# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-michael-k8s-namespace/LuxOSC.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 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 def LUX_SuperTrendOscillator(dtloc, source='close', length=6, mult=9, smooth=72): """ // 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 """ def_proc_name = '_LUX_SuperTrendOscillator' atrcol = 'atr' + def_proc_name hl2col = 'hl2' + def_proc_name upcol = 'up' + def_proc_name dncol = 'dn' + def_proc_name uppercol = 'upper' + def_proc_name lowercol = 'lower' + def_proc_name trendcol = 'trend' + def_proc_name sptcol = 'spt' + def_proc_name osc1col = 'osc1' + def_proc_name osc2col = 'osc2' + def_proc_name osccol = 'osc' + def_proc_name alphacol = 'alpha' + def_proc_name amacol = 'ama' + def_proc_name histcol = 'hist' + def_proc_name dtS = dtloc.copy().fillna(0) dtS[atrcol] = ta.ATR(dtloc, timeperiod=length) * mult dtS[hl2col] = (dtS['high'] + dtS['low']) / 2 dtS[upcol] = dtS[hl2col] + dtS[atrcol] dtS[dncol] = dtS[hl2col] - dtS[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) dtS[uppercol] = dtS.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) dtS[lowercol] = dtS.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) dtS[trendcol] = dtS.apply(calc_trend, axis=1) dtS[sptcol] = dtS[trendcol] * dtS[lowercol] + (1 - dtS[trendcol]) * dtS[uppercol] dtS[osc1col] = (dtS[source] - dtS[sptcol]) / (dtS[uppercol] - dtS[lowercol]) dtS[osc2col] = np.where(dtS[osc1col] < 1, dtS[osc1col], 1) dtS[osccol] = np.where(dtS[osc2col] > -1, dtS[osc2col], -1) dtS[alphacol] = dtS[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) dtS[amacol] = dtS.apply(calc_ama, axis=1) dtS[histcol] = ta.EMA(dtS[osccol] - dtS[amacol], timeperiod=smooth) return (dtS[osccol] * 100, dtS[amacol] * 100, dtS[histcol] * 100, dtS[sptcol]) class Github_DerSalvador_freqtrade_helm_chart__LuxOSC__20260115_122204(IStrategy): INTERFACE_VERSION = 3 # Buy hyperspace params: entry_params = {'cross_entry': -100, 'length_entry': 6, 'mult_entry': 9, 'smooth_entry': 72} # Sell hyperspace params: exit_params = {'cross_exit': 50} length_entry = IntParameter(2, 100, default=int(entry_params['length_entry']), space='entry') mult_entry = IntParameter(2, 100, default=int(entry_params['mult_entry']), space='entry') smooth_entry = IntParameter(2, 100, default=int(entry_params['smooth_entry']), space='entry') cross_entry = IntParameter(-100, 100, default=int(entry_params['cross_entry']), space='entry') cross_exit = IntParameter(-100, 100, default=int(exit_params['cross_exit']), space='exit') stoploss = -0.99 # Trailing stoploss trailing_stop = False timeframe = '5m' custom_info = {} process_only_new_candles = False use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False startup_candle_count: int = 30 # 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'} # Main plot indicators (Moving averages, ...) # Subplots - each dict defines one additional plot 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: dataframe['osc'], dataframe['signal'], dataframe['histogram'], dataframe['supertrend'] = LUX_SuperTrendOscillator(dataframe, length=int(self.length_entry.value), mult=int(self.mult_entry.value), smooth=int(self.smooth_entry.value)) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[qtpylib.crossed_above(dataframe['osc'], int(self.cross_entry.value)) & (dataframe['supertrend'] > dataframe['close']) & (dataframe['volume'] > 0), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[qtpylib.crossed_below(dataframe['osc'], int(self.cross_exit.value)) & (dataframe['volume'] > 0), 'exit_long'] = 1 return dataframe