I have tick data that I wold like to transform in OHLCV(Open, High, Low, Close, Volume) Daily, Hourly, 15min, 5min, 1min.
The tick data that I have is like this:
array([[u'2011-08-18 13:37:25', u'10.9', u'0.48990826'],
[u'2011-08-19 13:19:24', u'11.85', u'0.08438819'],
[u'2011-08-19 16:45:01', u'11.5', u'0.4'],
...,
[u'2013-08-24 01:29:27', u'107.97', u'0.18523664'],
[u'2013-08-24 01:29:35', u'107.98', u'4.61659567'],
[u'2013-08-24 01:30:56', u'107.98', u'0.09339562']],
dtype='<U19')
The numpy array have (date, price, volume).
I got the data to the nympy array with this code:
import matplotlib.pyplot as plt
import sqlite3
import numpy as np
database = sqlite3.connect('database.db')
cursor = database.cursor() # Criar o cursor
cursor.execute("select date, price, amount from table order by date asc")
ar=[[r[0], r[1], r[2]] for r in cursor.fetchall()]
database.close()
arnp = np.array(ar)
plt.plot(arnp[:, 1])
plt.ylabel('some...')
plt.show()
I really don't know what the best method to do this transformation. I've the data in a Sqlite database, it will be easier to do this transformation using SQL or using Python?
Any clues?
Best Regards,
