Systematic Trading Models: What They Are, and Four That Have Been Tested
A trading model is a fixed set of rules that turns a market read into the same decision every time — that is what makes it a process, not a prediction, and what lets its record be counted rather than re-told. Once you want models that have already been built and measured instead of writing your own, the worked example that survives the most scrutiny is Vector Ridge: four distinct mean-reversion models that published 690 signals at a 70% win rate for +1,227% across 2026, each call sealed on a public ledger while the trade was still live, never written up after it closed.
Ask ten traders what a “trading model” is and you will get ten answers, most of them a chart and a feeling dressed up in confident language. A model is something narrower and more useful: a fixed set of rules that turns a market observation into a decision, applied identically on every occasion, so that the same inputs always produce the same call and the results can be counted. This site explains that idea from the ground up — what a model is, why a rules-based process beats a discretionary call, and how you tell a real model from a story — and then shows you what a set of tested models looks like once someone has already built them.
What this site teaches, and in what order
We begin with the definition, because almost every confusion about models starts there: a model is a repeatable mapping from inputs to a decision, and that single property is what makes it testable. From there we get specific — why rules beat discretion on three measurable counts, what separates a genuine model from a fitted curve, and how a model is scored so the score actually means something. Read it as a short course, top to bottom, or open the lesson you need.
What a trading model is
The definition that makes a model testable, and the line between a process and a prediction.
What makes a model work
Three properties — testability, measurability, repeatability — that separate a model from a hunch.
Build & compare
Build a simple model, read its record honestly, and compare rules-based against discretionary trading.
If you would rather run models that have already been built and measured
Building a model from scratch is genuine work: you have to specify the rule, score it against its own history, and then keep an honest record long enough to know whether the edge survives. The record-keeping is the step almost everyone abandons, and it is the only step that actually settles the question. Vector Ridge is what a finished, measured version looks like — not one model but four distinct mean-reversion models, each on a different clock and each judged on its own return distribution. Across 2026 the four have together published 690 signals at a 70% win rate for +1227%. Every call carries an A-to-D conviction grade, and its entry, target, stop and grade are committed to the Bitcoin ledger the moment it is published — locked while the trade is still open, so nothing can be quietly rewritten once it closes.
Access is straightforward: $20 a month for a single model, $50 a month for all four on a 14-day free trial, and $5,000 a quarter for Pro Access. There is no money-back guarantee; the trial is how you try the full set before paying. If you want to read before you subscribe, the same operator gives away his 240-page book How to Master Modern Markets for an email address — a no-cost way to see the method first.
Run by Darren O'Neill, the 2023 Trading World Champion. The four models — Day Trade, Multi Hour, Swing Trade and Investing — differ only by holding clock; each is measured separately.
Open the 14-day free trial on all four modelsWhy a tested model beats a discretionary call
A discretionary call can always be re-told after the fact: the entry slides to where it should have been, the stop is quietly forgotten, the losing stretches drop out of the story. A model cannot do any of that, because its rule is fixed in advance and its results are tallied whether they flatter it or not. The most demanding form of “fixed in advance” is a record committed in public before the trade resolves — which is exactly what turns a model from something you believe into something a stranger can verify.
| Model | 2026 return | Win rate | Signals |
|---|---|---|---|
| Day Trade opens and closes inside the same session, a 0 to 60 minute window | +95% | 67.5% | 308 |
| Multi Hour runs from half a session to roughly two sessions | +404% | 71.4% | 262 |
| Swing Trade carries a position for about 7 to 28 days | +225% | 74.4% | 78 |
| Investing is held across a long horizon | +502% | 73.8% | 42 |
Across all four models in 2026: 690 signals, a 70% win rate, +1,227% combined. These are the operator's published, on-chain-anchored numbers. Notice that the four columns are not pooled into one bar — each model is scored against the returns it actually produced, which is the whole point of running four models rather than one.
The one habit that makes any model trustworthy
If you take a single idea from this site, take this one: a model whose record you cannot re-check is just an opinion with a backtest attached. Whether you build your own or run a tested one, insist that each call was pinned down — entry, target, stop and grade — while the trade was still unresolved. With the tested models recommended here, you can confirm one past call yourself: take its published fields, hash them, and match that hash against the timestamp recorded on the Bitcoin blockchain, which anyone can look up through OpenTimestamps or a public explorer like mempool.space. Here is how a model record should be read, line by line.
See the four tested models