While retail traders are still watching candlestick charts and searching for entry points, another group of market participants may already have placed, executed, and closed multiple orders within milliseconds. They are not necessarily trying to predict where BTC will be one month from now. Instead, they are focused on what may happen to the order book in the next second—or, or even the next millisecond.
In the latest episode of KTX Live(Sep 16), Mia from the KTX marketing team invited fund manager and quant expert Lurker to discuss high-frequency trading, arbitrage strategies, exchange liquidity, APIs, AI-powered quant research, and how ordinary users can begin exploring quantitative trading. Mr. Sun explained that he entered the crypto market in 2018 after gaining experience in quantitative trading in traditional markets. His team currently trades crypto assets, U.S. equities, and gold-related products. More trading discussions, Web3 industry insights, and upcoming livestreams are available through KTX Live.
1. How Is Quantitative Trading Different From Manual Trading?
When people first hear terms such as “quantitative trading,” “high-frequency trading,” and “market making,” they may picture complex algorithms, powerful servers, and large numbers of automated bots. According to Mr. Sun, however, the most basic idea behind quantitative trading is relatively straightforward: turning a trader’s logic into a system that can execute consistently.
Even traders with a well-developed strategy can still be influenced by emotion. They may refuse to cut losses when a stop-loss should be triggered, hesitate when the strategy calls for action, or close a position too early because of short-term market volatility. One of the main advantages of quantitative trading is that it converts these rules into code, allowing the same logic to be executed more consistently.
Quantitative trading also allows traders to test strategies with historical data and statistical methods. For example, a trader may develop a strategy based on candlestick patterns, moving averages, or the MACD indicator. Testing that strategy manually could take a considerable amount of time, while a historical backtest can provide a faster indication of how the strategy would have performed under past market conditions. Traders can also monitor leading assets, price movements, and broader market data through the KTX real-time crypto market page.
The difference between humans and machines becomes even more apparent in high-frequency trading. Mr. Sun provided an order-book example: suppose the best ask initially contains 20 BTC, but the available quantity quickly falls to 10 BTC, then 5 BTC, and finally 2 BTC. Before a human trader has time to respond, an automated system may already have interpreted the order-book change and decided whether to take the remaining liquidity.
The price movement captured by such a strategy may be only one basis point—or even less—but the opportunity itself may last only a few milliseconds. In other words, a retail trader may be asking, “Will BTC rise or fall over the next month?” while a high-frequency trader is asking, “What is likely to happen over the next few seconds?”
2. How Does High-Frequency Trading Generate Returns?
There is no single profit model for high-frequency trading. Different strategies rely on very different sources of return. Mr. Sun explained that common approaches include cross-exchange arbitrage, short-horizon predictive strategies, and market making.
Cross-exchange arbitrage attempts to capture temporary price differences between trading venues. Short-horizon predictive strategies examine market microstructure to estimate extremely short-term price movements. Market-making strategies seek returns by providing liquidity, managing inventory, capturing spreads, and, in some cases, receiving trading-fee rebates. Mr. Sun’s team currently focuses more heavily on statistical arbitrage.
A common question is whether cross-exchange arbitrage still exists when prices across major platforms are already closely aligned. The answer is yes—but these opportunities are becoming increasingly short-lived. Traders can compare the KTX spot market with the KTX USDT perpetual futures market to better understand the relationship between spot and derivatives prices, order books, and recent trades.
Capital does not enter every exchange at exactly the same moment. One venue may react first, while another follows shortly afterward, briefly creating a price difference. However, as more professional firms enter the market, traditional sources of alpha have become increasingly difficult to capture.
Large institutions generally have stronger infrastructure, lower trading fees, and faster execution systems. As a result, the returns available from relatively straightforward, low-risk cross-exchange arbitrage are being compressed. Mr. Sun therefore believes that, instead of competing directly with large institutions on speed, smaller traders may be better served by searching for specialized, less crowded sources of alpha.
3. Speed Matters, but Strategy May Matter More
The defining characteristic of “high-frequency trading” appears to be speed. Milliseconds, microseconds, and even lower latency can be critical for certain strategies. However, Mr. Sun believes that, for most smaller teams, competing purely on speed offers limited value relative to its cost.
Large institutions may invest heavily in data centers, networking, and execution infrastructure to reduce latency. Smaller traders are unlikely to gain a sustainable advantage through hardware competition alone. What remains more important is strategy and alpha. Identifying a signal that others have overlooked may be more valuable than reducing server latency by a few additional milliseconds.
High-frequency strategies also tend to have short lifespans. A strategy that works today may not continue generating returns tomorrow. As participation increases, market structure evolves, and similar strategies enter the market, some forms of alpha gradually disappear. Quantitative teams must therefore continue researching, testing, and iterating.
Developers interested in automated trading can review the KTX API documentation. It covers market data, order books, candlestick data, trade records, account information, order creation, order cancellation, and other interfaces that can support market research and automated trading tools.
4. What Do Professional Quant Teams Look for in an Exchange?
Retail users may select an exchange based on its brand, number of listed assets, rankings, or promotional campaigns. Professional quantitative teams tend to use a different set of criteria.
Mr. Sun summarized the three main factors as API and matching-engine stability, genuine liquidity, and trading costs. Stability can be even more important than speed. For a high-frequency strategy, one of the greatest risks is not necessarily making the wrong market prediction—it. It is being unable to place an order when needed or cancel an order when market conditions change.
This risk becomes especially serious during extreme market conditions. When large numbers of users attempt to trade at the same time, an overloaded API may prevent a quantitative system from checking positions, submitting orders, or cancelling existing orders. Even if a strategy is profitable over the long term, one major system failure could erase a substantial amount of previously accumulated profit.
Mr. Sun explained that when evaluating a new exchange, he first examines the stability of its API and matching engine, then its liquidity, and finally its trading costs. “Strong liquidity” cannot be judged solely by the amount displayed in the order book. An order book showing 10 BTC at a particular level does not necessarily mean that a trader can execute the full 10 BTC at the displayed price.
Professional teams generally begin with a small amount of capital and observe several levels of the order book before evaluating actual slippage and market impact. Depending on the strategy, a high-frequency test may begin with only a few hundred USDT, while other strategies may require USDT 10,000–20,000. Teams typically begin with limited capital and increase their allocation only after reviewing live execution results.
Trading fees must also be included in both backtests and live-performance assessments. The applicable rates can be reviewed on the KTX fee rates page.
5. Why Does a Healthy Exchange Need Quantitative Trading Teams?
To ordinary users, high-frequency bots may appear to be programs that simply buy and sell continuously. From a market-structure perspective, however, different types of quantitative teams are important components of a healthy liquidity ecosystem.
Mr. Sun believes that a mature trading market normally needs multiple strategies operating at the same time, including market-making, trend-following, and arbitrage strategies. If every participant follows the same strategy, traders may concentrate on one side of the market, leaving insufficient counterparties on the other side.
When market makers, arbitrageurs, trend-following strategies, and retail users participate together, trading activity becomes more balanced, while price discovery and market depth become easier to maintain. Quantitative teams and exchanges therefore have a mutually dependent relationship.
When personally evaluating an exchange, Mr. Sun focuses primarily on asset security, market depth, and API stability. He may initially allocate approximately 1,000 USDT and allow the system to operate through both normal and extreme market conditions before deciding whether to increase the allocation.
In addition to liquidity and technical stability, asset transparency can be another consideration when assessing a trading venue. Users can visit the KTX Proof of Reserves page to review information about reserve ratios, asset-storage practices, and related verification mechanisms.
6. Extreme Markets Offer the Greatest Opportunities and the Greatest Risks
Crypto markets operate 24 hours a day, seven days a week, and large price movements or rapid liquidations are not uncommon. For high-frequency teams, these conditions have two sides: greater volatility may create more short-term opportunities, but it also introduces greater risk.
According to Mr. Sun, API instability remains one of the team’s biggest concerns during extreme market conditions. If the system cannot place orders, cancel orders, or retrieve position information, the trading strategy may effectively lose control of the account.
A high-frequency strategy designed for long-term operation must therefore do more than generate profits under normal conditions. It must also account for network latency, extreme volatility, and exchange-level disruptions. Otherwise, a strategy that produces stable daily returns may still lose a significant portion of its accumulated profit during a single extreme event.
Before trading derivatives, users should understand order types, margin requirements, liquidation mechanisms, and other relevant rules. Futures trading involves substantial risk, and strategies should be backtested and evaluated with limited capital before larger amounts are deployed.
7. AI Has Already Changed Quantitative Trading
When asked whether AI will change quantitative trading, Mr. Sun gave a direct answer: the question is no longer whether AI will change quant trading already has.
Tools such as Claude and ChatGPT are now widely used for software development, data analysis, factor research, and strategy backtesting. In the past, researching an alpha factor could require writing code, constructing a framework, and processing data manually. The complete process might take one or two weeks.
Today, once researchers form an idea, they can use AI to develop an initial implementation much more quickly and then test it against historical data to determine whether the strategy has potential or consistently produces negative returns. This can substantially reduce the research cycle.
AI is also valuable as a learning tool. A beginner interested in building an order-book model may not know where to start. AI can help identify research directions, organize academic papers, explain model structures, and support the gradual reproduction and testing of existing methods. This is lowering the entry barrier to both programming and quantitative research.
However, AI-generated code or strategies should not be deployed directly into live trading without review. Developers must still examine data quality, API requirements, model parameters, execution logic, and risk controls. The English KTX API Developer Guide can be used as a reference when testing market-data and trading integrations.
8. Order Books Are Becoming an Important Source of High-Frequency Alpha
The easiest way to understand traditional arbitrage is to compare prices across two exchanges. When the price difference is larger than the combined trading fees and market-impact costs, a trader may buy on one venue and sell on another, attempting to profit as the prices converge.
The problem is that these opportunities have become extremely thin. As a result, some high-frequency teams are now studying what happens before the visible price changes.
For example, the rapid disappearance of sell-side liquidity or a sudden shift in aggressive order flow may produce a signal before the market price moves. Professional quantitative teams are therefore no longer asking only, “What is the current price?” They are also asking, “Why might the price change in the next second?”
Mr. Sun described this type of market-microstructure research as an important source of alpha currently being studied by many high-frequency firms.
KTX trading interfaces provide order-book and recent-trade information. Traders can observe changes through the KTX BTC/USDT spot trading page or the KTX BTCUSDT perpetual futures page. Developers can also use the API to retrieve order-book, candlestick, ticker, and trade data for further market-microstructure research.
9. Can Ordinary Users Still Begin Quantitative Trading?
The answer is yes, and the entry barrier is considerably lower than it once was.
Mr. Sun believes that beginning quantitative trading no longer requires someone to first become an expert programmer. A basic understanding of programming, mathematics, and algorithms—combined with modern AI tools—can be enough to begin learning and experimenting.
He suggested a clear path for ordinary users. First, learn how the market works, including basic concepts such as futures contracts and funding rates. Next, acquire the minimum programming knowledge needed to work with data. AI tools can then assist with learning, analysis, and code development.
After that, users can place strategies they have designed, discovered, or studied into a historical backtesting environment. If the results suggest room for improvement, the strategy can be adjusted and tested again. Only after this process should a user consider moving to live testing with a small amount of capital.
Users who do not know where to begin can start by reproducing classic strategies, such as Turtle Trading, moving-average crossovers, or basic cross-market arbitrage. AI can help construct the initial backtest, but users should still understand the parameters, assumptions, and optimization process before attempting to develop a personal trading system.
Users who want to learn more about crypto markets, trading products, and basic strategies can visit KTX Learn. Before entering live markets, they can also monitor price action and market conditions through the KTX Markets page and build a trading plan that reflects their experience and risk tolerance.
10. The Future of High-Frequency Trading: More Difficult, but Opportunities Will Remain
During the final rapid-fire segment, Mr. Sun provided several direct answers. For high-frequency trading, he currently prefers centralized exchanges. Between speed and strategy, he considers strategy more important. Among common approaches, arbitrage is relatively easier to understand and may provide a clearer starting point for beginners.
If he could use only one metric to evaluate execution quality on an exchange, he would examine market impact—the difference between the expected execution price and the price ultimately received when a large order is completed.
Will high-frequency trading become more profitable over the next three years? Mr. Sun’s view is that competition will become increasingly difficult, but new specialized opportunities will continue to emerge.
A source of alpha that works this year may gradually disappear next year. At the same time, changes in market structure can generate entirely new opportunities. The sustainable competitive advantage of a high-frequency team is therefore not necessarily the discovery of one strategy that “works forever,” but the ability to continue researching, validating, and iterating.
For teams exploring automated trading, stable data interfaces, genuine market depth, reasonable transaction costs, and comprehensive risk controls are all essential. Developers can review the KTX API documentation and use limited-capital testing to evaluate how a strategy performs under real execution conditions.
Conclusion: Retail Traders See Price; Professional Traders See Market Structure
One of the clearest distinctions highlighted during this KTX Live session was the difference in how market participants view trading. Retail users often focus on the BTC price, candlestick charts, and whether the market is rising or falling. Professional quantitative traders may instead be examining the order book, liquidity, market impact, API latency, matching-engine performance, and the broader microstructure behind each price movement.
For a trading platform, a mature ecosystem is not determined solely by the number of active users. Its ability to provide a stable, efficient, and genuinely liquid environment for retail traders, professional quantitative teams, arbitrageurs, and market makers also plays an important role in overall market quality.
As discussed during this episode of KTX Live, competition in high-frequency trading is becoming increasingly intense. At the same time, AI, changing market structures, and emerging trading platforms continue to create new areas for research. Execution may become faster and alpha may become shorter-lived, but strategy, disciplined implementation, and continuous iteration remain the foundations of long-term competitiveness.
Visit the KTX official website to explore crypto markets, spot trading, perpetual futures, and other products. For additional educational content and livestream information, follow KTX Learn and KTX Live.
This article was adapted from the KTX Live session “Retail Traders Are Still Watching Charts—Has Quant Already Made Its Move? Inside Crypto High-Frequency Trading.” The guest’s comments reflect personal experience and opinions and do not constitute investment advice. Crypto assets are highly volatile. Please make decisions based on your own experience, financial circumstances, and risk tolerance.