What I Build
I built this for myself first.
Before I help a client untangle their systems, I apply the same methodology to my own. This is a systems story — about what happens when a career infrastructure engineer gets tired of managing chaos and decides to build something instead.
Not financial advice. Not a product. A demonstration of methodology.
The Problem
The assets were diverse by design. The system wasn't.
The real chaos wasn't just the number of platforms. It was the fundamental diversity of what was being managed — multiple asset classes, each with a completely different strategy, time horizon, and definition of success:
Options. Short-term income. Premium selling. Systematic, rules-based execution. Measured in days and weeks.
Equities & ETFs. Thesis-driven growth. Frontier tech exposure. Short- to medium-term positions. Measured in quarters.
Crypto. Asymmetric upside. High volatility. Completely different risk profile. Spread across two exchanges.
Retirement Accounts. Long-term compounding. Tax-advantaged structure. Completely different rules, restrictions, and time horizons. Measured in decades.
Each of those lived on a completely different platform with its own dashboard, its own logic, and no awareness of anything else. I was context-switching between apps just to answer basic questions: Am I overexposed to semiconductors across all accounts? Did that options trade hit its target? Is the crypto position still sized correctly against the rest of the portfolio?
On top of that, I follow a specific frontier tech thesis — AI and compute, semiconductors, data centers, energy, cybersecurity, robotics, space, quantum, defense, biotech and longevity. Staying current across those sectors while actively managing across asset classes while running a business meant something had to give.
Different assets. Different goals. Different platforms. No architecture connecting any of it. That's the chaos.
The Approach
Assess first. Design second. Build third.
Before touching a tool or writing a line of code, I did what I do for clients: I assessed. The real problems were fragmentation, time cost, thesis drift, the need for income without active trading, and a system that had to be promotable to automation as confidence was established.
Then I defined constraints: free or low-cost tools only. No black-box vendors. No manual single points of failure. Human approval required at every execution stage until automated execution was explicitly earned.
This is the Arcwise methodology. Applied to myself first.
The Architecture
Three domains. One intelligence engine.
A shared intelligence layer monitors market regime, sector and theme context, macro calendar, earnings risk, and company news — filtered through the frontier tech thesis. It produces context ratings, thesis-fit scores, and risk flags. It does not execute trades. It informs. Three operational domains sit on top of it.
Long-Term Holdings Review
Ingests position data from broker exports and APIs. Compares holdings against the investment thesis. Checks concentration, diversification, tax location, and cash deployment. Surfaces rebalance recommendations weekly or after major market events. All execution is human and manual.
Options Income Lab
Eight strategies across four framework lanes on TastyTrade — from directional credit verticals to neutral range strategies to asymmetric structures to the Wheel. A Python-based scanner scores candidates mathematically. An AI review layer adjusts scores for market context. Paper trading first. Live execution only when explicitly promoted.
Stock / ETF Short-Term Trading
Swing-style trades on frontier-tech equities and sector ETFs in E*Trade. Read-only broker connection, order preview, paper trade ledger. Scan, score, human approves, execute.
The Stack
The answer isn't fewer tools. It's intentional ones.
There's a critical distinction between the left column and the middle one. The asset platforms on the left represent accidental accumulation — each chosen independently for a narrow purpose, with no shared architecture, no integration, and no unified view. The platform layer in the middle is intentional design — every tool has a single defined role, a clear boundary, and a deliberate connection to everything else. They all report to the same command center. They all feed the same intelligence layer. That's not the same kind of chaos. That's the solution to it.
The starting point — accidental accumulation
Options Trading
TastyTrade
Income strategies, premium selling, options scanning
Stock Trading
E*Trade, Merrill Edge
Equities, ETFs, short- and long-term positions
Crypto
Coinbase, Kraken
Digital asset holdings across two exchanges
Retirement & Crypto IRA
Merrill Lynch IRA, Schwab IRA, iTrustCapital
Long-term retirement accounts, crypto IRA exposure
Platform layer — intentional architecture
Notion
Finance command center — all trackers, queues, and review workflows
Paperclip
Visual intelligence dashboard — live portfolio view layer
Linear
Project and task management — because even a personal OS needs a backlog
GitHub
Source of truth for all code, docs, and promoted skills
OpenClaw + Hermes
Agentic runtime — always-on execution and research layer
Build layer — what assembled it
Claude Co-Work / Code
System design, strategy documentation, skill architecture
ChatGPT Codex
Code generation, scanner logic, skill scaffolding
Lovable
AI-native rapid prototyping for front-end components
The Complete System
A unified investment OS spanning options income, equity trading, long-term portfolio management, retirement accounts, and crypto — connected through a shared AI intelligence layer, managed from a single command center, and backed by an agentic runtime that runs research, scanning, and execution workflows automatically.
Why This Is Here
The complexity is different. The problem is identical.
Most businesses I work with aren't managing a dozen investment platforms. They're managing five disconnected SaaS tools, or a vendor stack that grew through a decade of one-off decisions, or a tech footprint that nobody fully owns. The problems look different on the surface. Underneath, they're the same: fragmentation, time cost, no unified view, and no one accountable for making it coherent.
Yes, there are platforms out there that handle 80–90% of what a business needs. But they're built for the median user — which means they come loaded with features you'll never touch, priced for enterprise budgets you don't have, and they still won't get you all the way there. You end up paying for 100% of a product you use 50% of — and that 50% still doesn't fit quite right. The last 10–20% is usually exactly where your actual competitive advantage lives.
The better answer is to start with what you actually need. Use the right low-cost tools in precise, defined roles. Build the last mile to fit your specific workflow, your specific goals, your specific constraints. That's what this system is. And that's what Arcwise does for businesses — assess what exists, cut what doesn't serve you, and build something that gets you to 100% for less than the off-the-shelf alternative.
I'm not a consultant who theorizes about systems. I build them — and I run them daily. If you want an AI systems architect who deploys the same methodology on his own infrastructure before yours, that's the offer.
Ready to cut through your complexity?
Start with a 20–30 minute conversation. No pitch, no commitment. Just an honest look at where you are and whether Arcwise is the right fit.
Or text / call 919.578.8026