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How to Create a Candlestick Chart in Matplotlib?

  • Last Updated : 28 Nov, 2021

A candlestick chart, often known as a Japanese candlestick chart, is a financial chart that shows the price movement of stocks, derivatives, and other financial instruments in real-time, there are simply four essential components that must be examined. The open, high, low, and close are the four key elements, the candlestick chart has been used. It’s one of the world’s oldest charts.

The syntax of making a candlestick chart is as follows. Here we have used plt.bar method to build the candlestick chart.

Syntax:

plt.bar(up.index,up.close-up.open,bottom=up.open,color)

where,

  • “up” dataframe will store the stock_prices when the closing stock price is greater than or equal to the opening stock prices

plt.bar(down.index,down.close-down.open,bottom=down.open,color)

 where ,

  • “down” dataframe will store the stock_prices when the closing stock price is lesser than the opening stock prices

Example 1: Python program to plot 4 columns with up and down sticks

Python3




import pandas as pd
import matplotlib.pyplot as plt
  
# DataFrame to represent opening , closing, high 
# and low prices of a stock for a week
stock_prices = pd.DataFrame({'open': [36, 56, 45, 29, 65, 66, 67],
                             'close': [29, 72, 11, 4, 23, 68, 45],
                             'high': [42, 73, 61, 62, 73, 56, 55],
                             'low': [22, 11, 10, 2, 13, 24, 25]},
                            index=pd.date_range(
                              "2021-11-10", periods=7, freq="d"))
  
  
plt.figure()
  
# "up" dataframe will store the stock_prices 
# when the closing stock price is greater
# than or equal to the opening stock prices
up = stock_prices[stock_prices.close >= stock_prices.open]
  
# "down" dataframe will store the stock_prices
# when the closing stock price is
# lesser than the opening stock prices
down = stock_prices[stock_prices.close < stock_prices.open]
  
# When the stock prices have decreased, then it
# will be represented by blue color candlestick
col1 = 'blue'
  
# When the stock prices have increased, then it 
# will be represented by green color candlestick
col2 = 'green'
  
# Setting width of candlestick elements
width = .3
width2 = .03
  
# Plotting up prices of the stock
plt.bar(up.index, up.close-up.open, width, bottom=up.open, color=col1)
plt.bar(up.index, up.high-up.close, width2, bottom=up.close, color=col1)
plt.bar(up.index, up.low-up.open, width2, bottom=up.open, color=col1)
  
# Plotting down prices of the stock
plt.bar(down.index, down.close-down.open, width, bottom=down.open, color=col2)
plt.bar(down.index, down.high-down.open, width2, bottom=down.open, color=col2)
plt.bar(down.index, down.low-down.close, width2, bottom=down.close, color=col2)
  
# rotating the x-axis tick labels at 30degree 
# towards right
plt.xticks(rotation=30, ha='right')
  
# displaying candlestick chart of stock data 
# of a week
plt.show()

Output:

We can also make a candlestick chart by using the mpl_finance module. To use mpl_finance we need to install it first, which can be done by using the code. We have to install mpl_finance.

pip install mpl_finance

Syntax:



candlestick_ohlc(ax, ohlc.values, width, colorup, colordown)

where

  • ac is the axis
  • values are the input values
  • width is the width of each candle stick
  • colorup is the color for up sticks
  • colordown is the color for down sticks

Example 2: Here, we define a dataset of stock prices that contains 5 parameters i.e open, close, high, low, and index (i.e date) and after that, we used pandas.to DateTime to convert the date, and then pandas.astype to convert all of the data to float ().

Python3




# Importing all the required libraries
  
import matplotlib.pyplot as plt
from mpl_finance import candlestick_ohlc
import pandas as pd
import matplotlib.dates as mpl_dates
import numpy as np
import datetime
  
  
# Defining a dataframe showing stock prices 
# of a week
stock_prices = pd.DataFrame({'date': np.array([datetime.datetime(2021, 11, i+1)
                                               for i in range(7)]),
                             'open': [36, 56, 45, 29, 65, 66, 67],
                             'close': [29, 72, 11, 4, 23, 68, 45],
                             'high': [42, 73, 61, 62, 73, 56, 55],
                             'low': [22, 11, 10, 2, 13, 24, 25]})
  
ohlc = stock_prices.loc[:, ['date', 'open', 'high', 'low', 'close']]
ohlc['date'] = pd.to_datetime(ohlc['date'])
ohlc['date'] = ohlc['date'].apply(mpl_dates.date2num)
ohlc = ohlc.astype(float)
  
# Creating Subplots
fig, ax = plt.subplots()
  
candlestick_ohlc(ax, ohlc.values, width=0.6, colorup='blue',
                 colordown='green', alpha=0.4)
  
# Setting labels & titles
ax.set_xlabel('Date')
ax.set_ylabel('Price')
fig.suptitle('Stock Prices of a week')
  
# Formatting Date
date_format = mpl_dates.DateFormatter('%d-%m-%Y')
ax.xaxis.set_major_formatter(date_format)
fig.autofmt_xdate()
  
fig.tight_layout()
  
plt.show()

Output:

We can also use a dataframe rather than defining it on our own. The dataset can be downloaded by clicking here.

Example 3:

Python3




import matplotlib.pyplot as plt
from mpl_finance import candlestick_ohlc
import pandas as pd
import matplotlib.dates as mpl_dates
import numpy as np
import datetime
  
  
# Extracting Data for plotting
data = pd.read_csv("C:/Users/aparn/Desktop/data.csv")
ohlc = data.loc[:, ['Date', 'Open', 'High', 'Low', 'Close']]
  
# Converting date into datetime format
ohlc['Date'] = pd.to_datetime(ohlc['Date'])
ohlc['Date'] = ohlc['Date'].apply(mpl_dates.date2num)
ohlc = ohlc.astype(float)
  
# Creating Subplots
fig, ax = plt.subplots()
  
candlestick_ohlc(ax, ohlc.values, width=0.6,
                 colorup='green', colordown='red', alpha=0.8)
  
# Setting labels & titles
ax.set_xlabel('Date')
ax.set_ylabel('Price')
fig.suptitle('Daily Candlestick Chart of NIFTY50')
  
# Formatting Date
date_format = mpl_dates.DateFormatter('%d-%m-%Y')
ax.xaxis.set_major_formatter(date_format)
fig.autofmt_xdate()
  
fig.tight_layout()
  
plt.show()

Output:


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