# source: https://raw.githubusercontent.com/kanaxe/freqtrade_kanaxe/b674562eaa4915192ec3a58cf3f86b64f3fba6e4/hma.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, Series from freqtrade.strategy import IStrategy from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter from freqtrade.exchange import timeframe_to_prev_date from datetime import datetime from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from technical.indicators import indicators class github_kanaxe_freqtrade_kanaxe__hma__20211115_132756(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: minimal_roi = { "0": 0.0015, "10": 0.0018, "15": 0.0015, "45": 0.002, "65": 0 } # Stoploss: stoploss = -0.01 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.021 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True # 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": { }, "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=35, high=100, default=40, space='sell', optimize=True, load=True) use_custom_sell = True # use_custom_stoploss = 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: # 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['ohlc4']=(dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4 dataframe['hlc3']=(dataframe['high'] + dataframe['low'] + dataframe['close']) / 3 dataframe['hl2']=(dataframe['high'] + dataframe['low'] ) / 2 dataframe['hma16'] = qtpylib.hma(dataframe['ohlc4'], 20) dataframe['hma8'] = qtpylib.hma(dataframe['hl2'], 8) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=30) # 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['sar'] = ta.SAR(dataframe) 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 custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # evaluate highest to lowest, so that highest possible stop is used if current_profit > 0.021: return stoploss_from_open(0.02, current_profit) elif current_profit > 0.011: return stoploss_from_open(0.01, current_profit) elif current_profit > 0.003: return stoploss_from_open(0.002, current_profit) # return maximum stoploss value, keeping current stoploss price unchanged return 1 def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() candlem = dataframe.iloc[0].squeeze() # Above 20% profit, sell when rsi < 80 # if current_profit > 0.2: # if last_candle['rsi'] < 60: # return 'rsi_below_60' # Between 2% and 10%, sell if EMA-long above EMA-short # if candlem['close'] < candlem['open']: if current_profit > 0.031: return 1 elif current_profit > 0.021: return 1 elif current_profit > 0.015: return 1 elif current_profit > 0.011: return 1 elif current_profit > 0.002: return 1 # if candlem['hma8'] < candlem['hma16']: # if current_profit < 0.001: # return 1 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'].shift(2) > dataframe['close'])& # (dataframe['rsi'] < 45)& (qtpylib.crossed_above(dataframe['close'], dataframe['hma16'])) ), '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['open'] < dataframe['close'] ) & (dataframe['open'] < dataframe['close']) # (dataframe['rsi'] > 37 ) # (qtpylib.crossed_below(dataframe['hma16'], dataframe['close'])) ), 'sell'] =0 return dataframe