Tools I've built, programs I've led, apps I've shipped.

Investment Research Database & Stock Dashboard

The investment team's stock research lived in ~3,900 individual Excel model workbooks — great for depth, terrible for cross-sectional questions. This turned them into a queryable database and a browsable dashboard.

• Python ingest reads every workbook into SQLite tables for security master data, earnings-trend time series, and historical snapshots — 10,000+ trend rows on first load.
• Screening, cross-sectional analysis, ticker history, and metric-drift functions surface names that may warrant renewed analyst review.
• Companion React dashboard reads the same workbooks for search, company overviews, side-by-side comparison, sector and industry filters, and relative-positioning charts.

Earnings Automation Suite

Two pieces that replaced a manual earnings data-entry process and a manual weekly scan.

• Earnings-trend pipeline: pulls earnings data from LSEG and writes it into macro-enabled Excel stock models through COM, preserving macros and external links. Safeguards for protected sheets, missing values, broken SharePoint links, and repeat runs.
• Weekly earnings alert: scans the full coverage universe (~428 primary, ~3,483 secondary names), queries LSEG for the period's releases, and sends a formatted Outlook summary on a schedule with logging, date anchoring, and manual overrides.

Equity Concentration & Sleeve-Drift Monitoring

Two monitoring tools built on a reusable Python package I wrote for querying the trust-accounting system over ODBC (validated across ~1,167 open accounts).

• Concentration dashboard: flags individual-equity concentration against investment-policy thresholds at book, master-account, and account level, with trim / watch / pledged / off-target categories and a comparison to current S&P 500 sector weights. Test output never exposes client identifiers.
• Sleeve-drift module: joins daily managed-account feed files with accounting data to produce a buy/sell drift signal per account, sleeve, and security — replacing a manual monthly Excel workflow while matching its output format so adoption was painless.

38-Year Asset Allocation Study

An allocation study over ~38 years of market data across U.S. equities, international equities, and intermediate fixed income, built as an interactive Plotly dashboard for use in client conversations.

• Efficient-frontier portfolios, rolling-period analysis, and how the optimal mix shifts over time.
• 60/40 and 70/30 versus all-equity, and growth of a hypothetical investment.
• Also: a scheduled Treasury yield-curve pipeline (1M–30Y, LSEG → Plotly → auto-published to the team workspace) and a P/E research script computing forward and trailing multiples across ~344 names with missing-data controls.

AI Wednesday

A recurring internal AI intelligence briefing written for finance and wealth-management professionals rather than technical specialists.

• Repeatable format: big picture and industry developments, AI in finance and wealth management, model and technology releases, ideas and society, risks and warnings, and a term of the week.
• Source attribution, practical business implications, plain-language definitions and analogies, and consistent branding on every edition.
• A Node.js document-generation workflow produces each edition from a written spec. First edition presented to the CIO; 8+ editions produced.

AI Task Force & Enterprise AI Pilot

Selected for Argent's AI Task Force, the group that operationalizes direction from the AI Steering Committee: evaluating and risk-tiering use-case requests, administering AI platforms and vendor service levels, maintaining acceptable-use policy, and reporting quarterly.

• Principal contributor to the enterprise AI pilot, with recurring feedback to senior leadership on adoption, governance, and scaling; built a custom check-in workflow tracking use cases, time saved, and helpfulness.
• Documented and presented ~18 automation and AI projects in a leadership showcase (descriptions, screenshots, business applications, risks, data considerations); produced a formal use-case inventory with proposed risk tiers.
• Worked directly with information security on data residency, retention, code review, workflow documentation, and potential data-warehouse architecture.
• Developed a board-level briefing supporting continued AI investment, and a business case for an LSEG analytics connector.

AI Basics — Executives of Tulsa & Argent Lunch & Learn

• Executives of Tulsa: invited presentation to ~25 business and finance executives on what AI is, how large language models work, how the major tools differ, hallucinations and fact-checking, and how non-technical users can start. 100+ audience questions; one-page take-home guide.
• Argent Tulsa Lunch & Learn (May 2026): co-developed and co-presented an AI Basics session for ~20 employees, adapted for a trust-company audience — practical use cases, acceptable-use requirements, enterprise data protection, and a verification step for confirming enterprise protection is active.

Iron Ledger

Strength-training and nutrition tracker with a lift mode and an activity mode, used daily by me and a small group of friends.

• Custom, user-editable training programs stored in the database with friend sharing; friends graph with per-metric stat sharing and leaderboards.
• Recipes, meal slots, supplements, macro targets, weekly-average weigh-ins, Apple Watch workout import via HealthKit.
• Postgres with row-level security and server-side RPCs; native push; guided tour and What's New; 700+ automated tests. Native watchOS companion in progress.

Manifest

Offline-first planning app for weekly lists, packing lists, recipes, and grocery runs.

• IndexedDB mirror with an outbox for offline edits; email-OTP auth with row-level security; push reminders and scheduled notifications.
• Global search, locked/private lists, photo attachments, rich text, recipe import from URL, calendar view, onboarding tour and changelog.
• A Python archiver mirrors data nightly into Markdown notes with YAML frontmatter, with fixture-based tests. MVP plus ~50 post-MVP work orders shipped in roughly three weeks.

Quartermaster

Home inventory system for physical storage with printed QR labels that resolve straight to the item.

• Server-rendered Next.js with server actions and API routes; OTP auth and row-level security.
• AI-assisted itemization from photos, item types and links, consumables with low-stock tracking, maintenance reminders, multi-photo support.
• Crash observability, guided tour and help centre, ~20 verification scripts, nightly backup with restore drills.

Shell Oil Company / OU Corporate Valuation Challenge — Finalist

Produced an investment recommendation for a publicly traded corporation using comparable-company, precedent-transaction, and discounted-cash-flow models, presented to a panel of professional judges.