Personal software / Quantitative analysis
A private, end-to-end platform that plugs into a live brokerage, pulls market data, and runs a whole suite of analytical engines to model risk, income, and portfolio health. Every night it rebuilds itself and republishes a set of interactive dashboards, unattended; through the trading session, a live layer keeps the screens current in real time. It is strictly read-only: it analyzes and advises, and never places a trade.
Est. replacement cost
$2.7–4.4M
To rebuild it to the same level at Bay Area engineering rates.
Engineering effort
4.5–6.5 yrs
Person-years of senior / staff work. Up to ~11 to reach it from a blank page.
Put differently: a well-funded startup’s entire flagship product at its first major funding round is often this size. This one was built solo, on the side, while running against a real live account.
Lines of code are a rough ruler, but they give a feel for scale. This platform sits in genuinely serious company: past the first version of Photoshop, past the core of SQLite, within sight of the software that flew the Space Shuttle.
Figures are widely-cited approximations; sources count lines differently. Order-of-magnitude intuition, not a leaderboard. The Shuttle bar ends at the edge (~420k); the PostgreSQL bar (~1.4M) fades out because it keeps going for three more chart-widths.
Real-time market data flows in through a single hardened data layer that every tool shares.
The whole system refreshes, recomputes, and republishes on a schedule, with no one at the keyboard.
Each a focused tool, from scenario modeling to income and risk planning, sharing common building blocks.
Results render as clean web dashboards and deploy straight to the cloud, viewable anywhere.
A regression suite guards the math, so a change in one place can’t quietly break another.
Every tool is read-only. It models, ranks, and recommends. It never executes an order.
The nightly engines answer the morning’s questions. Through the trading session, a second layer keeps the key screens alive: the same analytical code, fed by streaming data, republishing continuously. And because a live screen is read to make decisions under time pressure, this layer holds itself to a rule most software never states: it must prove it is current, and say so loudly when it is not.
Prices stream on persistent subscriptions. Between broker fetches, every option is repriced from its own greeks against the moving market, at zero data cost. The contracts that actually drive decisions get real quotes re-verified on a rotating schedule, and immediately on any significant move.
A live position feed watches every account. A fill in the trading platform re-scopes the affected position within seconds: alerts drop, coverage flips, and nothing needs a restart.
A new recommendation must persist through a confirmation window and survive a real-quote check before it replaces the old one. Every change that lands is journaled: timestamp, before, after, and the price that caused it.
Every page carries the moment its data was generated and ages it in the browser once a second. A frozen dashboard announces itself as a growing number instead of sitting there looking confident.
The status banner distinguishes conditions that normally look identical: current, running behind, busy but alive, and genuinely stopped. It never accuses a live process of being dead, and never lets a dead one look alive.
Every processing cycle is time-bounded, so heavy market-data work can never silently stall the display. Tabs left in the background for hours wake correctly and catch up within a second.
The easy way to describe any position is a single figure: its worst case. That number hides more than it shows. Two positions can carry an identical worst case and behave nothing alike, one spending its loss budget only in extremes that almost never happen, the other in the ordinary middle of the range. The platform’s job is to make that difference visible before it matters, so a decision rests on the whole curve rather than the headline.
Instead of one worst-case figure, it traces loss across the full range of outcomes and shows where the damage actually concentrates: thinly across improbable extremes, or densely in the likely middle.
Every candidate is run through dozens of end states, from strong gains to deep declines, and scored against a plain buy-and-hold baseline, so the trade-off is explicit at each point rather than averaged into a single promise.
Two routes to the same endpoint can end very differently. The models account for that, and mark which figures are certain and which are estimates that move with the road taken.
A position can look alarming on paper and still never force your hand. It tests whether a decline would actually threaten the account, separately from whether it merely shows a loss.
Borrowing to invest normally cuts against you exactly when it hurts most: it deepens a fall and can force a sale at the bottom. These structures are built to do the opposite. The same amplification that turns an ordinary rise into a handsome one automatically eases off as a decline deepens, and holds firm as a rise extends. The leverage, in other words, leans the right way in both directions.
As prices drop, the built-in protection grows and the position quietly sheds exposure. Each further decline bites less than the one before, so the loss curve bends flat instead of steepening. The structure de-risks itself into a downturn rather than doubling down.
Left to run, the same position keeps its full amplified exposure as the market climbs. A market that doubles turns into far more than double on the position, with no ceiling until you choose to add one.
Normal margin magnifies losses and is forced to sell into weakness, which is how leveraged accounts get wiped out at the bottom. This carries a similar headline amplification but the reverse behaviour: shielded on the way down, rewarded on the way up.
On each holding you choose the trade: convert the upside into steady cash income now, or keep it for the climb. Income on the names that can’t travel far, room to run on the ones that can, changeable at any time. The one condition: the downside protection only holds while its insurance is kept current.
It’s the fair question, and the honest answer is that owning the index outright is a perfectly good choice. The reason to do something more involved comes down to a single decision: which market outcomes do you want to be productive?
Simple and permanent. No maintenance, no horizon, nothing anyone can take from you. It earns very little while you wait, and in a downturn the plan is to sit still until it recovers, however long that takes. You are buying the freedom to never look at the screen.
Commits less cash than owning the index outright, participates more when markets rise, and turns the ordinary market, up, flat, or slightly down, into a far larger cash return. Those ordinary outcomes are most of any given stretch, so most of the time it earns many multiples of what simply holding would.
The bill for that edge is real, and it comes due in two places: it takes sharp declines harder than plain stock does, and it runs on a finite horizon rather than forever, so a recovery has to arrive inside its window. It also asks something of the person running it, to manage it, and to stay calm when a downturn looks alarming on paper. Held stock asks none of that. That is the whole trade: the index buys peace of mind and pays little; the active approach pays far more and charges the bill in bear markets and attention. Neither is wrong. It comes down to which bill you would rather pay.
The cost and time estimates use standard software-sizing models together with fully-loaded Bay Area pay for the senior and staff engineers this kind of work requires. If anything, the line count understates the difficulty: there is almost no filler here, no framework scaffolding, no borrowed libraries, no interface padding, so each line carries more logic than a typical line of commercial software. On a difficulty-adjusted basis, the real replacement cost lands at the upper end of the range.