# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/Discord_M5FftRsi.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from freqtrade.strategy.interface import IStrategy # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import math class Github_remiotore_freqtrade__Discord_M5FftRsi__20260111_210550(IStrategy): # Strategy interface version - allow new iterations of the strategy interface. INTERFACE_VERSION = 2 minimal_roi = { "0": 0.10 } stoploss = -0.075 trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.03 ticker_interval = '1h' process_only_new_candles = True use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False startup_candle_count: int = 260 plot_config = { 'main_plot': { }, 'subplots': { "fourier": { 'input': {'color': 'red'}, 'fourierf': {'color': 'green'}, 'fouriers': {'color': 'blue'} } } } def informative_pairs(self): """ """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ ################################################################################## ## --> Made in ITALY JN54oe in 2021 <-- ## # MikeFive RSI-FFT Convolution Strategy # # ------------------------------------- # # Transform the Rsi signal with the FFT algorithm # # Using 2 different convolution for generating valid signal # # -------------------------------------------------------------------- # # Change the values in the section 'Variabili Generali' for different result # # Also you can add a section valid for only one pair (example below). # # Try to change the values and SHARE the best config values or every pair!! # ### ------------------------------------------------------------------------- ### # Disclaimer: This strategy is experimental. I'm not responsable for money loss # # ------------------------------------------------------------------- # # TEST IT BEFORE USING IT! ONLY YOU ARE RESPONSABLE OF YOUR MONEY! # # ------------------------------------------------------------------- # # # # If you like my work, feel free to donate or use one of █▀▄ ▄▀█ █▀▀▀▀ # # my referral links, that would also greatly be appreciated █ █ █ █▄▄▄ # # https://accounts.binance.com/it/register?ref=LV0YOVHW █ █ █ # # BTC: bc1q0c0a5tez6lhjatdjkcccrg7xx0z3c2f5yk8c62 ▀ ▀ ▀▀▀▀ # ## ## ################################################################################## """ #Costanti pi = math.pi #Variabili Generali if(True): Nrsi = 14 # Timeperiod for RSI (standard value is 14) N = 22 # Sample for FFT (experiment Prime numbers) QtF = 16 # N of armonics for fast curve < N (if 0 no phase) QtS = 1 # N of armonics for slow curve < N (if 0 no phase) PhF = 0.3 # Phase offset - + fast curve (Relative to RSI) PhS = 0.1 # Phase offset - + slow curve (Relative to Fast) AmpArm0F = 1.005 # Amp of dc value fast curve (dc no phase) AmpArm0S = 1.000 # Amp of dc value slow curve (dc no phase) deltaT = 1 # Delta Timeframe for signal (1 to n+) deltaVb = 0.995 # ratio buy signal deltaVs = 1.002 # ratio sell signal ''' #Variabili per singole coppie if(metadata['pair'] == "BTC/EUR"): Nrsi = 15 # Timeperiod for RSI (standard value is 14) N = 22 # Sample for FFT (experiment Prime numbers) QtF = 16 # N of armonics for fast curve QtS = 1 # N of armonics for slow curve PhF = 0.0 # Phase offset - + fast curve (not for dc) PhS = 0.0 # Phase offset - + slow curve (not for dc) AmpArm0F = 1.006 # Amp of dc value fast curve (no phase) AmpArm0S = 1.000 # Amp of dc value slow curve (no phase) deltaT = 1 # Delta Timeframe for signal (1 to n+) deltaVb = 0.998 # ratio buy signal deltaVs = 1.001 # ratio sell signal ''' #Filtro in ingresso prima di fft #dataframe['media'] = (dataframe['open'] + dataframe['close'] + dataframe['low'] + dataframe['high']) / 4 dataframe['input'] = ta.RSI(dataframe['close'], timeperiod=Nrsi) #Parte Reale nel Dominio di Frequenza def ReX(k): sum = 0.0 for i in range(0, N): sum = sum + dataframe['input'].shift(i)*math.cos(2*pi*k*i/N) return sum #Parte Imaginaria nel Dominio di Frequenza def ImX(k): sum = 0.0 for i in range(0, N): sum = sum + dataframe['input'].shift(i)*math.sin(2*pi*k*i/N) return -sum #Conversione da Dominio di Frequeza a Dominio di Tempo (Sinusoide) def ReX_(k): case = 0.0 if(k!=0 and k!=N/2): case = 2*ReX(k)/N if(k==0): case = ReX(k)/N if(k==N/2): case = ReX(k)/N return case #Conversione da Dominio di Frequeza a Dominio di Tempo (Sinusoide) def ImX_(k): return -2*ImX(k)/N ''' #FFT NON USATA def x(i, N): sum1 = 0.0 sum2 = 0.0 for k in range(0, N/2): #for k=0 to N/2 sum1 = sum1 + ReX_(k)*math.cos(2*pi*k*i/N) for k in range(0, N/2): #for k=0 to N/2 sum2 = sum2 + ImX_(k)*math.sin(2*pi*k*i/N) return sum1+sum2 ''' #DFFT Ricostruzione in singole sinusoidi (Fase,Armonica) def sx(i, k): sum1 = ReX_(k)*math.cos(2*pi*k*i/N) sum2 = ImX_(k)*math.sin(2*pi*k*i/N) return sum1+sum2 #Costruzione segnale fast FaseF = PhF + N dataframe['fourierf'] = sx(FaseF,0) * AmpArm0F for i in range(1, QtF+1): dataframe['fourierf'] = dataframe['fourierf'] + sx(FaseF,i) #Costruzione segnale slow FaseS = PhS + FaseF dataframe['fouriers'] = sx(FaseS,0) * AmpArm0S for i in range(1, QtS+1): dataframe['fouriers'] = dataframe['fouriers'] + sx(FaseS,i) #Regole buy long dataframe['compra'] = ((dataframe['fourierf'] / dataframe['fouriers']) > deltaVb) & ((dataframe['fourierf'].shift(deltaT) / dataframe['fouriers'].shift(deltaT)) <= deltaVb) #Regole sell long dataframe['vendi'] = ((dataframe['fourierf'] / dataframe['fouriers']) < deltaVs) & ((dataframe['fourierf'].shift(deltaT) / dataframe['fouriers'].shift(deltaT)) >= deltaVs) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['compra'] > 0) & (dataframe['compra'].shift(1) == 0) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['vendi'] > 0) & (dataframe['vendi'].shift(1) == 0) ), 'sell'] = 1 return dataframe