# source: https://raw.githubusercontent.com/ExaByt3s/strategies/056a7e4f99f428796a7b3145708dd4de0b441a1c/archived/DonchianBounce.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.hyper import CategoricalParameter, DecimalParameter, IntParameter from user_data.strategies import Config class github_ExaByt3s_strategies__DonchianBounce__20221107_180033(IStrategy): """ Simple strategy based on Contrarian Donchian Channel Bounce from the bottom band How to use it? > python3 ./freqtrade/main.py -s github_ExaByt3s_strategies__DonchianBounce__20221107_180033.py """ # Hyperparameters # Buy hyperspace params: buy_params = { "buy_adx": 43.0, "buy_adx_enabled": False, "buy_dc_gain": 0.05, "buy_dc_period": 60, "buy_ema_enabled": False, "buy_sar_enabled": False, "buy_sma_enabled": True, } buy_dc_period = IntParameter(10, 120, default=43, space="buy") buy_dc_gain = DecimalParameter(0.01, 0.10, decimals=2, default=0.05, space="buy") buy_adx = DecimalParameter(1, 99, decimals=0, default=60, space="buy") buy_sma_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_ema_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_adx_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_sar_enabled = CategoricalParameter([True, False], default=False, space="buy") sell_sar_enabled = CategoricalParameter([True, False], default=False, space="sell") sell_hold = CategoricalParameter([True, False], default=True, space="sell") # set the startup candles count to the longest average used (EMA, EMA etc) startup_candle_count = max(buy_dc_period.value, 20) # set common parameters 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_sell_signal = Config.use_sell_signal sell_profit_only = Config.sell_profit_only ignore_roi_if_buy_signal = Config.ignore_roi_if_buy_signal order_types = Config.order_types 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_middleband'] = bollinger['mid'] dataframe['bb_lowerband'] = bollinger['lower'] # Donchian Channels dataframe['dc_upper'] = ta.MAX(dataframe['high'], timeperiod=self.buy_dc_period.value) dataframe['dc_lower'] = ta.MIN(dataframe['low'], timeperiod=self.buy_dc_period.value) dataframe['dc_mid'] = ((dataframe['dc_upper'] + dataframe['dc_lower']) / 2) dataframe["dc_gain"] = ((dataframe["dc_upper"] - dataframe["close"]) / dataframe["close"]) # Fibonacci Levels (of Donchian Channel) dataframe['dc_dist'] = (dataframe['dc_upper'] - dataframe['dc_lower']) dataframe['dc_hf'] = dataframe['dc_upper'] - dataframe['dc_dist'] * 0.236 # Highest Fib dataframe['dc_chf'] = dataframe['dc_upper'] - dataframe['dc_dist'] * 0.382 # Centre High Fib dataframe['dc_clf'] = dataframe['dc_upper'] - dataframe['dc_dist'] * 0.618 # Centre Low Fib dataframe['dc_lf'] = dataframe['dc_upper'] - dataframe['dc_dist'] * 0.764 # Low Fib ##print("\nupper: ", dataframe['dc_upper']) ##print("\nlower: ", dataframe['dc_lower']) ##print("\nmid: ", dataframe['dc_mid']) # 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_hf'].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['adx'] > self.buy_adx.value) & # (dataframe['dm_plus'] >= dataframe['dm_minus']) ) # Potential gain greater than goal conditions.append(dataframe['dc_gain'] >= self.buy_dc_gain.value) # TRIGGERS # closing price above SAR #conditions.append(dataframe['sar'] < dataframe['close']) # price crosses or jumps above lower band, green candle conditions.append( (dataframe['dc_lower'].notnull()) & # (dataframe['close'] >= dataframe['open']) & # ( # (dataframe['close'] >= dataframe['dc_lower']) & # (dataframe['low'] <= dataframe['dc_lower']) # ) (dataframe['close'] >= dataframe['open']) & ( (qtpylib.crossed_above(dataframe['close'], dataframe['dc_lower'])) | ( (dataframe['close'] >= dataframe['dc_lower']) & (dataframe['close'].shift(1) < dataframe['dc_lower'].shift(1)) ) ) ) # 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 = [] # price crosses or jumps below high band, red candle conditions.append( (dataframe['dc_upper'].notnull()) & (dataframe['close'] < dataframe['open']) & ( (qtpylib.crossed_below(dataframe['close'], dataframe['dc_upper'])) | ( (dataframe['close'] <= dataframe['dc_upper']) & (dataframe['close'].shift(1) > dataframe['dc_upper'].shift(1)) ) ) ) if self.sell_sar_enabled.value: conditions.append(dataframe['sar'].notnull()) conditions.append(dataframe['close'] < dataframe['sar']) # build the dataframe using the conditions if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe