Recently, I’ve noticed more and more people around me discussing quantitative trading, but honestly, there’s a lot going on beneath the surface. Most of what so-called quantitative trading looks like for retail investors is really just buying someone else’s app or code scripts—and these kinds of things are full of pitfalls. Just think about it: if someone really made a fortune through quantitative trading, even managed to achieve unending profits, why would they come and sell it to you? This logic holds true anywhere.

So what is real quantitative trading? In simple terms, it means using mathematical models and algorithms to drive trading strategies—so computers can automatically recognize market opportunities, generate signals, and execute buy and sell orders. The whole process is entirely data-driven and automated. It may sound high-end, but at its core it’s about using historical data to find patterns, building statistical models, validating strategy effectiveness through backtesting, and then having the program carry it out rigorously—this helps avoid interference from human emotions.

Look at those truly large quant firms—take the well-known domestic firm Fantasia Quant in China, for example. Has its founder, Liang Wenfeng, ever gone out selling any quantitative software? No. What they offer externally is asset management; they earn management fees, and they don’t sell so-called quant systems. That’s the difference.

As for the quantitative trading everyone is talking about in the market right now—let’s be real—most of it has already been simplified into automated trading tools. Basically, it means using off-the-shelf platforms or software, setting a few simple rules (like moving average crossovers, price grids, and other basic indicators), and letting bots automatically generate trading signals. The entry barrier really is low, but that’s still completely different from real, institution-level quantitative trading. Some people who are a bit more advanced can adjust strategy parameters themselves, but putting it bluntly, it’s still just fine-tuning on top of tools built into a major exchange, rather than building complex mathematical models from scratch.

Quantitative trading’s share in the global market is so high (in mature markets, more than 70% of trading is driven by programs) because its advantages are obvious—objective, consistent, and able to process massive amounts of data efficiently. But there are also problems: models are based on historical data, so if a black swan event happens, they may stop working; and over-optimization can lead to overfitting—no matter how good the historical performance looked, real-time trading can still lose money.

As for whether retail investors can get involved in quantitative trading—the answer is yes, but you need a programming foundation and mathematical ability. With API tools and automation platforms available now, ordinary people can participate too. The key is choosing reliable tools. Compared with blindly fumbling around on your own, choosing certain built-in automation features from major exchanges can help you avoid emotional problems in your trading.

But I still want to remind you—don’t keep trying to take shortcuts. If there really were a method so simple that it could easily make you rich, the person who created it would never put it up for sale. There are certainly opportunities in blockchain, but the key to success is patience and discipline, not expecting some quant system to make you rich overnight. Real wealth comes from sustained accumulation—step by step—which is better than anything else.