To match RSI(14) & Stochastic RSI(14) with Binance or TradingView - Python only
$30-250 USD
En curso
Publicado hace alrededor de 4 años
$30-250 USD
Pagado a la entrega
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.
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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
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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]
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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]))
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Carlos
Experianced developer on Quantopian, i have been building trading models for 2 years. I can help build RSI and Stochastic RSI . Contact Me for more details.