# source: https://raw.githubusercontent.com/vaskosmihaylov/nfi-custom-strategies/f12b9b6bd2554931091c940e4fa11b84f51da9d9/user_data/strategies/Bounce/BollingerBounce.py # directory_url: https://github.com/vaskosmihaylov/nfi-custom-strategies/blob/main/user_data/strategies/Bounce/ # User: vaskosmihaylov # Repository: nfi-custom-strategies # -------------------- # --- Do not remove these libs --- 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 freqtrade.vendor.qtpylib.indicators as qtpylib import numpy # noqa from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter import Config class Github_vaskosmihaylov_nfi_custom_strategies__BollingerBounce__20260725_180414(IStrategy): """ Simple strategy based on Bollinger Band Bounce from bottom How to use it? > python3 ./freqtrade/main.py -s Github_vaskosmihaylov_nfi_custom_strategies__BollingerBounce__20260725_180414 """ # Hyperparameters # Buy hyperspace params: buy_params = { "buy_bb_gain": 0.04, "buy_fisher": -0.81, "buy_fisher_enabled": True, "buy_mfi": 13.0, "buy_mfi_enabled": False, } buy_mfi = DecimalParameter(10, 40, decimals=0, default=37.0, space="buy") buy_fisher = DecimalParameter(-1, 1, decimals=2, default=0.15, space="buy") # Bollinger Band 'gain' (% difference between current price and upper band). # Since we are looking for potential swings of >2%, we look for potential of more than that buy_bb_gain = DecimalParameter(0.01, 0.10, decimals=2, default=0.05, space="buy") # Categorical parameters that control whether a trend/check is used or not buy_mfi_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_fisher_enabled = CategoricalParameter([True, False], default=True, space="buy") sell_fisher = DecimalParameter(-1, 1, decimals=2, default=-0.62, space="sell") sell_hold = CategoricalParameter([True, False], default=True, space="sell") # set the startup candles count to the longest average used (SMA, EMA etc) startup_candle_count = 20 # set common parameters can_short: bool = False minimal_roi = Config.minimal_roi trailing_stop = Config.trailing_stop trailing_stop_positive = Config.trailing_stop_positive trailing_stop_positive_offset = Config.trailing_stop_positive_offset trailing_only_offset_is_reached = Config.trailing_only_offset_is_reached stoploss = Config.stoploss timeframe = Config.timeframe process_only_new_candles = Config.process_only_new_candles use_exit_signal = Config.use_exit_signal exit_profit_only = Config.exit_profit_only ignore_roi_if_entry_signal = Config.ignore_roi_if_entry_signal order_types = Config.order_types @property def protections(self): return [ { "method": "StoplossGuard", "lookback_period_candles": Config.stoploss_guard_lookback_candles, "trade_limit": Config.stoploss_guard_trade_limit, "stop_duration_candles": Config.stoploss_guard_duration_candles, "only_per_pair": True, } ] def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. """ # MFI dataframe['mfi'] = ta.MFI(dataframe) # SMA - Simple Moving Average dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # RSI dataframe['rsi'] = ta.RSI(dataframe) # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) # Bollinger bands #bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) bollinger = qtpylib.weighted_bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) # A little different than normal - adjust band values based on buy_bb_ratio #dataframe['bb_upperband'] = bollinger['mid'] + (bollinger['upper']-bollinger['mid'])*self.buy_bb_uratio.value #dataframe['bb_middleband'] = bollinger['mid'] #dataframe['bb_lowerband'] = bollinger['mid'] - (bollinger['mid']-bollinger['lower'])*self.buy_bb_lratio.value dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_lowerband'] = bollinger['lower'] dataframe["bb_gain"] = ((dataframe["bb_upperband"] - dataframe["close"]) / dataframe["close"]) # EMA - Exponential Moving Average dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # SAR Parabol dataframe['sar'] = ta.SAR(dataframe) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # GUARDS AND TRENDS if self.buy_mfi_enabled.value: conditions.append(dataframe['mfi'] <= self.buy_mfi.value) if self.buy_fisher_enabled.value: conditions.append(dataframe['fisher_rsi'] < self.buy_fisher.value) # TRIGGERS # potential gain > goal conditions.append(dataframe['bb_gain'] >= self.buy_bb_gain.value) # current candle is green conditions.append(dataframe['close'] > dataframe['open']) # candle crosses lower BB boundary conditions.append( (dataframe['open'] < dataframe['bb_lowerband']) & (dataframe['close'] >= dataframe['bb_lowerband']) ) # check that volume is not 0 conditions.append(dataframe['volume'] > 0) # build the dataframe using the conditions if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ if self.sell_hold.value: dataframe.loc[(dataframe['close'].notnull() ), 'sell'] = 0 return dataframe # Exit long position if price is above upper band or strong sell signal dataframe.loc[ ( ( (dataframe['open'] > dataframe['bb_upperband']) | (dataframe['close'] > dataframe['bb_upperband']) ) | ( (dataframe['fisher_rsi'] > self.sell_fisher.value) & (dataframe['sar'] > dataframe['close']) #(dataframe['mfi'] > 70) ) ), 'sell'] = 1 return dataframe def leverage(self, pair: str, current_time, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag, side: str, **kwargs) -> float: return min(Config.trade_leverage, max_leverage)