Digital asset ETF log in to Hong Kong, institutional era arrives

The Hong Kong digital asset ETF officially launched on April 15, bringing new development momentum and investment opportunities to the digital asset market. As an investment product, digital assets are rapidly developing globally.

In the past month, mainstream digital assets such as BTC and ETH have experienced significant volatility, indicating the arrival of a new bull market. This has not only attracted the attention of many investors but has also raised higher technical demands on digital currency trading platforms.

Challenges in Data Storage and Processing

The cryptocurrency trading market is very different from traditional financial markets, with around-the-clock continuous trading generating over 10TB of market data daily, and this is continuously growing. The data volume between different currencies is extremely unbalanced, with top assets accounting for most of the data volume.

In addition, the market depth data of different currencies varies greatly, ranging from a dozen levels to thousands of levels. Even more challenging is the extreme volatility of digital currency prices, which places very high demands on system latency. Even a slight delay can lead to transaction failures, causing significant losses for investors.

Advantages of Time-Series Databases

In the face of these challenges, time-series databases have become the ideal choice for financial trading platforms and quantitative investment funds for data storage and processing. Time-series databases are specifically designed to handle time-series data, enabling efficient storage and querying of massive amounts of data. Their advantage lies in the ability to quickly process a large number of data writes and query requests, meeting the real-time data requirements of the digital currency trading market.

Time series databases can effectively compress time series data, reducing storage costs. They can also efficiently query historical data and support various complex time series analyses. Thanks to these advantages, time series databases have been widely adopted by traditional financial institutions, providing a solid foundation for the secure and stable operation of platforms.

In terms of application scenarios, financial institutions can use technical analysis methods to predict market price change trends through charts, data analysis, and other means to assist trading decisions. Since technical analysis only studies prices and does not focus on asset types, it is applicable to various trading markets. After the formation of trading markets for cryptocurrencies, technical analysis has become an important aspect of cryptocurrency trading.

The following will demonstrate how to implement 9 commonly used technical indicators through high-performance real-time computing, and construct a digital asset trading dashboard using visualization technology. Through these visual dashboards, market trends can be identified, price fluctuations observed, and market structures explored, providing comprehensive references and insights for decision-making.

This demonstration uses the DolphinDB database to perform real-time calculation of indicators. DolphinDB is a real-time computing and analysis platform based on a high-performance time-series database, characterized by its lightweight, one-stop service, and powerful computing performance. Its scalable visualization capabilities allow for the easy construction of interactive dashboards.

Currently, DolphinDB has provided data computing services for over a hundred institutions in the traditional finance sector, including Guotai Junan Securities, Franklin Templeton, and China Merchants Bank. In the digital asset field, it has also served several institutions such as exchanges and market makers.

8 Common Technical Indicators

Currently, there are more than 100 technical indicators in the market, and we will select the 9 most commonly used technical indicators, including moving average price, candlestick, momentum oscillation, and Bollinger Bands.

moving average price

The sliding average price, also known as the moving average line, generates a curve by calculating the average price over a certain period of time. It is often used to identify trend reversal points, support levels, and resistance levels. DolphinDB provides over 1500 commonly used financial trading calculation functions, which can quickly compute the sliding average price indicator.

K-line chart

The K-line is one of the most important technical indicators, and multiple K-line charts connected together form the price trend line.

Relative Strength Index

The Relative Strength Index is used to measure the speed and magnitude of price changes, calculated based on the average changes in price increases and decreases over a certain period. This indicator can identify overbought and oversold trends in the trading market and is one of the most popular oscillators.

Smooth Convergence Divergence

The MACD utilizes the convergence and divergence between the short-term and long-term exponential moving averages of the closing price to make judgments on the timing of buying and selling. It is a trend-following indicator that works well in volatile market conditions.

Bollinger Bands

The Bollinger Bands display the price's volatility range and trend by plotting the middle band ( moving average ) and two standard deviation lines above and below the price chart. They are commonly used to analyze market volatility, confirm trend direction, and identify buy and sell signals.

correlation between trading pairs

You can draw the correlation between different trading pairs and analyze the degree of association between different digital assets.

real-time trading table

Real-time trading information, as the most basic data, can help assess the actual trading situation in the current trading pair, such as the sentiment of buyers and sellers, and the comparison of long and short forces.

real-time transaction volume

The real-time transaction volume indicator can indicate the activity level of the trading market and the scale of funds, helping to understand market sentiment.

Performance Advantages of Time Series Databases

Time-series databases have powerful capabilities in processing massive data, complex metric calculations, multi-table association queries, real-time data analysis, financial derivative valuation, distributed computing, and high-concurrency computing. They can provide real-time insights into data, quickly identify patterns, and assist in decision-making, becoming an important component of the new generation of data infrastructure, leading the future development of data analysis technology.

Summary

With the ETF receiving approval from regulatory authorities, digital assets officially enter the "institutional era". Time-series databases, with their high throughput, efficient querying, and good scalability, can record every transaction, every event, and every decision, constructing a complete digital asset lifecycle.

By analyzing historical data, traders can gain insights into market trends, predict future directions, and develop the most timely trading strategies, providing strong data support for the investment, trading, and management of digital assets.