Retail traders' quantitative trading misconceptions: From hot topics to rational thinking

Recently, discussions about quantitative trading are everywhere in the community. It seems profound and mysterious, but in reality, many people can’t clearly distinguish between true quantitative trading and those “simplified automated trading tools” on the market. Today, let’s clarify the logic behind this.

Why are some people selling quantitative systems, yet the founders of real quantitative companies are not coming out to sell?

This is the most illustrative point. Industry giants like Fantom Quant and Liang Wenfeng only provide asset management services externally; they would never package their systems as software to sell to retail investors. The reason is simple: if a strategy can truly generate stable profits, why would the creator bother to sell it? This reveals a harsh reality—that most of the so-called “quantitative trading systems” being sold on the market may not actually be trustworthy.

In contrast, the built-in trading bots and strategy tools on Binance are at least backed by the platform, surpassing many so-called “market quantitative systems.”

What does true quantitative trading look like?

From an academic perspective, quantitative trading (Quantitative Trading) involves using mathematical models, statistical analysis, and computer algorithms to automatically identify trading opportunities, generate signals, and execute buy and sell orders based on historical data and real-time market information (prices, trading volume, economic indicators, etc.).

In plain language: it’s driven by data and formulas, fully automated, with no human emotional involvement.

Real institutional-level quantitative trading operates through several key steps:

Finding patterns in massive data — Hidden within historical data are various “high probability” events, such as price trends or abnormal fluctuations under certain conditions.

Building mathematical models — Using tools like statistics and machine learning to construct predictive models that attempt to capture the intrinsic logic of market behavior.

Backtesting and validation — Testing strategies against historical data to evaluate returns, risks, and stability, filtering out many ineffective strategies.

Strict algorithm execution — Avoiding human subjective judgment and emotional interference (greed, fear), acting strictly according to rules.

Built-in risk control — Position limits, stop-loss mechanisms, etc., to ensure losses in a single trade do not spiral out of control.

However, this system also has obvious limitations: models trained on historical data tend to fail during black swan events; over-optimization (“overfitting”) can produce impressive historical performance but perform poorly in real-time; market changes often outpace model adaptations.

Retail traders’ “quantitative trading” versus institutional-level systems are completely different

This is a common confusion. The so-called “quantitative trading” accessible to retail investors is actually a simplified version of automated trading tools—using ready-made platforms, software, or robots, based on simple rules (moving average crossovers, grid trading, time triggers, etc.) to generate signals or directly place orders. Some advanced ones allow parameter adjustments to optimize strategies.

In essence, this automates manual trading processes—letting algorithms perform “buy low, sell high” or high-frequency operations—mainly to avoid emotional interference. Strictly speaking, calling it “automated trading tools” is more accurate than “quantitative trading.”

Reality in mature markets

In global stock, futures, and forex markets, over 70% of trading volume is now driven by algorithms. Institutions and hedge funds have long regarded quantitative strategies as core competitive advantages. While retail traders can also get started with API tools and trading platforms, the barrier is high—requiring programming skills, mathematical foundation, and ongoing optimization and backtesting. Success often depends less on how advanced the tools are and more on strategy quality, data integrity, discipline in execution, and risk management.

Why are most products on the market scams?

This is a simple logical chain. If retail traders could easily achieve stable profits using ready-made “quantitative tools,” why do over 85% of retail traders still lose money? The answer is that most tools are more marketing hype than actual effectiveness.

Genuine reliable options are actually few. Binance’s built-in trading bots are relatively trustworthy because they are regulated by the platform, not products cobbled together by small teams. In contrast, those independently developed systems claiming to “always profit” are often just selling illusions of hope.

Final wake-up call

Don’t expect any simple method to make you rich quickly. If such a method truly existed, its inventor would not be selling it—they would quietly use it to make money instead of earning a little by selling software.

Blockchain and crypto markets are indeed full of opportunities, but opportunities never favor impatient people. Patience, rationality, continuous learning, and risk control are the real core. Quantitative trading is the same—rather than blindly following trends and buying various systems, it’s better to deeply understand market logic and gradually accumulate knowledge and experience.

Wealth accumulation is the result of compound interest, not a night’s fantasy. Stay focused, take one step at a time, and you will go further.