Plot Candlestick Chart using mplfinance module in Python

Candlestick chart are also known as a Japanese chart. These are widely used for technical analysis in trading as they visualize the price size within a period. They have four points Open, High, Low, Close (OHLC). Candlestick charts can be created in python using a matplotlib module called mplfinance. 

Installation:

pip install mplfinance

mplfinance.candlestick_ohlc()

This function is used to plot Candlestick charts.

Syntax: mplfinance.candlestick_ohlc(ax, quotes, width=0.2, colorup='k', colordown='r', alpha=1.0)
Parameters: 

• ax: An Axes instance to plot to.
• quotes: sequence of (time, open, high, low, close, …) sequences.
• width: Fraction of a day for the rectangle width.
• colorup: The color of the rectangle where close >= open.
• colordown: The color of the rectangle where close < open.
• alpha: (float) The rectangle alpha level.

Returns: returns (lines, patches) where lines are a list of lines added and patches is a list of the rectangle patches added.

To plot the chart, we will take data from NSE for the period 01-07-2020 to 15-07-2020, the data is available for download in a csv file, or can be downloaded from here. For this example, it is saved as 'data.csv'.
We will use the pandas library to extract the data for plotting from data.csv.

Below is the implementation:

# import required packages
import matplotlib.pyplot as plt
from mplfinance import candlestick_ohlc
import pandas as pd
import matplotlib.dates as mpdates

plt.style.use('dark_background')

# extracting Data for plotting
df = pd.read_csv('data.csv')
df = df[['Date', 'Open', 'High', 
         'Low', 'Close']]

# convert into datetime object
df['Date'] = pd.to_datetime(df['Date'])

# apply map function
df['Date'] = df['Date'].map(mpdates.date2num)

# creating Subplots
fig, ax = plt.subplots()

# plotting the data
candlestick_ohlc(ax, df.values, width = 0.6,
                 colorup = 'green', colordown = 'red', 
                 alpha = 0.8)

# allow grid
ax.grid(True)

# Setting labels 
ax.set_xlabel('Date')
ax.set_ylabel('Price')

# setting title
plt.title('Prices For the Period 01-07-2020 to 15-07-2020')

# Formatting Date
date_format = mpdates.DateFormatter('%d-%m-%Y')
ax.xaxis.set_major_formatter(date_format)
fig.autofmt_xdate()

fig.tight_layout()

# show the plot
plt.show()

Output :