimize_bull = True optimize_wild = True optimize_buy_1 = False optimize_buy_2 = False optimize_buy_3 = True optimize_stoploss = False optimize_ignore_roi = False optimize_protections = False btc_smadelta_buy = DecimalParameter(0.9, 1.10, default=1.03, space='buy', optimize=False) # -------------------------- # Buy condition (#1) params: # -------------------------- strict_bbdelta_close = DecimalParameter(0.0, 0.045, default=0.03344, space='buy', optimize=optimize_buy_1 and optimize_bear) strict_closedelta_close = DecimalParameter(-0.015, 0.045, default=0.00681, space='buy', optimize=optimize_buy_1 and optimize_bear) strict_tail_bbdelta = DecimalParameter(0.0, 2.0, default=1.73588, space='buy', optimize=optimize_buy_1 and optimize_bear) strict_mfi_limit = IntParameter(-15, 70, default=25, space='buy', optimize=optimize_buy_1 and optimize_bear) loose_bbdelta_close = DecimalParameter(0.0, 0.045, default=0.03344, space='buy', optimize=optimize_buy_1 and optimize_bull) loose_closedelta_close = DecimalParameter(-0.015, 0.045, default=0.00681, space='buy', optimize=optimize_buy_1 and optimize_bull) loose_tail_bbdelta = DecimalParameter(0.0, 2.0, default=1.73588, space='buy', optimize=optimize_buy_1 and optimize_bull) loose_mfi_limit = IntParameter(-15, 100, default=25, space='buy', optimize=optimize_buy_1 and optimize_bull) wild_bbdelta_close = DecimalParameter(0.0, 0.045, default=0.03344, space='buy', optimize=optimize_buy_1 and optimize_wild) wild_closedelta_close = DecimalParameter(-0.015, 0.045, default=0.00681, space='buy', optimize=optimize_buy_1 and optimize_wild) wild_tail_bbdelta = DecimalParameter(0.0, 2.0, default=1.73588, space='buy', optimize=optimize_buy_1 and optimize_wild) wild_mfi_limit = IntParameter(-15, 100, default=25, space='buy', optimize=optimize_buy_1 and optimize_wild) # -------------------------- # Buy condition (#2) params: # -------------------------- loose_ewo_bottom = DecimalParameter(-20.0, -8.0, default=-12.0, space='buy', optimize=optimize_buy_2 and optimize_bull) strict_ewo_bottom = DecimalParameter(-20.0, -8.0, default=-12.0, space='buy', optimize=optimize_buy_2 and optimize_bear) wild_ewo_bottom = DecimalParameter(-20.0, -8.0, default=-12.0, space='buy', optimize=optimize_buy_2 and optimize_wild) # -------------------------- # Buy condition (#3) params: # -------------------------- loose_ewo_bull = DecimalParameter(1.0, 20.0, default=6.0, space='buy', optimize=optimize_buy_3 and optimize_bull) loose_ewo_rsi = IntParameter(30, 70, default=55, space='buy', optimize=optimize_buy_3 and optimize_bull) strict_ewo_bull = DecimalParameter(1.0, 20.0, default=6.0, space='buy', optimize=optimize_buy_3 and optimize_bear) strict_ewo_rsi = IntParameter(30, 70, default=55, space='buy', optimize=optimize_buy_3 and optimize_bear) wild_ewo_bull = DecimalParameter(1.0, 20.0, default=6.0, space='buy', optimize=optimize_buy_3 and optimize_wild) wild_ewo_rsi = IntParameter(30, 70, default=55, space='buy', optimize=optimize_buy_3 and optimize_wild) # ---------------- # StopLoss params: # ---------------- btc_bail_pct = DecimalParameter(-0.1, 0, default=-0.018, space='sell', optimize=optimize_stoploss) btc_bail_pct_2 = DecimalParameter(-0.1, 0, default=-0.02, space='sell', optimize=optimize_stoploss) btc_bail_roc = DecimalParameter(-20.0, -1.0, default=-5.5, space='sell', optimize=optimize_stoploss) bail_after_period = CategoricalParameter([60, 120, 240, 280, 340, 400, 480, 600, 800, 1200], default=480, space='sell', optimize=optimize_stoploss) bail_ng_profit = CategoricalParameter([0, -0.01, -0.02, -0.03, -0.05, -0.06], default=-0.03, space='sell', optimize=optimize_stoploss) bail_ng_profit_2 = CategoricalParameter([-0.05, -0.06, -0.07, -0.08, -0.09], default=-0.07, space='sell', optimize=optimize_stoploss) bail_ewo = CategoricalParameter([0, -0.5, -1, -1.5, -2, -2.5], default=-1, space='sell', optimize=optimize_stoploss) bail_ewo_2 = CategoricalParameter([0, -0.5, -1, -1.5, -2, -2.5], default=0, space='sell', optimize=optimize_stoploss) # ------------------ # Ignore ROI params: # ------------------ ignore_roi_ewo = DecimalParameter(0.0, 10.0, default=3.0, space='sell', optimize=optimize_ignore_roi) ignore_roi_rsi = IntParameter(30, 90, default=50, space='sell', optimize=optimize_ignore_roi) # ------------------- # Protections params: # ------------------- btc_minus_pct = DecimalParameter(-0.1, 0, default=-0.004, space='buy', optimize=optimize_protections) btc_plus_pct = DecimalParameter(-0.01, 0.05, default=-0.005, space='buy', optimize=optimize_protections) btc_low_rsi = IntParameter(10, 70, default=35, space='buy', optimize=optimize_protections) btc_sma_ratio = DecimalParameter(1.0, 1.05, default=1.01, space='buy', optimize=optimize_protections) # Buy hyperspace params: buy_params = { "loose_ewo_bull": 16.831, "loose_ewo_rsi": 38, "strict_ewo_bull": 4.377, "strict_ewo_rsi": 35, "wild_ewo_bull": 19.547, "wild_ewo_rsi": 51, "btc_low_rsi": 35, # value loaded from strategy "btc_minus_pct": -0.004, # value loaded from strategy "btc_plus_pct": -0.005, # value loaded from strategy "btc_sma_ratio": 1.01, # value loaded from strategy "btc_smadelta_buy": 1.03, # value loaded from strategy "loose_bbdelta_close": 0.041, # value loaded from strategy "loose_closedelta_close": 0.044, # value loaded from strategy "loose_ewo_bottom": -9.059, # value loaded from strategy "loose_mfi_limit": 75, # value loaded from strategy "loose_tail_bbdelta": 0.099, # value loaded from strategy "strict_bbdelta_close": 0.012, # value loaded from strategy "strict_closedelta_close": 0.042, # value loaded from strategy "strict_ewo_bottom": -18.952, # value loaded from strategy "strict_mfi_limit": 38, # value loaded from strategy "strict_tail_bbdelta": 0.103, # value loaded from strategy "wild_bbdelta_close": 0.012, # value loaded from strategy "wild_closedelta_close": 0.036, # value loaded from strategy "wild_ewo_bottom": -8.294, # value loaded from strategy "wild_mfi_limit": 58, # value loaded from strategy "wild_tail_bbdelta": 1.081, # value loaded from strategy } # Sell hyperspace params: sell_params = { "ignore_roi_ewo": 2.989, "ignore_roi_rsi": 57, "bail_after_period": 240, # value loaded from strategy "bail_ewo": -0.5, # value loaded from strategy "bail_ewo_2": -2.5, # value loaded from strategy "bail_ng_profit": -0.06, # value loaded from strategy "bail_ng_profit_2": -0.08, # value loaded from strategy "btc_bail_pct": -0.024, # value loaded from strategy "btc_bail_pct_2": -0.091, # value loaded from strategy "btc_bail_roc": -1.824, # value loaded from strategy } def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] informative_pairs.append((self.btc_pair, self.btc_timeframe)) return informative_pairs def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # --------------- # BTC indicators: # --------------- informative = self.dp.get_pair_dataframe(self.btc_pair, self.btc_timeframe) informative['btc-open'] = informative['open'] informative['btc-low'] = informative['low'] informative['btc-close'] = informative['close'] informative['btc-volume'] = informative['volume'] informative['btc-sma25'] = ta.SMA(informative, timeperiod=25) informative['btc-sma50'] = ta.SMA(informative, timeperiod=50) informative['btc-sma100'] = ta.SMA(informative, timeperiod=100) informative['btc-bull'] = informative['btc-sma25'].lt(informative['close']) informative['btc-bear'] = informative['btc-sma25'].gt(informative['close']) informative['btc-roc'] = ta.ROC(informative, timeperiod=6) informative['btc-rsi'] = ta.RSI(informative, timeperiod=14) informative['btc-mfi'] = ta.MFI(informative, timeperiod=14) informative['btc-pct-change'] = informative['close'].pct_change() informative['btc-plus-di'] = ta.PLUS_DI(informative) informative['btc-minus-di'] = ta.MINUS_DI(informative) informative['btc-red-candle'] = np.where(informative['btc-close'] < informative['btc-close'].shift(), 1, 0) 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) # ---------------- # Coin Indicators: # ---------------- 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() dataframe['pct-change'] = dataframe['close'].pct_change() dataframe['sma-25'] = ta.SMA(dataframe, timeperiod=25) dataframe['sma-50'] = ta.SMA(dataframe, timeperiod=50) dataframe['coin-bull'] = dataframe['sma-25'].lt(dataframe['close']) dataframe['coin-bear'] = dataframe['sma-50'].gt(dataframe['close']) dataframe['4h-pct-change'] = dataframe['close'].pct_change(48) # --------------- # 1h indicators: # --------------- informative = self.dp.get_pair_dataframe(metadata['pair'], '1h') informative['1h-rsi'] = ta.RSI(informative, timeperiod=14) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, '1h', ffill=True) skip_columns = [(s + "_1h") for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.rename( columns=lambda s: s.replace("_1h", "") if (not s in skip_columns) else s, inplace=True) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # --------------- # Buy conditions: # --------------- buy_conditions = [] # WILD MARKET CONDITIONS: buy_conditions.append( (dataframe['btc-close'] > dataframe['btc-sma100']) & ( (dataframe['mfi'] <= self.wild_mfi_limit.value) & ( dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * self.wild_bbdelta_close.value) & dataframe['closedelta'].gt(dataframe['close'] * self.wild_closedelta_close.value) & dataframe['tail'].lt(dataframe['bbdelta'] * self.wild_tail_bbdelta.value) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) ) | # Elliot wave trend: buy when it's really low and rsi started ascending ( (dataframe['EWO'] < self.wild_ewo_bottom.value) & (dataframe['rsi'] > dataframe['rsi'].shift()) ) | # Buy when Elliot wave trend is up, coin sma is up, and rsi not too high: ( (dataframe['coin-bull']) & (dataframe['EWO'] > self.wild_ewo_bull.value) & (dataframe['EWO'].rolling(16).min() < 0) & (dataframe['1h-rsi'] < self.wild_ewo_rsi.value) ) ) ) # BULL MARKET CONDITIONS: buy_conditions.append( (dataframe['btc-close'] > dataframe['btc-sma50'] * self.btc_smadelta_buy.value) & ( (dataframe['mfi'] <= self.loose_mfi_limit.value) & ( dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * self.loose_bbdelta_close.value) & dataframe['closedelta'].gt(dataframe['close'] * self.loose_closedelta_close.value) & dataframe['tail'].lt(dataframe['bbdelta'] * self.loose_tail_bbdelta.value) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) ) | # Elliot wave trend: buy when it's really low and rsi started ascending ( (dataframe['EWO'] < self.loose_ewo_bottom.value) & (dataframe['rsi'] > dataframe['rsi'].shift()) ) | # Buy when Elliot wave trend is up, coin sma is up, and rsi not too high: ( (dataframe['coin-bull']) & (dataframe['EWO'] > self.loose_ewo_bull.value) & (dataframe['EWO'].rolling(16).min() < 0) & (dataframe['1h-rsi'] < self.loose_ewo_rsi.value) ) ) ) # BEAR MARKET CONDITIONS: buy_conditions.append( (dataframe['btc-close'] < dataframe['btc-sma50'] * self.btc_smadelta_buy.value) & ( (dataframe['mfi'] <= self.strict_mfi_limit.value) & ( dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * self.strict_bbdelta_close.value) & dataframe['closedelta'].gt(dataframe['close'] * self.strict_closedelta_close.value) & dataframe['tail'].lt(dataframe['bbdelta'] * self.strict_tail_bbdelta.value) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) ) | # Elliot wave trend: buy when it's really low and rsi started ascending ( (dataframe['EWO'] < self.strict_ewo_bottom.value) & (dataframe['rsi'] > dataframe['rsi'].shift()) ) | # Buy when Elliot wave trend is up, coin sma is up, and rsi not too high: ( (dataframe['coin-bull']) & (dataframe['EWO'] > self.strict_ewo_bull.value) & (dataframe['EWO'].rolling(16).min() < 0) & (dataframe['1h-rsi'] < self.strict_ewo_rsi.value) ) ) ) if buy_conditions: dataframe.loc[reduce(lambda x, y: x | y, buy_conditions), 'buy'] = 1 # dataframe.loc[:, 'buy'] = 1 # ------------ # Protections: # ------------ protections = [] protections.append( (dataframe['pct-change'].rolling(2).sum() < -0.063) ) protections.append( (dataframe['4h-pct-change'].rolling(3).max() > 0.225) & (dataframe['pct-change'] < 0) & (dataframe['rsi'] > 53) ) protections.append( (dataframe['open'].shift(1) > dataframe['close'].shift(1)) & (dataframe['open'].shift(2) > dataframe['close'].shift(2)) & (dataframe['open'].shift(3) > dataframe['close'].shift(3)) & (dataframe['open'].shift(4) > dataframe['close'].shift(4)) & (dataframe['volume'] > dataframe['volume'].shift(1)) ) protections.append( ((dataframe['btc-close'] * self.btc_sma_ratio.value) < dataframe[f'btc-sma25']) & (dataframe['btc-rsi'] <= self.btc_low_rsi.value) & (dataframe['btc-pct-change'].rolling(3).sum() < self.btc_minus_pct.value) & (dataframe['btc-pct-change'] < self.btc_plus_pct.value) ) protections.append( (dataframe['btc-open'].shift(1) > dataframe['btc-close'].shift(1)) & (dataframe['btc-open'].shift(2) > dataframe['btc-close'].shift(2)) & (dataframe['btc-open'].shift(3) > dataframe['btc-close'].shift(3)) & (dataframe['btc-open'].shift(4) > dataframe['btc-close'].shift(4)) & (dataframe['btc-volume'] > dataframe['btc-volume'].shift(1)) ) if protections: dataframe.loc[reduce(lambda x, y: x | y, protections), 'buy'] = 0 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'sell'] = 0 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() # immediately bailout when BTC dumping: if current_candle['btc-pct-change'] < self.btc_bail_pct.value or \ current_candle['btc-roc'] < self.btc_bail_roc.value or \ (current_candle['btc-pct-change'] < self.btc_bail_pct_2.value and current_candle['btc-bear']): return -0.01 # Manage losing trades: if current_profit < 0 and (current_time - timedelta(minutes=10) > trade.open_date_utc): if current_candle['coin-bear'] and current_candle['pct-change'] < -0.033: return -0.01 # if current_candle['EWO'] < -4 and current_candle['EWO'] < before_candle['EWO']: # return -0.01 if current_profit > self.bail_ng_profit.value and \ current_time - timedelta(minutes=int(self.bail_after_period.value)) > trade.open_date_utc and \ current_candle['EWO'] < self.bail_ewo.value: return -0.01 if current_profit < self.bail_ng_profit_2.value and \ current_candle['EWO'] < self.bail_ewo_2.value: return -0.01 return 0.99 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, **kwargs) -> bool: # if trade.open_date_utc == current_time: # return False # 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 or \ last_candle['EWO'] > self.ignore_roi_ewo.value or \ last_candle['close'] > last_candle['3close']: return False return True # Shuffle that backtest bro # def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, # time_in_force: str, current_time: datetime, **kwargs) -> bool: # rand = random.randint(1, 10) # if rand == 1: # return False # # return True