# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/Discord_S096.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from freqtrade.strategy.interface import IStrategy # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from datetime import datetime, timedelta def bollinger_bands(stock_price, window_size, num_of_std): rolling_mean = stock_price.rolling(window=window_size).mean() rolling_std = stock_price.rolling(window=window_size).std() lower_band = rolling_mean - (rolling_std * num_of_std) return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band) class Github_remiotore_freqtrade__Discord_S096__20260111_210550(IStrategy): """ """ # 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 # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "4320": 0.01, "300": 0.01, "60": 0.04, # 0.01 "30": 0.06, # 0.02 "0": 0.50 # 0.04 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.05 # -0.05 # Trailing stoploss trailing_stop = True trailing_only_offset_is_reached = True # trailing_stop_positive = 0.005 trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.02 #trailing_stop_positive_offset = 0.015 # Optimal timeframe for the strategy. timeframe = '1h' # Run "populate_indicators()" only for new candle. process_only_new_candles = False # These values can be overridden in the "ask_strategy" section in the config. use_sell_signal = False sell_profit_only = True ignore_roi_if_buy_signal = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 # Custom stoploss use_custom_stoploss = True # 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': { 'tema': {}, 'sar': {'color': 'white'}, }, 'subplots': { # Subplots - each dict defines one additional plot "MACD": { 'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}, }, "RSI": { 'rsi': {'color': 'red'}, } } } def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # Manage losing trades and open room for better ones. if (current_profit < 0) & (current_time - timedelta(minutes=300) > trade.open_date_utc): return 0.01 return 0.99 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. :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 """ # Momentum Indicators # ------------------------------------ # ADX dataframe['adx'] = ta.ADX(dataframe) dataframe['adx50'] = ta.ADX(dataframe, timeperiod=50) # # 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['ao'] = qtpylib.awesome_oscillator(dataframe) # # 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) dataframe['cmo'] = ta.CMO(dataframe) # RSI dataframe['rsi'] = ta.RSI(dataframe) dataframe['rsi25'] = ta.RSI(dataframe, timeperiod=25) dataframe['rsi50'] = ta.RSI(dataframe, timeperiod=50) dataframe['rsi100'] = ta.RSI(dataframe, timeperiod=100) dataframe['rsi200'] = ta.RSI(dataframe, timeperiod=200) dataframe['rsi400'] = ta.RSI(dataframe, timeperiod=400) dataframe['rsi1000'] = ta.RSI(dataframe, timeperiod=1000) # # 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) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # MFI dataframe['mfi'] = ta.MFI(dataframe) dataframe['mfi20'] = ta.MFI(dataframe, timeperiod=20) dataframe['mfi50'] = ta.MFI(dataframe, timeperiod=50) dataframe['mfi100'] = ta.MFI(dataframe, timeperiod=100) dataframe['mfi200'] = ta.MFI(dataframe, timeperiod=200) dataframe['mfi400'] = ta.MFI(dataframe, timeperiod=400) dataframe['mfi1000'] = ta.MFI(dataframe, timeperiod=1000) # # 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['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema200'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema400'] = ta.EMA(dataframe, timeperiod=100) # # SMA - Simple Moving Average # 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) # Parabolic SAR dataframe['sar'] = ta.SAR(dataframe) # TEMA - Triple Exponential Moving Average dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) dataframe['tema3'] = ta.TEMA(dataframe, timeperiod=3) dataframe['tema5'] = ta.TEMA(dataframe, timeperiod=5) dataframe['tema14'] = ta.TEMA(dataframe, timeperiod=14) dataframe['tema21'] = ta.TEMA(dataframe, timeperiod=21) dataframe['tema50'] = ta.TEMA(dataframe, timeperiod=50) # 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'] # helps with pattern recognition dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4 dataframe['sma_slow'] = ta.SMA(dataframe, timeperiod=200, price='close') dataframe['sma_medium'] = ta.SMA(dataframe, timeperiod=100, price='close') dataframe['sma_fast'] = ta.SMA(dataframe, timeperiod=50, price='close') dataframe['trend'] = dataframe['plus_di'] / dataframe['minus_di'] * 100 dataframe['color'] = np.where(dataframe['plus_di'] > dataframe['minus_di'], 'green', np.where(dataframe['plus_di'] < dataframe['minus_di'], 'red', 'blue')) mid, lower = bollinger_bands(dataframe['close'], window_size=40, num_of_std=2) dataframe['lower'] = lower dataframe['bbdelta'] = (mid - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() # strategy ClucMay72018 bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=25) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() #print(dataframe['trend']) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ TREND > 120: Bullish TREND < 80: Bearish ELSE: Sideways """ dataframe.loc[ ( ( #BULLISH & BEARISH ( (dataframe['trend']>120) | (dataframe['trend']<80) ) & ( # Breakout: TEMA crosses over longer TEMA (qtpylib.crossed_above(dataframe['tema5'], dataframe['tema14'])) & (dataframe['adx'] > 25) ) | ( # Breakout: RSI plus momentum (qtpylib.crossed_above(dataframe['rsi'], 60)) & (dataframe['adx'] > 30) ) | ( # Breakout: crossing middle of Bollinger Bands (qtpylib.crossed_above(dataframe['close'], dataframe['bb_middleband'])) & (dataframe['adx'] > 30) & (dataframe['adx'] > dataframe['adx'].shift(1)) & (dataframe['adx'].shift(1) > dataframe['adx'].shift(2)) & (dataframe['adx'].shift(2) > dataframe['adx'].shift(3)) & (dataframe['close'] > dataframe['ema21']) & (dataframe['close'] > dataframe['ema50']) & (dataframe['roc'] > 0) & (dataframe['ema50'] > 0) & (dataframe['ema21'] > dataframe['ema50']) ) | ( # Breakout: Increasing momentum and positive price action (qtpylib.crossed_above(dataframe['adx'], 25)) & (dataframe['ema21'] > dataframe['ema50']) ) | ( # BTFD: CCI bounce # Doesn't like BTC Dominance tracking sideways # Enable by uncommenting the indicators (qtpylib.crossed_above(dataframe['cci'], -100)) ) | ( # BTFD: Price Crosses into the Bollinger Bands from below # Doesn't like BTC Dominance tracking sideways # Enable by uncommenting the indicators (qtpylib.crossed_above(dataframe['close'], dataframe['bb_lowerband'])) ) | ( # BTFD: V-shaped bottom. # Doesn't like BTC Dominance tracking sideways # Enable by uncommenting the indicators (dataframe['average'].shift(5) > dataframe['average'].shift(4)) & (dataframe['average'].shift(4) > dataframe['average'].shift(3)) & (dataframe['average'].shift(3) > dataframe['average'].shift(2)) & (dataframe['average'].shift(2) > dataframe['average'].shift(1)) & (dataframe['average'].shift(1) < dataframe['average'].shift(0)) & (dataframe['low'].shift(1) < dataframe['bb_middleband']) & (dataframe['cci'].shift(1) < -100) ) ) | ( #SIDEWAYS ? # Still experimenting (dataframe['trend']>80) & (dataframe['trend']<120) & ( ( # strategy BinHV45 dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * 0.008) & dataframe['closedelta'].gt(dataframe['close'] * 0.0175) & dataframe['tail'].lt(dataframe['bbdelta'] * 0.25) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) | ( # strategy ClucMay72018 (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.985 * dataframe['bb_lowerband']) & (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 20)) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) ) ) | ( #BEARISH?! (dataframe['trend']<80) & ( ( # strategy BinHV45 dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * 0.008) & dataframe['closedelta'].gt(dataframe['close'] * 0.0175) & dataframe['tail'].lt(dataframe['bbdelta'] * 0.25) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) | ( # strategy ClucMay72018 (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.985 * dataframe['bb_lowerband']) & (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 20)) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) ) ) ), '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 populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ dataframe.loc[ ( ( #BULLISH (dataframe['trend']>120) & ( (qtpylib.crossed_above(dataframe['mfi'], 95)) # Signal: RSI crosses above 70 | ( (dataframe['mfi'] > 90) & # Signal: RSI crosses above 70 (dataframe['tema'] < dataframe['tema'].shift(3)) & # Guard: negative trend (dataframe['volume'] > 0) # Make sure Volume is not 0 ) | ( (qtpylib.crossed_above(dataframe['close'], dataframe['bb_upperband'])) & (dataframe['mfi'] > 60) # High MFI ) | ( (qtpylib.crossed_below(dataframe['adx'], 28)) & (dataframe['rsi'] > 60) # High RSI ) | ( (qtpylib.crossed_below(dataframe['adx'], 28)) & (dataframe['mfi'] > 60) # High MFI ) ) ) | ( #BEARISH (dataframe['trend']<80) & ( (qtpylib.crossed_above(dataframe['mfi'], 95)) # Signal: RSI crosses above 70 | ( (dataframe['mfi'] > 90) & # Signal: RSI crosses above 70 (dataframe['tema'] < dataframe['tema'].shift(3)) & # Guard: negative trend (dataframe['volume'] > 0) # Make sure Volume is not 0 ) | ( (qtpylib.crossed_above(dataframe['close'], dataframe['bb_upperband'])) & (dataframe['mfi'] > 60) # High MFI ) | ( (qtpylib.crossed_below(dataframe['adx'], 28)) & (dataframe['rsi'] > 60) # High RSI ) | ( (qtpylib.crossed_below(dataframe['adx'], 28)) & (dataframe['mfi'] > 60) # High MFI ) ) ) | ( #SIDEWAYS (dataframe['trend']>80) & (dataframe['trend']<120) & ( (dataframe['close'] > dataframe['bb_middleband']) | ( # Improves the profit slightly. (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) ) ) ), 'sell'] = 1 return dataframe