# source: https://raw.githubusercontent.com/khimnguynnn/tqreedsatge-rafstieerq/fdb7d3070eceaf1413ff1e2e18f6262be76ebdf4/FrayLIVEBTC15m.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) # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib # This class is a sample. Feel free to customize it. class Github_khimnguynnn_tqreedsatge_rafstieerq__FrayLIVEBTC15m__20250515_063843(IStrategy): """ This is a sample strategy to inspire you. 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 # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "0":0.0136, "114":0.06, "192":0.021, "510":0.01 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.346 # Trailing stoploss trailing_stop = True trailing_only_offset_is_reached = False trailing_stop_positive = 0.039 trailing_stop_positive_offset = 0.048 # Disabled / not configured # Hyperoptable parameters #Buy Space #RSI Fisher for rising track buy_frsi = DecimalParameter(-0.5, 1, decimals = 3, default = 0.25, space="buy") #RSI Fisher for Dip Reversal buy_dip_frsi = DecimalParameter(-0.9, 0.2, decimals = 2, default = -0.7, space="buy") frsi_pct = DecimalParameter(0, 1, decimals = 4, default = 0.6, space="buy") #use pct rate to calc percentage of rsi rising against previous candles ema_pct = DecimalParameter(0, 0.1, decimals = 4, default = 0.08, space="buy") #percentages of Difference between EMA7 against EMA7-TEMA macdn_buy = DecimalParameter(0, 0.8, decimals = 2, default = 0.09, space="buy") #Sell Position sell_frsi = DecimalParameter(-1, 1, decimals=2, default=-0.84, space="sell") #Main F-RSI macd_diff = DecimalParameter(0, 0.01, decimals=4, default=0.0047, space='sell') #Distance between MACD and MACD SIGNAL macdn_sell = DecimalParameter(0.2, 0.99, decimals=2, default= 0.81, space="sell") #MACD signal position near middle line #Protection Space cooldown_lookback = IntParameter(2, 90, default=3, space="protection", optimize=True) stop_duration = IntParameter(12, 100, default=3, space="protection", optimize=True) use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True) #ProtectionGuard @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": self.cooldown_lookback.value }, { "method": "MaxDrawdown", "lookback_period_candles": self.cooldown_lookback.value, "trade_limit": 5, "stop_duration_candles": self.stop_duration.value, "max_allowed_drawdown": 0.9 }, { "method": "StoplossGuard", "lookback_period_candles": 20, "trade_limit": 3, "stop_duration_candles": 4, "only_per_pair": False }, { "method": "LowProfitPairs", "lookback_period_candles": 24, "trade_limit": 2, "stop_duration_candles": 4, "required_profit": 0.01 } ] # Optimal timeframe for the strategy. timeframe = '15m' # 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 = True sell_profit_only = False ignore_roi_if_buy_signal = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 40 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': True } # Optional order time in force. order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } plot_config = { 'main_plot': { 'tema': {}, 'sar': {'color': 'blue'}, 'ema7':{'color': 'red'}, 'ema12':{'color': 'yellow'} }, 'subplots': { "MACD": { 'macdn': {'color': 'blue'}, 'macdnsig': {'color': 'orange'} #'macdhist':{} }, "FISHERS RSI":{ 'frsi':{'color':'green'}, } } } #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) #return [("ETH/USDT", "15m"), # ("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['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['frsi'] = (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'] #Normalize MACD macdn = dataframe['macd'] macdmin = (dataframe['macd'].min()) macdmax = (dataframe['macd'].max()) dataframe['macdn'] = (macdn - macdmin) / (macdmax - macdmin) macdnsig = dataframe['macdsignal'] macdnsigmax = (dataframe['macdsignal'].max()) macdnsigmin = (dataframe['macdsignal'].min()) dataframe['macdnsig'] = (macdnsig - macdnsigmin) / (macdnsigmax - macdnsigmin) # MFI #dataframe['mfi'] = ta.MFI(dataframe) # Overlap Studies # ------------------------------------ # Bollinger Bands #bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=18, 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['ema7'] = ta.EMA(dataframe, timeperiod=7) #dataframe['ema30'] = ta.EMA(dataframe, timeperiod=30) dataframe['ema12'] = ta.EMA(dataframe, timeperiod=14) #dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # Parabolic SAR dataframe['sar'] = ta.SAR(dataframe) # TEMA - Triple Exponential Moving Average dataframe['tema'] = ta.TEMA(dataframe, timeperiod=7) # # 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][1] dataframe['best_ask'] = ob['asks'][0][0] return dataframe #def informative_pairs(self): # get access to all pairs available in whitelist. # pairs = self.dp.current_whitelist() # Assign tf to each pair so they can be downloaded and cached for strategy. # informative_pairs = [(pair, '1d') for pair in pairs] #return informative_pairs # fetch current dataframe #if self.dp: # if self.dp.runmode.value in ('live', 'dry_run'): # dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'], # timeframe=self.timeframe) # 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 """ frsi_last3 = dataframe['frsi'].tail(3) frsi3rdlast, frsi2ndlast, frsilast = frsi_last3 last_ema7 = dataframe['ema7'] last_tema = dataframe['tema'] macdn_buy_low = float(self.macdn_buy.value) dataframe.loc[ #New FrayStrategy #Buy Rising Track Signal ( (qtpylib.crossed_above(dataframe['frsi'], self.buy_frsi.value)) & #(dataframe['ema7'] > dataframe['ema12']) & (dataframe['macdnsig'] > dataframe['macdnsig'].shift(1)) & (dataframe['macdnsig'] < dataframe['macdn']) & #(dataframe['sar'] > dataframe['sar'].shift(1)) & (dataframe['sar'] <= dataframe['tema']) & (dataframe['volume'] > 0) ), 'buy'] = 1 return dataframe dataframe.loc[ #Buy Dip as best as you can ( (last_tema < last_ema7 ) & (last_ema7 < dataframe['ema12']) & (qtpylib.crossed_below(dataframe['frsi'], self.buy_dip_frsi.value)) & (abs(frsilast - frsi2ndlast) < abs(frsi2ndlast - frsi3rdlast) < self.frsi_pct.value ) & (((last_ema7 - last_tema) / last_tema ) >= self.ema_pct.value ) & #Guard against False Bottom (dataframe['macdn'] <= macdn_buy_low ) & (dataframe['volume'] > 0) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ HUEHUEHUE """ last_macd = dataframe['macd'] last_macdsig = dataframe['macdsignal'] dataframe.loc[ ( (dataframe['tema'] > dataframe['ema7']) & (dataframe['tema'] < dataframe['tema'].shift(1)) & #(dataframe['frsi'] < dataframe['frsi'].shift(1)) & (qtpylib.crossed_above(dataframe['frsi'], self.sell_frsi.value )) & #(dataframe['macd'] < dataframe['macdsignal']) & ((( last_macd - last_macdsig ) / last_macdsig ) <= self.macd_diff.value ) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'sell'] = 1 return dataframe dataframe.loc[ ( (((last_ema7 - last_tema) / last_tema ) >= self.ema_pct.value ) & (dataframe['macdn'] <= self.macdn_sell.value) & (dataframe['volume'] > 0 ) ), 'sell'] = 1 return dataframe