# source: https://raw.githubusercontent.com/Bldnr94/FreqTrade/ac41453a47a5efd9aa04107c103b430ceddc2af7/ft_userdata/user_data/strategies-internet/freqtrade-strategies/strategies/Bandtastic-v2.py import talib.abstract as ta import numpy as np # noqa import pandas as pd from functools import reduce from pandas import DataFrame import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.strategy import IStrategy, CategoricalParameter, DecimalParameter, IntParameter, RealParameter __author__ = "Robert Roman" __copyright__ = "Free For Use" __license__ = "MIT" __version__ = "1.0" __maintainer__ = "Robert Roman" __email__ = "robertroman7@gmail.com" __BTC_donation__ = "3FgFaG15yntZYSUzfEpxr5mDt1RArvcQrK" # Optimized With Sharpe Ratio and 1 year data # 199/40000: 30918 trades. 18982/3408/8528 Wins/Draws/Losses. Avg profit 0.39%. Median profit 0.65%. Total profit 119934.26007495 USDT ( 119.93%). Avg duration 8:12:00 min. Objective: -127.60220 class Github_Bldnr94_FreqTrade__Bandtastic_v2__20240622_175427(IStrategy): INTERFACE_VERSION = 3 can_short = True timeframe = '15m' # ROI table: minimal_roi = { "0": 0.162, "69": 0.097, "229": 0.061, "566": 0 } # Stoploss: stoploss = -0.345 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.058 trailing_only_offset_is_reached = False # Hyperopt Entry Parameters entry_fastema = IntParameter(low=1, high=236, default=211, space='entry', optimize=True, load=True) entry_slowema = IntParameter(low=1, high=126, default=364, space='entry', optimize=True, load=True) entry_rsi = IntParameter(low=15, high=70, default=52, space='entry', optimize=True, load=True) entry_mfi = IntParameter(low=15, high=70, default=30, space='entry', optimize=True, load=True) entry_rsi_enabled = CategoricalParameter([True, False], space='entry', optimize=True, default=False) entry_mfi_enabled = CategoricalParameter([True, False], space='entry', optimize=True, default=False) entry_ema_enabled = CategoricalParameter([True, False], space='entry', optimize=True, default=False) entry_trigger = CategoricalParameter(["bb_lower1", "bb_lower2", "bb_lower3", "bb_lower4"], default="bb_lower1", space="entry") # Hyperopt Exit Parameters exit_fastema = IntParameter(low=1, high=365, default=7, space='exit', optimize=True, load=True) exit_slowema = IntParameter(low=1, high=365, default=6, space='exit', optimize=True, load=True) exit_rsi = IntParameter(low=30, high=100, default=57, space='exit', optimize=True, load=True) exit_mfi = IntParameter(low=30, high=100, default=46, space='exit', optimize=True, load=True) exit_rsi_enabled = CategoricalParameter([True, False], space='exit', optimize=True, default=False) exit_mfi_enabled = CategoricalParameter([True, False], space='exit', optimize=True, default=True) exit_ema_enabled = CategoricalParameter([True, False], space='exit', optimize=True, default=False) exit_trigger = CategoricalParameter(["exit-bb_upper1", "exit-bb_upper2", "exit-bb_upper3", "exit-bb_upper4"], default="exit-bb_upper2", space="exit") # Update strategy level settings use_exit_signal = True exit_profit_only = True exit_profit_offset: 0.01 ignore_roi_if_entry_signal = False # Update unfilled timeout settings unfilledtimeout = { "entry": 10, "exit": 10, "exit_timeout_count": 0, "unit": "minutes" } # Update order pricing settings order_types = { 'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60 } entry_pricing = { "price_side": "same", "use_order_book": True, "order_book_top": 1, "price_last_balance": 0.0, "check_depth_of_market": { "enabled": False, "bids_to_ask_delta": 1 } } exit_pricing = { "price_side": "same", "use_order_book": True, "order_book_top": 1, "price_last_balance": 0.0 } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # RSI dataframe['rsi'] = ta.RSI(dataframe) dataframe['mfi'] = ta.MFI(dataframe) # Bollinger Bands 1,2,3 and 4 bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1) dataframe['bb_lowerband1'] = bollinger1['lower'] dataframe['bb_middleband1'] = bollinger1['mid'] dataframe['bb_upperband1'] = bollinger1['upper'] 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'] bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3) dataframe['bb_lowerband3'] = bollinger3['lower'] dataframe['bb_middleband3'] = bollinger3['mid'] dataframe['bb_upperband3'] = bollinger3['upper'] bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=4) dataframe['bb_lowerband4'] = bollinger4['lower'] dataframe['bb_middleband4'] = bollinger4['mid'] dataframe['bb_upperband4'] = bollinger4['upper'] # Build EMA rows - combine all ranges to a single set to avoid duplicate calculations. for period in set( list(self.entry_fastema.range) + list(self.entry_slowema.range) + list(self.exit_fastema.range) + list(self.exit_slowema.range) ): dataframe[f'EMA_{period}'] = ta.EMA(dataframe, timeperiod=period) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # GUARDS if self.entry_rsi_enabled.value: conditions.append(dataframe['rsi'] < self.entry_rsi.value) if self.entry_mfi_enabled.value: conditions.append(dataframe['mfi'] < self.entry_mfi.value) if self.entry_ema_enabled.value: conditions.append(dataframe[f'EMA_{self.entry_fastema.value}'] > dataframe[f'EMA_{self.entry_slowema.value}']) # TRIGGERS if self.entry_trigger.value == 'bb_lower1': conditions.append(dataframe["close"] < dataframe['bb_lowerband1']) if self.entry_trigger.value == 'bb_lower2': conditions.append(dataframe["close"] < dataframe['bb_lowerband2']) if self.entry_trigger.value == 'bb_lower3': conditions.append(dataframe["close"] < dataframe['bb_lowerband3']) if self.entry_trigger.value == 'bb_lower4': conditions.append(dataframe["close"] < dataframe['bb_lowerband4']) # Check that volume is not 0 conditions.append(dataframe['volume'] > 0) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # GUARDS if self.exit_rsi_enabled.value: conditions.append(dataframe['rsi'] > self.exit_rsi.value) if self.exit_mfi_enabled.value: conditions.append(dataframe['mfi'] > self.exit_mfi.value) if self.exit_ema_enabled.value: conditions.append(dataframe[f'EMA_{self.exit_fastema.value}'] < dataframe[f'EMA_{self.exit_slowema.value}']) # TRIGGERS if self.exit_trigger.value == 'exit-bb_upper1': conditions.append(dataframe["close"] > dataframe['bb_upperband1']) if self.exit_trigger.value == 'exit-bb_upper2': conditions.append(dataframe["close"] > dataframe['bb_upperband2']) if self.exit_trigger.value == 'exit-bb_upper3': conditions.append(dataframe["close"] > dataframe['bb_upperband3']) if self.exit_trigger.value == 'exit-bb_upper4': conditions.append(dataframe["close"] > dataframe['bb_upperband4']) # Check that volume is not 0 conditions.append(dataframe['volume'] > 0) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1 return dataframe