# source: https://raw.githubusercontent.com/Boring-Mind/freqtrade-bot/d2309c2cede99da3881ff5519552792871859da5/user_data/strategies/new_strategy.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from freqtrade.strategy import ( BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, ) # -------------------------------- import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from typing import Final class TradingViewFunctions: """Contains utility functions that was cloned from TradingView.""" @staticmethod def crossover(x: pd.Series, y: pd.Series) -> bool: """Returns True if x crossed over y. Clones crossover function from TradingView. """ return x.iloc[-1] > y.iloc[-1] and x.iloc[-2] <= y.iloc[-2] @staticmethod def crossover_series(x: pd.Series, y: pd.Series) -> pd.Series: shifted_x = x.shift(-1) shifted_y = y.shift(-1) return x > y & shifted_x <= shifted_y class UTBotAlertsIndicator: """UT Bot Alerts indicator from TradingView.""" BUY_SIGNAL: Final[int] = 1 SELL_SIGNAL: Final[int] = -1 @staticmethod def _get_atr_trailing_stop_for_prev_bar(dataframe: DataFrame) -> float: """Get ATR trailing stop for previous bar. UT Bot Alerts indicator compares ATR trailing stop for current bar with previous bar. So at the bot start, indicator will need a value for previous bar as well, which is not calculated yet. We will return 0 as a default value then. """ if dataframe["atr_trailing_stop"].iloc[-2].isnull(): return 0 else: return dataframe["atr_trailing_stop"].iloc[-2] @staticmethod def _get_atr_trailing_stop( dataframe: DataFrame, sensitivity: int, atr_period: int ) -> float: atr = ta.ATR( dataframe["high"], dataframe["low"], dataframe["close"], timeperiod=atr_period, ) stop_n = sensitivity * atr current_close_price = dataframe["close"].iloc[-1] previous_close_price = dataframe["close"].iloc[-2] atr_trailing_stop_prev = ( UTBotAlertsIndicator._get_atr_trailing_stop_for_prev_bar(dataframe) ) if ( current_close_price > atr_trailing_stop_prev and previous_close_price > atr_trailing_stop_prev ): return max(atr_trailing_stop_prev, current_close_price - stop_n) if ( current_close_price < atr_trailing_stop_prev and previous_close_price < atr_trailing_stop_prev ): return min(atr_trailing_stop_prev, current_close_price + stop_n) if current_close_price > atr_trailing_stop_prev: return current_close_price - stop_n return current_close_price + stop_n @classmethod def get_data(cls, dataframe: DataFrame, sensitivity=1, atr_period=10) -> DataFrame: if dataframe["atr_trailing_stop"].iloc[-2].isnull(): dataframe["atr_trailing_stop"].iloc[-2] = cls._get_atr_trailing_stop( dataframe[:-1], sensitivity, atr_period ) dataframe["atr_trailing_stop"].iloc[-1] = cls._get_atr_trailing_stop( dataframe, sensitivity, atr_period ) ema = ta.EMA(dataframe["close"], timeperiod=1) above = TradingViewFunctions.crossover_series( ema, dataframe["atr_trailing_stop"] ) below = TradingViewFunctions.crossover_series( dataframe["atr_trailing_stop"], ema ) dataframe.loc[ (dataframe["stop"] > dataframe["atr_trailing_stop"] & dataframe[above]), "ut_bot_alert", ] = cls.BUY_SIGNAL dataframe.loc[ (dataframe["stop"] < dataframe["atr_trailing_stop"] & dataframe[below]), "ut_bot_alert", ] = cls.SELL_SIGNAL # if ( # dataframe["stop"].iloc[-1] > dataframe["atr_trailing_stop"].iloc[-1] # and above # ): # signal = cls.BUY_SIGNAL # elif ( # dataframe["stop"].iloc[-1] < dataframe["atr_trailing_stop"].iloc[-1] # and below # ): # signal = cls.SELL_SIGNAL return dataframe class Github_Boring_Mind_freqtrade_bot__new_strategy__20221105_171813(IStrategy): """ Scalping strategy from IQTrade channel. Original: https://www.youtube.com/watch?v=hPWDYDSbIAw 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_entry_trend, populate_exit_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 = 3 # Can this strategy go short? can_short: bool = True # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = {} # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". # ToDo: set stop loss dynamically on each trade # https://www.freqtrade.io/en/stable/stoploss/ stoploss = -0.02 # Trailing stoploss trailing_stop = False # trailing_only_offset_is_reached = False # trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.0 # Disabled / not configured # Optimal timeframe for the strategy. timeframe = "1m" # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Hyperoptable parameters buy_rsi = IntParameter( low=1, high=50, default=30, space="buy", optimize=True, load=True ) sell_rsi = IntParameter( low=50, high=100, default=70, space="sell", optimize=True, load=True ) short_rsi = IntParameter( low=51, high=100, default=70, space="sell", optimize=True, load=True ) exit_short_rsi = IntParameter( low=1, high=50, default=30, space="buy", optimize=True, load=True ) # Number of candles the strategy requires before producing valid signals # Hull Suite requires 55 bars # STC indicator requires 80 bars minimum startup_candle_count: int = 80 # Optional order type mapping. order_types = { "entry": "limit", "exit": "limit", "stoploss": "market", "stoploss_on_exchange": False, } # Optional order time in force. order_time_in_force = {"entry": "GTC", "exit": "GTC"} plot_config = { "main_plot": { "tema": {}, "sar": {"color": "white"}, }, "subplots": { "MACD": { "macd": {"color": "blue"}, "macdsignal": {"color": "orange"}, }, "RSI": { "rsi": {"color": "red"}, }, }, } 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 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) # UT Bot alerts # Sensitivity and atr period can be improved later using hyperopt dataframe = UTBotAlertsIndicator.get_data( dataframe, sensitivity=2, atr_period=6 ) # # 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) # 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"] # MACD macd = ta.MACD(dataframe) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] dataframe["macdhist"] = macd["macdhist"] # MFI dataframe["mfi"] = ta.MFI(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) # # 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) # 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'] # Retrieve best bid and best ask from the orderbook # ------------------------------------ """ # first check if dataprovider is available if self.dp: if self.dp.runmode.value in ('live', 'dry_run'): ob = self.dp.orderbook(metadata['pair'], 1) dataframe['best_bid'] = ob['bids'][0][0] dataframe['best_ask'] = ob['asks'][0][0] """ return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the entry signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with entry columns populated """ dataframe.loc[ ( # Signal: RSI crosses above 30 (qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value)) & (dataframe["tema"] <= dataframe["bb_middleband"]) & ( # Guard: tema below BB middle dataframe["tema"] > dataframe["tema"].shift(1) ) & ( # Guard: tema is raising dataframe["volume"] > 0 ) # Make sure Volume is not 0 ), "enter_long", ] = 1 dataframe.loc[ ( # Signal: RSI crosses above 70 (qtpylib.crossed_above(dataframe["rsi"], self.short_rsi.value)) & (dataframe["tema"] > dataframe["bb_middleband"]) & ( # Guard: tema above BB middle dataframe["tema"] < dataframe["tema"].shift(1) ) & ( # Guard: tema is falling dataframe["volume"] > 0 ) # Make sure Volume is not 0 ), "enter_short", ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the exit signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with exit columns populated """ dataframe.loc[ ( # Signal: RSI crosses above 70 (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value)) & (dataframe["tema"] > dataframe["bb_middleband"]) & ( # Guard: tema above BB middle dataframe["tema"] < dataframe["tema"].shift(1) ) & ( # Guard: tema is falling dataframe["volume"] > 0 ) # Make sure Volume is not 0 ), "exit_long", ] = 1 dataframe.loc[ ( # Signal: RSI crosses above 30 (qtpylib.crossed_above(dataframe["rsi"], self.exit_short_rsi.value)) & # Guard: tema below BB middle (dataframe["tema"] <= dataframe["bb_middleband"]) & (dataframe["tema"] > dataframe["tema"].shift(1)) & ( # Guard: tema is raising dataframe["volume"] > 0 ) # Make sure Volume is not 0 ), "exit_short", ] = 1 return dataframe