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Case study 01AI & DataIn development

TraderMindAI

A SwiftUI and Firebase-based trading journal and decision-support concept I’m actively developing while I refine the product experience.

Demo pendingRepository pending

Project brief

Context and intent

SwiftSwiftUIFirebase AuthenticationFirestoreMVVMReusable viewsOnboarding flowAI-assisted functionality

I’m building TraderMindAI as a mobile-first concept that helps me practice product thinking around journaling, reflection, and small AI-assisted features. The app is still evolving, so I’m treating it as a working project with a clear direction rather than a finished platform.

Problem

I want to help users capture context around each trade without relying on scattered notes or memory alone.

Target user

Self-directed traders who want a practical journal and decision-support tool.

Architecture

SwiftUI interfaces, Firebase Authentication, Firestore, MVVM-inspired state ownership, reusable views, and a layered structure designed to support future growth.

My contribution

I’m shaping the product direction, interface work, authentication flows, profile and onboarding flows, Firebase integration, reusable view design, and the app’s overall structure as it grows.

Current status

The app is still under development and currently includes onboarding, authentication, profile creation, dashboard and settings flows, plus an AI-assisted experience I’m continuing to refine.

Future improvements

I plan to strengthen journal analytics, improve simulated-market experiences, refine AI prompts and guardrails, and expand testing as the app matures.

Content confirmation required

Repository and demo links will be added once the app is shared more broadly.

01

Key features

  • Onboarding and authentication flow
  • User profile and dashboard structure
  • Trading journal and reflection concepts
  • AI-assisted product experience
02

Engineering challenges

  • Designing a clear experience that balances useful automation with thoughtful user judgment
  • Keeping the app architecture flexible as the product grows
03

Lessons learned

  • User trust matters as much as model capability in AI-assisted products
  • A consistent architecture helps new features ship without creating avoidable complexity

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