Plotting Stock Charts in MATLAB using Python’s yfinance

This tutorial explains how to fetch and visualize stock price data from Yahoo Finance using MATLAB. We use Python’s yfinance library from within MATLAB to avoid the need for expensive toolboxes or data subscriptions.

What You’ll Learn

How to set up MATLAB to work with Python
How to fetch Indian stock data using yfinance
How to plot closing prices or other fields using simple MATLAB scripts

Prerequisites

MATLAB installed (R2019b or later is recommended) in my case i used Matlab R2024b
Python 3.8 to 3.13+ installed on your system
The yfinance Python package installed
Basic knowledge of MATLAB scripting

Step 1: Install MATLAB (Select Required Components)

During installation:

Select only “MATLAB” (core component)
You do not need Simulink or engineering toolboxes
Optional toolboxes for quant work:
- Financial Toolbox
- Statistics and Machine Learning Toolbox

Step 2: Install Python and yfinance

Install Python from https://www.python.org or use Anaconda.

Then open Command Prompt and run:

pip install yfinance

This installs the yfinance package used to access Yahoo Finance data.

Step 3: Check Python Integration

MATLAB usually detects your Python installation automatically. Just run:

pyenv

If it shows a valid Python path and status is either Loaded or NotLoaded, you’re good.

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Test the integration with:

py.importlib.import_module('yfinance')

If no error is returned, you are good to proceed.

Step 4: Create and Run the Data Script

Save the following as datafeed_test.m and run it from the MATLAB editor.

symbols   = {'RELIANCE.NS'};      
startDate = '2024-01-01';
endDate   = '2025-05-05';
field     = 'Close';

py.importlib.import_module('yfinance');  

if numel(symbols)==1
    hist = py.yfinance.Ticker(symbols{1}).history( ...
             pyargs('start',startDate,'end',endDate));
else
    hist = py.yfinance.download(py.list(symbols), ...
             pyargs('start',startDate,'end',endDate));
end

idx = hist.index;
py_strs = cell(py.list(idx.strftime('%Y-%m-%d')));
date_strings = cellfun(@char, py_strs, 'UniformOutput', false);
dates = datetime(date_strings, 'InputFormat', 'yyyy-MM-dd');

figure; hold on;
for s = 1:numel(symbols)
    if hist.columns.nlevels == 1
        series = hist.get(field);
    else
        series = hist.get(py.tuple({symbols{s},field}));
    end
    plot(dates, double(series.values), 'LineWidth',1.8);
end
hold off; grid on;
title(sprintf('%s – %s', strjoin(symbols,', '), field));
xlabel('Date'); ylabel('Price');
legend(arrayfun(@(i) sprintf('%s-%s',symbols{i},field),1:numel(symbols), ...
       'UniformOutput',false), 'Location','best');

Notes

Use .NS for NSE stocks (e.g., RELIANCE.NS, INFY.NS)
for Nifty index use ^NSEI and for US market S&P500 use ^GSPC
You can use 'Open', 'High', 'Low', 'Close', 'Adj Close', or 'Volume' for the field
For multiple tickers, use {'RELIANCE.NS', 'TCS.NS'}

MATLAB has always been close to my heart — it was the first vectorized programming language I learned during my college days while studying Digital Signal Processing. That early exposure laid a strong foundation for later mastering Amibroker AFL and TradingView Pine Script, and it made understanding Pandas DataFrames significantly easier.