# 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, Series from freqtrade.strategy import IStrategy from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from technical.indicators import indicators #https://github.com/kanaxe/freqtrade_kanaxe class Hmar_github_kanaxe_20211115(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 # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". # ROI table: # ROI table: minimal_roi = { "0": 0.015, "13": 0.018, "15": 0.012, "31": 0 } # Stoploss: stoploss = -0.031 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.002 trailing_stop_positive_offset = 0.004 trailing_only_offset_is_reached = False # Optimal timeframe for the strategy. timeframe = '5m' # 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 = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 500 # 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": { "hma20": { "color": "#811125", "type": "line" } }, "subplots": { "RSI": { "rsi": { "color": "red" } }, "hlow": { "h-low": { "color": "#2de7cc", "type": "line" } }, "htsine": { "htsine": { "color": "#688669", "type": "line" } } } } # Hyperoptable parameters buy_rsi = IntParameter(low=1, high=30, default=30, space='buy', optimize=True, load=True) sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True) 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) # # 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) # # 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'] # 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['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'] dataframe['hma20'] = qtpylib.hma(dataframe['close'], 20) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=20) # MACD macd = ta.MACD(dataframe, timeperod=9) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] dataframe['cci'] = ta.CCI(dataframe, timeperiod=10) dataframe['h-low']= dataframe['hma20'] / dataframe['low'] dataframe['vwap'] = Series.vwap(dataframe) devUD = [1.28, 2.01, 2.51, 3.09, 4.01] # my std dev calculation = an incrementing std dev of df.VWAP dataframe['DEV'] = dataframe['vwap'].expanding().std() # dataframe['vwup1']= dataframe['vwap'] + devUD[0] * dataframe['DEV'] # dataframe['vwdow1']= dataframe['vwap'] - devUD[0] * dataframe['DEV'] # dataframe['vwup2']= dataframe['vwap'] + devUD[1] * dataframe['DEV'] # dataframe['vwdow2']= dataframe['vwap'] - devUD[1] * dataframe['DEV'] for dev in devUD: up = 'vwup{}'.format(dev) dow = 'vwdow{}'.format(dev) dataframe[up]=dataframe['vwap'] + dev * dataframe['DEV'] dataframe[dow]= dataframe['vwap'] - dev * dataframe['DEV'] # Retrieve best bid and best ask from the orderbook # ------------------------------------ """ # first check if dataprovider is available if self.dp: if self.dp.runmode 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_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[ ( # ( (qtpylib.crossed_below(dataframe['rsi'], 32)) & # (dataframe['rsi'] < 33) & # (dataframe['cci'] < -90 ) & # (dataframe['macdhist'].shift(1) > 0) & # (dataframe['adx'] > 29) & # (dataframe['ema21'] > dataframe['hma20']) & # (dataframe['close'].shift(1) < dataframe['hma20']) & # (dataframe['open'] > dataframe['hma20']) & # (dataframe['close'] > dataframe['hma20']) (dataframe['h-low'] > 1.005) & (qtpylib.crossed_below(dataframe['htsine'].shift(1), 0)) # (qtpylib.crossed_above(dataframe['close'], dataframe['hma20'])) # ) # | # ( # (dataframe['cci'] < -90 ) & # # (dataframe['macdhist'] < -2.5) & # (dataframe['close'].shift(1) < dataframe['hma20']) & # (qtpylib.crossed_above(dataframe['close'], dataframe['hma20']))) # Signal: RSI crosses above 30 ), '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[ ( (dataframe['rsi'] > 46) & # | # (dataframe['cci'].shift(1) > dataframe['cci'] ) & (dataframe['h-low'] <= 1) | (qtpylib.crossed_below(dataframe['htsine'], 98)) # ( # (dataframe['close'].shift(1) > dataframe['hma20']) & # (qtpylib.crossed_below(dataframe['close'], dataframe['hma20'])) # ) # | # (dataframe['close'] == dataframe['hma20'])) # (qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) ), 'sell'] = 1 return dataframe