Author: KK.aWSB

First, let's correct a misconception: Many people think of "quantitative strategies" as some black technology that only PhDs can understand.

This impression is only half right.

Among the nine mainstream strategies in quantitative trading, some can be played by ordinary people with AI, while others require billions in infrastructure to even qualify to engage. The problem is that most popular science articles either mix them together in a confusing manner or skip the crucial question of "Can ordinary people touch this?".

In today's article, I will use a very simple framework——traffic lights——to go through all nine schools: which green lights ordinary people + AI can start with now; which yellow lights require extra investment but are worth learning; and which red lights ordinary people should give up on early—not because you aren't smart enough, but because the entry threshold is wrong.

No formulas will be discussed, only what each strategy "is really betting on".

First, a hard rule: Beware of "the perfect results from backtesting"

Before we go through the nine schools, let me give you a warning shot.

There is a consensus in the industry: In 2026, if the Sharpe ratio (a measure of "how consistent the profits are") of any strategy exceeds 3 after backtesting, your first reaction should not be ecstasy, but skepticism—there’s a high probability that the backtesting method is flawed (such as inadvertently using future data, or selecting samples from survivors).

Only those institutional strategies that use real money, extreme leverage, and compete at millisecond speed can "reasonably" score absurdly high numbers. If an ordinary person backtests a strategy that yields a Sharpe of 5, it's not a path to wealth; it's a miscalculation. Keep this in mind, so you won't be deceived by "the backtest looks beautiful" when examining each strategy below.

🟢 Green Light Zone: Ordinary people + AI can play right now

These three schools have simple logic, public data, and AI can directly assist you; this is where beginners should start.

1. Momentum Strategy——Go with the trend, but substitute discipline for emotion

One-sentence principle: Assets that have risen significantly often continue to rise in the short term; those that have fallen significantly often continue to fall. This phenomenon has been repeatedly verified in academia across stocks, commodities, forex, and bond markets—the reason being that information dissemination takes time, coupled with human nature's tendency to follow the crowd.

Can ordinary people engage: Yes, and it is the first choice for beginners. Essentially, this is "buy high, sell low," but the key in its quantitative version is to use fixed rules instead of emotions—for example, "buy when the 20-day moving average crosses above the 60-day moving average," rather than buying high based on feelings.

What AI can help you with: Just explain your momentum rules to the AI in layman's terms, and it will directly help you write runnable backtesting code to see historical performance in minutes.

Risk warning: The biggest enemy of momentum is "sharp turns"——trends can suddenly reverse without warning, and at that point, momentum strategies can face severe backlash.

2. Mean Reversion——The rubber band snaps back

One-sentence principle: If the price strays too far from the historical average, it is highly likely to be "pulled back"——like a stretched rubber band that will eventually snap back to its original position.

Can ordinary people engage: Yes. This is the "opposite sibling" of the momentum strategy——one bets on "trend continuation," the other bets on "extreme correction." Both are effective under different time scales and market environments and are classic combinations for constructing strategy portfolios.

What AI can help you with: Determining "what counts as too far off" requires some statistical background (in layman’s terms: calculating the current price and measuring how many standard deviations it is above the historical average). AI can directly help you calculate and visualize this, so you don't have to do it manually.

Risk warning: Mean reversion can be disastrous in extreme one-sided trends——"undervalued" assets may continue to fall because they have no intention of reverting.

3. Breakout Strategy——Follow after a critical level is breached

One-sentence principle: When the price breaks through a key range of long-term consolidation (for example, a new yearly high), it often indicates the start of a new trend, and following this breakout can be profitable.

Can ordinary people engage: Yes, this is the one with the simplest rules. "Buy when it breaks the previous high, sell when it falls below the previous low"—the logic is straightforward enough for elementary school students to understand.

What AI can help you with: It can help you scan a basket of stocks to automatically identify those "breaking through key levels," so you don’t have to monitor them yourself.

Risk warning: The biggest pitfall is called "false breakout"——where the price briefly breaks out and then immediately pulls back, trapping those who chased it. This is also why breakout strategies usually need to be confirmed by trading volume.

🟡 Yellow Light Zone: AI can significantly lower the threshold, but more effort is required

These four schools are more complex than the green light zone; ordinary people will struggle to do it alone, but AI tools in 2026 have lowered the threshold to a level that "serious study can allow you to engage."

4. Pairs Trading / Statistical Arbitrage——Two assets that usually move in sync, suddenly one gets distracted

One-sentence principle: Find two assets that have historically moved in high correlation (such as Coca-Cola and Pepsi), and when their price difference suddenly widens—one goes up while the other goes down—simultaneously buy the cheaper one and short the more expensive one, betting that their price difference will ultimately converge back to normal levels.

Can ordinary people engage: A simplified version can be engaged, but be cautious. Institutional statistical arbitrage manages hundreds or thousands of positions simultaneously, aiming for "complete market neutrality" (unfazed by price movements, only profiting from price differences). Ordinary individuals play a simplified version—selecting a few pairs of highly correlated assets for small-scale spread trading.

What AI can help you with: Determining "whether two assets truly have a stable statistical relationship" requires some mathematical tools (the technical term is "cointegration test"), and AI can run this calculation directly, so you don’t need to understand the underlying mathematical principles.

Reality reminder: This type of strategy has a "capacity ceiling"——the profits come from very small price differences; once the fund size increases, your own trading may actually eliminate the price difference. This is precisely the natural advantage of ordinary people: your small fund size won’t face this issue, while institutions may be limited by their greater scale.

5. Factor Investing——Label stocks and select stocks based on labels

One-sentence principle: Group stocks based on certain common characteristics and label them (such as "cheap," "high profitability," "recently performing well"), and then systematically buy stocks of certain labeled categories, as historical data show that some labels outperform the market over the long run.

Can ordinary people engage: Yes, and it is the most "academically rigorous" route. There is decades of publicly available academic research supporting this path; it's not a mystical practice.

What AI can help you with: Using open-source tools like Qlib, ordinary people can run through the complete "factor extraction → testing → portfolio" process—this was something only institutional quantitative teams could do a few years ago.

Risk warning: Factors that were once effective may gradually become ineffective due to overuse (this is called "factor crowding"). Factors that work today do not guarantee they will still work tomorrow.

6. News Sentiment Trading——Let AI read the news 24 hours a day for you

One-sentence principle: Market sentiment can be rapidly influenced by news, earnings reports, and discussions on social media. If you can understand the emotional tendencies behind this information faster and more accurately than others, you can gain an edge.

Can ordinary people engage: This is a school that has only truly opened up to ordinary people in 2026. In the past, handling vast amounts of text and determining sentiment was something only teams supported by professional institutions could afford to do. Now, a trained open-source financial language model can be run by ordinary people on a consumer-grade graphics card.

What AI can help you with: This is almost an AI-native strategy—letting AI read earnings call transcripts, regulatory documents, and news bulletins in real-time, providing sentiment judgments, which used to be the most expensive part of this school but is now almost free.

Risk warning: AI's sentiment judgments are not infallible, especially when the information itself is contradictory or when "expectations have already been priced in," which can lead to misjudgments.

7. Machine Learning Strategies——Let AI find patterns by itself instead of you setting rules for it

One-sentence principle: In the previous strategies, the rules are defined by humans first, then executed by computers. This category reverses that——it throws a massive amount of data at the model and lets it discover complex patterns that are hard for humans to find.

Can ordinary people engage: Yes, but you need to be psychologically prepared: this is the most likely area among the nine where you can "fool yourself." The more complex the model, the more likely it is to memorize patterns from historical data that don't actually exist (this is termed "overfitting")—gorgeous backtesting may appear great, but it will reveal its true nature when applied in real conditions.

What AI can help you with: Current open-source tools have standardized the process of "training a decent model," so ordinary people do not need to write code from scratch.

Hard rule: The more complex the model, the stricter the "out-of-sample testing" (validating with completely new data that the model has never seen) needs to be. If you can't conduct this step, the risks of machine learning strategies outweigh the potential rewards for you.

🔴 Red Light Zone: Ordinary people should give up early; it's not about ability but qualifications

For the last two schools, to be frank: Ordinary people shouldn't waste time. This is not an issue of intelligence; it's about admission tickets.

8. Market Making——Acting as an intermediary to earn the spread, but the competitors are the fastest institutions in the world

One-sentence principle: Simultaneously post two quotes, "I am willing to buy" and "I am willing to sell," to earn through a very small spread, essentially providing liquidity to the market as an intermediary.

Can ordinary people engage: No. The key to winning in this game is speed and capital scale——whoever's quoting system reacts a millisecond faster will be able to seize the price spread before others. This requires institutional-level technical investment; ordinary accounts and network latency don't even qualify for entry.

9. High-Frequency Trading (HFT)——An arms race measured in microseconds

One-sentence principle: Capture fleeting price differences between trading venues in extremely short time frames (at the microsecond level).

Can ordinary people engage: Absolutely not, and there’s no need to feel burdened by it. This field requires: renting data centers next to exchanges (the professional term is "colocation"), customized network hardware, and specialized execution systems at the chip level. This is not a gap that "just learning a bit of Python" can close; it's a gap caused by physical distance and hardware investment. Even if you are a world-class mathematician, without that infrastructure, you can't sit at the table.

Ordinary people's mindset should be: Upon seeing the phrase "high-frequency trading," skip it entirely—there's no need to envy; it simply is another game. Your battlefield lies in the green and yellow light zones.

A diagram to understand: Which path should you learn now

If you are a complete beginner, the suggested sequence is:

Step One: Choose the simplest one from the green light zone (momentum or mean reversion), use the backtesting tools you've set up previously, and personally run through a complete process——the focus is not on making money but understanding "how a strategy transforms from an idea into results."

Step Two: Once you've successfully navigated the green light zone, move to the yellow light zone——factor investing is the most worthwhile to learn because its academic background is the most solid, and AI tools are the most mature.

Step Three: News sentiment trading and machine learning strategies can be advanced attempts, but make sure to adhere to the hard rule "If backtesting Sharpe exceeds 3, be suspicious" to avoid fooling yourself.

Red light zone, no need to learn; just knowing it exists and understanding why ordinary people shouldn't engage is enough.

Three Insights for Ordinary People

First, "complex" does not equal "valuable"; matching your resources is what brings value.

Red light zone strategies are not ranked behind because they are "higher level," but because they require resources (capital scale, hardware, speed) that ordinary people inherently lack. The first principle in choosing a strategy is not to select the "most powerful," but the one that "matches your existing resources."

Second, what AI is doing is making "information processing," which used to be very expensive, cheap.

Among the nine schools, the most significant changes are in "news sentiment trading" and "machine learning strategies"——they used to be exclusive to institutions, but thanks to AI, ordinary people have finally gained admission qualifications. This reminds us that any field that was previously "monopolized due to high information processing costs" is worth re-examining—AI may have already lowered the ticket price.

Third, "simple" strategies are, in fact, the natural advantage of ordinary people.

The statistical arbitrage section mentioned an intuitive fact: institutions actually find it difficult to "engage" in certain strategies due to their large capital scale. Ordinary people's smaller fund size makes them more agile in limited opportunities where capacity is constrained, compared to giants. Not every environment benefits from "bigger is better"; in some fields, being smaller is precisely the advantage.

Finally

Nine schools, three colors.

Green light zone, ready to engage today. Yellow light zone, worth putting in serious study. Red light zone, not your battlefield; there's no need to feel burdened.

True wisdom is not about learning all nine schools, but clearly knowing where you should start under which light.

Those who stubbornly pursue high-frequency trading and fantasize about competing with institutions using just a laptop are truly wasting their talent——because they chose the wrong field, not because they lack ability.

Start with one green light; thoroughly explore it. It’s much faster than grappling with nine lights at once.

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