How to Build a Stock Screener: Criteria, Logic, and Automation in 2026

What Is a Stock Screener?

A stock screener is a filtering tool that narrows thousands of publicly traded stocks down to a short list of candidates that meet your specific trading criteria. Instead of reviewing 500 charts by hand every morning, a screener does the work in milliseconds — returning only the stocks that match your exact setup requirements.

The concept is simple: you define rules (price above $5, volume above 1 million shares, RSI between 40 and 60, gapping up more than 2%), and the screener returns every stock that currently meets all of them. The result is a focused watchlist you can analyze and act on.

The challenge is choosing the right criteria. Most beginner screeners use too many filters and return zero results, or too few and return 200 noisy candidates. Building a good screener means understanding what each filter measures, how filters interact, and how to weight them into a coherent scoring system.

Why Traders Use Screeners

The core problem screeners solve is coverage at scale. There are roughly 8,000 publicly traded stocks in the US market. Active day traders typically focus on a subset of high-liquidity names, but even within the S&P 500 and Russell 2000, manually scanning for setups is impractical.

What a screener replaces:

• Manual chart reviews across hundreds of tickers each morning
• Spreadsheet-based filtering of end-of-day data
• Relying on stock tip forums or social media for ideas

What a screener provides:

• A consistent, emotion-free process that runs the same rules every day
• Discovery of opportunities outside your usual watchlist
• Prioritization — the highest-scoring candidates appear at the top

Beyond finding trades, screeners create discipline. When your entry criteria are codified in filters, you stop chasing stocks that "feel" interesting and focus on stocks that meet an objective standard.

See Stock Screener Filters Guide for a comprehensive reference on individual filter types and their optimal ranges.

Step 1: Define Your Trading Style First

Before writing a single filter, answer these questions:

What is your holding period?

• Day trader → focus on intraday momentum, gap, volume, and liquidity filters
• Swing trader → focus on multi-day setups: breakouts from consolidation, RSI divergence, trend alignment
• Position trader → focus on fundamental filters: earnings growth, sector rotation, relative strength vs. index

What is your account size?

• Under $25,000 → PDT rule limits you to 3 day trades per 5 business days; focus on swing setups or higher-conviction day trades
• Over $25,000 → PDT restrictions lift; day trading screeners become practical

What market conditions are you in?

• Trending bull market → momentum and breakout screens perform well
• Range-bound / choppy market → mean reversion and oversold screens outperform
• High volatility → widen ATR thresholds; tighten position sizing

Your answers determine which filters matter. A day trader doesn't need P/E ratio filters; a position trader doesn't need intraday gap filters.

Step 2: Core Screening Criteria Explained

Volume

Volume is the most important filter in any day-trading screener. High volume means tight bid-ask spreads, easy fills, and the ability to exit quickly. Low-volume stocks move erratically on small orders and are hard to exit at your intended price.

Absolute volume measures total shares traded. A baseline of 500,000 shares per day is the minimum for most day traders; 1 million+ is preferred for large-cap stocks.

Relative volume (RVOL) is more powerful: it compares today's volume so far to the average volume at the same time on previous days. An RVOL of 3.0 means the stock is trading 3× its normal volume at this time of day — a signal that something meaningful is happening. See Relative Volume in the glossary for full details.

Recommended filters:

• Minimum absolute volume: 500,000 shares (intraday screener) or 1M (conservative)
• Minimum RVOL: 1.5× for general screens; 2.0× for gap-and-go setups
• Dollar volume minimum: price × volume > $5M (ensures enough liquidity in dollar terms)

ATR (Average True Range)

ATR measures how much a stock moves on an average day, in dollar terms. A stock with a $1.50 ATR moves roughly $1.50 from high to low on a typical day. This matters for setting realistic profit targets and stop-losses.

ATR% (ATR as a percentage of price) is more useful for cross-stock comparison. A $5 stock with $0.50 ATR has 10% ATR — very volatile. A $200 stock with $0.50 ATR has only 0.25% ATR — barely moves. See ATR Indicator Guide for setup and calculation details.

Recommended filters:

• Minimum ATR%: 1.5–2.0% (ensures the stock moves enough to make meaningful profits after commissions)
• Maximum ATR%: 5–8% (very high ATR stocks are harder to trade consistently — wider stops, unpredictable moves)

For day trading, the sweet spot is typically 2–4% ATR, giving enough range to profit while remaining manageable.

RSI (Relative Strength Index)

RSI measures momentum on a 0–100 scale. Values above 70 indicate overbought conditions; values below 30 indicate oversold. See RSI glossary entry for the full formula.

How you use RSI depends on your strategy:

Momentum trading — Look for RSI between 50–70 (strong but not overextended). Stocks in this zone are trending with room to run further. Avoid RSI above 80 for entries — the stock is overextended and vulnerable to a pullback.

Mean reversion trading — Look for RSI below 35 (oversold) in an otherwise strong stock. The logic: the stock has pulled back too far and is likely to bounce. This strategy works best in uptrending markets.

Breakout confirmation — RSI crossing above 50 from below signals momentum building. Combine with volume expansion for higher-confidence entries.

Recommended filters by strategy:

• Momentum entry: RSI 50–68
• Oversold bounce: RSI < 35 with price above 50-day MA
• Breakout confirmation: RSI crossing 50, with 20%+ RVOL

Gap

A gap occurs when today's opening price is significantly different from yesterday's closing price. Gaps signal news — earnings surprises, FDA approvals, analyst upgrades, sector events — that the market hasn't fully priced in yet.

Gap up % = (today's open − yesterday's close) / yesterday's close × 100

Gaps create momentum because:

1. Traders who missed the initial move buy into the opening to avoid missing the continuation
2. Short sellers who were wrong get stopped out, adding to buying pressure
3. News creates new buyers who weren't previously interested in the stock

Recommended filters:

• Minimum gap up: 2–5% for day trading (smaller gaps often fill quickly without continuation)
• Gap combined with RVOL > 2.0 significantly improves quality
• Avoid gaps above 15–20% — too much uncertainty about where fair value is; wide spreads at open

See Gap and Go Strategy Guide for how to trade gap stocks systematically.

Price Range

Basic price filters prevent you from screening into stocks that are impractical to trade:

• Minimum price: $3–$5 (below $3, stocks are often penny stock territory with wide spreads and manipulation risk)
• Maximum price: Optional — some traders cap at $500 to ensure whole-share positions are practical
• 52-week high proximity: Stocks within 5–10% of their 52-week high are often in strong uptrends. Conversely, stocks near 52-week lows may be falling knives.

Step 3: Combining Filters Into a Scoring System

Individual filters are binary — a stock either passes or fails. A scoring system is more nuanced: it ranks candidates by how strongly they meet your criteria.

Here's a simple composite scoring approach for a day-trading screener:

Composite score = weighted sum of all factors.

The top 10–15 stocks by composite score become your morning watchlist. This approach is far more robust than simple pass/fail filters because it surfaces stocks that score strongly on multiple dimensions simultaneously.

This is exactly how Tradewink's automated screener works — see the next section for the full implementation.

Step 4: Adding a Universe Filter

Before applying momentum and technical filters, define your universe — the pool of stocks you're willing to trade:

Index membership: Restricting to S&P 500 or Russell 2000 components filters out illiquid micro-caps automatically. Index stocks have institutional coverage, predictable volume patterns, and better liquidity.

Sector exclusion: Some sectors are systematically harder to day trade. Biotech stocks gap wildly on FDA news that's impossible to predict; bank stocks move on macro factors that dwarf technical signals. Many day traders exclude biotech entirely from their automated screener.

Exchange listing: NYSE and NASDAQ-listed stocks have better regulation and liquidity than OTC or pink sheet stocks.

Float size: Float (shares available to trade publicly) affects volatility. Low-float stocks (under 20 million shares) move violently on moderate volume — great for momentum plays, but risky to size large. High-float stocks are more stable but require more volume to create momentum.

Step 5: Time-of-Day Filters

Stock behavior changes dramatically throughout the trading day. Your screener should adapt:

Pre-market (4–9:30 AM ET): High-gap stocks dominate. Screener criteria should focus on gap size and catalyst quality (news, earnings). Volume is thin, so RVOL comparisons against pre-market historical data are more reliable than absolute volume.

Opening range (9:30–10:30 AM ET): Highest volatility of the day. Screener output is most actionable here — gap stocks with high RVOL often set their intraday direction in the first 30 minutes. See Opening Range Breakout Strategy for how to trade this window.

Mid-day (11 AM–2 PM ET): Volume and volatility typically drop. Screener should tighten criteria — require higher RVOL multiples and larger ATR% to filter out sluggish mid-day drift.

Power hour (3–4 PM ET): Volume picks back up. Mean reversion plays on stocks that overextended at open are common. Screener criteria shift toward overbought/oversold RSI at extreme levels.

How Tradewink's Screener Works

Tradewink's autonomous day-trading screener runs continuously during market hours, scanning 500+ stocks in real time and ranking them by a composite score. Here's how it's built:

Static Universe + Dynamic Sourcing

The screener starts with a static universe of 50+ default tickers — the most liquid, most-traded names in each sector. These are stocks that consistently meet minimum volume and ATR requirements.

Beyond the static universe, Tradewink pulls candidates from two dynamic sources:

Finviz scanning — Tradewink queries Finviz every scan cycle to pull the current top movers by percentage change, volume, and relative strength. This ensures that stocks outside the default list that are moving significantly on a given day get evaluated. A biotech that just announced Phase 3 results or a tech stock up 8% on earnings beats won't be in any static list but will appear prominently in Finviz movers.

S&P 500 heatmap integration — Tradewink automatically pulls the day's top movers from the S&P 500 by sector. This catches large-cap rotation plays: if the energy sector is up 3% while the broader market is flat, energy names become screener candidates even if their individual stats don't meet the gap threshold.

This three-source approach (static universe + Finviz + S&P heatmap) ensures the screener doesn't miss the day's most active setups regardless of which sector or market cap range they come from.

Scoring and Ranking

Each candidate receives scores across six dimensions:

1. Volume score — Absolute volume vs. minimum threshold + RVOL multiplier
2. ATR% score — Volatility in the 2–4% sweet spot scores highest
3. Gap score — Gap up/down percentage, with 5–10% gaps scoring at the top
4. RSI score — Momentum zone (50–68) for trend plays; extreme zones for mean reversion
5. Relative strength — Performance vs. SPY on the day
6. 52-week proximity — Distance from 52-week high (proximity = strength)

The composite score ranks all candidates. The top candidates are flagged for AI analysis, where a Claude model evaluates each setup in context — analyzing whether the catalyst, market regime, and technicals align before the trade is sized and submitted.

See Screener Composite Score Guide and How AI Day Trading Screeners Work for deeper implementation detail.

Watchlist Prioritization

Stocks on a user's personal watchlist get a +15 point score boost in Tradewink's screener. This ensures that stocks you've specifically identified as setups appear near the top of the ranked list even if their raw scores are slightly below market leaders. Watchlist tickers are always evaluated first in each scan cycle.

Step 6: Backtesting Your Screener

A screener is a hypothesis — it assumes that stocks meeting these criteria tend to produce good trades. Before using it with real money, backtest it:

What to measure:

• Hit rate of screener output → what percentage of screener candidates resulted in a profitable setup within the holding period?
• Average profit/loss on screener candidates vs. random picks
• Performance by market regime (trending vs. choppy)

Common backtest findings:

• High-RVOL + gap screens work best in trending markets; they produce false signals in choppy regimes
• RSI oversold screens (RSI < 35) work in bull markets; in bear markets, oversold gets more oversold
• ATR% filters need adjustment for different market volatility regimes (VIX below 15 vs. above 25)

See Backtesting Trading Strategies Guide and Walk-Forward Optimization Guide for methodologies.

Step 7: Automating Your Screener

Manual screeners (running searches in ThinkorSwim, Finviz, or TradingView) work for daily pre-market preparation. For intraday opportunities, you need automation.

Key automation requirements:

1. Real-time data feed — Streaming quotes from a provider like Polygon.io or Alpaca Markets. Historical end-of-day data isn't sufficient for intraday gap and RVOL calculations.

2. Scheduled scan loops — The screener should run on a timer (every 60–120 seconds during market hours) rather than waiting for manual triggers.

3. Alert delivery — When a stock crosses your score threshold mid-day, you need immediate notification: Discord message, email, mobile push, or direct trade execution.

4. Regime awareness — The screener should adjust its behavior based on market conditions. In a choppy, low-volatility regime, tighten RVOL and ATR thresholds. In a high-volatility trending market, loosen them.

5. Deduplication — When a stock scores high in three consecutive scan cycles, you don't want three separate alerts. A good screener tracks recently flagged candidates and suppresses repeat notifications within a time window.

This is exactly the architecture Tradewink implements — a continuously running Python async loop that queries market data, scores candidates, and sends alerts to Discord, all while adapting to the current market regime detected by the regime detection system.

Common Screener Mistakes to Avoid

Mistake 1: Too many filters, zero results. Adding 15 filters creates a screener that returns zero stocks on most days. Start with 3–4 core filters (volume, ATR%, gap or RSI), get results, then incrementally tighten.

Mistake 2: Ignoring liquidity. A stock can pass every technical filter but have $200,000 average daily volume — impossible to enter and exit cleanly as a day trader. Always include a minimum dollar volume or absolute volume gate.

Mistake 3: Static thresholds in different regimes. A gap of 3% in a low-volatility environment is significant; in a high-VIX environment, 3% gaps are common noise. Adjust your thresholds based on current market conditions or build regime-aware dynamic thresholds.

Mistake 4: Confusing screener output with trade signals. A screener produces candidates. Each candidate still needs individual analysis: Is the gap based on solid news or a rumor? Is the sector in favor or being sold? Does the chart show a clean pattern, or is it extended after a multi-day run? A screener shortens your research list; it doesn't replace the research.

Mistake 5: Never updating the screener. Markets evolve. A screener that worked well in 2023 may underperform in 2026's market structure. Review screener performance quarterly. Remove filters that haven't added value. Test new factors.

Putting It All Together: A Sample Day-Trading Screener

Here's a complete example screener configuration for day trading:

Universe: S&P 500 + Russell 2000 components, excluding biotech (GICS sub-industry code 352010)

Hard filters (all must pass):

• Price: $5–$500
• Average daily volume (20-day): > 500,000 shares
• ATR%: > 1.5%

Scoring factors (ranked by composite score):

• RVOL > 1.5× today vs. 20-day average at this time (30% weight)
• Gap up > 2% from prior close (25% weight)
• RSI 50–68 or RSI < 32 (15% weight)
• ATR% 2–4% (20% weight)
• Within 10% of 52-week high (10% weight)

Time gates:

• Pre-market scan: filter for gap > 3% with news catalyst
• Opening range scan (9:30–10:30 AM): full composite score, top 15 candidates
• Mid-day scan (11 AM–2 PM): require RVOL > 2.0 to filter noise
• Power hour (3–4 PM): add RSI extremes (< 30 or > 75) for reversal plays

Alert threshold: Composite score > 70 out of 100

This configuration produces 5–15 actionable candidates per scan cycle during peak hours — a manageable list that rewards focused analysis.

For the algorithmic implementation of this type of screener, see Algorithmic Trading Strategies. For how AI analysis is layered on top to validate each candidate, see AI Day Trading Strategies.