I Build the Software
I Invest With
A fundamental investor who engineers his own research stack.
30+ automated research tools, designed and built solo. A decade in public markets across healthcare, SMID equities, technology, and multi-asset investing.
Research Tools
A selection from 30+ automated research tools I've designed, built, and run — spanning idea generation, monitoring, due diligence, and macro.
BIOTECH TRIAGE
PRIVATESystematically scores and prioritizes biotech names across quality and valuation dimensions to focus deep research where it is most likely to generate alpha.
With 400+ US-listed biotechs, deciding where to spend deep research time is itself a research problem. Without a structured framework, coverage tends to skew toward familiar names rather than the best opportunities.
Developed a two-axis scoring framework that evaluates every name on fundamental quality across six categories and valuation margin of safety using a sum-of-parts NPV model, sorting names into Priority Buy, Monitor, Avoid Trap, or Remove.
Makes idea prioritization systematic rather than intuitive, increasing the likelihood that research effort is concentrated where the risk/reward is most attractive.
FORENSIC TRIAGE
PRIVATEScreens the coverage universe for accounting quality concerns using sector-specific diagnostic frameworks built for healthcare services and medtech.
Accounting irregularities are often visible in filings before they affect the stock — but identifying them consistently across a large universe requires time and sector-specific knowledge that most workflows don't support.
Built a systematic screen that evaluates every name across eight flag families — including accruals quality, revenue recognition, balance sheet trends, and disclosure patterns — using rubrics calibrated for healthcare services and medtech business models.
Surfaces names with elevated accounting risk before they become consensus concerns, supporting better short idea generation and reducing the risk of holding deteriorating longs.
SIGMA ALERT
PUBLICMonitors the full coverage universe in real time and flags statistically abnormal price moves throughout the trading day.
Significant price moves across a large universe happen constantly. Most analysts catch them for their core names but miss the periphery — by the time a move is visible, the opportunity to understand the cause has often passed.
Built a statistical screener that computes z-scores for every ticker against its trailing return distribution and fires alerts three times per trading day — open, midday, and close — with company name, sector, and magnitude of the move.
Ensures no meaningful move across the coverage universe goes unnoticed, creating more opportunities to act on price dislocations before they are fully explained by consensus.
13F ANALYZER
PRIVATETracks institutional positioning across 52 funds each quarter to surface emerging conviction and identify names with significant smart-money activity.
Institutional 13F filings are public and information-rich, but extracting meaningful signal across dozens of funds each quarter is time-consuming and difficult to do consistently.
Built an automated pipeline that pulls four quarters of holdings data for 52 funds across seven investment style categories, diffs positions quarter over quarter, reconstructs 10-year holding history, and generates AI-assisted investment thesis summaries for top positions.
Converts a time-intensive manual process into a systematic quarterly input for idea generation, making institutional positioning a reliable part of the research process rather than an occasional reference.
COVERAGE MANAGER
PRIVATEMaintains a live investment universe of ~1,100 tickers with automated performance tracking and fundamentals enrichment across multiple data providers.
Managing a large coverage universe manually is operationally intensive — tickers go stale, sector tags drift, and performance data lives in disconnected spreadsheets. Most analysts either limit their universe or sacrifice data quality.
Built a weekly automated pipeline that validates, enriches, and segments the coverage universe, pulling from multiple fundamentals providers with automatic failover, and delivers performance reports by email and Slack every Friday morning.
Creates a reliable system of record for the coverage universe that feeds every downstream research workflow, freeing time for analysis rather than data maintenance.
EARNINGS AGENT
PRIVATEAutomates earnings calendar maintenance across the full coverage universe, with consensus estimates surfaced directly in the calendar event.
Tracking earnings dates and consensus estimates across a large coverage list requires constant manual upkeep across multiple data sources, creating operational drag during the most information-intensive periods of the quarter.
Built a daily agent that pulls upcoming earnings dates from Finnhub for every name in the coverage universe and automatically creates Google Calendar events with timing, consensus EPS, and revenue estimates baked into the description.
Eliminates calendar maintenance entirely, ensures no earnings event is missed, and keeps consensus expectations visible without requiring a separate lookup during earnings season.
DAILY READS
PRIVATEImproves information intake by aggregating and prioritizing high-signal content across curated sources, with selection criteria that improve over time based on feedback.
The volume of information relevant to investing has expanded dramatically, while most workflows for consuming it remain manual and fragmented. This creates a risk of missing important signals or spending time on low-value inputs.
Built a system that aggregates content from newsletters, curated web sources, and financial media, uses Claude to select four high-signal articles each morning, and refines its selection criteria over time based on explicit feedback.
Improves pattern recognition and allows more time to be spent on synthesis and judgment rather than information collection — and gets more useful the longer it runs.
EXEC INTERVIEWS
PRIVATEBuilds a substitute for the management access a solo investor lacks — a citation-backed synthesis of what every executive in the coverage universe has said publicly, plus a psychological profile of each leader.
Direct management access is one of the biggest advantages large institutions hold over independent investors. Public executive interviews carry much of the same signal, but they are scattered across podcasts, conferences, and media and impossible to track by hand.
Built a pipeline that discovers and ingests public executive interviews by company and by person, then maintains a durable people database anchored to SEC reporting-owner identities so an executive stays tracked across job changes — producing academic-style strategy and character profiles for ~360 current and former executives.
Recreates a piece of the institutional information edge — a structured, queryable read on management quality and company culture — for an independent investor.
HC MACRO POLICY
PRIVATEA healthcare-specific intelligence hub covering Washington policy, the sector operating environment, and the government rate-setting calendar that drives reimbursement.
Healthcare investing is uniquely exposed to policy and reimbursement decisions made in Washington — market-moving information that is real but almost impossible to monitor manually across subsectors.
Built three connected feeds on a healthcare-subsector spine: a running policy-news digest, an operating-conditions dashboard built from ~39 industry data series, and a decade-plus calendar and history of CMS reimbursement rate cycles — all auto-published to interactive web pages.
Turns the most distinctive risk in healthcare — policy and reimbursement — into a variable that can be monitored like any other, rather than a source of surprises.
PORTFOLIO DAILY
PRIVATEA daily and weekly read on the actual portfolio — and, for every unusual mover, a side-by-side panel of explanations drawn from eight different AI models and search tools.
Knowing a position moved is easy; getting a fast, honest read on why — rather than one analyst's single guess — is the hard part, especially across multiple brokerage accounts.
Built a pipeline that pulls holdings from two brokerages, builds a clean per-position performance table, and for every statistically large mover assembles a "why did this move" panel by asking eight AI models and search tools the same question — then shows all eight answers side by side with their sources.
Replaces a single explanation with a range of cited, comparable second opinions on every surprise in the book.
DEEP DD
PRIVATEOn-demand, deeply-sourced due-diligence memos on healthcare companies — produced in under an hour instead of a day.
A proper first-pass due-diligence write-up on a new name takes hours of reading and synthesis, and that cost is what stops an investor from seriously vetting more ideas.
On request for any ticker, commissions an autonomous multi-source research job and renders the result as a structured investment-committee memo — business, pipeline, market size, moat, regulatory landscape, management, financials, catalysts, bull/bear, and risks — with every claim's source logged.
Compresses time-to-first-opinion on a new idea from a day to under an hour, with citations, so far more candidates can be triaged seriously.
MACRO MONITOR
PRIVATEA daily digest of U.S. economic releases and macro research, paired with a top-down market-data charting suite, viewed through a healthcare lens.
Macro context drives markets, but the relevant releases and commentary are spread across dozens of government sites and newsletters — staying current is a daily chore that is easy to let slip.
Built a system that tracks the U.S. release calendar, ingests a wide set of research feeds, and posts a single organized briefing each morning — paired with a charting module that visualizes growth-rate trends and valuation history in the style of a well-known macro strategist.
Keeps a macro edge sharp without the daily time sink, and packages the picture in a form that can be acted on before the open.
Design Philosophy
Designed to reduce time spent on manual data work and increase time spent on analysis and judgment.
Built to improve consistency across the research process — the same rigor applied to every name, not just the highest-conviction ones.
Focused on surfacing signal, not generating conclusions — the tools inform judgment, they don't replace it.
Structured to integrate into a fundamental, bottoms-up workflow — outputs land in Slack, Gmail, Google Calendar, and PDF reports, not in a separate dashboard.
How They're Built
Cron-driven, server-free.
Runs on GitHub Actions or Windows Task Scheduler. No servers to keep alive. The system exists only when it's executing.
Secrets hygiene.
No keys in code. GitHub Actions secrets or local .env only. Security is architectural, not accidental.
Idempotent reruns.
Re-trigger without duplicates or corruption. Scripts check state before mutation to ensure safety.
Real-world data tolerance.
Holiday handling, stale-data guards, multi-currency, and robust API rate limit management.
Composable, not monolithic.
Projects communicate through files (JSON/CSV), not live APIs. Failure in one node doesn't bring down the stack.
Reasoning Models + Structured Analysis.
Claude where reasoning matters, code where structure matters. Knowing where that line goes is the primary skill.
About
I am a public markets investor with a decade of experience investing across healthcare, SMID equities, and technology, with a foundation in multi-asset investing.
I have worked across long-only and long/short strategies within large, global investment platforms and on small, focused teams, contributing to differentiated investment outcomes over both short and long time horizons.
I build AI-native research workflows that enhance fundamental research and decision-making.