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:
Python3
# import required packagesimport matplotlib.pyplot as pltfrom mplfinance import candlestick_ohlcimport pandas as pdimport matplotlib.dates as mpdatesplt.style.use('dark_background')# extracting Data for plottingdf = pd.read_csv('data.csv')df = df[['Date', 'Open', 'High', 'Low', 'Close']]# convert into datetime objectdf['Date'] = pd.to_datetime(df['Date'])# apply map functiondf['Date'] = df['Date'].map(mpdates.date2num)# creating Subplotsfig, ax = plt.subplots()# plotting the datacandlestick_ohlc(ax, df.values, width = 0.6, colorup = 'green', colordown = 'red', alpha = 0.8)# allow gridax.grid(True)# Setting labels ax.set_xlabel('Date')ax.set_ylabel('Price')# setting titleplt.title('Prices For the Period 01-07-2020 to 15-07-2020')# Formatting Datedate_format = mpdates.DateFormatter('%d-%m-%Y')ax.xaxis.set_major_formatter(date_format)fig.autofmt_xdate()fig.tight_layout()# show the plotplt.show() |
Output :
Candlestick Chart
