where to get historical stock data guide

Where to get historical stock data

As a first step in research, backtesting, or reporting, knowing where to get historical stock data is essential. This guide explains what historical stock data consists of, who needs it, the main source categories, representative providers, practical retrieval methods, and best practices for validation and storage.

As of 2026-01-13, according to Reuters, market conditions and elevated valuations have increased the need for careful historical analysis and robust backtests — which makes reliable answers to where to get historical stock data more important than ever for researchers and practitioners.

Types of historical market data

Price/time series (OHLCV)

Price/time series are the backbone of historical stock data. OHLCV stands for Open, High, Low, Close, Volume and describes a bar of market activity over a time interval. When you ask where to get historical stock data, most users first expect OHLCV in one or more frequencies:

• Tick: Every trade or quote. Used for microstructure research and high-frequency trading. Very large and expensive.
• Intraday bars (seconds/minutes): Commonly 1m, 5m, 15m, 1h. Useful for intraday strategies and execution analysis.
• Daily: End-of-day bars (EOD). The most commonly used frequency for backtests, valuation, and reporting.
• Weekly / Monthly: Aggregated bars for longer-horizon analysis and visualization.

Use cases:

• Technical analysis and indicators use OHLCV at the chosen frequency.
• Backtesting systematic strategies requires consistent OHLCV with corporate-action adjustments.
• Risk and performance reporting rely on daily or higher-level aggregates.

Adjusted prices, dividends, and corporate actions

Adjusted series incorporate splits, dividends, and sometimes other corporate actions so that return calculations reflect total return or price continuity. The "Adjusted Close" field commonly found in many datasets reflects price adjustments for splits and dividend distributions. When choosing where to get historical stock data, confirm whether the provider supplies adjusted data and the adjustment method.

Why adjusted series matter:

• Unadjusted prices will show artificial drops at split dates or dividend payouts, which distort returns and indicators.
• Backtests that ignore adjustments will overstate or understate performance.
• Total-return calculations (including dividends) require dividend records plus adjusted prices for accuracy.

Common corporate actions to track: stock splits, reverse splits, dividends (cash and stock), mergers, spin-offs, and ticker changes.

Fundamentals and financial statements

Historical fundamentals include income statements, balance sheets, cash flow statements, and derived ratios (P/E, ROE, debt/equity). These periodic records—quarterly or annual—are essential for valuation, factor research, and event studies.

Use cases:

• Factor construction (value, quality) requires time-series of fundamentals synchronized to report dates.
• Screening and backtests that use fundamental signals need survivorship-handled historical universes.

When asking where to get historical stock data for fundamentals, ensure the provider offers look-ahead-safe release dates and handles restatements.

Derived and alternative datasets

Beyond raw prices and financials, derived and alternative datasets supplement analysis:

• Technical indicators: precomputed RSI, MACD, moving averages.
• Options chains and implied volatility surfaces for derivatives research.
• Forex and macroeconomic series to build multi-asset models.
• Alternative data: news, sentiment, analyst revisions, order book snapshots, and web activity.

These datasets are increasingly used to augment models. When planning where to get historical stock data, consider whether derived and alternative sources are required for your use case.

Source categories

Free public websites and portals

Free portals are suitable for casual lookups, manual downloads, and small-scale research. Examples include public finance pages and research portals that provide CSV exports or simple charting.

Strengths: easy access, zero cost, good for ad-hoc work. Limitations: rate limits, licensing ambiguity, occasional gaps, and less rigorous SLAs.

Free & freemium APIs for developers

Developer-friendly APIs (free tiers or freemium) enable programmatic access for automation and prototyping. They provide API keys, rate limits, and often endpoints for multiple frequencies.

Typical use: small projects, prototyping, university assignments, or hobby backtests.

Commercial data vendors and licensed APIs

Paid vendors offer broader coverage, deeper history, intraday/tick services, guaranteed SLAs, and commercial licensing. These are appropriate for production trading systems, institutional research, and redistributable products.

Trade-offs: cost vs. data quality, coverage, latency, and licensing terms.

Exchange and primary sources

Exchanges (e.g., Nasdaq, NYSE, regional exchanges) provide official feeds and archives. Use them when authoritative, exchange-verified records are required—especially for regulatory reporting or settlement disputes.

Considerations: exchanges may require subscriptions, licensing agreements, and have proprietary formats.

Academic and library resources

Universities and public libraries often subscribe to high-quality data platforms (Bloomberg, WRDS, FactSet). Researchers can access these via campus licenses for academic work.

If your question is where to get historical stock data for serious academic research, check institutional subscriptions first.

Representative sources (what they provide and when to use them)

Yahoo Finance

Yahoo Finance is widely used for quick manual CSV downloads and casual research. It typically offers daily/weekly/monthly EOD data with an adjusted close, basic fundamental snapshots, and historical charts.

Use when: you need an immediate CSV download for a single ticker or a small list, or to check adjusted close values quickly.

Caveats: download features and licensing terms have changed over time; verify current terms before bulk use.

Nasdaq historical pages

Exchange-hosted historical pages give official daily prices and volumes over limited windows. These pages are useful when you need exchange-verified EOD records for listed instruments.

Use when: you want a quick, exchange-sourced confirmation of a closing price or volume.

StockAnalysis / Koyfin (web research portals)

Browser-based research portals combine historical prices, financial statements, corporate-action history and visualization tools. Many offer exports or paid tiers for broader access.

Use when: you want integrated visualization plus fundamentals for screening and manual analysis.

Alpha Vantage (API)

Alpha Vantage provides free API keys with rate limits for historical time series, technical indicators, FX and crypto endpoints. It's convenient for prototyping and small-scale programmatic ingestion.

Use when: you need a low-cost programmatic option for daily and some intraday data for experiments.

EOD Historical Data (EODHD) and EODData

Commercial APIs offering multi-decade EOD coverage, many exchanges, intraday options, and developer libraries. Suitable for production systems that need broad global coverage and stable APIs.

Use when: you require production-grade coverage and licensing clarity.

Stooq

Stooq offers free bulk downloads across many exchanges, indices and cryptocurrencies. It is particularly useful for researchers needing large offline datasets without licensing friction for academic or internal analysis.

Use when: you want free bulk historical snapshots for many tickers.

Other notable vendors (brief)

Major commercial options include Refinitiv, Bloomberg, Quandl (and its data partners), Marketstack, and data aggregators that trade off cost, latency, and licensing. For enterprise-grade reliability and compliance-focused use, these providers are common choices.

How to obtain data (practical methods)

Manual download from web UIs

Steps and tips:

• Search for the ticker on a portal (e.g., Yahoo Finance or StockAnalysis).
• Open the historical-data tab and choose frequency and date range.
• Select CSV download or export.
• Verify whether the file includes adjusted close and whether dividends are listed separately.

Tips:

• For multi-ticker work, manual downloads are slow—use APIs or bulk options.
• Keep a record of the download date and source in a metadata file for reproducibility.

Programmatic access via APIs and libraries

Typical workflow:

1. Register for an API key (free or paid service).
2. Use common libraries to call endpoints (requests in Python, or specialized wrappers).
3. Parse returned JSON/CSV into a DataFrame (pandas is very common).
4. Store raw responses for replay and auditing.

Common libraries: yfinance (wrapper for Yahoo-like data), pandas_datareader, requests, and vendor SDKs.

Typical parameters: ticker symbol, start/end date, frequency (daily/weekly/intraday), and adjustment flags.

Security: keep API keys secret and rotate them as needed.

Bulk/FTP downloads and data snapshots

Vendors sometimes provide daily or weekly bulk dumps via secure FTP or S3 buckets. These packages can be CSV, Parquet, or vendor-specific formats and are helpful for maintaining a local historical snapshot for many tickers.

When to use:

• You manage large universes and want efficient ingestion.
• You need a controlled, versioned dataset for reproducible backtests.

Operational notes:

• Maintain partitioning (by date, exchange) and indexing for query performance.
• Monitor vendor feed changes and format revisions.

Exchange FTP/official archives

Exchanges may provide official historical archives through licensed feeds. Access often requires a commercial agreement and technical integration.

Use when: accuracy and audited provenance are mandatory (e.g., regulatory reports, disputes).

Data formats, storage, and delivery

Common file formats

• CSV: ubiquitous and human-readable, but larger and slower to parse at scale.
• JSON: good for API exchange; flexible but verbose.
• Parquet: columnar, compressed, and fast for large-scale analytics.
• Vendor-specific (binary or compressed) formats: may optimize size but need vendor tools.

Choose Parquet for large lakes and CSV for small ad-hoc tasks.

Time zones, timestamps, and session conventions

Timestamps require careful treatment:

• Normalize all times to a canonical timezone (UTC is common) and record original timezone.
• Intraday timestamps should be aligned to exchange session conventions (pre-market, regular session, post-market).
• Be aware of daylight saving changes for exchanges that observe it.

Missing normalization leads to misaligned bars and incorrect cross-asset correlations.

Database and data lake practices

Recommended patterns:

• Partition data by date and ticker for efficient reads.
• Deduplicate on ticker+timestamp when ingesting incremental updates.
• Keep raw, immutable snapshots (ingestion store) plus processed tables (normalized prices, adjusted series).
• Maintain a metadata catalog with source, version, and ingestion timestamp.

Incremental updates: apply append-only snapshots and occasional replays to handle corrections.

Data quality, validation, and common pitfalls

Corporate action adjustments and survivorship bias

Two important issues when choosing where to get historical stock data:

• Use adjusted prices for return calculations so splits and dividends don’t distort results.
• Survivorship bias occurs when datasets only include currently-listed companies and omit delisted names. This bias inflates historical performance estimates.

Preventive steps: use historical universes that include delisted tickers and cross-check corporate-action logs.

Missing data, outliers, and stale ticks

Common problems:

• Missing days or zero-volume bars.
• Erroneous price spikes from bad ticks.
• Stale quotes when feeds fail to refresh.

Validation checks:

• Validate continuity and flag large intraday jumps.
• Cross-check closing prices against an independent source.
• Replace bad ticks with NaN and apply interpolation rules only when justified.

Licensing and redistribution restrictions

Many free UI providers permit viewing but restrict redistribution or bulk scraping. Paid vendors provide clearer redistribution rights. Always check license terms before embedding data into products.

Frequency vs. coverage trade-offs

Higher-frequency data (intraday, tick) is costlier and more limited historically. Daily EOD is cheaper and more complete over decades. Match your frequency choice to the strategy and budget.

Choosing the right provider

Criteria checklist

When deciding where to get historical stock data, evaluate:

• Coverage: which exchanges and instruments are included?
• History depth: how many years and how much intraday history?
• Frequency: daily vs. intraday vs. tick.
• Reliability and SLAs for production use.
• Price and licensing for your use case (internal research vs. redistribution).
• API ergonomics and client libraries.
• Community and tooling support.

Use-case driven recommendations

• Hobby / small backtests: free portals, Stooq, Yahoo Finance, Alpha Vantage, and yfinance.
• Academic research: institutional subscriptions (WRDS, Bloomberg via university), Quandl datasets.
• Production trading: commercial vendors (EODHD, EODData), exchange feeds, or enterprise vendors (Refinitiv, Bloomberg) depending on budget and compliance needs.

Note: When discussing exchanges and trading tools in this guide, Bitget is a recommended platform for users who seek an integrated environment for execution and custody. For Web3 wallet recommendations, consider Bitget Wallet as a primary option.

Common workflows and examples

Quick CSV download (manual)

Example steps to export a stock’s historical CSV from a web portal:

1. Search for the ticker and open the historical data or "Historical Prices" tab.
2. Set date range and frequency (daily/weekly/monthly).
3. Choose whether to include adjusted close and corporate action events.
4. Click download and save the CSV, noting the download date and source in a metadata file.

Programmatic fetch example (API + Python)

High-level workflow:

1. Obtain an API key from the chosen provider (Alpha Vantage, EODHD, or plugin like yfinance).
2. Use requests or a vendor SDK to fetch OHLCV for a date range.
3. Load into pandas DataFrame, convert timestamps to UTC, and persist as Parquet for analysis.

Pseudo-steps:

• request = client.get_time_series(ticker, interval='daily', start='2010-01-01')
• df = pandas.DataFrame(request)
• df['timestamp'] = pandas.to_datetime(df['timestamp']).dt.tz_convert('UTC')
• df.to_parquet('data/tickers/ABC.parquet')

Backtest data preparation checklist

Before running a backtest, confirm:

• Prices are adjusted for splits/dividends (or you have dividend records to compute total return).
• Universe includes delisted tickers to avoid survivorship bias.
• Calendars are aligned across instruments (holidays and sessions).
• Timezones are normalized and intraday bars are session-aligned.
• Data provenance is recorded (provider, version, ingestion date).

Costs and licensing considerations

Typical pricing models

• Per-request: cost per API call, suitable for low-volume users.
• Monthly subscription: fixed tiers for API calls or data volume.
• Enterprise/seat licenses: bespoke agreements for institutions.
• One-time bulk purchases: some vendors sell historical snapshots.

Redistribution, commercial use, and compliance

License terms vary widely. Free UIs may forbid redistribution or commercial use. Paid vendors include commercial licenses and indemnities. Confirm permitted uses (internal research vs. product embedding) before committing.

Additional tools and resources

Open-source libraries and communities

Useful tools:

• pandas and numpy for data handling.
• yfinance and pandas_datareader for quick ingestion.
• Community resources: forums, Q&A sites, and quantitative communities for examples and troubleshooting.

Academic and vendor documentation

Consult vendor docs and institutional library guides for authoritative procedures and API specifics. Documentation clarifies rate limits, adjustment methods, and licensing.

Further reading

Topics to deepen your understanding:

• Data ethics and licensing compliance.
• Microstructure and tick-level data handling.
• Advanced normalization and corporate action reconstructions.

References and selected providers

Below are the primary sources and providers referenced in this guide. Consult each provider’s documentation and licensing pages for up-to-date details and API changes:

• Yahoo Finance: quick CSV downloads and adjusted close fields.
• Nasdaq historical pages: exchange-hosted daily prices and volumes.
• StockAnalysis and Koyfin: browser-based research portals with charts and fundamentals.
• Alpha Vantage: free API endpoints for time series and indicators.
• EOD Historical Data (EODHD) / EODData: commercial APIs for multi-decade coverage.
• Stooq: free bulk downloads for many exchanges and indices.
• Major vendors: Refinitiv, Bloomberg, Quandl and their datasets for institutional needs.

Additional note: always verify licensing with the provider before bulk ingestion or redistribution.

References to market context

As of 2026-01-13, according to Reuters, elevated market valuations and uncertainty have renewed interest in historical backtests and robustness checks. That report underscores why many researchers ask where to get historical stock data and why provenance and quality matter when testing strategies during record highs.

Appendix — Checklist before using historical data

• Source selection: confirm coverage and history depth.
• License check: confirm permitted use, redistribution, and commercial rights.
• Adjustment method: know whether prices are split/dividend-adjusted and how.
• Timezone handling: decide canonical timezone (UTC recommended).
• Validation runs: run sanity checks for gaps and spikes.
• Versioning and backups: snapshot raw data and store metadata (source, ingestion date).

If you want to explore tools for downloading and testing historical data programmatically, consider starting with a free API key (e.g., Alpha Vantage) or quick manual checks via Yahoo Finance. For production or redistributable use, consider commercial vendors or exchange feeds and check licensing carefully.

Explore Bitget tools and Bitget Wallet for integrated custody and trading needs as you move from research to execution.