Datasets:

sebdg
/

crypto_data

	import pandas as pd
	import numpy as np

	df = pd.read_csv('candles.csv')


	from talib import RSI, BBANDS, MACD, ATR, EMA, SMA


	# group by market
	grouped = df.groupby('market')

	# for each market calculate the indicators and add them to the dataframe
	for market, group in grouped:
	# calculate the indicators
	print('Calculating indicators for', market)
	df.loc[group.index,'rsi'] = RSI(group['close'], timeperiod=14)

	upper, middle, lower = BBANDS(group['close'], timeperiod=20)
	df.loc[group.index,'bb_upper'] = upper
	df.loc[group.index,'bb_middle'] = middle
	df.loc[group.index,'bb_lower'] = lower

	macd, macdsignal, macdhist = MACD(group['close'], fastperiod=12, slowperiod=26, signalperiod=9)
	df.loc[group.index,'macd'] = macd
	df.loc[group.index,'macdsignal'] = macdsignal
	df.loc[group.index,'macdhist'] = macdhist

	df.loc[group.index,'atr'] = ATR(group['high'], group['low'], group['close'], timeperiod=14)

	df.loc[group.index,'ema'] = EMA(group['close'], timeperiod=30)
	df.loc[group.index,'sma'] = SMA(group['close'], timeperiod=30)

	# drop the rows with NaN values
	df = df.dropna()


	# save the dataframe to a new file
	print(df.tail())
	df.to_csv('indicators.csv', index=False)