HALO Stock Performance Tracker

2.65

Derivation Chain

Step 1 AI → HALO investment rotation trend

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Step 2 HALO sector performance data collection Infrastructure

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Step 3 HALO stock performance tracking dashboard for retail investors

Problem

Korean retail investors (MZ generation in their 20s-30s, overseas stock investors) who built AI-stock-heavy portfolios need to rebalance toward HALO (Hardware, Agriculture, Logistics, Old Economy) sectors, but performance data — earnings, order backlogs, and capacity utilization — is scattered across 10+ sources, wasting 3-5 hours of research per week.

Solution

Automatically aggregates quarterly earnings, order backlogs, and utilization metrics for 50 representative HALO stocks (e.g., McDonald's, Deere, Caterpillar) into a Korean-language summary dashboard. Differentiated by a relative momentum chart versus the AI sector and a rebalancing simulator as core features.

Target: Salaried retail investors in their 20s-30s with overseas stock experience, investing ₩1M+ (~$750+) per month

Revenue Model: SaaS Monthly Subscription at ₩49,000 (~$37)/account; Free Plan (limited to 5 stocks) → targeting 8% Premium conversion rate

Ecosystem Role: Consumer

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

2.0/5

U Urgency

2.0/5

M Market

3.0/5

R Realizability

3.0/5

V Validation

3.0/5

NUMR-V Scoring System

N Novelty	1-5	How uncommon the service is in market context.
U Urgency	1-5	How urgently users need this problem solved now.
M Market	1-5	Market size and growth potential from proxy indicators.
R Realizability	1-5	Buildability for a small team with realistic constraints.
V Validation	1-5	Validation signal quality from competition and demand data.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (64%)

Tech Complexity

24.0/40

Data Availability

19.6/25

MVP Timeline

20.0/20

API Bonus

0.0/15

Feasibility Breakdown

Tech Complexity	/ 40	Difficulty of core implementation stack.
Data Availability	/ 25	Practical availability and cost of required data.
MVP Timeline	/ 20	Expected time to ship a usable MVP.
API Bonus	/ 15	Bonus for viable public API leverage.

Market Validation (50/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

14.0/20

Revenue Signals

7.5/15

Pick-Axe Fit

7.5/15

Solo Buildability

7.0/10

Validation Breakdown

Competition	/ 20	Signal quality from competitor landscape.
Market Demand	/ 20	Demand proxies from search and mention patterns.
Timing	/ 20	Fit with current shifts in tech, behavior, and regulation.
Revenue Signals	/ 15	Reference evidence for monetization viability.
Pick-Axe Fit	/ 15	How well the concept serves participants in a trend.
Solo Buildability	/ 10	Practicality for lean-team implementation.

Technical Requirements

Backend [medium] Frontend [medium] Data Pipeline [medium]

Dashboard