# source: https://raw.githubusercontent.com/XQuant42/ft_pantheon/b90b70c3a5d12124eb8ee2637cd4e96f1724caaf/user_data/strategies/DCBBBounce.py # --- 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 class Github_XQuant42_ft_pantheon__DCBBBounce__20251011_084903(IStrategy): """ Simple strategy based on Contrarian Donchian Channels crossing Bollinger Bands How to use it? > python3 ./freqtrade/main.py -s Github_XQuant42_ft_pantheon__DCBBBounce__20251011_084903.py """ # Hyperparameters # Buy hyperspace params: buy_params = { "buy_adx": 25.0, "buy_adx_enabled": True, "buy_ema_enabled": False, "buy_period": 52, "buy_sar_enabled": True, "buy_sma_enabled": False, } buy_period = IntParameter(10, 120, default=52, space="buy") buy_adx = DecimalParameter(1, 99, decimals=0, default=25, space="buy") buy_sma_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_ema_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_adx_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_sar_enabled = CategoricalParameter([True, False], default=True, space="buy") 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 = buy_period.value # The ROI, Stoploss and Trailing Stop values are typically found using hyperopt # if hold enabled, then use the 'common' ROI params if sell_hold.value: # ROI table: minimal_roi = { "0": 0.278, "39": 0.087, "124": 0.038, "135": 0 } # Trailing stop: trailing_stop = True trailing_stop_positive = 0.172 trailing_stop_positive_offset = 0.212 trailing_only_offset_is_reached = False # Stoploss: stoploss = -0.333 else: # ROI table: minimal_roi = { "0": 0.261, "40": 0.087, "95": 0.023, "192": 0 } # Stoploss: stoploss = -0.33 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.168 trailing_stop_positive_offset = 0.253 trailing_only_offset_is_reached = False # Optimal timeframe for the strategy timeframe = '5m' # run "populate_indicators" only for new candle process_only_new_candles = False # Experimental settings (configuration will overide these if set) use_sell_signal = True sell_profit_only = True ignore_roi_if_buy_signal = True # Optional order type mapping order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': 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. """ bollinger = qtpylib.weighted_bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_lowerband'] = bollinger['lower'] # Donchian Channels dataframe['dc_upper'] = ta.MAX(dataframe['high'], timeperiod=self.buy_period.value) dataframe['dc_lower'] = ta.MIN(dataframe['low'], timeperiod=self.buy_period.value) dataframe["dcbb_diff_upper"] = (dataframe["dc_upper"] - dataframe['bb_upperband']) dataframe["dcbb_diff_lower"] = (dataframe["dc_lower"] - dataframe['bb_lowerband']) # ADX dataframe['adx'] = ta.ADX(dataframe) dataframe['dm_plus'] = ta.PLUS_DM(dataframe) dataframe['dm_minus'] = ta.MINUS_DM(dataframe) # MFI dataframe['mfi'] = ta.MFI(dataframe) # 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) # 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 Parabolic dataframe['sar'] = ta.SAR(dataframe) # SMA - Simple Moving Average dataframe['sma'] = ta.SMA(dataframe, timeperiod=200) #print("\nSMA: ", dataframe['sma']) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # GUARDS AND TRENDS # check that volume is not 0 (can happen in testing, or if there are issues with exchange data) # conditions.append(dataframe['volume'] > 0) # during back testing, data can be undefined, so check conditions.append(dataframe['dc_upper'].notnull()) if self.buy_sar_enabled.value: conditions.append(dataframe['sar'].notnull()) conditions.append(dataframe['close'] < dataframe['sar']) if self.buy_sma_enabled.value: conditions.append(dataframe['sma'].notnull()) conditions.append(dataframe['close'] > dataframe['sma']) if self.buy_ema_enabled.value: conditions.append(dataframe['ema50'].notnull()) conditions.append(dataframe['close'] > dataframe['ema50']) # ADX with DM+ > DM- indicates uptrend if self.buy_adx_enabled.value: conditions.append( (dataframe['adx'] > self.buy_adx.value) & (dataframe['dm_plus'] >= dataframe['dm_minus']) ) # TRIGGERS # closing price above SAR #conditions.append(dataframe['sar'] < dataframe['close']) # green candle, Lower Bollinger goes below Donchian conditions.append( (dataframe['dcbb_diff_lower'].notnull()) & (dataframe['close'] >= dataframe['open']) & (qtpylib.crossed_above(dataframe['dcbb_diff_lower'], 0)) ) # build the dataframe using the conditions if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe def populate_sell_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 hold, then don't set a sell signal if self.sell_hold.value: dataframe.loc[(dataframe['close'].notnull() ), 'sell'] = 0 else: conditions = [] # Upper Bollinger goes above Donchian conditions.append( (dataframe['dcbb_diff_upper'].notnull()) & #(dataframe['close'] <= dataframe['open']) & (qtpylib.crossed_below(dataframe['dcbb_diff_upper'], 0)) ) # build the dataframe using the conditions if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe