Getting Profit AI's chart analysis into your own systems

The valuable thing inside Profit AI is not a balance — it is a reading. Point the app at a chart screenshot and its backend returns a structured verdict: trend direction, the patterns it detected, support and resistance zones, indicator state, and a buy or sell suggestion drawn from that one chart. Per the app's own description on the Google Play listing, it does this across forex pairs, global stocks and indices, commodities and metals, and crypto. That analysis output, the history behind it, and the account's watchlists and alerts are what an integrator wants to reach.

None of it sits behind an open-banking regime — this is a consumer analysis tool published by SwiftSoft, not a deposit account. So the route is authorized interface integration: we work with a consenting account, observe the documented traffic between the app and its backend, and rebuild the calls that matter as code you own.

What Profit AI keeps server-side

These are the surfaces worth wiring. The granularity column reflects how the app presents each one; field names are pinned during the build against a consenting account.

Ways in

Three routes genuinely apply here. Each lists what it reaches, the effort, how durable it is, and what we set up to run it — the access side is arranged with you during onboarding, not something you hand over before we start.

Interface integration of the analysis backend

The app talks to its own backend over HTTPS to submit a chart and collect the reading. We observe that exchange on a consenting account, document the auth chain and the request shape, and rebuild the analysis call plus the history and watchlist endpoints as code. Reaches the richest data, including the structured signal itself. Moderate effort. Durable as long as the backend contract holds; we add a re-validation step for when it shifts. This is the route we would build on, because the structured analysis is the asset and it lives here.

User-consented credential access

For a deployment that needs to act as the user — pulling their own history, their own watchlists — we wire a consented login flow and hold the session the way the app does. Reaches everything tied to that account. Low-to-moderate effort once the auth chain from route one is mapped. Pairs naturally with the first route rather than competing with it.

Native export as a fallback

Where the app or the platform exposes a share or export path for an analysis or a watchlist, we parse that artifact as a backstop for a single account. Lowest effort, narrowest reach, and the least sensitive to backend changes — useful for one-off pulls or as a sanity check against the live integration.

What lands in your repo

Delivery is source you run, not a slide deck. For Profit AI that means:

• An OpenAPI specification covering the analysis call, analysis history, watchlists, alerts and entitlement lookup, with the request and response fields named as observed.
• A protocol and auth-flow report: the login exchange, token handling, and how the session is presented on each call — including the multipart image upload the analysis endpoint expects.
• Runnable client source in Python and Node.js for the key endpoints, with the normalized schema applied so a result is JSON your code can read directly.
• Automated tests against recorded fixtures, including the quota-error path and an expired-session retry.
• Interface documentation, plus data-retention and consent-handling notes that match the footing below.

The analysis call

The central surface is the image-to-reading request. Field names here are illustrative and get pinned during the build; the shape — a multipart upload in, a structured reading out — is the part that matters.

POST /api/v1/analysis            # host resolved during the build
Authorization: Bearer <session token from the app login flow>
Content-Type: multipart/form-data

  chart=@eurusd_4h.png
  market=forex
  timeframe=4h

200 OK
{
  "analysis_id": "an_9f2c...",
  "direction":   "bearish",                 // bullish | bearish | neutral
  "patterns":    ["double_top", "lower_high"],
  "levels":      { "support": [1.0820, 1.0775], "resistance": [1.0910] },
  "indicators":  { "rsi": 62.4, "macd": "cross_down" },
  "signal":      { "side": "sell", "confidence": 0.71 },
  "created_at":  "2026-06-07T09:12:04Z"
}

# 402 / 429 when the daily free analysis is spent and no plan is active —
# the client checks entitlement first and backs off rather than hammering.

What we account for

Two specifics on this app shape the build, and we handle both as part of the work:

• The free tier allows one analysis a day with unlimited requests on a paid plan, per the listing. We carry the account's entitlement state through the client and pace requests around that cap, so a free-tier account does not start returning quota errors mid-sync. Where a plan is active, the same client runs without the throttle.
• TradingView's Advanced Charts run inside a WebView on mobile, per TradingView's own mobile documentation. Symbol, timeframe and layout state therefore live in that web layer, not on a native screen, so we read chart context from the WebView bridge and the data feed it calls — kept separate from the analysis backend so each can be queried on its own.
• Analysis output mixes free-form model text with structured fields. We pin a normalized schema during the build and add a re-validation pass for when the backend response shape changes, so a silent field rename surfaces as a failing test rather than corrupt data.

Consent footing

Profit AI holds user content and preferences — the charts you upload, the readings they produce, your watchlists and alerts. That is the footing it sits on: general data-protection law, not open banking. EU and UK GDPR cover account holders in those regions, and CCPA with CPRA covers California users. The SwiftSoft privacy policy is the document that governs the app's own handling. We work on the consenting account holder's authorization, keep consent and access records, minimize the pull to the fields you actually need, and sign an NDA where the engagement calls for it. The app itself is positioned for education and information, with the readings stated as not financial advice — we keep that framing intact and never present the output as anything more than what the app produces.

Where teams plug it in

• A research desk that logs every analysis into its own store, so the model's calls can be reviewed against what the market actually did.
• An alerting product that mirrors a user's watchlist and re-routes Profit AI's price triggers to its own notification channel.
• A backtest pipeline that replays historical chart images through the analysis call and scores the signal side against later price action.

Screens we mapped

The published screens, used to orient the interface mapping. Select to enlarge.

The chart-analysis app cluster

Profit AI sits in a crowded category of upload-a-chart, get-a-reading tools. Naming the neighbours widens the picture for a team weighing a single integration across several of them.

• ChartScan — snap or upload a stock, crypto or forex chart and get pattern, signal and level detection; holds per-account scan history much like Profit AI.
• Chartify — AI chart analysis with per-user analysis records behind an account.
• ChartAnalyst.ai — web tool that returns trend, support and resistance, and risk zones from an uploaded candlestick chart.
• GPTChart.ai — chart-reading AI that logs analyses per user and produces explainable trade notes.
• Chartby — mobile chart-analysis assistant with saved readings and watchlists.
• Snappchart — AI stock-chart analysis keeping a user's prior scans.
• TrendSpider — automated technical analysis and scanning with substantial per-account configuration and alert state.
• Tickeron — AI signal and pattern service with per-user watchlists and signal history.

How this was put together

Checked in June 2026 against the app's store listings, the developer's policy pages, and TradingView's charting documentation, cross-read with the app's stated feature set. Primary sources: the Google Play listing, the App Store listing, the SwiftSoft privacy policy, and TradingView's mobile charting docs. Endpoint paths and field names in this brief are illustrative and confirmed live during a build against a consenting account.

Mapped by the OpenBanking Studio integration desk · 2026-06-07

Questions integrators ask

Can you return the chart-analysis output as structured JSON instead of the in-app card?

Yes. The app's core surface takes an uploaded chart image and returns a reading — trend direction, detected patterns, support and resistance zones, indicator state and a buy or sell suggestion. We map that response to a stable schema your code can consume, so each analysis arrives as JSON fields rather than a screen you would have to scrape.

Does the one-free-analysis-a-day limit get in the way of an integration?

The listing describes one free analysis daily with unlimited requests on a paid plan. The integration carries the account's entitlement state, so calls run under whatever plan the consenting account holds, and we design the request cadence around the cap so a free-tier account does not silently start returning quota errors.

Which data-protection rules apply, given Profit AI is not a bank?

It holds user content and preferences — uploaded charts, analysis history, watchlists, alerts — not regulated bank records, so the footing is general data-protection law: EU and UK GDPR, plus CCPA and CPRA for California users. There is no open-banking or account-aggregation regime in play. We run on the account holder's own authorization, keep consent records, and minimize what is pulled to the fields you actually need.

How do you handle the TradingView charts that load inside the app?

TradingView's Advanced Charts run inside a WebView on mobile, per their own docs, so symbol, timeframe and layout state live in that web layer rather than on a native screen. We capture chart context from the WebView bridge and the data feed it calls, and keep that separate from the analysis backend so the two surfaces can be queried independently.

One brief, one app, one quote. Engagement runs whichever way suits you: pay from $300 for source-code delivery — runnable client source, the OpenAPI spec, tests and interface docs for Profit AI's analysis and account surfaces, billed only after delivery once you are satisfied — or skip the upfront cost entirely and call our hosted endpoints on a pay-per-call basis, paying only for what you use. Either way the build runs in one to two weeks, access and compliance are arranged with you as we go, and you can tell us the app and what you need from its data to start.