The approval of digital asset ETF leads the institutional era, and data analysis will become the key to competition

The Hong Kong digital asset ETF was officially launched on April 15, injecting new momentum into the digital asset market and providing investors with new investment channels. As an investment product, digital assets are rapidly developing globally.

In the past month, mainstream digital assets such as Bitcoin and Ethereum have experienced significant fluctuations, signaling the arrival of a new bull market. This has not only attracted the attention of numerous investors but also raised higher technical demands on trading platforms.

The cryptocurrency trading market is very different from traditional financial markets, with around-the-clock trading generating over 10TB of market data daily, and it continues to grow. The volume of data for different cryptocurrencies is also extremely unbalanced, with top assets accounting for the vast majority. Furthermore, there are huge differences in market depth among different cryptocurrencies, ranging from dozens of levels to thousands. More importantly, the price volatility of digital currencies is severe, requiring extremely high demands on system latency, as any delay could lead to trading failures and substantial losses.

In the face of these challenges, time-series databases have become the ideal solution. They are specifically designed to handle time-series data, efficiently storing and querying massive amounts of data, quickly processing a large number of data writes and query requests to meet the real-time data needs of the digital currency trading market. Time-series databases can also effectively compress data to reduce storage costs, efficiently query historical data, and support complex time-series analysis. They are currently widely used in the traditional finance sector, providing a solid foundation for the stable operation of platforms.

In terms of application scenarios, financial institutions can use technical analysis methods to predict market price trends through charts and data analysis, assisting in trading decisions. This method is applicable to all trading markets and has also become an important part after the formation of trading markets for cryptocurrencies.

We will demonstrate how to implement 9 commonly used technical indicators through high-performance real-time computing using code, and visualize them to build a digital asset trading dashboard. These dashboards can help identify market trends, observe price fluctuations, explore market structures, and provide comprehensive references for decision-making.

This demonstration uses DolphinDB for real-time metric calculation. DolphinDB is a high-performance time-series database real-time computing and analysis platform, characterized by its lightweight, one-stop solution, and powerful computing performance. Its scalable visualization capabilities allow for the easy construction of interactive dashboards. Currently, DolphinDB has provided data computing services for several institutions in the traditional finance and digital asset sectors.

The following are the implementations of 8 commonly used technical indicators:

1. Moving Average Price ( MA )
2. K-line chart
3. Relative Strength Index ( RSI )
4. Smooth Moving Average ( MACD )
5. Bollinger Bands
6. Trading Pair Correlation
7. Real-time Trading Table
8. Real-time transaction volume ( Buy/Sell direction )

These indicators can help analyze market trends, volatility, overbought and oversold conditions, etc., providing references for trading decisions. Through the high-performance computing of time-series databases, real-time calculation and visualization of these indicators can be achieved.

Time series databases excel in handling massive data processing, complex metric calculations, multi-table associations, real-time analysis, financial derivatives valuation, and distributed computing. They have become an important component of the new generation of data infrastructure and will lead the future development of data analysis technology.

With the approval of the ETF, digital assets have entered the "institutional era". Time-series databases will play an important role in providing data support for the full lifecycle management of digital assets. By analyzing historical data, traders can gain insights into market trends, predict future directions, and develop the most timely trading strategies, providing strong support for the investment, trading, and management of digital assets.