MoodLedger – Mood-Based Personal Finance Tracking App for Women

Existing alternatives

• Budgeting apps (YNAB, Mint-like tools, Monarch, Copilot): strong transaction categorization and budgeting, weak on emotional context.
• Expense trackers: manual logging, limited analytics beyond totals.
• Mood/journaling apps (Daylio, journaling tools): track mood but not tied to financial behavior.
• Therapy/coaching content: generic advice, not personalized to spending data.

Why they are insufficient

• They treat spending as purely rational and optimize for constraints (budgets), not triggers.
• They lack a structured way to capture emotional state at the moment of purchase.
• Insights are typically aggregate (“you spent $X on shopping”), not causal or contextual (“stress correlates with late-night online shopping”).
• Many finance apps are data-hungry; users concerned about privacy avoid deeper behavioral tracking.

Pain points

• Users recognize “emotional spending” but can’t quantify patterns.
• Hard to connect mood cycles (stress/anxiety) with spending spikes.
• Shame/avoidance: strict budgeting UIs can feel punitive, causing churn.
• Lack of actionable interventions: users need gentle prompts and alternatives.

Market inefficiencies

• Behavioral finance insights are mostly delivered via coaching or therapy; software rarely operationalizes it with transaction-level data.
• Existing apps optimize for broad demographics; a supportive, privacy-first approach tailored to women’s needs is under-served.

Primary persona

Women (25–45) with stable income who want to improve spending habits and reduce emotional spending without feeling judged.

• Goals: understand triggers, reduce impulse purchases, feel in control.
• Behaviors: uses mobile-first tools; may already use a bank app; may have tried budgeting apps and churned.
• Sensitivities: privacy, tone, and non-judgmental guidance.

Secondary persona

Women (18–24) early-career / students building financial habits.

• Goals: learn patterns early; avoid debt; build awareness.
• Behaviors: more manual tracking tolerance; high engagement with mood tracking.

Technical maturity level

• Mixed; assume non-technical.
• Must support “quick log” flows and minimal setup.

Buying behavior (SaaS)

• Will pay for perceived personal value (insights, trend detection, supportive prompts).
• Prefers monthly subscription with free trial.
• Low tolerance for complex onboarding; value must appear within 7 days.

MVP feature set (mobile-first)

1. Account & onboarding

   • Email + password or Apple/Google sign-in.
   • Privacy choices: cloud sync on/off (MVP can default to cloud sync; add local-only later).
   • Baseline questions (optional): goals, common triggers, preferred tone.

2. Mood model

   • Predefined moods: stressed, happy, bored, anxious, confident, sad, tired, celebratory.
   • Mood intensity (1–5).
   • Optional note (free text).

3. Transaction logging (manual)

   • Fields: amount, currency, date/time, merchant (free text), category, channel (online/in-store), payment method (optional), tags.
   • Attach mood at creation; allow adding mood later.
   • Receipt photo upload (optional MVP; can defer).

4. Categorization

   • Simple category list (e.g., groceries, dining, shopping, beauty, subscriptions, transport, entertainment, health).
   • Basic merchant-to-category suggestions (local mapping table + last-used).

5. Analytics dashboards

   • Mood → spend totals (weekly/monthly).
   • Mood → category breakdown.
   • Time-of-day heatmap by mood (simple bins).
   • “Top merchants when stressed” list.

6. Pattern detection (rules + stats)

   • Rules:
     • Spike detection: spend in last 7 days vs prior 28-day baseline by mood.
     • Late-night pattern: transactions after 10pm correlated with specific moods.
     • Category coupling: mood disproportionately associated with a category.
   • Output: human-readable insight cards.

7. Recommendations (non-clinical)

   • Template-based suggestions tied to detected patterns (e.g., “When stressed, you spend more on online shopping. Try a 10-minute pause before checkout.”).
   • “Pause button” flow: quick grounding prompt + alternative action list.

8. Data export & deletion

   • Export CSV/JSON.
   • Delete account + data.

9. Notifications (optional MVP)

   • Daily mood check-in reminder.
   • Weekly insight summary.

Explicit non-goals for MVP

• Bank account aggregation (Plaid) unless needed for validation.
• Complex budgeting (envelopes, bill planning).
• ML-heavy personalization; start with interpretable heuristics.

High-level components

• Mobile App (iOS/Android): primary UI for logging moods and transactions, viewing insights.
• Backend API: auth, CRUD for moods/transactions, analytics aggregation, insight generation.
• Database: relational store for users, transactions, moods, insights.
• Job runner: scheduled analytics recomputation (daily/weekly) and notification dispatch.
• Object storage: optional receipt images.
• Observability: error tracking + basic metrics.

Deployment approach

• Start with a single-region deployment.
• Use managed Postgres.
• Stateless API containers.
• Background jobs via managed queue/cron.

Mermaid diagram

flowchart LR
  A[Mobile App
(React Native)] -->|HTTPS + JWT| B[API Gateway / Backend
(FastAPI)]
  B --> C[(Postgres)]
  B --> D[Object Storage
(S3/R2) optional]
  B --> E[Queue/Worker
(Celery/Redis)]
  E --> C
  E --> F[Notifications
(FCM/APNs)]
  B --> G[Analytics/Insights Module
(rule+stats)]
  G --> C

Key implementation notes

• Keep analytics logic server-side for consistency and easier iteration.
• Use idempotent insight generation jobs keyed by (user_id, period_start, period_end, insight_type).
• Prefer interpretable metrics (lift, z-score, ratio) over opaque models.

Core tables (Postgres)

users

column type notes id uuid (pk) email text (unique) nullable if social login only password_hash text nullable for OAuth created_at timestamptz timezone text default 'UTC' settings jsonb notification prefs, privacy flags Indexes:

• unique(email)

moods

Represents a mood event (can be linked to a transaction or standalone check-in).

column type notes id uuid (pk) user_id uuid (fk users.id) index mood text enum-like constraint intensity smallint 1–5 note text optional occurred_at timestamptz when user felt it created_at timestamptz Indexes:

• (user_id, occurred_at desc)
• (user_id, mood, occurred_at desc)

transactions

column type notes id uuid (pk) user_id uuid (fk users.id) index amount_cents int store integer cents currency char(3) ISO merchant text category text simple enum-like channel text 'online'/'in_store' occurred_at timestamptz purchase time mood_id uuid (fk moods.id) nullable; index note text optional created_at timestamptz Indexes:

• (user_id, occurred_at desc)
• (user_id, category, occurred_at desc)
• (user_id, mood_id)

insights

Materialized insight cards generated by jobs.

column type notes id uuid (pk) user_id uuid (fk users.id) index insight_type text e.g., 'mood_spend_spike' title text body text markdown/plain severity smallint 1–5 data jsonb metrics, charts inputs period_start date period_end date created_at timestamptz dismissed_at timestamptz nullable Indexes:

• (user_id, created_at desc)
• unique(user_id, insight_type, period_start, period_end)

merchant_category_map (optional MVP)

column type notes id uuid (pk) user_id uuid per-user personalization merchant_norm text normalized merchant key category text updated_at timestamptz Indexes:

• unique(user_id, merchant_norm)

Constraints

• Check constraints for mood in (...), channel in ('online','in_store'), intensity between 1 and 5.

Auth

• JWT access token (short-lived) + refresh token.
• Authorization: user-scoped; every resource filtered by user_id from token.

Endpoints (v1)

Auth

• POST /v1/auth/signup
• POST /v1/auth/login
• POST /v1/auth/refresh

Example: POST /v1/auth/login

{ "email": "a@b.com", "password": "..." }

Response:

{ "access_token": "...", "refresh_token": "..." }

Moods

• POST /v1/moods
• GET /v1/moods?from=...&to=...
• PATCH /v1/moods/{id}
• DELETE /v1/moods/{id}

POST /v1/moods

{ "mood": "stressed", "intensity": 4, "note": "Work deadline", "occurred_at": "2026-02-17T21:10:00Z" }

Transactions

• POST /v1/transactions
• GET /v1/transactions?from=...&to=...&mood=...&category=...
• PATCH /v1/transactions/{id}
• DELETE /v1/transactions/{id}

POST /v1/transactions

{
  "amount_cents": 2599,
  "currency": "USD",
  "merchant": "Sephora",
  "category": "beauty",
  "channel": "online",
  "occurred_at": "2026-02-17T21:12:00Z",
  "mood": { "mood": "stressed", "intensity": 4, "note": "Rough day" }
}

Response includes created transaction and mood_id.

Analytics

• GET /v1/analytics/mood-spend?from=...&to=...&bucket=week|month
• GET /v1/analytics/mood-category?from=...&to=...
• GET /v1/analytics/mood-time-heatmap?from=...&to=...

Example response (mood-spend):

{
  "from": "2026-01-01",
  "to": "2026-02-17",
  "series": [
    { "bucket_start": "2026-02-10", "mood": "stressed", "spend_cents": 12000, "tx_count": 5 },
    { "bucket_start": "2026-02-10", "mood": "happy", "spend_cents": 4500, "tx_count": 2 }
  ]
}

Insights

• GET /v1/insights?from=...&to=...
• POST /v1/insights/recompute (admin/user-triggered; rate limited)
• PATCH /v1/insights/{id} (dismiss)

Error model

{ "error": { "code": "VALIDATION_ERROR", "message": "...", "fields": {"amount_cents":"must be > 0"} } }

Mobile

• React Native (Expo)
  • Fast iteration, cross-platform, strong ecosystem for charts and secure storage.
  • Expo EAS for builds and OTA updates.

Backend

• Python FastAPI
  • High productivity, strong typing via Pydantic, good for analytics endpoints.
• Background jobs: Celery + Redis (or RQ) for scheduled insight generation.

Database

• Postgres (managed: Supabase, Neon, RDS)
  • Relational integrity for user-scoped financial data.
  • JSONB for insight payloads.

Hosting

• API: Fly.io / Render / AWS ECS
• DB: managed Postgres
• Object storage: Cloudflare R2 or S3

Observability

• Sentry (mobile + backend)
• Structured logs (JSON) + basic metrics

Optional alternatives

• Backend in Node.js (NestJS) if team is TS-heavy.
• Use Supabase Auth + PostgREST to reduce backend code, but custom analytics often still needs an API layer.

Authentication

• OAuth (Apple/Google) + email/password.
• Passwords hashed with Argon2id.
• JWT access tokens (15 min) + refresh tokens (rotating, stored hashed).

Authorization

• Row-level authorization enforced in API: every query scoped by user_id.
• Consider Postgres RLS if using Supabase; otherwise enforce in service layer + tests.

Data protection

• TLS everywhere.
• Encrypt sensitive fields at rest if needed (notes can be sensitive). Practical approach:
• DB-level encryption via managed provider + optional application-layer encryption for note fields.
• Secure storage on device for tokens (Keychain/Keystore).

Privacy-first controls

• Clear data retention policy.
• Export + delete flows.
• Avoid selling/sharing data; minimize third-party SDKs.

Rate limiting & abuse

• Rate limit auth endpoints and insight recompute.
• Basic WAF rules if on Cloudflare.

Threat considerations

• Account takeover: protect with MFA later; for MVP add device/session management.
• Inference risk: insights could reveal sensitive mental state; ensure notifications are discreet and configurable.
• Receipt images: scan uploads for malware; use signed URLs; private buckets.

Pricing model

• Freemium:
• Free: manual logging, basic charts (last 30 days), limited insights.
• Pro ($6–$10/month): full history, advanced insights, weekly reports, pattern alerts, export formats.

Revenue streams

• Subscriptions (primary).
• Optional add-on: “guided programs” (one-time purchase) with structured prompts (non-therapeutic).

Expansion pricing

• Bank sync add-on (covers Plaid costs): +$3–$5/month.
• Family/partner mode (shared insights, separate data): higher tier.

Unit economics assumptions

• COGS dominated by:
• Hosting (low early).
• Push notifications (low).
• If bank sync: Plaid per-user costs; gate behind paid tier.
• Target: LTV > 6–12 months of subscription; keep churn low via weekly value delivery.

Distribution strategy

• Mobile app stores + content-led acquisition.
• Position as emotional spending awareness tool, not a budgeting replacement.

Channels

• TikTok/Instagram Reels: short “pattern” stories (without claiming clinical outcomes).
• Partnerships: women-focused financial educators, newsletters, podcasts.
• Communities: Reddit personal finance sub-communities, women-in-tech/finance groups.

Launch plan

1. Landing page + waitlist with 2–3 core screenshots and privacy stance.
2. Private beta (100–300 users) with manual logging only.
3. Public launch with Pro trial (7–14 days).

Growth loops

• Weekly “Your patterns this week” shareable (privacy-safe, no amounts by default).
• Referral: give 1 free month Pro for both sides.
• Habit loop: daily mood check-in → transaction log → weekly insight.

Validate before building full product

1. Concierge MVP (1–2 weeks)

   • Use a simple form (Typeform) for mood + purchase logging.
   • Weekly email report manually generated (Google Sheets) showing mood-spend correlations.
   • Validate that users find insights valuable and change behavior.

2. Prototype test

   • Figma clickable prototype for logging + insight cards.
   • Measure time-to-log and perceived tone.

MVP testing approach

• Build manual transaction + mood logging + 3 dashboards + 5 insight templates.
• Run a 30-day cohort test.

Metrics to track

• Activation: % who log ≥3 transactions with mood in first 7 days.
• Engagement: weekly active users; logs per week.
• Insight consumption: open rate, dismiss rate.
• Behavior proxy: reduction in spend in “trigger categories” after insight exposure (careful: correlation, not causation).
• Retention: D7, D30.
• Willingness to pay: conversion to Pro after trial.

Phase 1 (MVP) — 6–8 weeks

• React Native app: auth, mood + transaction CRUD.
• Basic analytics endpoints and charts.
• Insight generation v1 (rules + simple stats) daily job.
• Export + delete.
• Beta distribution + instrumentation.

Phase 2 — 8–12 weeks

• Bank sync (Plaid) as paid add-on.
• Better merchant normalization and categorization.
• Personalization: user-configurable triggers and goals.
• Local-only mode (optional) or end-to-end encryption for notes.
• Improved notifications (pattern alerts).

Phase 3 — 3–6 months

• On-device ML suggestions (privacy-preserving) for mood prediction prompts (not automatic labeling).
• “Programs”: structured habit-building modules.
• Partner mode (shared goals, separate private logs).
• Advanced analytics: seasonality, anomaly detection, cohort comparisons.

Technical risks

• Data quality: manual entry is noisy; mitigate with fast logging UX and defaults.
• Analytics correctness: avoid misleading correlations; show confidence and sample sizes.
• Timezones and bucketing errors; store UTC + user timezone.

Market risks

• Users may resist logging mood at purchase time; mitigate with retrospective tagging and reminders.
• Niche positioning could limit TAM; ensure messaging is inclusive while designed for women.

Legal risks

• Avoid medical/mental health claims; insights must be framed as behavioral observations.
• Privacy compliance: GDPR/CCPA deletion/export, clear consent.

Competitive risks

• Budgeting apps could add mood tags; differentiation via UX tone, privacy stance, and insight quality.
• Journaling apps could add spending; defend with finance-native analytics.

Product expansions

• Bank sync + enrichment: merchant normalization, category suggestions, recurring detection.
• Interventions: configurable “pause rules” (e.g., if stressed + shopping category + >$50, show prompt).
• Goal tracking: not budgets, but behavioral goals (e.g., “reduce stressed shopping nights”).
• Content layer: short, evidence-based behavioral tips tied to detected patterns.
• Privacy upgrades: end-to-end encryption for notes; local-first storage with optional sync.

Scalability direction

• Precompute aggregates per user per day/week to reduce query load.
• Move insight generation to event-driven pipeline when bank sync increases volume.
• Add feature flags for experiments on insight templates.

You are a senior full-stack engineer building the MVP of “MoodLedger”, a mood-based personal finance tracking app. Implement a production-ready MVP with a React Native (Expo) mobile app and a FastAPI backend with Postgres.

TECH STACK
- Mobile: React Native (Expo), TypeScript, React Query, React Navigation
- Charts: victory-native or react-native-svg-charts
- Backend: Python 3.12, FastAPI, Pydantic, SQLAlchemy 2.0, Alembic
- Auth: JWT access + refresh tokens, Argon2 password hashing
- DB: Postgres
- Jobs: Celery + Redis (or RQ) for daily insight generation
- Storage: S3-compatible (optional; can stub)

DELIVERABLES
1) Monorepo with /apps/mobile and /services/api
2) Working auth (signup/login/refresh) and user settings
3) CRUD for moods and transactions
4) Analytics endpoints for mood-spend, mood-category, mood-time-heatmap
5) Insight generation job that writes insight cards into DB
6) Mobile UI: log mood, log transaction (with mood), dashboards, insights list, dismiss insight
7) Export data endpoint + delete account endpoint
8) Docker Compose for local dev (api + postgres + redis)
9) Seed script with demo data
10) Minimal test coverage: auth, one analytics endpoint, insight job idempotency

PROJECT STRUCTURE
- apps/mobile
  - src/screens (Login, Signup, Home, AddTransaction, AddMood, Analytics, Insights, Settings)
  - src/components (MoodPicker, TransactionForm, InsightCard, Charts)
  - src/lib/api.ts (typed client)
  - src/state (React Query hooks)
- services/api
  - app/main.py
  - app/core (config, security, deps)
  - app/models (SQLAlchemy models)
  - app/schemas (Pydantic)
  - app/routes (auth, moods, transactions, analytics, insights, user)
  - app/services (analytics.py, insights.py)
  - app/jobs (celery_app.py, tasks.py)
  - alembic/

DATABASE SCHEMA (Postgres)
- users(id uuid pk, email unique, password_hash, timezone, settings jsonb, created_at)
- moods(id uuid pk, user_id fk, mood text, intensity smallint 1-5, note text, occurred_at timestamptz, created_at)
- transactions(id uuid pk, user_id fk, amount_cents int, currency char(3), merchant text, category text, channel text, occurred_at timestamptz, mood_id fk nullable, note text, created_at)
- insights(id uuid pk, user_id fk, insight_type text, title text, body text, severity smallint, data jsonb, period_start date, period_end date, created_at, dismissed_at)
Constraints: mood in predefined list; channel in ('online','in_store'); intensity 1..5
Indexes: (user_id, occurred_at desc) on moods/transactions; unique(user_id, insight_type, period_start, period_end) on insights

API ENDPOINTS
Auth:
- POST /v1/auth/signup {email,password}
- POST /v1/auth/login {email,password}
- POST /v1/auth/refresh {refresh_token}

Moods:
- POST /v1/moods
- GET /v1/moods?from&to
- PATCH /v1/moods/{id}
- DELETE /v1/moods/{id}

Transactions:
- POST /v1/transactions (supports nested mood creation)
- GET /v1/transactions?from&to&mood&category
- PATCH /v1/transactions/{id}
- DELETE /v1/transactions/{id}

Analytics:
- GET /v1/analytics/mood-spend?from&to&bucket=week|month
- GET /v1/analytics/mood-category?from&to
- GET /v1/analytics/mood-time-heatmap?from&to

Insights:
- GET /v1/insights?from&to
- PATCH /v1/insights/{id} {dismissed:true}
- POST /v1/insights/recompute (rate limited)

User:
- GET /v1/user/me
- DELETE /v1/user/me (deletes all user data)
- GET /v1/user/export (returns JSON export)

INSIGHT GENERATION (IMPLEMENT)
- Daily job per user computes last 7 days vs prior 28 days baseline.
- Generate at least 3 insight types:
  1) mood_spend_spike: if spend for a mood in last 7d is > baseline_mean + 2*std (or > 1.5x baseline if std unavailable) and tx_count>=3
  2) late_night_pattern: if >40% of transactions for a mood occur after 22:00 local time and tx_count>=5
  3) mood_category_lift: compute lift = P(category|mood)/P(category); if lift>=1.8 and tx_count>=5
- Store insight card with metrics in insights.data and human-readable title/body.
- Ensure idempotency using unique(user_id, insight_type, period_start, period_end).

MOBILE UI REQUIREMENTS
- Fast logging: Add Transaction screen with amount keypad, merchant, category, channel toggle, mood picker.
- Analytics screen: filters (date range), charts for mood spend and mood-category.
- Insights screen: list of cards, dismiss action.
- Settings: timezone, notification toggles (can be stubbed), export, delete account.

DEPLOYMENT
- Provide Dockerfiles for api and a docker-compose.yml for local.
- Provide environment variables template (.env.example).
- Include a simple deploy guide for Render/Fly.io and managed Postgres.

QUALITY BAR
- Typed API client on mobile.
- Server-side validation with clear error responses.
- Basic unit tests for analytics computations.
- No third-party analytics SDKs by default; keep privacy-first.

Now implement the repository with the above requirements.