[Red Envelope] Top Yuzu Science Series --- Quantification is not mysticism, the survival rule of the quantitative era

Short-term trading is an art. It takes the ever-changing market as a canvas, sharp insight as a brush, and decisive execution as paint. True masterpieces are not born from chasing random fluctuations but are created through precise capture and resonance with the market’s core “strength”—drawing directions at emotional peaks, structuring during sector rotations, and ultimately adding the finishing touch at the moment individual stocks lead the rally. ————Reinvestment Artist [Taogu Ba]

To do a good job, one must first equip oneself properly. Whether in life or work, preparation and having the right tools increase the chances of success. The same applies to our trading market. In a quant-driven environment, to better respond to quant strategies, we must prepare in advance. Given the unique behavioral style of quant trading, we need to upgrade and maintain our arsenal. When deploying troops and arranging formations, those who understand their opponents better tend to seize the initiative. Similarly, to handle quant trading, understanding its principles and formation is essential for effective countermeasures.

Understanding Quantitative Trading

Quantitative trading, before executing systematic trades, is fundamentally no different from ordinary investors’ trading—both are based on expectations of a certain asset, leading to buy or sell decisions. Ordinary investors might base their decisions on K-line patterns, such as three consecutive days of gains, combined with experience accumulated in the market, leading to expectations of continued rise on the fourth day, prompting early buying. Or, for example, when the stock price touches the 10-day moving average, we might judge a short-term support and expect a rebound, leading to a buy. Similarly, quant trading analyzes specific factors to assess the probability of a stock’s rise or fall the next day, deciding whether to buy or sell. Some might think, “Quant and regular trading are the same, just different frequencies, so we can beat it easily.” Not quite.

How Quantitative Trading Develops Its Buying Logic

Ordinary traders accumulate experience through repeated trades, based on historical behaviors and results. Quant trading, on the other hand, automatically analyzes vast amounts of historical data to summarize relevant patterns. Out of over 5,000 stocks in the market, we might only trade three or four daily, and the stocks we observe are usually the strong and core stocks within certain sectors—roughly a dozen or two. From their intraday and K-line patterns, the experience we gather is limited. But machines don’t need rest—they can backtest every minute of every stock’s movement, 24/7, tirelessly. Even with Level 2 data, every transaction detail can be analyzed. While humans think and summarize based on logic, quant models derive rules from massive data backtests, which are objective and reliable. Some rules may seem nonsensical, like “When stock A gains 3%, stock B, unrelated, has a 90% chance to decline,” which we cannot observe with the naked eye. These are all factors driving stock price fluctuations.

Many such factors exist—market-driven and outside influences, including sudden positive or negative news, corporate announcements, or even social sentiment from forums like Baidu Tieba, where quant algorithms can automatically retrieve and analyze data. Financial data is not publicly disclosed, but you now understand the reasons behind these mechanisms. Overall, quant systems, due to their convenience and intelligence, can access far more data than ordinary investors. There are also various other influencing factors—some observable, some only detectable by machines—creating an information gap that offers profit opportunities.

How Quantitative Trading Optimizes Its Trading Rules

Although quant systems can analyze a wide array of influencing factors, they assign weights to each based on their accuracy. Think of each factor as a popular influencer in Taobao—each has different performance in various market conditions. These weights vary across market phases: during short-term emotional surges, certain factors are more reliable, so their influence is increased; during consolidation, their accuracy drops, and other factors become more relevant, prompting weight adjustments. With daily analysis of massive market data, quant models can iterate and improve themselves continuously—progressing faster than human learning.

High-Frequency Characteristics

While quant models can identify certain reliable factors from vast data, the probability of these factors appearing in intraday movements isn’t very high. To generate more profit within limited trading hours, quant strategies often increase trading frequency—triggering multiple trades to capture small gains—this is high-frequency trading. As quant models evolve through self-learning, they can artificially create certainty by setting specific trading rules that steer stock movements in predictable directions. Coupled with high-frequency execution, this allows capturing more profits within a limited time frame.

How to Recognize Quantitative Trading

Based on the previous analysis of quant’s essence, we can distill key indicators to identify it. Quantitative trading aims for high profits, certainty, and high frequency—essentially buy low and sell high repeatedly, often within short timeframes.

1. On daily K-line charts, frequent long upper or lower shadows suggest potential for price differences; prices often precisely touch the 5-day or 10-day moving averages, especially at key or round-number levels, due to the rigid nature of code-based algorithms; short-term volatile movements with certain patterns also indicate quant activity.

For example, recent hot stock Huasheng Tiancheng experienced intense fluctuations around January 12, with frequent appearances of quant funds on the龙虎榜 (list of top traders). You can also observe whether quant participation is involved by analyzing the seatings on the龙虎榜. The intraday chart shows jagged, choppy movements, indicating non-smooth trading.

2. On intraday charts, some stocks show large, straight-line jumps, but market prices are limited by price cages. If the rapid rise is purely driven by manual orders, such speed is hard to sustain. When a strong upward move occurs, it’s often quant-driven. During such moves, there are usually opposing orders or sell-offs from quant funds, causing brief pauses or oscillations—evidence of bid-ask battles.

For example, recent cases like Zhongke Environmental’s rapid late-day surge, dominated by institutional and quant buying, can be seen on龙虎榜, where the seats are mostly institutional. The price movement shows a zigzag pattern, not smooth.

3. Another pattern involves repeated, small oscillations—like zigzag movements—where the stock price fluctuates slightly and repeatedly, often driven by quant funds placing buy and sell orders to profit from small differences. Such patterns are visible in stocks hitting daily limit-ups recently, with frequent zigzag intraday movements and quant seats on龙虎榜.

4. Rapid price surges followed by quick declines, or vice versa, with movements unrelated to sector trends or other stocks, suggest quant-driven operations.

5. During key trading times, such as midday or slow late-day periods, abnormal surges or drops—unrelated to overall market liquidity—indicate quant activity. Quant funds tend to execute more gradual, controlled moves during these times.

6. On turnover rate, stocks with significantly higher turnover than peers but little price movement or stagnation often reflect internal manipulations or “cross-trading” by quant funds, leading to high turnover without real price appreciation.

7. Volume patterns that are unusually regular—such as consistent volume over multiple days or stable intraday volume—resemble mathematical patterns, indicating algorithmic control.

8. On the order book, frequent order cancellations and placements—small lots, high speed, and regular patterns—are clear signs of quant activity.

9. A common phenomenon is that placing a buy order just below the current price often results in no execution, but when you cancel, the price moves to your order level immediately. Repeating this cycle, or seeing the price jump when you sell, are typical quant manipulations targeting retail traders.

Quant models are constantly improving, utilizing massive data sources to analyze and imitate retail behaviors and other patterns, masking their presence. However, since code is ultimately code, careful analysis can often reveal telltale signs. Contributions and insights on this topic are welcome.

Quantitative Traits

Supporting the Rise
In bullish markets, quant strategies often serve as a supporting force, especially when new capital enters the market. During such phases, the influx of funds can absorb the sell pressure from quant algorithms, preventing prices from falling back and instead pushing higher, which triggers further quant buy signals. This creates a collective effort among main funds, retail, and quant funds, driving prices upward. Additionally, different quant funds have varying trading rules, some with rules for hitting daily limit-ups, which can help stocks stay at the top but may also turn into sell-off triggers later.

Supporting the Fall
In declining markets, the same logic applies in reverse. Main funds, retail, and quant funds may rush to sell, with quant algorithms executing at high speed. Different quant systems may trigger sell-offs at different times, causing a cascade effect. During such periods, quant’s core principle of high selling and low buying dominates, and market sentiment tends to be low, with rapid rotation and difficulty sustaining short-term gains.

Discipline
Quant systems are driven by emotionless mathematical formulas and code, making them highly disciplined. Once sell conditions are met, they execute immediately without hesitation. Human traders, however, may delay, hoping for higher gains, leading to slower responses. Learning from quant’s discipline is valuable—maintaining strict rules is crucial for short-term trading success.

How to Respond to Quant

First, distinguish whether the market is in an accumulation, shrinking, or expanding phase. Different strategies apply accordingly. During expansion, you can increase positions and follow short-term trades, as new capital can offset quant’s sell pressure. In accumulation or contraction phases, the market is dominated by quant, which relies on high sell-low buy tactics. Adjust your positions accordingly—favoring low buy strategies and avoiding chasing highs intraday. Different stages require different trading systems; fighting quant directly is futile. Respect the market’s style.

Second, avoid stocks heavily involved in quant activities. These are dominated by algorithms, which can control short-term movements entirely. Participating in such stocks may lead to frequent intraday volatility, making it hard to profit or even causing losses. Instead, focus on core sector stocks, popular stocks, or those with clear upward trends and sector influence. Stocks with genuine institutional or main fund participation are driven by real capital, and quant can serve as a follow-up or supporting force, helping you profit.

Manage your positions carefully—avoid over-concentrating on single stocks, especially if your mindset isn’t stable. Quant is everywhere, constantly calculating, and often exploits intraday volatility for T+0 trading. Overleveraging can cause emotional swings and lead to premature selling. For trending stocks, hold on and don’t overreact to daily fluctuations—focus on the overall trend. Quant funds have time costs; they’re not afraid of your patience, but they do care if you understand the trend and can hold.

Currently, the scale of quant funds in the market is substantial. We should view them correctly: they add liquidity and activity but also monitor retail accounts closely. Everything has pros and cons. If used wisely, quant strategies can help achieve limit-up gains. Rational analysis and proper utilization are key—complaining blindly is unproductive.

Having heard many market theories and principles, many still struggle with practical application. That’s why I share my “Strength Pyramid System,” which can help you grow. Those seeking zero-cost gains will remain on the surface, unable to grasp the core logic of profits. The purpose of sharing is to help those interested not to be lost. The market will evolve, and so will our “Strength Pyramid” system. In future, I will adapt it to different market conditions by adding new “dimensions,” making it more suitable for various market cycles.