Quant trading strategies: 4 main types and how to test each one

Contents

1. What are the main types of quant trading strategies?
2. How is a quant strategy different from ordinary technical analysis?
3. How do you turn a trading idea into testable rules?
4. How many trades do you need before a result means anything?
5. How do you backtest a quant strategy and check it out-of-sample?
6. Where do quant strategies usually fail?
7. Frequently asked questions
8. References

What are the main types of quant trading strategies?

Quant trading strategies fall into four main families: trend following, mean reversion, breakout and momentum. Each is a fixed rule that says when to enter, when to exit and how much to hold, applied to every bar by a computer. The family decides what a test must check first: profit concentration, cost per trade, false breakouts or turnover.

A quant trading strategy is a complete set of written trading rules that a computer can apply to historical and live prices without a human decision in between. Quantitative trading strategies and systematic trading strategies are two names for the same thing. The four families below cover most of what retail futures traders run, and the way a family makes its money shapes the way it fails, which is why the test for each one starts in a different place.

Family	Trend following	Mean reversion	Breakout	Momentum
What it bets on	Price keeps going in its current direction	Price returns to an average	Price leaves a range and keeps going	Recent winners keep winning
Typical trade shape	Few big wins, many small losses	Many small wins, rare big losses	Mixed	Similar to trend
Typical holding time	Days to weeks	Hours to days	Hours to days	Weeks to months
First thing to check in a test	Profit concentration	Cost per trade	False breakout rate	Turnover

Trend following and momentum are close relatives. In futures, momentum usually means holding a market long when its own return over the past weeks or months is positive and short when it is negative; Moskowitz, Ooi and Pedersen (2012) studied this rule across many futures markets and called it time series momentum. Trend following reacts to the shape of the price path, for example a moving average or a channel, and tends to trade more often. Mean reversion bets the other way: it sells strength and buys weakness and expects a return to a recent average. Breakout trades are the entry side of trend following written as one event: price leaves a defined range. The five futures algo trading strategy types map onto these families, and the S&P 500 futures strategy rule types show them on one market.

How is a quant strategy different from ordinary technical analysis?

A quant strategy uses the same indicators a chart reader uses, but every decision is written as a rule that a computer applies to every bar, so the result can be counted, repeated and tested; a discretionary chart read cannot. Technical analysis is the practice of reading past prices and volume, usually on a chart, to decide what to do next. The difference is not the tool. A 20-day moving average is the same line on both screens. The difference is whether the decision depends on the person looking at it.

Question	Chart reading	Quant rule
Who decides the entry?	The trader, looking at the chart	A written condition checked on every bar
Is the exit defined before entry?	Often not	Always
Can you count the trades over 10 years?	No, each read is different	Yes, the computer takes every signal
Can two people get the same result?	Rarely	Yes, with the same data and settings
Can it be checked on data you never saw?	Only by trading it forward	Yes, on an out-of-sample period

This is why a chart pattern you trade by eye has no backtest record, and a quant rule does. The chart reader took some signals and skipped others for reasons that are hard to write down, so there is no fair way to count the misses. The computer takes them all, including the bad ones, and that is the point: the count of bad ones is the information you need. The same logic applies to execution, which is covered in what algo trading is and what it costs; a rule that a computer can test is also a rule that a computer can trade.

How do you turn a trading idea into testable rules?

An idea becomes testable when you write the entry, the exit, the position size, the market, the bar size and the costs down before the first run, so the backtest has nothing left to interpret. Most ideas start as a sentence like "buy pullbacks in an uptrend". A backtest cannot run on that sentence. It needs six fixed decisions, in this order:

1. Write the idea in one sentence that names the family. "Buy pullbacks in an uptrend" is mean reversion inside a trend filter. Naming the family tells you which test comes first.
2. Choose the market and the bar size. Daily bars on one index future give a few trades a month; 5-minute bars give a few a day. Bar size sets the trade count and the cost burden; testing a day trading strategy after costs shows how much the intraday choice changes.
3. Write the entry rule with a parameter range. "Close above the 20-day average for 2 bars" is a rule. "Lookback 10 to 50" is a range the search can try. Fewer parameters mean fewer ways to fool yourself.
4. Write the exit rule. An exit is a condition that closes the trade: a reverse signal, a stop, a target or a time limit. Without one the strategy is not complete.
5. Fix the position size and the costs. One contract, a stated commission and a stated slippage per trade. Costs go in before the first run, not after.
6. Write the pass or fail threshold. For example: at least 200 trades, average trade after costs above three times the round-trip cost, no single quarter holding more than a third of the profit. Write it before the first run so the result cannot move the bar.

A useful check at the end: hand the written rules to someone else and ask whether they could mark every trade on a chart without asking you a question. If they would need to ask, the rule is not finished.

How many trades do you need before a result means anything?

Judge a result by the number of completed trades after costs, not by years or by net profit; a run with a few dozen trades is a hint that needs more evidence, not a finding. The reason is how an average behaves. An average built from 35 trades can move a long way if three trades change. An average built from a few hundred barely moves. Only one formula matters here:

uncertainty of the average trade = spread of single trades ÷ √N

The spread of single trades is how far one trade's result typically sits from the average (the standard deviation). N is the number of completed trades, and √N is its square root. Because a square root grows slowly, four times as many trades are needed to halve the uncertainty.

A real run shows why this matters. The run below was made with AlphaPrime (Valynox) on NQ daily bars from 2019-01-02 to 2025-06-30, long and short, one contract. It used a Standard Deviation Bands rule as its only entry and exit, a stand-in built from standard indicators to illustrate the trade-count problem, not a test of any one of the four families. The candidate shown is the one the search kept out of 1,024 it tried, chosen for its return against its drawdown.

Three things to read in this box. First, 35 trades over six and a half years: the average trade of $3,324.57 sits inside a wide band of uncertainty, so the next 35 trades could look very different. Second, the max drawdown of $110,535 is almost as large as the net profit of $116,360, which is the shape of a position held almost all the time (time in market 95.01%) through the swings of 2020 and 2022. Third, the result is the best of 1,024 candidates, picked after the fact, and that selection flatters it; the rule did not earn this money in any sense a reader can expect to repeat. What the box does show is how a report should be read: trades first, then average trade against costs, then profit.

How do you backtest a quant strategy and check it out-of-sample?

Backtest with fees, slippage and next-bar execution from the first run, then grade the frozen rules on data the search never touched, with the pass or fail rule written before you look. Quant strategy backtesting is the replay of written rules on past prices with every cost charged. It has three parts that must stay separate.

1. Backtest with real frictions. Commission and slippage per trade, in the same currency as the results, and fills on the next bar after the signal. A rule that decides on the close and fills on the same close uses a price it could not have traded at.
2. Search on the earlier part of the history only. In-sample data is the part used to choose rules and settings. Out-of-sample data is the part held back and run once with the rules frozen. A 70:30 to 80:20 time split is common; how to split in-sample and out-of-sample data covers the split date and how much out-of-sample is enough.
3. Grade the out-of-sample result against the threshold from step 6 above. Pass or fail. Changing a setting because the out-of-sample curve looked soft turns that data into in-sample data and the grade is lost.

What a good result looks like: the out-of-sample slope is shallower than the in-sample slope but still positive, the drawdown is of similar size, and most quarters contribute. What a bad result looks like: the curve goes flat or turns down at the split date, or one month holds most of the gain. Some drop is normal, because the in-sample result was the best of many tries and carries a bonus the new data does not. AlphaPrime's strategy validation lets you set these thresholds once, before the search, and stops the later checks on a candidate as soon as it fails one, so the out-of-sample data is seen by fewer candidates and stays closer to untouched.

Where do quant strategies usually fail?

The two usual failure points are keeping the best of many variants and calling it skill, and an average trade too small to survive real costs; both are visible before any live trade if you look for them. Overfitting is tuning a strategy so closely to one stretch of history that it describes the noise in that stretch rather than a repeatable pattern. It does not need many parameters. It only needs many tries and one winner kept.

Think of a thousand people each flipping a coin ten times. Someone will get nine heads. If you then hire that person as a coin expert, the next ten flips will disappoint you. A search over 1,024 candidates works the same way: the kept one is partly a real pattern and partly the luckiest draw, and the in-sample number cannot tell you the proportion. Bailey, Borwein, López de Prado and Zhu (2014) show that the expected out-of-sample result of the best backtest falls as the number of trials rises, and their later paper (2017) gives a way to estimate how likely the kept candidate is to be overfit. Harvey and Liu (2015) reach the same conclusion and propose discounting a result for the number of tests behind it. The research is summarized in why the best backtest is usually inflated.

The second failure is quieter. A strategy with a gross average trade of a few ticks looks smooth before costs because it trades often, and loses after costs for the same reason. The check is one division: average trade after costs over round-trip cost. A rule of thumb many systematic traders use is that the gross average trade should be several times the round-trip cost, so a modest rise in slippage does not erase the edge. How to set commission and slippage, and why high-frequency candidates are the first to fail, is in trading costs in backtests. Mean reversion families are the most exposed here, because their profit per trade is small by design.

Three checks catch most of both failures before anything goes live:

1. Neighbouring settings. If a 20-bar lookback is good and 18 and 22 are poor, the 20 is noise.
2. Top trades removed. Take out the five largest winners. A trend rule will shrink; it should not turn negative.
3. Doubled slippage. Rerun with twice the slippage. A rule that fails was depending on cheap fills.

A practical next step: take one idea you already trade by eye, write its six decisions from the third section, including the pass or fail threshold, run it on daily bars of one market with costs charged, and count the completed trades before you read the profit line. If the count is under a hundred, the first job is more data, not a better rule.

Frequently asked questions

What are quant trading strategies?

Quant trading strategies are complete sets of written trading rules that a computer applies to prices without a human decision in between. Most belong to one of four families: trend following, mean reversion, breakout and momentum. Because every decision is a rule, the strategy can be backtested on past prices, counted trade by trade and checked on data it has never seen.

What is the difference between trend following and momentum?

Both bet that what has been moving keeps moving. Momentum in futures usually means holding a market long when its own return over the past weeks or months is positive and short when it is negative, with holding times of weeks to months. Trend following reacts to the shape of the price path, such as a moving average crossing or a channel breakout, and tends to trade more often with shorter holds. Both make most of their profit from a few large trades, so both should be tested with the largest winners removed.

Is 35 trades enough to judge a quant strategy?

No. The uncertainty of the average trade shrinks with the square root of the trade count, so 35 trades leave the average wide open and the next 35 can look very different. Treat a result with fewer than about a hundred trades as a reason to collect more data: a longer history, a smaller bar size or a walk-forward design. Many systematic traders want several hundred out-of-sample trades before they trust a per-trade figure.

Do I need to code to test quant trading strategies?

Not necessarily. The rules must be exact enough that a computer can apply them to every bar, but several kinds of software accept rules without code, and strategy generation tools can turn an idea with parameter ranges into candidates and test them. What you cannot skip is the written part: entry, exit, size, market, bar size, costs and a pass or fail threshold set before the first run.

Why does a quant strategy that passed a backtest still fail live?

The two usual reasons are visible in the backtest if you look. First, the strategy was the best of many variants, and the best of many carries a selection bonus that new data does not repeat. Second, the average trade after costs was too small to survive slightly worse fills than the backtest assumed. Checking neighbouring settings, removing the top trades and doubling slippage before going live catches most cases of both.

References

• Bailey, D. H., Borwein, J., López de Prado, M., & Zhu, Q. J. (2014). Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance. Notices of the American Mathematical Society, 61(5), 458–471.
• Bailey, D. H., Borwein, J., López de Prado, M., & Zhu, Q. J. (2017). The Probability of Backtest Overfitting. Journal of Computational Finance, 20(4), 39–69.
• Harvey, C. R., & Liu, Y. (2015). Backtesting. The Journal of Portfolio Management, 42(1), 13–28.
• Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time Series Momentum. Journal of Financial Economics, 104(2), 228–250.