To match RSI(14) & Stochastic RSI(14) with Binance or TradingView - Python only

Request To write a python script or use existing formula for RSI and Stochastic RSI which would match one or both Binance and/ or TradingView platform. You can use any of TA-lib, finta, tulipy or your own function or library in order to accomplish the task. No scraping please. Just APIs and formula based solutions. __________________________________________________________________ To consider a) trading window is 1 minute/ kline b) 14 means trading trading period, so last 14 klines of 1 minute to be considered c) might be possible to adjust the code using a smoothness (Wilder's Smoothing) to get the right result d) in order to use the last 14 klines, you might need more than 14 entries, to close the gap of potential NaN e) the above can be done by fetching the client.get_klines for past 250 entries of Client.KLINE_INTERVAL_1MINUTE for a specific pair f) Stochastic RSI smoothness tried so far was EMA and SMA __________________________________________________________________ Charts Make sure the trading window is 1 minute and selected from tech indicators the RSI(14) and the StochRSI(14, 14, 3, 3) aka Stochastic RSI [login to view URL] [login to view URL] __________________________________________________________________ Helper and Misc import pandas as pd pd.set_option('display.float_format', lambda x: '%.12f' % x) import numpy as np np.set_printoptions(suppress=True) from [login to view URL] import Client import talib from talib import MA_Type from talib import abstract from [login to view URL] import * from finta import TA import tulipy as ti import logging [login to view URL](filename='log_'+[login to view URL]('%Y_%m_%d')+'.log',level=[login to view URL]) # the input to be saved into a csv with the following head csvhead = [["date", "open", "high", "low", "close"],] csvdata = [] client = Client(api_key, api_secret) [login to view URL]() # fetch a good chunk of data to avoid NaN when applying the formulas, despite we only need the last entry from result or the RSI/ StochRSI # according to [login to view URL] klines = get_historical_klines("RENBTC", Client.KLINE_INTERVAL_1MINUTE, "1 day ago UTC") # populate a csv called "[login to view URL]" with above klines response # you might want to reverse the dates in the csv to calculate in the chronological order df = pd.read_csv('[login to view URL]',index_col ='date') close = df['close'] ### RSI outputs ### rsi_finta = [login to view URL](close, 14) print("RSI_finta = {}".format(rsi_finta[-1])) rsi_ta = [login to view URL](close * 100000, 14) / 100000 print("RSI_talib = %s" % (rsi_ta[-1])) rsi_ti = [login to view URL](close, 14) print("RSI_tulipy = %s" % (rsi_ti[-1])) ### Stochastic RSI outputs ### fastk_1, fastd_1 = [login to view URL](rsi_*one_from_above, rsi_*one_from_above, rsi_*one_from_above, fastk_period=14, slowk_period=3, slowk_matype=[login to view URL], slowd_period=3, slowd_matype=[login to view URL]) fastk_2, fastd_2 = [login to view URL](rsi_*one_from_above, rsi_*one_from_above, rsi_*one_from_above, fastk_period=14, slowk_period=3, slowk_matype=[login to view URL], slowd_period=3, slowd_matype=[login to view URL]) print("StochRSI K_1 is: %s \n" % (fastk_1[-1])) print("StochRSI D_1 is: %s \n" % (fastd_1[-1])) print("StochRSI K_2 is: %s \n" % (fastk_2[-1])) print("StochRSI D_2 is: %s \n" % (fastd_2[-1]))