Nine Strategies of Quantitative Trading: Which Ones Can Ordinary People and AI Easily Handle?

Author: KK.aWSB

First, let’s correct a misconception: when many people hear "quantitative strategies," they think of black technology that only PhDs can understand.

This impression is only half correct.

Among the nine mainstream strategies in quantitative trading, some can be easily managed by ordinary people with the help of AI, while others require billions in infrastructure just to participate. The problem is that most popular science articles either mix them all together in a confusing way or skip over the crucial question of whether "ordinary people can get involved."

In this article, I will use a simple framework—traffic lights—to go through all nine strategies: which ones are green lights that ordinary people and AI can start using right now; which ones are yellow lights that require additional investment but are worth learning; and which ones are red lights that ordinary people should give up on early—not because they aren’t smart enough, but because the entry barriers are too high.

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

First, a Rule of Thumb: Beware of "Perfect Backtests"

Before going through the nine strategies, let me give you a precautionary warning.

There is a consensus in the industry: by 2026, if any strategy shows a Sharpe ratio (a measure of how stable profits are) exceeding 3 in backtesting, your first reaction should not be joy, but skepticism—there’s a high probability that the backtesting method has issues (for example, inadvertently using future data or selecting samples that are survivors).

Only those institutional strategies that use real money, extreme leverage, and compete for speed at the millisecond level can "reasonably" achieve absurdly high numbers. If an ordinary person backtests a strategy with a Sharpe ratio of 5, it’s not a windfall; it’s a miscalculation. Remember this rule so you won’t be deceived by "beautiful backtests" when looking at each strategy.

🟢 Green Light Zone: Strategies Ordinary People + AI Can Use Now

These three strategies are simple in logic, have publicly available data, and AI can help you implement them directly. This is where beginners should start.

1. Momentum Strategy—Go with the Flow, but Use Discipline Instead of Emotion

One-sentence principle: Things that have risen a lot tend to continue rising in the short term; things that have fallen a lot tend to continue falling. This phenomenon has been repeatedly verified in academia across stock, commodity, forex, and bond markets—the reason being that information diffusion takes time, and human nature tends to follow trends.

Can ordinary people get involved? Yes, and it’s the best starting point. Essentially, this is "buying high and selling low," but the key in the quantitative version is to replace emotion with fixed rules—like "buy when the 20-day moving average crosses above the 60-day moving average," rather than relying on gut feelings.

What can AI help you with? Tell AI your momentum rules in simple terms, and it will write the backtesting code for you, allowing you 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 backfire.

2. Mean Reversion—Like a Rubber Band Bouncing Back

One-sentence principle: If prices deviate too far from historical averages, they are likely to "snap back"—like a stretched rubber band that will eventually return to its original position.

Can ordinary people get involved? Yes. This is the "opposite brother" of the momentum strategy—one bets on "trend continuation," while the other bets on "extreme correction." Both strategies can be effective in different time scales and market environments, making them a classic pairing for constructing a portfolio strategy.

What can AI help you with? Determining "what counts as too far off" requires some statistical knowledge (in layman's terms: calculating how many standard deviations the current price is above the historical average). AI can directly help you calculate and visualize this without needing to do it manually.

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

3. Breakout Strategy—Follow Up Once Key Levels Are Crossed

One-sentence principle: When prices break through a key range that has been consolidating for a long time (like a new yearly high), it often signals the start of a new trend, and following this breakout can be profitable.

Can ordinary people get involved? Yes, it has the simplest rules. "Buy when breaking above the previous high, sell when breaking below the previous low"—the logic is straightforward enough for elementary school students to understand.

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

Risk warning: The biggest pitfall is called "false breakout"—prices may break out momentarily and then immediately retreat, trapping those who jumped in. This is why breakout strategies usually require volume confirmation.

🟡 Yellow Light Zone: AI Can Significantly Lower Barriers, but More Effort Is Needed

These four strategies are a bit more complex than those in the green light zone. Ordinary people will struggle to manage them alone, but AI tools in 2026 have lowered the barriers to a level where serious learning is feasible.

4. Pairs Trading / Statistical Arbitrage—Two Assets That Usually Move Together, One Suddenly Goes Awry

One-sentence principle: Find two assets that have historically moved in sync (like Coca-Cola and Pepsi), and when their price difference suddenly widens—one rises while the other falls—simultaneously buy the cheaper one and short the expensive one, betting that their price difference will eventually revert to normal levels.

Can ordinary people get involved? A simplified version can be attempted, but caution is needed. The institutional version of statistical arbitrage involves managing hundreds of positions simultaneously, pursuing "complete market neutrality" (not fearing price movements, just profiting from price differences). Ordinary people play a simplified version—selecting a few pairs of highly correlated assets for small-scale arbitrage trading.

What can AI 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 process for you without needing to understand the underlying mathematical principles.

Reality check: This type of strategy has a "capacity ceiling"—the profits come from very small price differences, and once the capital scale increases, your own trading can erase the price difference. This is precisely the natural advantage of ordinary people: your capital is small, so you won’t encounter this issue, while institutions may be limited by their large scale.

5. Factor Investing—Labeling Stocks and Selecting Based on Labels

One-sentence principle: Group stocks by certain common characteristics (like "cheap," "high profitability," "recently performed well"), and systematically buy stocks of a certain label because historical data shows that some labels outperform the market over the long term.

Can ordinary people get involved? Yes, and it’s the most "academically rigorous" path. This approach is supported by decades of publicly available academic research, not pseudoscience.

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

Risk warning: Factors that were once effective may gradually lose their effectiveness as more people start using them (this is called "factor crowding"). Factors that work well today do not guarantee they will still work tomorrow.

6. News Sentiment Trading—Let AI Read News for You 24/7

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

Can ordinary people get involved? This is a strategy that truly opens up to ordinary people in 2026. In the past, processing vast amounts of text and judging emotional tendencies was something only teams from professional institutions could afford. Now, an open-source financial language model can run on a consumer-grade graphics card.

What can AI help you with? This is almost a native strategy for AI—allowing AI to read earnings call transcripts, regulatory documents, and news flashes in real-time to provide sentiment judgments. This was once the most expensive part of this strategy, but now it’s almost free.

Risk warning: AI’s sentiment judgments are not infallible, especially in cases where the information itself is contradictory or where "expectations have already been priced in," leading to potential misjudgments.

7. Machine Learning Strategies—Let AI Find Patterns Instead of You Setting the Rules

One-sentence principle: In the previous strategies, the rules were set by humans first, and then the computer executed them. This category reverses that—feeding vast amounts of data to the model and letting it find complex patterns that are hard for the human brain to discover.

Can ordinary people get involved? Yes, but be prepared: this is the easiest strategy to "fool yourself" among the nine. The more complex the model, the more likely it is to "memorize" non-existent patterns from historical data (the technical term is "overfitting")—backtests may look beautiful, but once you go live, the truth will be revealed.

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

Ironclad rule: The more complex the model, the stricter the "out-of-sample testing" (validating the model with new data it has never seen). If you can’t do this step, machine learning strategies may pose more risk than reward for you.

🔴 Red Light Zone: Ordinary People Should Give Up Early—It’s Not a Matter of Ability, but of Qualification

For the last two strategies, to be frank: ordinary people should not waste their time. This is not an issue of intelligence but of entry tickets.

8. Market Making—Earning the Spread as an Intermediary, but Competing Against the Fastest Institutions in the World

One-sentence principle: Simultaneously post "I’m willing to buy" and "I’m willing to sell" quotes, making money through tiny spreads, essentially providing liquidity to the market as an intermediary.

Can ordinary people get involved? No. The key to winning this game is speed and capital scale—whoever’s quoting system reacts a millisecond faster can seize the price difference first. This requires institutional-level technical investment, and ordinary people’s accounts and network delays don’t even qualify to enter.

9. High-Frequency Trading (HFT)—An Arms Race Measured in Microseconds

One-sentence principle: Capturing fleeting price differences between different trading venues within extremely short time frames (microseconds).

Can ordinary people get involved? Absolutely not, and there’s no need to feel any psychological burden. This field requires renting server rooms next to exchanges (the technical term is "co-location"), customized network hardware, and execution systems at the chip level. This is not a gap that can be solved by "learning more Python"; it’s a difference in physical distance and hardware investment. Even if you are a world-class mathematician, without that infrastructure, you still can’t get to the table.

The mindset ordinary people should have: When you see the words "high-frequency trading," just skip it. There’s no need to envy it; it’s a completely different game. Your battlefield is in the green light and yellow light zones.

A Visual Guide: Which Strategy Should You Learn Now?

If you are a complete beginner, the recommended order is:

Step 1: Choose the simplest one from the green light zone (momentum or mean reversion), and use the backtesting tools you’ve set up to run through a complete process yourself—focus not on making money, but on understanding "how a strategy transforms from an idea to a result."

Step 2: Once you’re comfortable with the green light zone, move to the yellow light zone—factor investing is the most worthwhile one to learn because it has the most solid academic foundation and the most mature AI tools.

Step 3: News sentiment trading and machine learning strategies can be considered as advanced attempts, but be sure to adhere to the ironclad rule of "if the backtested Sharpe ratio exceeds 3, you should be skeptical"—don’t let yourself be fooled.

The red light zone is not worth learning; just knowing it exists and why ordinary people can’t get involved is enough.

Three Insights for Ordinary People

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

The strategies in the red light zone are not ranked lower because they are "more advanced," but because they require resources (capital scale, hardware, speed) that ordinary people inherently lack. The first principle of selecting a strategy is not to choose the "most powerful" one, but the one that "matches your existing resources."

Second, AI is making what was once the most expensive part of "information processing" cheaper.

Among the nine strategies, the most significant changes are in "news sentiment trading" and "machine learning strategies"—they were once exclusive to institutions, but now, thanks to AI, ordinary people have gained entry qualifications for the first time. This reminds us that any field that was once "monopolized due to expensive information processing" is worth re-evaluating—AI may have already lowered the ticket price.

Third, "simple" strategies are actually a natural advantage for ordinary people.

As mentioned in the statistical arbitrage section, there’s a counterintuitive fact: institutions may find it difficult to operate certain strategies due to their large capital scale. Ordinary people, with smaller capital, can be more flexible in limited-capacity opportunities. Not every area benefits from being "bigger"; in some fields, being small can actually be an advantage.

Finally

Nine strategies, three colors.

Green light zone: You can start today. Yellow light zone: Worth serious investment in learning. Red light zone: Not your battlefield; feel no psychological burden.

True intelligence is not about learning all nine strategies, but clearly knowing where to start under which light.

Those who stubbornly pursue high-frequency trading, fantasizing about competing with institutions using just a laptop, are truly wasting their talent—because they’ve chosen the wrong path, not because they lack ability.

Start with a green light, and mastering it will be much faster than being torn between nine lights.

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