Best Free Stock Screener API
Filters, Limits & Python

Best free stock screener API: choose by workload

There is no universal winner. A stock-screener API must provide a programmable way to filter an eligible universe—not merely fetch one ticker’s quote. The best fit exposes the required fundamental or technical filters, covers the target exchanges, returns a stable schema, and permits the intended use.

Choose by workload before choosing by brand. A server-side fundamental screen needs expressive conditions and reliable fiscal-period semantics; a live technical scanner needs broad quote coverage and synchronized timestamps; a small research notebook may work better with bulk data and local filtering; a historical strategy test requires point-in-time fundamentals, old universes, and delisted securities. One free plan rarely leads all four categories.

Stock screener API filters and limits to compare

Filters, exchange coverage, freshness, pagination, rate limits, authentication, and licensing are separate pass/fail requirements. Confirm every one before comparing SDK convenience or response speed.

Use a fixed four-stock fixture to verify filter semantics

Define the universe before the rules: US primary-listed common stocks, excluding ETFs and ADRs, observed at the June 30 close. Use only fundamentals that were publicly available by that cutoff. Apply market_cap_usd >= 1,000m, ttm_revenue_growth > 10%, and avg_dollar_volume_20d_usd >= 5m. Exclude a security when a required field is null rather than treating null as zero.

The API should return the same two matches and an explainable reason for each exclusion. Record whether market cap uses the closing price or a later quote, which four fiscal quarters form TTM revenue, how non-USD values are converted, and whether the threshold operators are inclusive. Without those details, two syntactically identical screens can implement different decisions.

Free stock screener API options to verify

This is a verification shortlist, not a permanent ranking. “Best” depends on whether you need server-side screening or raw data for client-side filtering. Build a scorecard before testing: assign pass/fail gates for market, security type, required fields, freshness, historical behavior, license, and budget; score convenience only after every gate passes. This prevents an attractive SDK or generous headline quota from masking a missing core capability.

Free stock screener API Python test workflow

from decimal import Decimal, InvalidOperation
import requests


FILTERS = {
    "as_of": "2026-06-30",
    "security_type": "common_stock",
    "country": "US",
    "filters": [
        {"field": "market_cap_usd", "op": "gte", "value": 1_000_000_000},
        {"field": "ttm_revenue_growth", "op": "gt", "value": 0.10},
        {"field": "avg_dollar_volume_20d_usd", "op": "gte", "value": 5_000_000},
    ],
    "sort": ["market_cap_usd:desc", "security_id:asc"],
    "limit": 100,
}


def as_decimal(row, field):
    value = row.get(field)
    if value is None:
        return None
    try:
        return Decimal(str(value))
    except InvalidOperation as exc:
        raise ValueError(f"{field} is not numeric: {value!r}") from exc


def fetch_screen(endpoint, api_key):
    cursor = None
    snapshot = None
    seen = set()
    matches = []

    with requests.Session() as session:
        while True:
            body = {**FILTERS, "cursor": cursor}
            response = session.post(
                endpoint,
                headers={"Authorization": f"Bearer {api_key}"},
                json=body,
                timeout=(5, 30),
            )
            response.raise_for_status()
            payload = response.json()

            page_snapshot = payload.get("snapshot")
            snapshot = snapshot or page_snapshot
            if page_snapshot != snapshot:
                raise RuntimeError("Provider snapshot changed during pagination")

            for row in payload.get("results", []):
                identity = (row.get("security_id"), row.get("exchange"))
                if not all(identity):
                    raise ValueError("Stable security_id and exchange are required")
                if identity in seen:
                    raise ValueError(f"Duplicate security across pages: {identity}")
                seen.add(identity)

                cap = as_decimal(row, "market_cap_usd")
                growth = as_decimal(row, "ttm_revenue_growth")
                liquidity = as_decimal(row, "avg_dollar_volume_20d_usd")
                if None in (cap, growth, liquidity):
                    continue
                if cap >= 1_000_000_000 and growth > Decimal("0.10") \
                        and liquidity >= 5_000_000:
                    matches.append(row)

            cursor = payload.get("next_cursor")
            if not cursor:
                return matches

How QVeris helps connect stock screening tools

• Open the QVeris provider details for stock screening and financial-data capabilities.
• Inspect provider documentation, authentication, operators, units, response shape, pagination, timestamp behavior, and known limitations before connecting.
• Give an agent explicit requirements for market, universe, filter logic, as-of date, null policy, sorting, result cap, and freshness instead of asking it for a vague “best screener.”
• Keep provider attribution, retrieval time, query version, and license notes with every result; capability access is not investment advice.

Best free stock screener API FAQ

Authoritative references and related guides