To build a stock screener with AI that covers the whole market, do not fetch tickers one at a time. Pull the entire trading day in a single bulk end-of-day request, store it in your own database on a nightly schedule, and run the filters against that table. The screen then returns in milliseconds and costs one API call a day.
That one paragraph is the whole article, and it is the part every tutorial on this topic leaves out. Searched in a browser on 5 October 2026, the results for this phrase were two app-store listings, a Perplexity finance page, a broker's marketing lesson, four YouTube walkthroughs and a Substack post. The written guides all loop over a list of symbols, hit a free API key, and stop before the loop meets a rate limit. None of them says what a full market sweep actually costs, which endpoint makes it cheap, or whether the licence on the data lets you show the results to anyone but yourself.
This article does. Every quota, price and plan gate below was opened in a browser on 5 October 2026 and is dated at the point it appears. The worked example uses US equities because that is the market these vendors publish quotas for. The structure is the same everywhere: the exchange owns the feed, the vendor licenses it, and your rights come from the vendor, so check the rules of whichever market you are consuming.
What a whole-market screener actually has to do
A screener is three jobs wearing one coat, and people usually only build the third.
- Acquire. Get a current price and a set of fundamentals for every symbol in your universe, not just the ones you already like.
- Store. Keep them somewhere you can query with ordinary filters and sorting, with yesterday still present so you can compute change, momentum and averages.
- Screen. Apply the filters, rank the survivors and show them.
A watchlist tool only needs the third. That is a genuinely different build, and if a few dozen symbols is all you want, the smaller pattern is a better fit.
If what you actually want is live prices and charts for a short list of names you already follow, that is a different article: adding live market data to a Lovable dashboard covers the per-symbol route and its caching. A screener is the opposite problem. You do not know which symbols matter until after the scan.
The universe maths nobody shows you
Start with the size of the thing. There are roughly 5,000 to 6,000 common stocks listed on US exchanges at any time. Count every instrument the exchanges actually print, which is what a bulk feed returns, and the number is far larger: EODHD documents a full US trading day, 18 August 2026, as 44,376 rows, delivered as a 2.3 MB CSV or a 6.7 MB JSON file. ETFs, preferred shares, warrants and units are all in there. Your screener has to decide which of those it keeps.
Now put the naive one-call-per-symbol loop against the free tiers, read on 5 October 2026:
Read the right-hand column twice. The quota is the problem everyone writes about. The licence is the problem that decides whether the thing you build can ever have a second user, and it is almost never mentioned in a screener tutorial.
The pattern that works: one bulk call, a nightly job, a database screen
Every serious screener inverts the loop. Instead of asking the API about each symbol, you ask it once for the whole day.
Step one: the bulk end-of-day call
Massive publishes a Daily Market Summary endpoint at /v2/aggs/grouped/locale/us/market/stocks/{date}. Its own description says it returns daily open, high, low, close, volume and volume-weighted average price “for all U.S. stocks on a specified trading date” in a single request. The page's plan matrix, read on 5 October 2026, says Plan Access: “Included in all Stocks plans”. That includes Stocks Basic at $0, where the recency is end-of-day and the history is two years. Records go back to 10 September 2003.
So the free tier's 5 calls per minute is not the constraint people think it is. One call a day gets you the entire market. Five calls a minute is 7,200 calls a day. You need one.
EODHD sells the same shape differently. Its bulk endpoint is documented as “One request, one exchange, one day” and costs a flat 100 API calls for a whole exchange no matter how many rows come back. The page adds a detail worth knowing before you optimise the wrong way: adding the symbols parameter costs 100 calls plus one per ticker, measured at 102 calls for two tickers and 104 for four. Asking a bulk endpoint for a handful of names is more expensive than taking the entire exchange.
Both vendors are telling you the same thing. Bulk is the cheap path and per-symbol is the expensive one, which is the exact reverse of how a beginner writes the first version.
Step two: one table, written once a night
Land the bulk response in a single prices table keyed on symbol and date. Keep enough history to compute what your filters need. Twenty trading days covers a 20-day average volume and a one-month momentum figure; 250 covers a 52-week high and a one-year return. At roughly 44,000 rows a day, a year of all instruments is about 11 million rows, which an ordinary Postgres table handles without complaint if you index on symbol and date and nothing else at first.
Fundamentals move on a different clock. Prices change daily, revenue and earnings change quarterly, so put them in their own table with their own refresh job. Rebuilding a fundamentals snapshot nightly for 5,000 companies wastes quota on data that did not move.
Step three: the screen is a query, not an API call
Once the data is local, a screen is a WHERE clause. Market cap above a floor, price above a floor to drop the sub-dollar noise, 20-day average dollar volume above a liquidity threshold, a price-to-earnings band, a revenue-growth minimum. It returns in milliseconds, it costs nothing per run, and users can change the filters as often as they like without touching your API quota. That property, not the AI part, is what makes a screener feel fast.
How to describe this to Lovable
People build with Lovable by describing the outcome, not by naming the files. The prompt that produces the right architecture names the schedule, the bulk call and the table explicitly, because a vaguer prompt tends to produce the per-symbol loop:
Build a stock screener. Create a scheduled job that runs every weekday at 22:00 UTC, calls the grouped daily bars endpoint once for the previous trading date, and upserts every row into a prices table keyed on symbol and date. Keep 250 trading days and delete older rows. Add a second table for quarterly fundamentals refreshed weekly. The screen page queries the database only, never the API, with filters for market cap, last price, 20-day average dollar volume, trailing P/E and revenue growth, and a sortable results table. Store the API key as a secret.
Three things in that prompt matter more than the rest. “Calls the endpoint once” rules out the loop. “Queries the database only, never the API” stops the screen page from becoming a quota bonfire the first time somebody drags a slider. “Store the API key as a secret” keeps the key out of the browser bundle, which is the single most common way a hobby screener leaks a paid key.
On that last point, the security basics for a finance app built with Lovable covers secrets, row-level security and the mistakes that put a key in front-end code.
Scheduled jobs in Lovable, and what they cost
Lovable's Jobs documentation, read on 5 October 2026, describes jobs as scheduled background tasks for recurring work such as “checking for new information, posting updates, or syncing data”. Four details decide whether your nightly load behaves:
- You create a job by asking in the project chat or through SQL. The Jobs view under More, Cloud, Jobs is for reviewing, enabling and disabling, not creating. Changing a schedule or deleting a job also happens from chat or SQL.
- The view shows the schedule in plain language, the last run, and a run history table with Succeeded, Failed or Running per execution. It does not update live, so use Refresh.
- Jobs use credits. The docs are explicit: each run consumes Cloud usage like any other backend work, so frequent or long-running jobs increase the project's run credit usage.
- A scheduled job keeps the project active. Lovable auto-pauses projects on the built-in backend after inactivity, and the docs name scheduled jobs as background activity that prevents that pause.
That fourth point is the one that bites. A screener nobody visits still runs its job every night, so it never auto-pauses and it keeps spending run credits in the background. If you build a screener and then lose interest, disable the job rather than abandoning the project.
One failure mode is worth designing for from the start. A bulk load that fails silently leaves yesterday's prices on screen looking perfectly current. Lovable's project monitoring can alert you when scheduled jobs fail, and a visible “data as of” timestamp on the screen page costs nothing and tells the user the truth.
If the retry behaviour matters more than that, Lovable documents an Inngest connector for background jobs and durable workflows that retry automatically, with “Every hour, pull new records from an external API and upsert them into the database” as one of its own example prompts. Inngest usage is billed by Inngest, not by Lovable. The wider connector picture for a finance stack is covered separately.
Your fundamentals will lie to you if you let them
Price data is mostly a plumbing problem. Fundamentals are a correctness problem, and four specific traps turn a screener into a confident generator of wrong answers.
Trailing and forward P/E are not interchangeable
Trailing P/E uses the last four reported quarters. Forward P/E uses an analyst estimate of the next four. They can differ by a factor of two on the same company on the same day, and most free APIs return one of them under a field simply called peRatio. If your filter says “P/E under 15” and your source is forward while your user assumes trailing, the screen is wrong in a way nobody will notice. Label the field in the interface with which one it is.
Negative earnings break the sort, not just the filter
A loss-making company has a negative or undefined P/E. Sort ascending on P/E and every loss-maker floats to the top of your “cheapest stocks” list. Decide explicitly whether negative earnings are excluded, shown as null, or ranked last, and put that choice in the interface rather than in a comment.
Survivorship bias is baked in by default
Most APIs return the symbols that exist today. Companies that were delisted, acquired or went to zero have left the list. Any backtest you run on that universe silently removes the worst outcomes and flatters every strategy. If you want to test a screen historically rather than just run it today, you need delisted tickers, which is a paid feature almost everywhere.
Point-in-time data is the one nobody buys
Today's database says a company's Q2 revenue was a certain number. It does not say that the number was restated in November, or that the original figure was only published six weeks after quarter end. A screen that filters on a quarter's figures using today's values is using information that was not available on the date it pretends to screen. For a live screen that runs today this does not matter. For anything that claims a historical result, it is the difference between research and fiction.
The licence question that decides whether you can ship
Quota is a cost. Licence is a permission, and it is the one that stops a side project from becoming a product. On 5 October 2026 the free and cheap tiers read like this.
- Alpha Vantage: free key is 25 requests per day; the paid ladder runs $49.99 a month for 75 requests per minute up to $249.99 for 1,200 per minute. Every one of those paid plans is still the personal, non-commercial licence.
- Twelve Data: Basic free is 8 API credits per minute, 800 a day, and its own tooltip limits the data to internal testing, evaluation or development, “not displayed to users, shared externally, or used in production systems”. Grow at $79 adds display rights but its tooltip says the data “may be displayed but cannot be programmatically processed”, which is worth reading carefully, because programmatic processing is exactly what a screener does.
- Massive: Stocks Basic $0 and every individual tier up to Advanced at $199 are badged for individual use. The business licence for the same data is Stocks Business at $2,499 a month. Starter at $29 removes the rate limit entirely with unlimited API calls, which solves quota without touching licence.
- Marketstack prints it on the card: Free is Non-Commercial Use and Basic at $9.99 a month is Commercial Use, which is the cheapest documented step from hobby to commercial anywhere in the category.
The full terms, quoted from each provider's own pages, are in what free stock API licences actually allow. The short version: price is set by who sees the number, not by how many calls you make, and there is no mainstream free stock API licensed for commercial use. If your screener is for you alone, a free or individual tier is usually fine. The moment a colleague, a client or the public can see the output, you are in a different licence bracket and the $29 tier does not cover you.
What this costs to run
Two bills, and people usually only budget for one. Lovable plan prices below were read logged out on lovable.dev/pricing on 5 October 2026.
The ladders are credit-tier dropdowns, not single prices. Pro starts at 100 credits for $25 a month and runs to 10,000 for $2,250; Business starts at 100 for $50 and runs to 10,000 for $4,300, all read on the pricing page on 5 October 2026. How Lovable credits translate into the cost of an actual MVP and the Free, Pro and Business comparison both go deeper on the credit side.
The Business line is not an upsell for its own sake. On Free and Pro, anyone with the link can open a published app. If your screener is meant for a team and not for the internet, restricting who can open it is a Business plan feature, and that is the honest reason to be on it.
When Finviz or TradingView is already enough
This is the section the build-it-yourself posts skip, and skipping it is why readers end up spending a weekend to recreate something that already exists for less than a dinner.
Finviz Elite, read on 5 October 2026, is $39.50 a month or $299.50 a year, and that includes real-time quotes and charts, the advanced screener, export to Excel and CSV, unlimited alerts, 100 portfolios and intraday maps. The free tier has no export and no alerts. TradingView shipped an AI Screener in August 2026 that takes a description in plain language and builds the filters, columns and sorting for you.
Buy, do not build, when all of these are true:
- The filters you want already exist in the product's field list.
- You are screening one well-covered market with standard fundamentals.
- You are the only user, and exporting a CSV is a fine way to get the data out.
- You have no interest in maintaining a nightly job for the next two years.
Build when at least one of these is true:
- Your filter is not in anybody's field list: a custom score, a blend of your own factors, a ratio you compute from two fields nobody exposes together.
- You need the screen joined to data that is yours, such as positions, a research backlog, client mandates or an exclusion list.
- Other people need to run it on a schedule and receive the output without logging into a terminal.
- The screen has to feed something downstream: alerts, a report, another system.
A screener that only reproduces Finviz's filters is a learning exercise, which is a perfectly good reason to build one. Just call it that in your own head before the weekend starts.
A realistic build order
- Day one, one symbol. Call the bulk endpoint for a single past date, print the row count, and confirm the licence on the tier you signed up for covers what you intend to do with it.
- Day one, one table. Land that one day into a prices table. Do not schedule anything yet.
- Day two, the schedule. Add the nightly job. Watch the run history for three days before trusting it, and put a visible “data as of” timestamp on the page.
- Day two, the screen. Build the filters against the table. Resist adding fundamentals until price filters work end to end.
- Week two, fundamentals. Separate table, separate refresh, labelled fields. Decide the negative-earnings rule before you write the sort.
- Later, and only if you need it. Delisted tickers, point-in-time history, intraday recency. Each one is a paid tier and each one roughly doubles the complexity of the thing you are maintaining.
If a trading journal rather than a screener is what you are actually missing, the build for that is written up separately, and the comparison against Cursor and Replit covers the choice of tool if you would rather write the code yourself.
Frequently asked questions
How can I build my own stock screener?
Pick a provider with a bulk end-of-day endpoint, load one whole trading day into your own database, schedule that load nightly, and run your filters as database queries rather than API calls. The architecture matters more than the tool: the same three steps apply whether you build with Lovable, write it yourself, or use a spreadsheet.
Is there a free AI tool for stock analysis?
There are free tiers, but read what they permit. Alpha Vantage's free key is 25 requests a day and Twelve Data's Basic free tier is limited by its own terms to internal testing and development rather than production. Free is usually real for personal use and rarely real for anything with a second user.
What is the best free stock screener app?
For most people the free tier of an established screener beats a weekend build, because it already has the field list and the data licence. The case for building is a filter nobody exposes, a join against your own data, or an output that has to feed something else automatically.
How do I build a stock scanner?
A scanner usually means intraday rather than end-of-day. That changes the economics, because 15-minute delayed and real-time feeds are separate licensed tiers: on Massive, end-of-day is on the free tier, 15-minute delayed starts at $29 a month and real-time at $199, all for individual use. Build the end-of-day version first and confirm the logic before paying for recency.
What is the best stock screener software?
It depends entirely on whether your filters are standard. Finviz Elite at $39.50 a month and TradingView's screener cover standard fundamental and technical fields extremely well. Custom scores, private data and scheduled delivery to other people are the things they cannot do, and they are the only honest reasons to build your own.
How many API calls does a whole-market scan actually take?
One, if you use a bulk endpoint. Massive's grouped daily endpoint returns every US stock for a date in a single request and is included on all Stocks plans; EODHD's whole-exchange request is a flat 100 API calls regardless of row count. Done per symbol instead, the same scan is roughly 5,000 calls, which is 200 days of Alpha Vantage's free quota.
Verified in the browser on 5 October 2026
- lovable.dev/pricing, logged out: Free $0, Pro from $25 a month for 100 credits, Business from $50 for 100 credits, Enterprise quoted. Credit ladders unchanged from previous readings.
- docs.lovable.dev/features/jobs: jobs created from chat or SQL, run history with Succeeded, Failed or Running, jobs consume Cloud credits, scheduled jobs prevent auto-pause.
- polygon.io/docs/rest/stocks/aggregates/daily-market-summary: all US stocks for one date in a single request, Plan Access “Included in all Stocks plans”, records back to 10 September 2003.
- polygon.io/pricing: Stocks Basic $0 at 5 API calls a minute, end-of-day, individual use; Starter $29 unlimited calls, 15-minute delayed; Advanced $199 real-time.
- alphavantage.co/premium: free tier 25 API requests per day; paid $49.99 to $249.99 a month.
- twelvedata.com/pricing: Basic free 8 API credits a minute, 800 a day, internal non-display; Grow $79; Pro $229; Ultra $999.
- eodhd.com bulk API page: one request, one exchange, one day; a full US day on 18 August 2026 is 44,376 rows; flat 100 API calls per exchange.
- finviz.com/elite.ashx: Elite $39.50 a month or $299.50 a year.
About the author
TJ Alam is a certified Lovable Expert (Website Builder track) and the founder of Digi Flock Enterprises. He built tjalam.com and cyberdance.in with Lovable. His Expert listing is in the Lovable partner directory. If you want a screener, dashboard or internal tool built properly rather than built twice, that is the page to start from.
Ready to build? Start on the Business plan if the screener is for a team, because restricting who can open a published app is a Business feature, or work with a certified Expert if you would rather skip the first two weekends.
Disclosure: MoneyFlock may earn a commission if you subscribe to a Lovable Business plan through links in this article, at no extra cost to you. TJ Alam is a certified Lovable Expert.