A couple of days ago, I had dinner with some friends in the crypto circle, and the topic turned to quantitative trading. One of the guys complained that now the market is flooded with all kinds of "quantitative" buzzwords, and even newbies who just entered the scene less than a week ago are starting to research it, constantly talking about "algorithms" and "backtesting." He said hearing it all the time gets a bit annoying because most people actually haven't really understood what true quantification means.

Actually, this is a pretty common issue. Let’s first clarify the concept of quantitative trading—don’t be intimidated by the jargon: Simply put, it’s a method that uses mathematical models, statistical analysis, and programming code to replace human decision-making in executing trades. The basic idea is straightforward—"exploiting profitable probability patterns from historical data." For example, by studying past price fluctuations and volume data, identifying potential patterns, validating whether these patterns are effective through models, and then letting programs trade automatically. This helps avoid the interference of human emotions like greed and fear.

When I first got into quant trading myself, I spent over half a month just on data cleaning. You should know that true institutional-level quant trading not only requires market trading data but also integrates macroeconomic indicators, industry trends, and other multi-faceted information. The models involve advanced knowledge like probability theory and machine learning. Most importantly, they undergo repeated backtesting to ensure stable performance across various market conditions.

Leading quant firms like Fantom and Jiukun usually have teams composed of top university graduates, and they invest astronomical amounts annually in data infrastructure and technological R&D. These institutions have comprehensive risk control systems, diverse data sources, and years of optimization and accumulation.

In contrast, retail traders’ access to "quantitative" tools and strategies? Honestly, it’s mostly just automated trading at best. What’s the difference? Institutional quant is systematic, multi-dimensional, and rigorously validated; retail quant often just involves stacking a few simple technical indicators or copying a ready-made trading logic. When market conditions change, these simple strategies can quickly become ineffective.

A more practical point: institutions have enough capital, data access, and technical resources. They can withstand the costs of backtesting failures and model adjustments. Retail traders, on the other hand, are often attracted by some influencer’s promotion, spend a little money on a "quant tool," watch a few tutorial videos, and think they’ve mastered the essence of quant trading. Only to realize they’re losing money in the end.

So, for friends who are new to the scene, instead of being dazzled by all the high-level quant concepts, it’s better to solidify your basic knowledge and understand how the market really works. If you truly want to do quant trading, either have professional technical and data support or start with simple automated strategies and experiment gradually. Don’t be fooled by the word "quantitative"—many times, it’s just a marketing gimmick.