import numpy as np
import csv
import matplotlib.pyplot as plt
from matplotlib import dates, ticker
import matplotlib as mpl
from mpl_finance import candlestick_ohlc

mpl.style.use(‘default’)

fname = ‘data.csv’

# Empty lists to extract the data from csv files
date_data = []
open_data = []
high_data = []
low_data= []
close_data= []
trade = []
turn = []

# Extracting data
with open(fname, ‘r’) as csvfile:
data = csv.reader(csvfile, delimiter=’,’)
for line in data:
date_data.append(line[0])
open_data.append(line[1])
high_data.append(line[2])
low_data.append(line[3])
close_data.append(line[4])
trade.append(line[5])
turn.append(line[6])
# removing ‘-‘ indata
trade[18] = 0
turn[18] = 0

# Conversion to numpy arrays
open_val = np.array(open_data[1:], dtype=np.float64)
high_val = np.array(high_data[1:], dtype=np.float64)
low_val = np.array(low_data[1:], dtype=np.float64)
close_val = np.array(close_data[1:], dtype=np.float64)
trade_val = np.array(trade[1:], dtype=np.float64)
turn_val = np.array(turn[1:], dtype=np.float64)

# Matplotlib needs dataes in floating numbers to plot them under the hood
data_dates=[]
for date in date_data[1:]:
new_Date = dates.datestr2num(date)
data_dates.append(new_Date)

i = 0
ohlc_data = []
while i < len(data_dates):
stats_1_day = data_dates[i], open_val[i], high_val[i], low_val[i], close_val[i]
ohlc_data.append(stats_1_day)
i += 1

fig, ax1 = plt.subplots()
candlestick_ohlc(ax1, ohlc_data, width=0.5, colorup=’g’, colordown=’r’, alpha=0.8)
plt.show()