Historical Market Data Sources

The article “Historical Market Data Sources” was originally published on QuantInsti blog.

A good trading or investment strategy is only as good as the data behind it. High-quality data is essential if you are backtesting a quant model, analyzing market trends, or building an algorithmic trading system.

Prerequisites:
To make the most of this blog, it is essential to have a strong foundation in market data sources, data handling techniques, and financial data processing.

Start with Market Data FAQ to understand the fundamentals of financial data sources, formats, and applications in trading. This blog covers common queries regarding data providers, access methods, and integration into trading models.For those interested in a structured learning approach, the Getting Market Data course provides a step-by-step guide on how to fetch, process, and use financial data for algorithmic trading.

In this blog, we will explore the following:

1. Top financial data sources
2. How to choose the right data provider?
3. Common data quality issues and how to handle them
4. How to handle time zone and data synchronization?

Top financial data sources

Some platforms provide intraday data (ideal for high-frequency and short-term strategies), while others focus on end-of-day (EOD) data for long-term analysis. Depending on the provider, data can be accessed via APIs, CSV downloads, or software terminals.

The table below breaks down the top financial data sources, highlighting whether they are free or paid, the type of data they offer, and how you can access it.

How to choose the right data provider?

Here are a few points to consider:

Accuracy and reliability – How trustworthy is the data?

Financial data must be clean, accurate, and free from inconsistencies. Errors in price feeds, missing data points, or incorrect adjustments for corporate actions (e.g., stock splits, dividends) distort backtesting results and lead to incorrect trading decisions.

Example:

A trader using Yahoo Finance may notice discrepancies in adjusted close prices due to inconsistent dividend adjustments. She’ll notice that a paid provider like Bloomberg would ensure adjustments are correctly applied.

Latency and speed – How fast do you get the data?

Low-latency, real-time data is crucial for high-frequency trading (HFT) and intraday strategies. A delay in receiving market prices can lead to slippage (executing trades at worse prices than expected).

Example:

A trader using Interactive Brokers (IB API) receives real-time bid-ask quotes, which is ideal for algorithmic execution. In contrast, if she uses Yahoo Finance, she will experience delayed prices, making it unsuitable for active trading.

Historical data availability – How much past data is available?

Backtesting a strategy requires long-term historical data. A dataset with only 1–2 years of data is insufficient for testing performance across different market conditions (e.g., bull and bear markets).

Example:

A quant researcher backtesting a strategy on Nifty 50 stocks may find NSE India provides 10+ years of daily data but lacks intraday historical data. In contrast, Bloomberg provides tick-level history for institutional users.

Cost and subscription plans – Is a free provider sufficient, or is a paid plan necessary?

Financial data providers offer different pricing tiers, from free limited access to enterprise-level subscriptions costing thousands of dollars per month. Your choice depends on your budget and trading needs.

Example:

A retail investor tracking long-term trends may find Yahoo Finance and NSE India sufficient. Meanwhile, a hedge fund running real-time execution algorithms will require a Bloomberg terminal or Reuters Refinitiv.

Common data quality issues and how to handle them

Financial data is often messy, incomplete, or inconsistent, leading to inaccurate analysis and poor trading decisions. Here are some of the most common data quality issues and how to handle them effectively.

1. Missing Data – How to handle gaps in data?

Missing data can occur due to trading holidays, exchange downtime, incomplete API responses, or data provider limitations. Gaps in data can distort technical indicators, backtests, and model predictions.

Example:

A stock has missing closing prices due to a trading halt. Instead of leaving gaps, we can:

• Use forward fill: Copy the last known price.
• Use sector index movements as an estimate.
• Exclude those days from the backtesting calculation

Python Example for Filling Missing Data:

data['Adj Close'] = data['Adj Close'].interpolate(method='linear') # Linear interpolation
data.fillna(method='ffill', inplace=True) # Forward fill

Filling_missing_data.py hosted with ❤ by GitHub

2. Adjustments for corporate actions – Handling stock splits, dividends, and mergers

Corporate actions like stock splits, dividends, and spin-offs impact stock prices and must be handled correctly for accurate analysis.

Common Corporate Actions & Their Effects

• Stock splits – Adjust the price and volume proportionally.
• Dividends – Cash dividends reduce the stock price; they must be accounted for in total return calculations.
• Mergers & acquisitions – May cause price discontinuities; use adjusted prices.

How to Handle Corporate Actions?

• Use adjusted prices – Most data providers (Yahoo Finance, Bloomberg) offer adjusted closing prices, which account for corporate actions.
• Manually adjust splits – If only raw prices are available, divide past prices and multiply volumes by the split ratio.
• Total Return Index (TRI) – If analyzing performance, consider using total return data that includes dividends.

Example:

A 2-for-1 stock split means:

• The stock price is halved.
• The number of shares doubles.
• Unadjusted price data would incorrectly show a 50% drop.

Python Example for Adjusting Stock Splits:

data['Split Factor'] = data['Split Factor'].cumprod()
data['Adj Close'] = data['Close'] / data['Split Factor']

Split_Factor.py hosted with ❤ by GitHub

3. Data Synchronization – Aligning time zones and different data sources

Market data often comes from multiple exchanges, sources, or time zones, leading to misaligned timestamps, missing data, or incorrect comparisons.

Common Data Synchronization Issues:

• Time Zone Differences – NYSE operates in Eastern Time, while NSE follows Indian Standard Time (IST).
• Asynchronous Data Feeds – Fundamental data updates quarterly, but price data updates in real time.
• Mismatched Data Granularity – One dataset might be minute-level, while another is daily-level.

How to handle time zone and data synchronization?

• Convert time zones—Before analysis, ensure all timestamps are in the same time zone. Use pytz in Python for conversions.
• Resample data – If combining intraday and daily data, convert them to a common frequency.
• Align data from different sources – If merging two datasets, use pd.merge() with the appropriate time alignment.

Example:

If merging intraday forex data (UTC) with stock data (EST), convert everything to UTC.

Python Example for Time Zone Conversion:

import pandas as pd
import pytz
data.index = data.index.tz_localize('US/Eastern').tz_convert('UTC') # Convert EST to UTC

Time_zone_conversion.py hosted with ❤ by GitHub

Conclusion

To sum up, this blog covered:

• A comparison of top free and paid financial data sources based on asset coverage, access type, and availability of intraday, daily, and fundamental data.
• Key factors to consider when choosing a data provider, include accuracy, latency, historical depth, and cost.
• Common data quality issues such as missing data, corporate actions, and synchronisation challenges—and how to handle them effectively.

Selecting the right financial data provider is critical for traders, investors, and researchers who rely on quantitative analysis. Factors such as accuracy, reliability, latency, historical depth, and cost play a key role in determining which provider best suits your needs. While free data sources may be sufficient for basic analysis, professional traders and institutions often require premium data with lower latency and better quality control.

Next steps

Here is a list of resources you use to expand your knowledge with advanced techniques in data retrieval, processing, and financial analysis.

To explore different libraries and tools for working with financial data, read Python Trading Library, which introduces Python-based solutions for financial data extraction, analysis, and visualisation.

Additionally, How to Use Financial Market Data for Fundamental and Quantitative Analysis provides insights into quantitative trading models, sentiment analysis, and data-driven decision-making.

If you’re interested in fundamental and sentiment analysis, the Fundamental and Sentiment Analysis Data blog offers guidance on extracting and processing alternative datasets for better market predictions.

For traders looking to retrieve futures, cryptocurrency, and forex price data, consider these hands-on tutorials:

Download Futures Data Using Yahoo Finance Library in Python

Download Cryptocurrency Data Using CryptoCompare API in Python

Download Forex Price Data Using YFinance Library in Python

Since data quality and preprocessing are crucial for financial modelling, explore Data Cleaning to learn best practices for handling missing values, outliers, and inconsistencies in trading datasets.

For a structured and hands-on approach to preparing financial data for machine learning and algorithmic trading, consider the Data and Feature Engineering for Trading course. This course covers essential topics such as feature selection, dataset transformation, and optimizing predictive models using financial data.

File in the download:

• Historical market data sources – Python notebook https://blog.quantinsti.com/financial-market-data-providers/