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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) |