Contract data work · Markets domain
I build the data tooling that trading and research operations run on.
Backtests, portfolio analytics, dashboards, and reporting that regenerates itself — in R and Python.
Most analysts who can build the pipeline don't know what theta decay or assignment risk are. Most people who know the markets can't build the pipeline. Hiring for the overlap is slow and expensive, so the work usually ends up being done by hand, every week, by someone whose time is worth more than that.
That's the work I take. Below are two pieces built end to end — the study and the dashboard that runs off it — so you can judge the standard before talking to me.
Published work
Leveraged ETF "decay" is not what you think it is
Six leveraged pairs over 16–18 years, separating the compounding effect from the financing cost — two things the usual argument conflates. Includes the volatility threshold where leverage flips from additive to destructive, and a limitations section that 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? Regenerates from one script — no manual updating, no spreadsheet.
6 pairstrailing return measured against a breakeven that needs no forecastBoth are reproducible from source. The backtest is 288 lines of R against freely available daily bars — no paid data, no API key, runs in under a minute. I'll send it to anyone who asks.
What I do
Your strategy, documented properly: full trade log, drawdown path, and the honest failure cases. Sells better than screenshots, because skeptics can check it.
A standing view of whatever you currently rebuild by hand — positions, risk, regime, performance attribution. Like the monitor above, but for your book.
The chart pack you rebuild before every issue, regenerating itself on a schedule. Pipeline, checks, and the published output.
Things that need to keep running: watched when they break, extended 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, that's mine to absorb, not yours.
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 hold up to 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 I am
I'm James Bowmar. I work in R and Python, I trade options and prediction markets with my own money, and I've spent the last several years building market-data tooling — backtest engines, volatility and regime analytics, options pricing, and position-level P&L accounting with proper set-lot handling. The research above is representative of how I work: separate the effects that actually differ, publish the limitations, and make it reproducible.
Adjusted Close is my consulting practice, a South Carolina LLC. Research is published under my own name; work is contracted through the company.
Getting in touch
Tell me what you're rebuilding by hand and roughly how often. That's usually enough for me to say whether it's a week's work or a month's, and what it would cost — no discovery call required to get a number.
I work async by default, which tends to suit clients in other time zones. If a call is useful I'm available early mornings, lunchtime, and evenings US Eastern.