| Quantitative | Discretionary | |
|---|---|---|
| How a decision is made | A rule specified in advance, applied identically every time it triggers. | Judgement applied in the moment, informed by experience and context. |
| How it can be checked | Replayed against history, so the process can be tested separately from the outcome. | Only by outcome, which conflates a good decision with a lucky one. |
| Where it is strong | Consistency, breadth, and the absence of mood. It will not abandon the plan in March. | Context a dataset does not contain — a regime change, a story, a thing that has never happened before. |
| How it fails | The rule was fitted to the past rather than derived from it, and nobody counted the attempts. | Hindsight, overconfidence, and an inability to say afterwards what the rule even was. |
| What it demands of you | Statistical discipline, and a tolerance for killing ideas you like. | Emotional discipline, and a tolerance for being wrong in public. |
How a quantitative strategy actually gets built
Start from a reason, not a pattern
A hypothesis about why an edge should exist — someone is forced to sell, a risk is being compensated, information travels slowly. Patterns found by searching data with no prior reason are the ones that disappear, and starting from the reason is the cheapest defence there is.
Get data that reflects what you would have known
Point-in-time data, including the companies that later went bankrupt and the figures as first reported rather than as later revised. This step is unglamorous, takes longer than everything else, and is where a large share of impossible results come from.
Split the data before you look at it
Decide now which period you will not touch, and then do not touch it. Held out after you have a result is not held out; it is a period you have already read about in the news.
Write the rule and test it, costs included
Spread, commission, slippage and financing come off the top. A strategy that is excellent before costs and unprofitable after them is a common outcome, not an unlucky one.
Count everything you tried
Every variant, parameter sweep and abandoned idea. This is the number that decides how much the winner is worth, it is the one nobody records, and it cannot be reconstructed honestly afterwards.
Decide, and expect the answer to be no
Judge the result against the held-out data and the trial count together. Most ideas should die here. A research process whose output is usually a strategy is not a research process, it is a generator.
What makes a strategy quantitative?
That the decision rule is specified completely enough for someone else — or a computer — to apply it without asking you what you meant. Everything else follows from that.
Two things it does not require, both of which put people off unnecessarily. It does not require high frequency: a rule that rebalances once a month is quantitative if the rule is explicit. And it does not require complexity: many durable strategies are one or two rules that would fit on an index card. Sophistication is a property of the validation, not of the rule.
The real dividing line is falsifiability. A discretionary trade can only be judged by whether it worked, which is a terrible signal in a domain this noisy. A quantitative rule can be judged by whether it would have worked across a period nobody used to design it — a much harder test, and the only one that separates a process from a run of luck.
What do quants actually spend their time on?
Almost none of it is the part that looks like the job.
- Data. Sourcing it, cleaning it, and establishing what was actually knowable on each date. This is the largest single time cost and the least discussed.
- Killing ideas. The great majority of hypotheses do not survive, and getting to that answer quickly and honestly is the skill.
- Infrastructure. Making a test reproducible, so that a result from March can be re-run in July and give the same number.
- Arguing about whether a result is real. Which is, in the end, the whole job.
What almost nobody spends their time on is finding a strategy that looks good in a backtest. That takes an afternoon, and the fact that it takes an afternoon is precisely the problem.
Do you need a PhD to trade quantitatively?
No, and the honest version of this answer has two halves.
The mathematics needed to build a sound simple strategy is roughly first-year statistics: distributions, variance, correlation, and enough understanding of significance to be suspicious of your own results. The programming is a few hundred lines. Neither is a barrier.
What is genuinely hard is not the technique — it is the discipline that surrounds it. Holding data back when you badly want to look at it. Recording every variant you tried when the count is embarrassing. Concluding that a beautiful result is noise. Institutions employ PhDs partly for the mathematics and largely because those habits are difficult to maintain alone, against your own enthusiasm.
Backtested and hypothetical results are not indicative of future performance. Nothing here is investment advice.
Why do most quantitative strategies fail?
Not usually because the mathematics was wrong. Usually because of how the strategy was chosen.
Test enough rules against the same history and some will look excellent through chance alone. The one that looks best is the one that gets written down, and its backtest is arithmetically correct — it simply describes the winner of a competition rather than the discovery of an effect. Nothing in the output records that the competition happened.
- The search was large and uncounted. The most common cause and the least visible.
- Costs were optimistic. A high-turnover rule can be excellent gross and unprofitable net.
- The data leaked. A rule acting on information that was not available at the time produces performance no strategy could achieve.
- The effect was real and then decayed, because enough people found it. No amount of validation catches this in advance, and any tool claiming otherwise is describing something it does not have.
The first three are preventable with method. The fourth is not, and a quantitative process that pretends otherwise is selling certainty rather than practising a discipline.
Common questions
What is the difference between quantitative trading and algorithmic trading?
They overlap and are often used interchangeably. In careful use, quantitative refers to how the decision is made — from data and explicit rules — while algorithmic refers to how it is executed, by software rather than by hand. A quantitative strategy can be executed manually, and an algorithm can execute a discretionary decision.
How much money do you need to start?
Less than most people assume for the research, more than most assume for it to be worth the effort. Testing ideas costs nothing but data and time. The constraint is that fixed costs and minimum position sizes make small accounts inefficient for strategies that trade often, so higher-turnover ideas need more capital before their edge survives the friction.
Is quantitative trading profitable?
The honest answer is that this question cannot be answered in general, and any source giving you a number is describing survivors. What can be said is that the discipline is a way of finding out whether a specific idea holds up, not a category of strategy that works. Most individual ideas do not, which is the expected outcome of testing them properly.
What programming language do quants use?
Python for research and analysis in most of the industry, with C++ where execution speed matters and R still common in statistics-heavy work. The language matters far less than the data quality and the validation discipline, which is where results are actually won or lost.
Can you do quantitative trading without coding?
Increasingly yes, through tools that turn a described rule into a tested strategy. The risk is that removing the effort of building a strategy also removes the friction that limited how many you tried, and the number of attempts is the main driver of a false result. Whichever tool you use, the question to ask it is how many variants it tested.