Algo Trading: How to Source Market Data, Guide to Data Handling and Market Feeds

If you're diving into the world of finance or stock markets, you’ll quickly realise how important data is. Every price movement, every trade, and every news update comes down to data. But how do traders, investors, or financial apps get access to this data? And once they get it, how do they make sense of it? In this chapter, we’ll take a simple and practical look at how to source market data, how to clean financial data, and how to manage it like a pro, even if you’re just starting out. 

What Is Market Data? 

Let’s begin with the basics. Market data refers to information about financial instruments like stocks, mutual funds, bonds, or currencies. This includes: 

• Price (open, high, low, close) 

• Trading volume 

• Bid and ask prices 

• Historical data 

• Real-time or delayed quotes 

• News, earnings reports, and analyst recommendations 

This data helps investors make informed decisions. Think of it as the raw ingredients in a recipe, you need the right data to cook up a solid investment strategy. 

Why Learn How to Source Market Data? 

Imagine trying to drive a car without a speedometer or fuel gauge. You wouldn’t know how fast you're going or if you’re about to run out of fuel. That’s what investing without data looks like. By learning how to source market data, you get: 

• Real-time updates to react quickly 

• Historical data to spot trends 

• Accurate information for analysis 

• A competitive edge over guesswork-based decisions 

Whether you’re a student learning about the stock market or an aspiring trader, sourcing reliable data is the first step toward smart investing. 

Types Of Market Data 

Before you source anything, you need to understand the different kinds of data available: 

1. Real-Time Data

This is data that updates instantly as trades happen. It’s useful for active traders who need to act fast. 

2. Delayed Data

Usually delayed by 15–20 minutes. It’s enough for long-term investors or learning purposes. 

3. End-of-Day (EOD) Data

This is data recorded at the end of each trading day. It’s perfect for backtesting strategies or doing homework on stocks. 

4. Historical Data

Covers days, months, or even years of past trading activity. Great for understanding patterns and trends. 

How To Source Market Data? 

So, how do you actually get hold of market data? Whether you're researching stocks, building a trading strategy, or working on a school project, there are several great ways to get started. Here are four practical options for how to source market data: 

1. Free Financial Websites

This is the easiest way to begin, especially for students and casual learners. These websites provide a wide range of market data for free: 

• NSE India (www.nseindia.com) – Offers end-of-day (EOD) prices, company announcements, and historical data. 

• BSE India (www.bseindia.com) – Includes stock quotes, past performance, and corporate actions. 

• Angel One (www.angelone.in) – In addition to stock data and financial tools, Angel One features a Knowledge Centre with educational articles on investing and trading, and a News section that keeps you updated on the latest market developments. 

These sites are perfect for getting familiar with market terms and basic analysis. 

2. Public APIs (Application Programming Interfaces)

For those interested in automation or coding, APIs are incredibly useful. They let you pull data directly into your programmes or apps. 

Some well-known and beginner-friendly APIs include: 

• Alpha Vantage – Provides free stock time series data, technical indicators, and crypto data. 

• Yahoo Finance API (via Python’s yfinance library) – Popular among students and hobbyists. 

• Quandl – Known for its high-quality financial and economic datasets, including global markets. 

• Twelve Data – Offers both real-time and historical data with easy-to-use API endpoints. 

All you usually need is a free account and an API key to get started. You can write simple scripts to pull data for analysis or personal projects. 

3. Data Aggregator Platforms

These are websites or services that gather market data from multiple sources and organise it in a user-friendly format. While some may offer premium versions, they usually provide plenty of free data for learners. 

Angel One: A popular platform in India that offers market insights, stock screeners, financial ratios, and research reports in one place. It’s useful for exploring market trends and understanding stock fundamentals with minimal effort. 

These platforms are great if you want data that’s easy to read, compare, and interpret without diving into spreadsheets or coding. 

4. Professional (Paid) Data Providers

As you advance and need cleaner, more detailed, or real-time data, paid providers come into play. These services are built for serious analysts, developers, and institutions. 

Top providers include: 

• Bloomberg Terminal – Industry standard with high-speed, reliable, and comprehensive data. Very costly, but unmatched in depth. 

• Refinitiv (by LSEG, formerly Reuters) – Offers global data feeds, analytics tools, and APIs tailored for professionals. 

• Tickertape and Screener.in (India-specific) – Affordable options for advanced stock screening and detailed financial reports. 

Paid data sources are ideal when free options aren’t enough—especially when accuracy and speed are critical. 

How To Use APIs To Pull Market Data? 

Let’s keep this simple and assume you’re using Python (a popular language in finance). Here’s a basic example using yfinance, a Python library: 

import yfinance as yf  # Download data for Reliance Industries reliance = yf.download('RELIANCE.NS', start='2023-01-01', end='2024-01-01')  # Show the first 5 rows print(reliance.head())  

This will pull Reliance Industries’ stock data from Yahoo Finance for 2023. Cool, right? 

How To Clean Financial Data? 

Once you have data, it’s not always ready to use. You’ll find missing values, wrong formats, or even duplicate records. That’s why understanding how to clean financial data is just as important. 

Here are the basics: 

1. Remove Missing Values

Sometimes data will have NaN (not a number). You can remove them like this: 

clean_data = reliance.dropna() 

2. Convert Data Types

Make sure dates are in the correct format: 

reliance.index = pd.to_datetime(reliance.index) 

3. Rename Columns

If columns have strange names, rename them: 

reliance.rename(columns={'Close': 'Close_Price'}, inplace=True) 

4. Handle Outliers

Sometimes a wrong price might sneak in (like a stock price of ₹1 lakh). You can remove such values using logic or statistical rules. 

Organising Market Data 

Now that your data is clean, it’s time to manage it properly. Here’s what you should do: 

1. Store Your Data Safely

First things first, make sure your data is stored in a secure and accessible place. Depending on how much data you’re working with, you can choose from different tools: 

• Excel or Google Sheets – Great for small projects, easy to read and share. 

• CSV Files (.csv) – Ideal for saving larger datasets in a simple format. 

Pick a format that suits your needs and make regular backups so you don’t lose your work. 

2. Organise Your Files and Folders

It’s easy to end up with a messy desktop full of files. To stay organised: 

• Use clear and consistent file names (e.g., NIFTY50_EOD_2024.csv). 

• Keep files in folders by date, company, or type of data. 

Being organised helps you and others find the right data quickly, especially when your project grows. 

3. Use Version Control

If you're writing scripts or updating your data often, version control is your best friend. It helps you keep track of changes and go back if something breaks. 

• Git is the most popular version control tool for coding projects. 

• It works well with platforms like GitHub or GitLab, where you can store your code and collaborate with others. 

Even if you're working solo, version control makes your work safer and more professional. 

4. Automate Your Data Updates

Instead of downloading or cleaning data manually every day, why not automate the process? You can schedule your scripts to run at set times using: 

• Cron Jobs (Linux) – A tool that runs commands on a schedule. 

• Task Scheduler (Windows) – Does the same thing but for Windows users. 

Automation saves time and ensures your data is always fresh and up to date—especially useful for tracking live markets or building dashboards. 

Use Cases for Clean Market Data 

Here’s how investors and learners can use well-sourced and clean data: 

• Backtesting: Try out trading strategies on historical data 

• Charts and Visuals: Build graphs to understand trends 

• Machine Learning Models: Predict stock movements (for advanced users) 

• Portfolio Analysis: Track performance of your investments 

• Classroom Projects: Impress your teacher with real data studies 

Common Mistakes Beginners Make 

Be careful not to: 

• Rely only on one data source 

• Ignore data cleaning 

• Use outdated data for live decisions 

• Forget to check data accuracy 

• Try to jump into real-time trading without understanding the basics 

Final Thoughts 

Learning how to source market data and how to clean financial data is a vital skill for anyone entering the financial world. Whether you're a student, a new investor, or an aspiring data scientist, mastering these basics will set you on the right path. 

You don’t need to be a tech genius. Start with free tools, try your hand at simple scripts, and gradually work your way up. Clean, accurate, and timely data is the foundation of every smart financial decision.