Keep your stack in sync with a Deepvue trader's account

The short read for an integrator

One Deepvue account drives a browser tab, an iPhone and an Android handset at the same time, and the screener behind all three refreshes on a WebSocket tick during market hours across what Deepvue describes as 1,150-plus data points. That cross-device sync is the useful clue. It means the watchlists, saved screens, alerts and AI-terminal history you would want downstream already live on a server of record, not trapped in one client.

So the integration problem splits cleanly. The account-owned artifacts - lists, screen definitions, alert rules, chat logs - are yours to mirror and keep in step. The live quote stream is licensed market data and gets handled within its own terms. We build the connector that reads the first set in real time and respects the second, and we hand it over as code you run or an endpoint you call. If it were our own build, the spine is direct interface integration against the same calls the app makes, with a user-consented session filling the gaps.

What the account actually holds

These are Deepvue's real surfaces, named the way the app names them, mapped to what an integrator does with each.

Routes into the data

Direct interface integration

We map the REST calls and the WebSocket subscription the web and mobile clients already make, then re-implement them as a clean client. This reaches the lists, screens, alerts, chat history, chart layouts and the live tick stream the app subscribes to. Effort is moderate; durability tracks the app's release cadence, which we cover with a re-check pass folded into maintenance. Access is arranged with you during onboarding - the build runs against an account you consent to, in a workspace you control. This is the route we would lead on.

User-consented session access

Deepvue logs in with an emailed login code rather than a stored password. We build the connector around that login-code exchange and the session token it returns, scoped strictly to the consenting account. It is the lightest path to everything that account can see, and it pairs well with the first route for the live-session pieces.

Native export, as a fallback

The account dashboard exposes a Downloads area. Where it yields structured files for the account-owned lists, we wrap them as a coarse, low-frequency fallback - useful for a first backfill, not for the intraday picture. We would not build the spine on it.

What ships to you

• Runnable clients in Python and Node.js for the watchlist, saved-screen and alert endpoints, plus a WebSocket subscriber for the live alert and quote channels.
• A push handler that takes alert triggers off the stream and fans them into your queue, Slack or Discord, with reconnect-and-resume so a dropped socket picks back up from the last cursor.
• A test suite with recorded fixtures for screen results and alert payloads, so a shape change in the upstream shows up as a failing assertion rather than a quiet gap.
• A sync design that separates concerns: real-time push for alerts and ticks, delta reconciliation for the slower-moving lists and screen membership.
• An OpenAPI description of the surface we mapped, secondary to the code.
• A protocol and auth-flow write-up: the login-code to session-token chain and the WebSocket subscribe handshake.
• Interface documentation and data-retention notes covering what is kept, for how long, and where the licensed quote data sits.

On the wire

Illustrative shapes, confirmed against a consenting account during the build rather than copied from any document. The point is the contour: a session from the login-code exchange, a saved screen read back as members, and a single socket carrying both alerts and ticks.

# Session from the passwordless login-code exchange
session = deepvue.auth.from_login_code(email, code)   # -> bearer token + refresh

# Read a saved screen's current members (REST)
screen = client.screens.results(
    screen_id="kell-momentum",
    fields=["symbol", "rs_line", "stage", "avwap_dist"],
)
for row in screen.rows:
    upsert_symbol(row)            # idempotent on (account_id, symbol)

# One socket, two channels: alert triggers and live quotes
async for ev in client.stream(channels=["alerts", "quotes"], token=session.token):
    if ev.type == "alert.triggered":
        push(ev)                  # hand off to your queue
    elif ev.type == "quote.tick":
        cache.update(ev.symbol, ev.last)   # display-only, per the data terms

# On drop: reconnect with backoff, resume from the last cursor.

Where teams plug this in

• An ops bot that mirrors a trader's watchlists and relays alerts into Slack or Discord the moment they fire.
• A portfolio dashboard backed by the same screen membership the user maintains in Deepvue, kept current through the session.
• A compliance archive that stores AI-terminal research sessions alongside the alerts that prompted a trade.
• A journaling tool that pairs each triggered alert with the chart workspace open at the time.

What we engineer around

Two judgments shape this particular build.

The live quotes are licensed, the lists are not

Deepvue publishes a real-time data agreement covering its market data, so the quote stream is not ours to redistribute. We design the connector to treat live quotes as display-only for the consenting user and cache them just long enough to render, while the account-owned artifacts - watchlists, screens, alerts, chat history - move freely. Keeping those two lanes separate in the schema is a design decision we make up front, not a patch later.

The server is the source of truth, not a device

Because one account drives web, iOS and Android together, the state is server-authoritative. We reconcile against that server of record rather than reading a single client, which is what stops two devices from disagreeing about a list. We also account for the difference between a saved screen as a stored filter and the result set it produces, capturing both so downstream membership stays right as it churns intraday. Where the login-code session needs a token refresh, the connector handles it without dropping the run.

Authorization, privacy and the quote question

Deepvue is a US-based research platform, not a bank or a payment account, so the work sits under data-protection law rather than any open-banking regime. The dependable basis for access is the account holder's own authorization: we operate on a consenting account, with the scope written down. Watchlists, alerts, screen definitions and AI-terminal logs are personal data under California's CCPA/CPRA, and under GDPR for any EU users, so the connector minimizes what it pulls and keeps a record of the consent it relies on. The licensed quote data is handled under Deepvue's real-time data agreement, separate from the personal artifacts. We work behind an NDA where the engagement calls for one, log what the integration accesses, and retain only what the brief needs.

Working with us

A first connector for the watchlist, saved-screen and alert endpoints typically lands inside one to two weeks. Take the work as source code from $300 - runnable clients, the push handler, tests and interface docs - and pay only after delivery, once it runs against your account and you are satisfied. Or skip hosting it and call our endpoints instead, pay-per-call with no upfront fee, while we keep the connector current as the app changes. Tell us the app and what you need out of it; the access and compliance pieces we arrange with you as part of the engagement.

Start a Deepvue integration

Screens from the app

Pulled from the public store listing for reference - the screener grid, charts and watchlist views an integration reads against.

Other tools in the same lane

Same category, each holding its own per-user lists, screens or alerts behind an account - the neighbours a unified research integration tends to touch. Listed for context, not ranked.

• Finviz - a web screener with the well-known heatmaps over US-listed names and a popular free tier for quick scans.
• TradingView - multi-asset charting and screening with a scripting language and a large social layer.
• MarketSurge - IBD's CANSLIM-oriented screening and pattern recognition aimed at growth traders.
• TrendSpider - automated technical analysis, backtesting and alerting.
• TC2000 - a fast scanning engine with an integrated brokerage for stocks and options.
• ChartMill - technical and fundamental screening across US, Canadian and European exchanges.
• Koyfin - fundamental research with global company snapshots and customizable dashboards.
• StockCharts - classic charting paired with technical-analysis education and saved scans.

Questions integrators ask about Deepvue

Can triggered alerts and live ticks reach our systems as they fire, or only by polling?

As they fire. We build a WebSocket subscriber against the same alert and quote channels the Deepvue client uses, so trigger events hand off to your queue in real time. Watchlists and saved screens, which change less often, ride a delta reconciliation instead.

Does a saved Deepvue screen carry its filter logic or only the current matches?

Both. A screen is an ANY/ALL filter definition plus a live result set, so we capture the rule groups and the current members. Downstream stays correct as membership churns through the trading day.

Can we redistribute the real-time quotes that Deepvue shows?

The live quote stream is licensed market data, governed by Deepvue's published real-time data agreement, so we treat it as display-only for the consenting user rather than something to resell. The account artifacts you own outright - watchlists, screens, alerts, and AI-terminal history - are the freely integrable part, and we keep the two cleanly separated.

We use the web app and both mobile apps - which one is the source of truth for a sync?

The server is. One account drives the browser, the iOS app, and the Android app, so we reconcile against the server of record rather than scraping a single device, which keeps state from drifting between them.

How this brief was put together

Checked in June 2026 against Deepvue's own pages: the product homepage for the screener, charts, watchlist and AI-terminal claims and the cross-device sync; the knowledge-base article on login and the account dashboard for the login-code flow; and the published real-time data agreement that places the quote stream under market-data licensing. Counts such as 1,150+ data points and the preset totals are Deepvue's figures, not independently audited. Sources: deepvue.com, account and profile knowledge base, real-time data agreement.

OpenFinance Lab - interface engineering notes, June 2026.