# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/Discord_BuzzzMoneyV1.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from freqtrade.strategy import IStrategy from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter from freqtrade.strategy import merge_informative_pair # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from functools import reduce from datetime import datetime, timedelta class Github_remiotore_freqtrade__Discord_BuzzzMoneyV1__20260111_210550(IStrategy): """ This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/ You can: :return: a Dataframe with all mandatory indicators for the strategies - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your strategy - Add any lib you need to build your strategy You must keep: - the lib in the section "Do not remove these libs" - the methods: populate_indicators, populate_buy_trend, populate_sell_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 2 buy_params = { "buy_dip_candles_1": 3, "buy_dip_candles_2": 29, "buy_dip_candles_3": 130, "buy_dip_threshold_1": 0.13, "buy_dip_threshold_2": 0.2, "buy_dip_threshold_3": 0.25, "base_nb_candles_buy": 19, # value loaded from strategy "low_offset": 0.969, # value loaded from strategy } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 30, # value loaded from strategy "high_offset": 1.012, # value loaded from strategy } # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "0": 10, } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.999 # Trailing stoploss trailing_stop = False trailing_only_offset_is_reached = False trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.03 # Custom stoploss use_custom_stoploss = False # Optimal timeframe for the strategy. timeframe = '5m' # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the "ask_strategy" section in the config. use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = True inf_1h = '1h' # informative tf # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } plot_config = { # Main plot indicators (Moving averages, ...) 'main_plot': { }, 'subplots': { # Subplots - each dict defines one additional plot "RSI": { 'rsi': {'color': 'black'}, }, "MACD": { 'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}, }, "ADOSC": { 'ADOSC' : { 'color': 'green'}, }, "STOCH": { 'slowk': { 'color': 'blue'}, 'slowd': { 'color': 'orange'}, }, } } optimize_dip = False optimize_non_dip = True base_nb_candles_buy = IntParameter(5, 80, default=30, space='buy', optimize=optimize_non_dip, load=True) low_offset = DecimalParameter(0.8, 0.99, default=0.958, space='buy', optimize=optimize_non_dip, load=True) buy_dip_threshold_1 = DecimalParameter(0.08, 0.2, default=0.12, space='buy', decimals=2, optimize=optimize_dip, load=True) buy_dip_threshold_2 = DecimalParameter(0.02, 0.5, default=0.28, space='buy', decimals=2, optimize=optimize_dip, load=True) buy_dip_threshold_3 = DecimalParameter(0.02, 0.5, default=0.28, space='buy', decimals=2, optimize=optimize_dip, load=True) buy_dip_candles_1 = IntParameter(1, 20, default=2, space='buy', optimize=optimize_dip, load=True) buy_dip_candles_2 = IntParameter(1, 40, default=10, space='buy', optimize=optimize_dip, load=True) buy_dip_candles_3 = IntParameter(40, 140, default=132, space='buy', optimize=optimize_dip, load=True) # sell params base_nb_candles_sell = IntParameter(5, 80, default=30, load=True, optimize=True, space='sell') high_offset = DecimalParameter(0.8, 1.1, default=1.012, load=True, optimize=True, space='sell') sell_rsi_main = DecimalParameter(72.0, 90.0, default=80, space='sell', decimals=2, optimize=True, load=True) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.inf_1h) for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) # EMA informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # SMA # informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200) # informative_1h['sma_200_dec'] = informative_1h['sma_200'] < informative_1h['sma_200'].shift(20) # RSI informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) # stochastic slow stoch = ta.STOCH(informative_1h) informative_1h['slowd'] = stoch['slowd'] informative_1h['slowk'] = stoch['slowk'] # SSL Channels #ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20) #informative_1h['ssl_down'] = ssl_down_1h #informative_1h['ssl_up'] = ssl_up_1h #dataframe['ADOSC'] = ta.ADOSC(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume']) return informative_1h 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. :param dataframe: Dataframe with data from the exchange :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) # Momentum Indicators # ------------------------------------ # ADX #dataframe['adx'] = ta.ADX(dataframe) # # Plus Directional Indicator / Movement # dataframe['plus_dm'] = ta.PLUS_DM(dataframe) # dataframe['plus_di'] = ta.PLUS_DI(dataframe) # # Minus Directional Indicator / Movement # dataframe['minus_dm'] = ta.MINUS_DM(dataframe) # dataframe['minus_di'] = ta.MINUS_DI(dataframe) # # Aroon, Aroon Oscillator # aroon = ta.AROON(dataframe) # dataframe['aroonup'] = aroon['aroonup'] # dataframe['aroondown'] = aroon['aroondown'] # dataframe['aroonosc'] = ta.AROONOSC(dataframe) # # Awesome Oscillator dataframe['lame_ao'] = qtpylib.awesome_oscillator(dataframe, weighted=False, fast=34, slow=5) # # Keltner Channel # keltner = qtpylib.keltner_channel(dataframe) # dataframe["kc_upperband"] = keltner["upper"] # dataframe["kc_lowerband"] = keltner["lower"] # dataframe["kc_middleband"] = keltner["mid"] # dataframe["kc_percent"] = ( # (dataframe["close"] - dataframe["kc_lowerband"]) / # (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) # ) # dataframe["kc_width"] = ( # (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"] # ) # # Ultimate Oscillator # dataframe['uo'] = ta.ULTOSC(dataframe) # # Commodity Channel Index: values [Oversold:-100, Overbought:100] # dataframe['cci'] = ta.CCI(dataframe) # 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'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) # # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy) # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # # Stochastic Slow stoch = ta.STOCH(dataframe) dataframe['slowd'] = stoch['slowd'] dataframe['slowk'] = stoch['slowk'] # Stochastic Fast #stoch_fast = ta.STOCHF(dataframe) #dataframe['fastd'] = stoch_fast['fastd'] #dataframe['fastk'] = stoch_fast['fastk'] # # Stochastic RSI # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this. # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results. # stoch_rsi = ta.STOCHRSI(dataframe) # dataframe['fastd_rsi'] = stoch_rsi['fastd'] # dataframe['fastk_rsi'] = stoch_rsi['fastk'] # MACD macd = ta.MACD(dataframe, fastperiod=8, slowperiod=21, signalperiod=5) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # MFI #dataframe['mfi'] = ta.MFI(dataframe) # # ROC # dataframe['roc'] = ta.ROC(dataframe) # Overlap Studies # ------------------------------------ # Bollinger Bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] # dataframe["bb_percent"] = ( # (dataframe["close"] - dataframe["bb_lowerband"]) / # (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) # ) # dataframe["bb_width"] = ( # (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"] # ) # Bollinger Bands - Weighted (EMA based instead of SMA) # weighted_bollinger = qtpylib.weighted_bollinger_bands( # qtpylib.typical_price(dataframe), window=20, stds=2 # ) # dataframe["wbb_upperband"] = weighted_bollinger["upper"] # dataframe["wbb_lowerband"] = weighted_bollinger["lower"] # dataframe["wbb_middleband"] = weighted_bollinger["mid"] # dataframe["wbb_percent"] = ( # (dataframe["close"] - dataframe["wbb_lowerband"]) / # (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) # ) # dataframe["wbb_width"] = ( # (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_middleband"] # ) # # EMA - Exponential Moving Average # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) # dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21) dataframe[f'ema_{self.base_nb_candles_sell.value}'] = ta.EMA(dataframe, timeperiod=int(self.base_nb_candles_sell.value)) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) # # SMA - Simple Moving Average dataframe[f'sma_{self.base_nb_candles_buy.value}'] = ta.SMA(dataframe, timeperiod=int(self.base_nb_candles_buy.value)) # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3) # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5) # dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10) # dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21) # dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50) # dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100) #dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) #dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) # Parabolic SAR #dataframe['sar'] = ta.SAR(dataframe) # TEMA - Triple Exponential Moving Average #dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) # Cycle Indicator # ------------------------------------ # Hilbert Transform Indicator - SineWave #hilbert = ta.HT_SINE(dataframe) #dataframe['htsine'] = hilbert['sine'] #dataframe['htleadsine'] = hilbert['leadsine'] # Pattern Recognition - Bullish candlestick patterns # ------------------------------------ # # Hammer: values [0, 100] # dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # # Inverted Hammer: values [0, 100] # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) # # Dragonfly Doji: values [0, 100] # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) # # Piercing Line: values [0, 100] # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] # # Morningstar: values [0, 100] # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] # # Three White Soldiers: values [0, 100] # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] # Pattern Recognition - Bearish candlestick patterns # ------------------------------------ # # Hanging Man: values [0, 100] # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) # # Shooting Star: values [0, 100] # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) # # Gravestone Doji: values [0, 100] # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) # # Dark Cloud Cover: values [0, 100] # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) # # Evening Doji Star: values [0, 100] # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) # # Evening Star: values [0, 100] # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) # Pattern Recognition - Bullish/Bearish candlestick patterns # ------------------------------------ # # Three Line Strike: values [0, -100, 100] # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) # # Spinning Top: values [0, -100, 100] # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] # # Engulfing: values [0, -100, 100] # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] # # Harami: values [0, -100, 100] # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] # # Three Outside Up/Down: values [0, -100, 100] # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] # # Three Inside Up/Down: values [0, -100, 100] # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] # # Chart type # # ------------------------------------ # # Heikin Ashi Strategy # heikinashi = qtpylib.heikinashi(dataframe) # dataframe['ha_open'] = heikinashi['open'] # dataframe['ha_close'] = heikinashi['close'] # dataframe['ha_high'] = heikinashi['high'] # dataframe['ha_low'] = heikinashi['low'] #Chaikin A/D Oscillator dataframe['ADOSC'] = ta.ADOSC(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume']) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ dataframe.loc[ ( (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (((dataframe['open'].rolling(int(self.buy_dip_candles_1.value)).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_1.value) & (((dataframe['open'].rolling(int(self.buy_dip_candles_1.value + self.buy_dip_candles_2.value)).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_2.value) & (((dataframe['open'].rolling(int(self.buy_dip_candles_1.value + self.buy_dip_candles_2.value + self.buy_dip_candles_3.value)).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_3.value) & #(dataframe['ADOSC'] > 0) & #(dataframe['rsi'] > 50) & #(dataframe['rsi'].shift() > 50) & #(dataframe['rsi'].shift(2) > 50) & #(dataframe['rsi_1h'] > 50) & #(dataframe['slowk'] > dataframe['slowd']) & #(dataframe['slowk'] > 80) & #(dataframe['slowk_1h'] > dataframe['slowd_1h']) & #(dataframe['macd'] > dataframe['macdsignal']) & (dataframe['lame_ao'] > dataframe['lame_ao'].shift()) & (dataframe['lame_ao'].shift() < dataframe['lame_ao'].shift(2)) & #(qtpylib.crossed_above(dataframe['ADOSC'], 0)) & #(qtpylib.crossed_above(dataframe['ao'], 0)) & #(qtpylib.crossed_above(dataframe['rsi'], 50)) & # Signal: RSI crosses above 50 #(qtpylib.crossed_above(dataframe['slowk'], dataframe['slowd'])) & # stochastic k crosses above d #(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) & # stochastic macd crosses above macdsignals (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ( (dataframe['lame_ao'] < dataframe['lame_ao'].shift()) & (dataframe['lame_ao'].shift() > dataframe['lame_ao'].shift(2)) & (dataframe['volume'] > 0) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ] = 1 return dataframe