A plain-English guide
This system has two halves that work together. Every trading day it reads a wide picture of the market — prices, company fundamentals, and the news — and then decides, share by share, what to buy, hold, or trim. No hunches, no headlines-chasing. Here's the whole thing, in plain terms.
Written for anyone weighing up the strategy — prospective backers, friends and family, or the simply curious. No finance or coding background needed.
The big picture
If you read only one thing, read this. Everything else on the page is just these two halves, in more detail. Data comes in, the model reads it, trades go out — then it all happens again tomorrow.
Prices, company fundamentals, and news — gathered and cleaned automatically each morning.
A trained model reads the day's snapshot and weighs every share against the others.
Buy, hold, or trim decisions become real orders through the broker, sized with care.
Part One
What the system looks at, where it comes from, and how it becomes one clean picture the model can read.
§ 01 · The ingredients
Think of what a careful human trader would want on their desk every morning. The system gathers the same three things — just for a whole basket of US shares at once, every single day.
Daily price and trading volume for every share in the basket — the raw pulse of the market.
Earnings, analyst views, and the financial signals that say how a business is actually doing.
Economic and company news, read and scored so the mood around each share becomes a number.
§ 02 · The gathering
The point here isn't the plumbing. It's that the whole collection routine runs itself, on a schedule, and is built to keep an honest, unbroken record even when a source has an off day.
Fresh data is pulled automatically each day. Nobody has to sit and press a button for the system to stay current.
Incoming data is checked for holes and oddities, and gaps are filled sensibly so the record stays continuous.
If a source stalls, the system carries on from the last good record rather than breaking or inventing numbers.
Nothing dramatic. The system keeps the last reliable figures, flags that something was missing, and picks the source back up on the next run. A bad-data day never quietly turns into a bad-trade day.
§ 03 · The translation
Raw prices and news aren't much use to a model on their own. They get translated into a consistent set of measurements — the same ones, computed the same way, for every share, every day.
How a share has been moving and how quickly — the measures that describe direction and strength.
Company health boiled down into comparable scores, so a strong balance sheet reads clearly to the model.
The tone of the news around each share, rated on a scale, so mood becomes something measurable.
§ 04 · The handoff
Everything in Part One exists to build one thing: a single tidy table, one row per share, refreshed each day. This is the picture the model reads — and the exact point where the data half hands over to the decision half.
| Share | Trend | Model reads → |
|---|---|---|
| MU | strong ↑ | candidate |
| SCHW | flat → | watch |
| ORCL | rising ↑ | candidate |
| DELL | slipping ↓ | review |
| … | … | … |
Part Two
How the model reads that daily snapshot and turns it into real, sensibly-sized trades — with guardrails.
§ 05 · The decision-maker
The model is the decision-maker. It reads the day's snapshot and, for each share, chooses one of a few simple moves: buy, hold, or trim a position. What makes it different from a rulebook is how it learned to choose.
Instead of following instructions like "buy when X happens," the model was trained on years of market history, learning which combinations of signals tended to pay off — and which didn't.
For every share, every day, the decision is deliberately simple. Complexity lives in reading the market well — not in exotic actions.
§ 06 · The judgement
The model doesn't look at each share in isolation. It weighs them against one another, and it leans in harder when a share's signals look unusual — the way a good analyst's eye is drawn to something out of the ordinary.
Attention is shared across the whole basket, so the portfolio stays balanced rather than betting the house on one name.
When a share's signals are extreme or out of character, the model gives it more weight — catching moments that a flat, mechanical rule would miss.
§ 07 · The last mile
A decision on a screen isn't a trade. The system turns each choice into an actual order through the broker — sized sensibly, and deliberately avoiding trades so small the costs would eat them.
Every trade carries a fee. The system won't place a trade so tiny that the fee swallows the benefit — a small but real discipline that stops the strategy quietly leaking money on pointless micro-trades.
§ 08 · The guardrails
The honest answer to "how do I know it won't do something reckless?" A handful of hard limits sit around the model, independent of whatever it decides on any given day.
No single share can grow beyond a set share of the portfolio, so one bet can't sink the whole book.
The minimum-trade rule keeps the system from churning through fees on trades too small to matter.
Suspect or missing data is caught before it can drive a trade, so a data glitch never becomes a bad order.
The results
Performance is judged against a benchmark — a plain comparison of "how did the system do versus simply holding the market?" The live, verified record is where the real evidence lives.
The shape above is illustrative. What matters is the live track record, measured the same honest way: returns after real trading costs, set against a simple market benchmark rather than a flattering one.
This is a long-term, iterative project — not a finished product making promises. The strategy is refined continually, and the verified record is what speaks for it.
Live track record — coming soonCommon questions
Yes. The system gathers its own data, makes its own decisions, and places its own orders on a daily cycle. A person watches over it, but the day-to-day trading runs without manual input.
A fixed basket of large US shares. It doesn't roam the whole market hunting for tips — it works the same defined universe every day, which keeps its behaviour consistent and measurable.
It reviews the whole basket once per trading day and adjusts where the model sees reason to. It's not a high-frequency system firing thousands of trades — and the cost rule actively discourages needless churn.
The same guardrails apply: position limits cap any single exposure, and the model can trim into weakness rather than sit frozen. It's built to respond, not to guarantee it never loses — no honest system can promise that.
Consistency. It reads the same signals the same way every day, across the whole basket at once, without fatigue, mood, or the pull of a dramatic headline. It's a disciplined process, not a personality.
A quick vocabulary
The project
This is a long-running, independently built systematic trading project. The goal is straightforward: run a disciplined, fully-automated strategy well enough, and for long enough, to build a verified public track record that can stand on its own.
That record — accumulated on a recognised platform where every trade is logged and independently measured — is the point. It's what turns a private strategy into something a third party can trust and, in time, back.
The work is continual. Models are tested, refined, and replaced as the evidence dictates; nothing here is presented as finished or guaranteed.
Questions about the strategy or the track record? Reach out.