SahamLens — Local-First Trading Intelligence for IDX
A local-first trading intelligence platform for Indonesian stock investors, combining market data, technical analysis, AI-powered research, portfolio tracking, and decision-support tools in a single workspace.
Outcome: Production · 18 months · 283 contributions
SahamLens
SahamLens is a local-first trading intelligence platform built for Indonesian stock investors. It combines market data, technical analysis, AI-assisted research, portfolio tracking, and journaling tools into a single decision-support workspace.
Unlike brokerage platforms or signal-selling services, SahamLens is designed to help investors build their own conviction through data, context, and structured analysis.
The Challenge
Retail investors often need to switch between multiple tools to research stocks, monitor watchlists, review news, track trades, and evaluate portfolio risk. Information becomes fragmented, workflows become repetitive, and important signals are easy to miss.
I wanted a single platform that could centralize research and decision-making while keeping data ownership local and under the user's control.
The Solution
I built SahamLens as a local-first platform that aggregates market data, technical indicators, company information, news, watchlists, and trading journals into a unified workflow.
The system combines quantitative analysis with AI-assisted research to help investors quickly understand market developments, evaluate opportunities, and maintain discipline through structured trade reviews and risk management tools.
What Shipped
The platform includes:
Watchlists and stock monitoring Technical indicators and market analytics AI-generated research briefs and summaries Portfolio tracking and performance monitoring Trading journal and post-trade review workflows Risk management and position-sizing tools Local-first data storage and analysis Modern web dashboard for daily decision support What I Learned
Building investment software is less about predicting markets and more about improving decision quality. The most valuable features were not the ones that generated insights automatically, but the ones that helped organize information, reduce noise, and create a repeatable investment process.
Local-first architecture also proved to be a strong design choice, providing better control over data, lower operating costs, and a faster feedback loop for experimentation.