Stock screener Guide

Stock screener

A stock screener is a software tool or web service that helps investors and traders filter and find equities (and sometimes ETFs, mutual funds, or tokens) by applying selectable criteria—fundamental, technical, performance, and ownership filters—to narrow a large investable universe to a manageable list of candidates. This guide explains what a stock screener does, the major types, core metrics, modern features, practical workflows, and known limitations. It also includes timely examples and best practices for building robust screens.

As of January 24, 2026, according to Barchart and Benzinga reporting, screeners remain central to idea generation: Barchart’s Stock Screener was used to identify dividend aristocrats outperforming the S&P 500, and Benzinga highlighted analyst screens that identified opportunities such as Meta. These use cases illustrate common screening workflows in both dividend and growth contexts.

Definition and purpose

A stock screener is a toolkit for filtering a broad set of securities into a focused list that meets user-defined conditions. Typical purposes include:

• Idea generation: finding stocks that match a thesis (value, growth, dividend, turnaround).
• Watchlist creation: compiling candidates for monitoring and further analysis.
• Screening trade setups: identifying technical set-ups (MA crossovers, breakouts) for short-term trading.
• Portfolio construction and rebalancing: narrowing a universe for allocation decisions, factor exposure checks, and risk limits.

Within a research or trading workflow, a stock screener sits between raw market data and deeper analysis. It translates broad goals into explicit, testable filters (for example: market cap > $5B AND P/E < 15 AND 52-week performance > 20%). Good use of a stock screener speeds research, enforces repeatability, and reduces the chance of missing opportunities across a large universe.

History and evolution

Stock screeners began as basic desktop or spreadsheet filter tools that applied a handful of metrics (market cap, P/E, sector). Over the past two decades they evolved along several lines:

• Web and cloud platforms: real-time or near‑real‑time feeds and centralized metric libraries made maintenance easier and broadened access.
• Metric expansion: from a dozen simple ratios to hundreds of metrics, including multi-year financials, analyst estimates, and more granular ratios.
• Chart and portfolio integration: outputs moved from static lists to interactive watchlists linked to charts, news, and position tracking.
• Customization and automation: custom formulas, saved templates, saved alerts, and APIs enabled systematic workflows and programmatic usage.
• Historical screening and backtesting: newer platforms allow screening against past snapshots (survivorship-aware) and testing how a screen would have performed historically.

Recent trends emphasize advanced fundamental capabilities (long-form financial histories and model-ready fields), historical screening (screen as-of dates and multi-year averages), and cloud APIs for quant workflows. These changes converted screeners from simple discovery tools into repeatable research engines.

Types of screeners

Screeners can be specialized by the metrics they emphasize and the user base they serve.

Fundamental screeners

Fundamental screeners filter using financial-statement derived metrics and valuation measures. Common filters include:

• Price-to-earnings (P/E), price-to-sales (P/S), EV/EBITDA
• Revenue growth, EPS growth, multi-year CAGR
• Profitability margins (gross margin, operating margin, net margin)
• Return metrics (ROE, ROIC)
• Balance-sheet ratios (debt/EBITDA, current ratio)

Fundamental screeners are used by value and quality investors, equity analysts, and anyone who wants to test valuation-based hypotheses at scale.

Technical screeners

Technical screeners focus on price action and indicator-based conditions. Typical filters:

• Moving average crossovers (50/200-day MA, 10/20-day MA)
• RSI, MACD, stochastic oscillators
• Pattern detection (triangles, head-and-shoulders, flags)
• Volatility filters (ATR, beta)
• Price breakouts and gap scans

Traders use technical screeners to find momentum entries, mean-reversion candidates, or pattern-based trades.

Performance & momentum screeners

These rank or filter by returns and relative strength. Common filters:

• Short- and long-term percent returns (week, month, 3‑month, 12‑month)
• Relative performance vs. benchmark (e.g., outperforming S&P 500 over 52 weeks)
• Volume/price breakout filters to catch accelerating moves

Performance screeners are popular for momentum strategies and identifying trending winners.

ETF / mutual fund screeners

Fund-level screeners offer specialized fields not present in equity screeners:

• Holdings and sector exposure
• Expense ratio
• AUM and flows
• Tracking error and fund-level performance metrics

These are essential for portfolio construction and fund selection.

Crypto-focused screeners

Crypto screeners differ significantly. They may include:

• On-chain metrics (active addresses, transaction counts, inflows/outflows)
• Liquidity measures and exchange listings
• Tokenomics (supply, inflation schedule, vesting)
• Smart-contract health and audit status

Many traditional stock screeners do not support crypto; when they do, the metrics and cadence differ. Bitget’s ecosystem products (including Bitget Wallet and platform tools) can complement crypto screening with wallet-level and exchange-level data where available.

Core screening criteria and metrics

A stock screener’s power comes from the available criteria. Key metric categories include:

• Descriptive: market capitalization, sector/industry, exchange, country
• Valuation: P/E, P/S, EV/EBITDA, PEG
• Growth: revenue growth, EPS growth, sales CAGR, forward estimates
• Profitability: ROE, ROIC, net/operator margins
• Balance‑sheet: debt/equity, debt/EBITDA, current and quick ratios
• Ownership: insider ownership, institutional holdings, recent insider buys/sells
• Liquidity: average daily volume, bid-ask spread
• Technical indicators: moving averages, RSI, MACD, ATR
• Dividends: dividend yield, payout ratio, dividend growth records
• Events/News: upcoming earnings dates, dividends ex-date, recent filings

Combining metrics across categories yields more robust results than relying on a single dimension.

Features of modern screeners

Modern screeners offer many advanced features that make screening more powerful and repeatable.

Large metric sets and global coverage

Top platforms provide hundreds of metrics and multi-market coverage—global financials, analyst estimates, and historical statement lines—enabling broad cross-market screens (example: 500+ metrics and 10+ years of financial history on some platforms).

Historical and backtesting support

Historical screening lets users run a screen as of a past date and evaluate multi-year averages. Backtesting measures how a screen would have performed historically, helping identify overfitting and survivorship biases.

Custom formulas and templates

Custom formulas let users derive new metrics (for example, revenue per employee or adjusted free cash flow yield). Templates and saved screens speed reuse and standardize processes across teams.

Integration with charts, watchlists, alerts, and portfolios

A useful screener integrates with charting tools, watchlists, and alerting systems. Outputs can be pushed to watchlists, linked to interactive charts, and set to trigger alerts (price, news, indicator cross).

For traders using exchange features, consider linking watchlists to a trusted trading venue; where applicable, Bitget’s platform tools and Bitget Wallet can be highlighted as integration options for execution and custody.

Exporting and APIs

Export options (CSV/Excel) and programmatic APIs enable quant researchers and developers to incorporate screening outputs into quantitative pipelines, model backtests, or automated trade systems.

Examples of popular screeners and distinguishing features

The ecosystem of screeners ranges from lightweight web tools to institutional platforms. Below are representative examples and their notable strengths.

Koyfin

Koyfin is known for deep fundamental coverage: 500+ metrics, multi-year historical financials, custom formulas, and historical screening capabilities. It is favored by fundamental analysts who need consolidated financials plus visualization.

Finviz

Finviz is a widely used web screener with a fast visual interface, a rich set of preset filters, heatmaps, and both fundamental and technical filters. It provides free access and a paid Elite tier for real-time data and enhanced features.

Yahoo Finance Screeners

Yahoo Finance offers broad, user-friendly screeners with many premade templates and integration into Yahoo portfolios and news. It’s a good starting point for retail investors building watchlists and linking to headline flow.

Trading/technical-focused providers

Platforms that emphasize charting and technical filters—such as providers with strong community scripting and charting—are popular among traders. Their strength lies in advanced charting, custom scripts, and community-shared indicators.

Benzinga / Market data & news platforms

Benzinga and similar platforms combine screening with editorial content, calendars, and trade idea lists. The integration of news and calendar events into a screener is useful for event-driven workflows (earnings, dividends, upgrades).

Niche/technical screeners (e.g., StockMonitor)

Niche providers focus on specific use cases such as pattern recognition, MA crosses, or short-term volatility scans. They can be quicker to adopt for traders with specialized setups.

Note: the examples above highlight differences in metric depth, user experience, and target user (long-term analyst vs. short-term trader). When selecting a screener, align its strengths with your workflow.

How to build and use effective screens

Building useful screens is part art, part discipline. Follow these steps:

1. Define objectives

   • Be explicit: are you seeking dividend income, value bargains, momentum winners, or technical entries? Objective clarity determines metrics.

2. Choose timeframes

   • Match filters to your holding period. Short-term traders emphasize intraday/weekly indicators; investors emphasize multi-year fundamentals.

3. Combine fundamental and technical filters

   • A common approach is to filter by a fundamental thesis (e.g., ROIC > X, revenue growth > Y) and then apply a technical filter (price above 50‑day MA, recent breakout) to time entries.

4. Avoid overly strict filters

   • If a screen returns zero results, relax thresholds. Overly constrained screens can be a sign of overfitting.

5. Save templates and document assumptions

   • Save screens as templates and document the rationale and parameter choices for future reviews.

6. Validate outputs with charts and news

   • Always inspect candidate charts and latest filings/news; screens provide candidates but not full due diligence.

7. Iterate and backtest where possible

   • Use historical screening and backtests to check whether the screen historically produced sensible outcomes; watch for look-ahead bias and survivorship bias.

Advanced workflows and automation

Screeners can become components of automated research and portfolio systems.

• Custom formulas and factor definitions: create derived metrics and factor scores for ranking.
• Alerts and webhooks: trigger notifications when a screeners’ result set changes or when a candidate hits a condition.
• API access and exports: pull screen results into analysis notebooks, backtesting engines, or execution systems.
• Integration with rebalancing: feed screened lists to a portfolio manager for periodic rebalancing and risk checks.

Automation requires careful governance: timestamped data snapshots, documented formulas, and audit trails help maintain reproducibility.

Limitations, pitfalls and risks

Stock screeners are powerful but have constraints that users must recognize:

• Data latency and subscription tiers

  • Real-time vs end‑of‑day data depends on subscription; intraday trading on EOD-only screeners is unsafe.

• Survivorship and selection biases

  • Screens run on current universes may exclude delisted names. Historical screening must use survivorship-aware datasets.

• Overfitting to past data

  • Complex filters tuned to past performance can fail in future markets. Prefer economic rationale over purely numeric tuning.

• Ignoring market context and news

  • Screens do not replace qualitative analysis. Company filings, macro events, and regulatory changes matter.

• False precision

  • Many metrics are estimates (analyst EPS, forecasts), and small differences may not be meaningful.

• Coverage and metric consistency

  • Not all providers cover every exchange or asset class consistently; region-specific accounting differences affect ratios.

Being aware of these limits reduces the risk of drawing misleading conclusions from screen outputs.

Differences between stock and crypto screening

Screening cryptocurrencies requires different inputs and caution:

• Different metrics: crypto screens use on‑chain metrics (active addresses, transfer volumes), token supply dynamics, and protocol-level data rather than income statements and balance sheets.
• Liquidity and exchange listing variance: many tokens trade on a small set of venues; liquidity thresholds matter more.
• No standard earnings reports: there are no GAAP financials for many tokens; revenue-like metrics may be protocol fees.
• Tokenomics: inflation schedule, vesting cliffs, and supply caps materially affect long-term value.

Do not apply equity-style fundamental filters (P/E, EPS) directly to tokens. Use tools designed for tokens or combine exchange and wallet-level data. Bitget Wallet and Bitget platform tools can be recommended options for custody and some token analytics where supported.

Best practices and governance

Short guidelines for rigorous, repeatable screening:

• Document screening criteria and assumptions
• Backtest or historical-screen where possible
• Confirm bankable numbers with primary sources (company filings, official reports)
• Combine screen outputs with qualitative research (management, strategy, industry structure)
• Track data provenance and licensing; use reliable data feeds for execution-sensitive screens
• Maintain versioned templates and change logs for team use

Governance reduces mistakes and improves auditability—critical for teams running screens as part of asset management or institutional workflows.

See also

• Fundamental analysis
• Technical analysis
• Screener API
• Backtesting
• On-chain analysis
• Dividend investing

Examples and timely use cases (data-backed)

Below are concrete examples showing how a stock screener is used in practice, with data as reported recently.

As of January 24, 2026, according to Barchart reporting, a practical dividend-focused screen identified several Dividend Aristocrats that outperformed the S&P 500 over a 52-week window. The Barchart screen used filters such as 52-week performance difference from market, analyst rating thresholds, and minimum analyst coverage. The top results included:

• Albemarle Corp (ALB): reported 52-week gain of 108.66% and a forward annual dividend of $1.62 (yield ~0.9%); recent quarterly sales were ~$1.3B, down ~4% YoY, and a reported net loss improvement of 85% to ~$161M.
• C.H. Robinson Ww (CHRW): 52-week gain of 69.03%, forward annual dividend ~$2.52 (yield ~1.4%); recent sales down 11% YoY to $4.1B, net income rose 68% to $163M.
• Cardinal Health (CAH): 52-week gain of 60.69%, forward annual dividend ~$2.04 (yield ~1%); reported sales up 22% YoY to $64B, net income up 8% to $450M.

These examples show how combining dividend filters with performance and analyst-consensus fields can produce candidate lists that meet both income and performance goals. Barchart’s methodology included leaving some fields blank (e.g., 52-week percent change) to sort by highest returns and setting analyst coverage thresholds (12+ analysts) to preference consensus liquidity and coverage.

Similarly, Benzinga reporting highlighted screens that identified potential opportunities in large-cap tech: analysts used comparative valuation screens (forward P/E comparisons) and narrative filters (AI investment and monetization signals) to contrast Meta with peers. Benzinga’s coverage reported price points (e.g., Meta around $648.50 on the snapshot) and analyst targets (an example $910 target referenced by an analyst) as inputs for screens focused on valuation gaps and thematic catalysts.

When using a stock screener for such cases, ensure you record the screen parameters, the data snapshot time, and verify company filings or official releases for any material changes.

Limitations specific to the news examples

• Reporting dates and snapshots matter: performance figures (52-week returns) and analyst targets can change rapidly. Always timestamp the screen and record the data source.
• Dividend yields depend on current price and forward dividend declarations; yields change with price moves.
• Analyst consensus and target prices are estimates and not guarantees.

As of January 24, 2026, the described examples illustrate reproducible screening steps: set objective, select filters (dividend yield, 52-week outperformance, analyst coverage), run screen, validate top candidates with charts and filings.

Practical checklist for your next screen

• Define the objective (e.g., dividend growth + outperformance)
• Select universe (US large caps, global, sector-specific)
• Choose fundamental filters (dividend growth history, payout ratio limits)
• Add performance filters (52-week outperformance vs benchmark)
• Add quality/coverage filters (minimum analyst count, liquidity thresholds)
• Run screen and inspect top 10–30 candidates visually
• Check recent news, earnings, and filings for material events
• Save the template and document the snapshot date and data provider

Governance note and data provenance

Accurate screening depends on reliable data. For sources cited in this guide:

• As of January 24, 2026, Barchart reporting was cited for dividend-focused screening examples and 52-week performance figures.
• Benzinga reporting was cited for analyst-driven valuation comparisons and price snapshots.

When using third-party screeners, confirm the data license and refresh cadence. For execution or custody, prefer trusted platforms; for Web3 custody and token analytics, Bitget Wallet and Bitget platform services are recommended within the Bitget ecosystem.

Final remarks and next steps

A stock screener is an efficient, repeatable way to translate research ideas into candidate lists. Whether you are hunting for dividend consistency, value bargains, or momentum leaders, a disciplined screening process—documented, backtested where possible, and validated with charts and filings—improves decision quality.

If you want to try building screens now, start with a simple template (universe + 3 core filters), save it, and iterate. For crypto-related screens, use tools that support on‑chain metrics and consider Bitget Wallet for custody and token-level information where available.

Explore Bitget platform features and Bitget Wallet to combine custody, trade execution, and wallet analytics for a more integrated screening-to-execution workflow.

References and further reading

• Koyfin — overview of deep fundamental coverage and historical screening features.
• Finviz — example of a fast visual web screener with preset filters and heatmaps.
• Yahoo Finance — user-friendly screeners and premade templates.
• Benzinga — reporting and screener use-cases (analyst comparisons and price action snapshots).
• Barchart — stock screener examples used for dividend and performance screens.
• StockMonitor — example niche technical screener for pattern and indicator-based scans.

(Above references represent reporting and platform feature descriptions used to build this guide; users should consult primary sources and platform documentation for up-to-date metric definitions and data policies.)

Reporting date: As of January 24, 2026, according to Barchart and Benzinga reporting referenced in the examples above.