Custom software · Markets domain
We build software for people who trade.
Adjusted Close is a custom software development firm with two arms. The consumer arm publishes iOS apps for investors. The business arm builds analytics software on contract: dashboards, backtests, and reports that run themselves, in R and Python.
Both arms come from the same place. Whether it's a retail trader keeping a spreadsheet of sold puts or a research shop rebuilding a chart pack by hand every issue, the person who understands the position usually can't build the software, and the person who can build it usually doesn't know the markets. We sit in the middle of that.
Apps
Each of our apps tracks the user's own positions and shows what the broker leaves out. Subscriptions, no ads, and no analytics SDKs in the binary.
Foresight
A prediction-market portfolio tracker for Polymarket and Kalshi. Real P&L across both venues, calibration of your own prices against the market, and exit analysis, so you can find out whether you actually have an edge.
Know if you have an edgecalibration, real P&L, and exit analysis for serious traders iOS app · launching soonTheta
An options income tracker for premium sellers. The wheel, covered calls, and cash-secured puts, with every roll netted out, so you can see how much premium you've actually kept: annualized, per underlying, and through every wheel.
Track your premium incomeyour real break-even, and the calls you can safely sell iOS app · launching soonDividend Track
A dividend income tracker for long-term investors. What your whole portfolio will pay you over the next twelve months, across every account, not just one broker's.
Forward income, all accountsa brokerage only shows the dividends it holdsCustom software
The business arm builds analytics software for firms in the markets. Two pieces below were built end to end, the study and the dashboard that runs off it, so you can judge the standard before you write to us.
Leveraged ETF "decay" is not what you think it is
Six leveraged pairs over 16 to 18 years. The usual argument treats the compounding effect and the financing cost as one thing; this study separates them. It also finds the volatility threshold where leverage flips from additive to destructive, and the limitations section names the survivorship bias.
6–8%the annual financing cost nobody quotes, against a ~0.9% stated expense ratio DashboardLeveraged ETF regime monitor
The study's central finding turned into a standing question: given where realized volatility sits today, is leverage additive or destructive in each pair right now? It regenerates from one script, so nothing on it is updated by hand.
6 pairstrailing return measured against a breakeven that needs no forecastBoth are reproducible from source. The backtest is 288 lines of R against free daily bars. It needs no paid data or API key and runs in under a minute. The script is published on the research page.
What we build
Your strategy, documented properly: the full trade log, the drawdown path, and where it fails. A skeptic can check it, which is why it sells better than a screenshot.
A standing view of whatever you rebuild by hand today: positions, risk, regime, performance attribution. Like the monitor above, built for your book.
The chart pack you rebuild before every issue, regenerating itself on a schedule. The pipeline, the checks, and the published output.
Things that need to keep running. We watch them when they break and extend them each quarter. Most projects turn into this once the first one lands.
Fixed scope and a fixed price, agreed before anything starts. If a project runs long, we absorb it.
Who this is for
- Finance newsletters and research shops rebuilding the same chart pack by hand every issue.
- Trading educators who make performance claims and want numbers behind them that survive a skeptic.
- Small funds and prop shops with no engineering headcount and a reporting job that eats a day a week.
- Fintech teams who need market-data pipelines built but can't justify a full-time hire yet.
- Analytics agencies needing overflow capacity from someone who already knows the domain.
Who we are
Adjusted Close LLC is a South Carolina company founded by James Bowmar. I'm the founder and the person who does the work. My first data science project was in 2016, and I've grown the skillset as the tools and the field have evolved since. I work in Swift, R, and Python, and I trade options and prediction markets with my own money, which is where the domain knowledge comes from: backtest engines, volatility and regime analytics, options pricing, and position-level P&L with proper tax-lot handling. The research above shows how I work. I separate the effects that actually differ, I publish the limitations, and I make it reproducible.
Research is published under my own name. Custom software is contracted through the company, and so are the apps.
Getting in touch
For custom work, tell us what you're rebuilding by hand and roughly how often. That's usually enough to say whether it's a week's work or a month's, and what it would cost. You don't need a discovery call to get a number.
We work async by default, which suits clients in other time zones. If a call helps, I'm available early mornings, lunchtime, and evenings US Eastern. App support goes through each app's support page.