# source: https://raw.githubusercontent.com/behradhesami/Projects/5ac4ca3ce403e5ccc9db229f5b32846bf2767a7d/Freqtrade/LuxOSC1_1/lux1_1.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 from functools import reduce import logging import math 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 logger = logging.getLogger(__name__) def LUX_SuperTrendOscillator(dtloc, source = 'close', length = 6, mult = 9, smooth = 1): """ // This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/ // ?? LuxAlgo https://www.tradingview.com/script/dVau7zqn-LUX-SuperTrend-Oscillator/ :return: List of tuples in the format (osc, signal, histogram) translated for freqtrade: viksal1982 viktors.s@gmail.com """ dtSPB = dtloc.copy() dtSPB['atrcol'] = np.nan dtSPB['hl2col'] = np.nan dtSPB['upcol'] = np.nan dtSPB['dncol'] = np.nan dtSPB['uppercol'] = np.nan dtSPB['lowercol'] = np.nan dtSPB['trendcol'] = np.nan dtSPB['sptcol'] = np.nan dtSPB['osc1col'] = np.nan dtSPB['osc2col'] = np.nan dtSPB['osccol'] = np.nan dtSPB['alphacol'] = np.nan dtSPB['amacol'] = np.nan dtSPB['histcol'] = np.nan dtSPB['atrcol'] = ta.ATR(dtloc, timeperiod = length) * mult dtSPB['hl2col'] = (dtSPB['high'] + dtSPB['low'] )/2 dtSPB['upcol'] = dtSPB['hl2col'] + dtSPB['atrcol'] dtSPB['dncol'] = dtSPB['hl2col'] - dtSPB['atrcol'] def calc_upper(dfr, init=0): global calc_Lux_STO_upper global calc_Lux_STO_src if init == 1: calc_Lux_STO_upper = 0.0 calc_Lux_STO_src = 0.0 return if calc_Lux_STO_src < calc_Lux_STO_upper: calc_Lux_STO_upper = min(dfr['upcol'], calc_Lux_STO_upper) else: calc_Lux_STO_upper = dfr['upcol'] calc_Lux_STO_src = dfr[source] return calc_Lux_STO_upper calc_upper(None, init=1) dtSPB['uppercol'] = dtSPB.apply(calc_upper, axis = 1) def calc_lower(dfr, init=0): global calc_Lux_STO_lower global calc_Lux_STO_src if init == 1: calc_Lux_STO_lower = 0.0 calc_Lux_STO_src = 0.0 return if calc_Lux_STO_src > calc_Lux_STO_lower: calc_Lux_STO_lower= max(dfr['dncol'], calc_Lux_STO_lower) else: calc_Lux_STO_lower = dfr['dncol'] calc_Lux_STO_src = dfr[source] return calc_Lux_STO_lower calc_lower(None, init=1) dtSPB['lowercol'] = dtSPB.apply(calc_lower, axis = 1) def calc_trend(dfr, init=0): global calc_Lux_STO_trend global calc_Lux_STO_lower global calc_Lux_STO_upper if init == 1: calc_Lux_STO_trend = 0.0 calc_Lux_STO_lower = 0.0 calc_Lux_STO_upper = 0.0 return if dfr[source] > calc_Lux_STO_upper: calc_Lux_STO_trend = 1 elif dfr[source] < calc_Lux_STO_lower: calc_Lux_STO_trend = 0 calc_Lux_STO_upper = dfr['uppercol'] calc_Lux_STO_lower = dfr['lowercol'] return calc_Lux_STO_trend calc_trend(None, init=1) dtSPB['trendcol'] = dtSPB.apply(calc_trend, axis = 1) dtSPB['sptcol'] = dtSPB['trendcol'] * dtSPB['lowercol'] + (1-dtSPB['trendcol'] ) * dtSPB['uppercol'] dtSPB['osc1col'] = (dtSPB[source] - dtSPB['sptcol']) / (dtSPB['uppercol'] - dtSPB['lowercol']) dtSPB['osc2col'] = np.where(dtSPB['osc1col'] < 1, dtSPB['osc1col'], 1 ) dtSPB['osccol'] = np.where(dtSPB['osc2col'] > -1, dtSPB['osc2col'], -1) dtSPB['alphacol'] = dtSPB['osccol'].pow(2)/length def calc_ama(dfr, init=0): global calc_Lux_STO_ama if init == 1: calc_Lux_STO_ama = 0.0 return calc_Lux_STO_ama = calc_Lux_STO_ama + dfr['alphacol'] * (dfr['osccol'] - calc_Lux_STO_ama) return calc_Lux_STO_ama calc_ama(None, init=1) dtSPB['amacol'] = dtSPB.apply(calc_ama, axis = 1) dtSPB['histcol'] = ta.EMA((dtSPB['osccol']- dtSPB['amacol']),timeperiod = smooth) return dtSPB['osccol'] * 100, dtSPB['amacol'] * 100 , dtSPB['histcol'] * 100, dtSPB['sptcol'] class Github_behradhesami_Projects__lux1_1__20240626_142549(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 0.13, "33": 0.046, "50": 0.024, "100": 0 } # Buy hyperspace params: buy_params = { "cross_buy": -61, "length_buy": 33, "mult_buy": 99, "short_cross": 87, "smooth_buy": 30, } # Sell hyperspace params: sell_params = { "cross_sell": 70, } length_buy = IntParameter(2, 50, default= int(buy_params['length_buy']), space='buy') mult_buy = IntParameter(2, 100, default= int(buy_params['mult_buy']), space='buy') smooth_buy = IntParameter(2, 100, default= int(buy_params['smooth_buy']), space='buy') cross_buy = IntParameter(-100, -50, default= int(buy_params['cross_buy']), space='buy') cross_sell = IntParameter(-100, 100, default= int(sell_params['cross_sell']), space='sell') short_cross = IntParameter(50, 100, default= int(buy_params['short_cross']), space='buy') stoploss = -0.145 trailing_stop = False timeframe = '5m' custom_info = {} process_only_new_candles = False use_exit_signal = False exit_profit_only = False ignore_roi_if_entry_signal = False startup_candle_count: int = 30 order_types = { 'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' } plot_config = { 'main_plot': { 'supertrend': {'color': 'green'}, }, 'subplots': { "OSC": { 'osc': {'color': 'blue'}, 'signal': {'color': 'orange'}, 'histogram': {'color': 'green'}, } } } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # for val in self.length_buy.range: # for val_1 in self.mult_buy.range: # dataframe[f'osc_{val}_{val_1}']=ta.EMA(dataframe, short_lenght = val,long_lenght = val_1) #------------------------------------------------------------------------------------------------------------------------------------------------------------ #for val in self.length_buy.range: #for val_1 in self.mult_buy.range: #dataframe[f'osc_{val}_{val_1}'] =LUX_SuperTrendOscillator(dataframe,source='close', length = int(self.length_buy.value), mult =int(self.mult_buy.value), smooth =int(self.smooth_buy.value)) # for val in self.length_buy.range: # for val_1 in self.mult_buy.range: # for val_2 in self.smooth_buy.range: # dataframe[f'supertrend_{val}_{val_1}_{val_2}'] =LUX_SuperTrendOscillator(dataframe,source='close', length = val, mult =val_1, smooth =val_2) # for val in self.length_buy.range: # for val_1 in self.mult_buy.range: # for val_2 in self.smooth_buy.range: # dataframe[f'signal_{val}'] =LUX_SuperTrendOscillator(dataframe,source='close', length = val, mult =val_1, smooth =val_2) # for val in self.length_buy.range: # for val_1 in self.mult_buy.range: # for val_2 in self.smooth_buy.range: # dataframe[f'histogram_{val}_{val_1}_{val_2}'] =LUX_SuperTrendOscillator(dataframe,source='close', length = val, mult =val_1, smooth =val_2) #-------------------------------------------------------------------------------------------------------------------------------------------------------- for val in self.length_buy.range: for val_1 in self.mult_buy.range: for val_2 in self.smooth_buy.range: dataframe[f'osc_{val}_{val_1}_{val_2}'], dataframe[f'signal_{val}_{val_1}_{val_2}'] , dataframe[f'histogram_{val}_{val_1}_{val_2}'],dataframe[f'supertrend_{val}_{val_1}_{val_2}'] =LUX_SuperTrendOscillator(dataframe, length = int(self.length_buy.value), mult = int(self.mult_buy.value), smooth = int(self.smooth_buy.value)) # for val in self.length_buy.range: # for val_1 in self.mult_buy.range: # for val_2 in self.smooth_buy.range: # dataframe[f'supertrend_{val}_{val_1}_{val_2}'], # dataframe[f'osc_{val}_{val_1}'], # dataframe[f'signal_{val}'] , # dataframe[f'histogram_{val}_{val_1}_{val_2}']= LUX_SuperTrendOscillator(dataframe,source='close', length = int(self.length_buy.value), # mult = int(self.mult_buy.value), smooth = int(self.smooth_buy.value)) #------------------------------------------------------------------------------------------------------------------------------------------------------------ # dataframe['osc'], dataframe['signal'] , dataframe['histogram'], dataframe['supertrend'] =LUX_SuperTrendOscillator(dataframe, length = int(self.length_buy.value), # mult = int(self.mult_buy.value), smooth = int(self.smooth_buy.value)) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: condination_long =[] condination_long.append(qtpylib.crossed_above( dataframe[f'osc_{self.length_buy.value}_{self.mult_buy.value}_{self.smooth_buy.value}'], int(self.cross_buy.value) )) condination_long.append( dataframe[f'supertrend_{self.length_buy.value}_{self.mult_buy.value}_{self.smooth_buy.value}'] > dataframe['close'] ) #enter_short condination_short =[] condination_short.append(qtpylib.crossed_below( dataframe[f'osc_{self.length_buy.value}_{self.mult_buy.value}_{self.smooth_buy.value}'], int(self.short_cross.value) )) condination_short.append( dataframe[f'supertrend_{self.length_buy.value}_{self.mult_buy.value}_{self.smooth_buy.value}'] < dataframe['close']) dataframe.loc[ reduce(lambda x, y: x & y,condination_long ), "enter_long", ] = 1 dataframe.loc[ reduce(lambda x, y: x & y,condination_short ), "enter_short", ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: condination_long =[] condination_long.append(qtpylib.crossed_below( dataframe[f'osc_{self.length_buy.value}_{self.mult_buy.value}_{self.smooth_buy.value}'], int(self.cross_sell.value) )) #exit_short condination_short =[] condination_short.append(qtpylib.crossed_above( dataframe[f'osc_{self.length_buy.value}_{self.mult_buy.value}_{self.smooth_buy.value}'], int(self.cross_sell.value) )) dataframe.loc[ reduce(lambda x, y: x & y, condination_long), "exit_long"] = 1 dataframe.loc[ reduce(lambda x, y: x & y, condination_short), "exit_short"] = 1 return dataframe