Accounting, financial operations, and automation professional focused on turning complex work into clear, repeatable systems.
I use data, rules, and practical tools to make decisions more reliable, traceable, and easier to execute.

Right now, I’m spending most of my time on:

• building small automation and reporting tools that solve real operational problems
• improving decision quality in finance, markets, and workflow design
• publishing more of my work publicly in a clearer, more professional way

I’m most comfortable working in environments where precision matters.

Across finance, data, and markets, I tend to approach problems by:

• breaking ambiguity into structured inputs
• defining clear rules and constraints
• validating data before acting on it
• removing emotion and noise from decision-making
• iterating, testing, and refining until outcomes are repeatable

I’m less interested in one-off solutions and more interested in systems that behave predictably under real-world conditions.

I’ve applied this mindset across a few overlapping domains:

Designing and maintaining clean, reliable financial records; supporting month- and year-end processes; and ensuring financial data is accurate, traceable, and usable for decision-making.

Personal research into programmatic trading (rule-based systems that execute predefined market decisions automatically), where I translate technical analysis into deterministic logic.

This work focuses on:

• converting discretionary market decisions into code
• enforcing risk constraints mechanically
• testing logic across varying market conditions
• removing emotional influence from execution

For non-traders, I think of this as building financial decision systems: well-defined rules, validated inputs, and consistent execution.

Building small internal tools and scripts to:

• clean and normalize data
• automate repetitive or error-prone tasks
• improve the clarity and usability of reports

These are pragmatic solutions built to solve specific problems rather than generalized products.

I’m comfortable working across modern accounting, data, and automation tools when they meaningfully reduce friction or improve clarity.

Accounting & Data

• QuickBooks (Online & Desktop)
• Excel (formulas, PivotTables)
• Power BI
• SQL
• Google Sheets

Programming & Analysis

• Python (data processing, analysis, automation)
• Pandas / NumPy
• Jupyter Notebooks

Automation & Integrations

• Power Automate
• Zapier / Make
• APIs and data connectors
• GitHub & GitHub Actions

Web & Supporting Tools

• WordPress / Elementor
• HTML / CSS / JavaScript
• Cloudflare
• VS Code

I approach these tools as instruments for exploration and problem-solving, not as an end in themselves.

Some areas I’m actively exploring:

• Programmatic access to accounting data using the QuickBooks Online API
• More readable and decision-oriented financial reporting formats
• Rule-based logic applied to accounting workflows and market analysis
• Small utilities that replace repetitive, low-value manual tasks

These explorations are iterative by design and evolve as tools and ideas change.

A few examples of the kinds of projects I share here:

• shopify_analytics - Python tooling for pulling and analyzing Shopify sales and inventory data
• questrade-reporting - automated financial reporting workflows built from API data
• Quantower-Momentum-Candle-Detection - signal detection logic for time-series and trading analysis
• openclaw-dashboard - a fork I am adapting to evaluate self-hosted operator dashboards for AI workflow systems

This GitHub profile contains:

• personal projects
• learning artifacts
• experiments
• small tools built to solve real problems

The code here reflects curiosity, iteration, and practical problem-solving rather than polished, commercial software.

🔗 LinkedIn
🌐 Personal Website ▶ YouTube (Professional Channel) 📧 Email