JIYIK CN >

In this demonstration, we will introduce candlestick charts or plots and see how to mplfinancecreate an OHLC (Open, High, Low, Close) candlestick chart using the module in Python Matplotlib.

mplfinanceCreate candlestick charts using the Matplotlib library

pandasThe Matplotlib Financial API makes it easy to plot stock prices. It requires the and modules to be installed on our machine mplfinance.

mplfinanceThe module makes it very easy to do various operations such as OHLC charts, candlestick charts and even point and figure charts, all on one line.

For example, the first thing to do is to get some data. We downloaded Amazon's stock price from 2019 to 2020 from Yahoo Finance.

To download, go to Yahoo Finance, search for Amazon, and go to the Historical Data tab to download. We are using Amazon's stock price, but you can use any stock price data you like.

If the module is not installed on your machine mplfinance, you must install it.

pip install mplfinance

Import the following required modules.

# %matplotlib inline
import pandas as pd
import mplfinance as mplf

The following line show()will display our plot or chart inline without using the method, but if we use a different IDLE, for example pycharm, we don't need to write this line as it will produce an error.

%matplotlib inline

Let's create a filevariable called and give it the name of the CSV file we downloaded. Make sure you are in the correct file path location, otherwise you will have to put the path here and use osthe Python package.

file = "AMZN.csv"

We will then create a data frame and read the CSV file.

data = pd.read_csv(file)

This is called a data frame below. It's 252 rows by 7 columns, and we have these columns here, and you can see that it's automatically indexed.

data

If you write your code into a different IDLE, the above line will not work. You need to print it to see your data.

Sometimes when we download data from yahoo, the names of the columns have spaces in front of them. This can be annoying, so you have to change it.

We will Dateconvert the column to datetime, since the object is a string. We need to use the pandas to_datetime()method to convert it to datetime.

We can info()view Datea summary of the columns and their data types using the method.

data.Date = pd.to_datetime(data.Date)
data.info()

When we execute it, we will now see that we have a datetimedata type, instead of before we had an object.

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 252 entries, 0 to 251
Data columns (total 7 columns):
 #   Column     Non-Null Count  Dtype
---  ------     --------------  -----
 0   Date       252 non-null    datetime64[ns]
 1   Open       252 non-null    float64
 2   High       252 non-null    float64
 3   Low        252 non-null    float64
 4   Close      252 non-null    float64
 5   Adj Close  252 non-null    float64
 6   Volume     252 non-null    int64
dtypes: datetime64[ns](1), float64(5), int64(1)
memory usage: 13.9 KB

If we want to manipulate this data to display our chart, we need to access 日期the columns since we will be displaying the stock price corresponding to a date. We need to make the index Datea column of our dataframe and the padding will be added automatically to that index.

data = data.set_index("Date")
data

DateThe column is now our index.

We will be using the Finance API in the following examples. We have our data and types mplf, which is our mplfinancereference to the module.

We call the method from this module plot().

# %matplotlib inline # remove this line when use do not use jupyter notebook
import pandas as pd
import mplfinance as mplf

file = "AMZN.csv"
data = pd.read_csv(file)
data.Date = pd.to_datetime(data.Date)
data.info()
data = data.set_index("Date")
mplf.plot(data)
# use this when you are not using jupyter notebook
mplf.show()

If we want to show volume, we will make a line graph because it is easier to see on this scale and the volume is True.

# %matplotlib inline # remove this line when we use jupyter notebook
import pandas as pd
import mplfinance as mplf

file = "AMZN.csv"
data = pd.read_csv(file)
data.Date = pd.to_datetime(data.Date)
data.info()
data = data.set_index("Date")
mplf.plot(data, type="line", volume=True)
# use this when you are not using jupyter notebook
mplf.show()

We have a line graph that is different from the OHLC graph. We also have volume, so we can see that it's doing something for us here.

It has labeled the axes and rotated those labels.

Let's find the months using the time series in pandas. We mavput the data from May to July into the moving average using the parameter, and then we specify the type as candlesticka candle named and we will keep the volume.

# %matplotlib inline # remove this line when wuse do not use jupyter notebook
import pandas as pd
import mplfinance as mplf

file = "AMZN.csv"
data = pd.read_csv(file)
data.Date = pd.to_datetime(data.Date)
data.info()
data = data.set_index("Date")

mplf.plot(
    data.loc["2020-03":"2020-07"],
    figratio=(20, 12),
    title="Amazon price 2019 - 2020",
    type="candle",
    mav=(20),
    volume=True,
)
# use this when you are not using jupyter notebook
mplf.show()

If we execute the code, we get a candlestick, an OHLC chart with volumes only for the moving average.

We can stylechange the style of the plot's appearance using the parameter, "yahoo"setting to the value of this method.

# %matplotlib inline # remove this line when wuse do not use jupyter notebook
import pandas as pd
import mplfinance as mplf

file = "AMZN.csv"
data = pd.read_csv(file)
data.Date = pd.to_datetime(data.Date)
data.info()
data = data.set_index("Date")

mplf.plot(
    data.loc["2020-03":"2020-07"],
    figratio=(20, 12),
    title="Amazon price 2019 - 2020",
    type="candle",
    mav=(20),
    volume=True,
    style="yahoo",
)
# use this when you are not using jupyter notebook
mplf.show()