# source: https://raw.githubusercontent.com/viktors1982/trading/e2fba2a4608c6bd6d1ca7e117c80cad2f692dec1/freqtrade/strategies/SupertrendBFutures.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 import math from freqtrade.persistence import Trade, PairLocks from datetime import datetime, timedelta import math import logging from datetime import datetime, timedelta, timezone #from py3cw.request import Py3CW logger = logging.getLogger(__name__) def funcLinearRegressionChannel2(dtloc, source = 'close', window = 180, deviations = 2): dtLRC = dtloc.copy() dtLRC['lrc_up'] = np.nan dtLRC['lrc_down'] = np.nan dtLRC['slope'] = np.nan dtLRC['average'] = np.nan dtLRC['intercept'] = np.nan i = np.arange(start=1, stop=window+1) # #print(i) i = i[::-1] # #print(i) i = i + 1.0 # #print(i) Ex = i.sum() Ex2 = (i * i).sum() ExT2 = math.pow(Ex, 2) def calc_lrc(dfr, init=0): global calc_lrc_src_value if init == 1: calc_lrc_src_value = list() return calc_lrc_src_value.append(dfr[source]) lrc_val_up = np.nan lrc_val_down = np.nan slope = np.nan average = np.nan intercept = np.nan Ey = np.nan Exy = np.nan vwap1 = np.nan sdev = np.nan dev = np.nan lrc_down = np.nan lrc_up = np.nan if len(calc_lrc_src_value) > window: calc_lrc_src_value.pop(0) if len(calc_lrc_src_value) >= window: src1 = np.array(calc_lrc_src_value) src = src1[::-1] Ey = src.sum() # Ey2 = (src * src).sum() EyT2 = math.pow(Ey,2) Exyi = i*src Exy = (Exyi).sum() # PearsonsR = (Exy - Ex * Ey / window) / (math.sqrt(Ex2 - ExT2 / window) * math.sqrt(Ey2 - EyT2 / window)) ExEx = Ex * Ex slope = 0.0 if (Ex2 != ExEx ): slope = (window * Exy - Ex * Ey) / (window * Ex2 - ExEx) average = Ey / window intercept = average - slope * Ex / window + slope vwap1 = intercept + slope * window sdev = np.std(src1) dev = deviations * sdev lrc_down = vwap1 - dev lrc_up = vwap1 + dev return slope,average,intercept,Ex,Ey,Ex2,Exy,vwap1,sdev,dev,lrc_down,lrc_up calc_lrc(None, init=1) dtLRC[['slope','average','intercept','Ex','Ey','Ex2','Exy','vwap1','sdev','dev','lrc_down','lrc_up']] = dtLRC.apply(calc_lrc, axis = 1, result_type='expand') return dtLRC[['slope','average','intercept','Ex','Ey','Ex2','Exy','vwap1','sdev','dev','lrc_down','lrc_up']] class github_viktors1982_trading__SupertrendBFutures__20220718_143423(IStrategy): INTERFACE_VERSION = 3 buy_params = { "lr_win" : 150, "lr_mult" : 2, } # ROI table: minimal_roi = { "0": 0.015, # This is 10000%, which basically disables ROI } lr_win = IntParameter(10, 500, default= int(buy_params['lr_win']), space='buy') lr_mult = IntParameter(1, 20, default= int(buy_params['lr_mult']), space='buy') stoploss = -0.15 # 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 can_short = True # 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", } plot_config = { # Main plot indicators (Moving averages, ...) 'main_plot': { 'lrc_up': {'color': 'green'}, 'lrc_down': {'color': 'blue'}, 'st_line': {'color': 'orange'} }, 'subplots': { } } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Super trend B # https://www.tradingview.com/v/veDICWLK/ # translated for freqtrade: viksal1982 viktors.s@gmail.com mult1 = 0.5 length = 20 st_mult = 3 st_period = 7 dataframe['basis'] = ta.SMA(dataframe, timeperiod = length) dataframe['upper1'] = dataframe['basis'] + mult1 * ta.STDDEV(dataframe, length) dataframe['lower1'] = dataframe['basis'] - mult1 * ta.STDDEV(dataframe, length) dataframe['ATR'] = ta.ATR(dataframe, timeperiod = st_period) dataframe['TR'] = ta.TRANGE(dataframe) dataframe['ATR1'] = dataframe['TR'].ewm(alpha=1 / st_period).mean() dataframe['up_lev'] = dataframe['upper1'] - st_mult * dataframe['ATR'] dataframe['dn_lev'] = dataframe['lower1'] + st_mult * dataframe['ATR'] dataframe['up_trend'] = dataframe['up_lev'] dataframe['down_trend'] = dataframe['up_lev'] dataframe['trend'] = np.where( dataframe['close'] > dataframe['down_trend'], 1, -1) dataframe['st_line'] = np.where( dataframe['trend'] == 1, dataframe['up_trend'],dataframe['down_trend']) dataframe[['slope','average','intercept','Ex','Ey','Ex2','Exy','vwap1','sdev','dev','lrc_down','lrc_up']] = funcLinearRegressionChannel2(dataframe, source = 'close', window = int(self.lr_win.value), deviations = int(self.lr_mult.value)) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (qtpylib.crossed_below(dataframe['close'], dataframe['lrc_down'])) & (dataframe['volume'] > 0) ), ['enter_long', 'enter_tag']] = (1, 'long') dataframe.loc[ ( (qtpylib.crossed_below(dataframe['close'], dataframe['st_line'])) & (dataframe['volume'] > 0) ), ['enter_short', 'enter_tag']] = (1, 'short') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (qtpylib.crossed_below(dataframe['close'], dataframe['lrc_down'])) & (dataframe['volume'] > 0) ), ['exit_short', 'exit_tag']] = (1, 'short') dataframe.loc[ ( (qtpylib.crossed_below(dataframe['close'], dataframe['st_line'])) & (dataframe['volume'] > 0) ), ['exit_long', 'exit_tag']] = (1, 'long') return dataframe