Digital asset ETF opens the institutional era, data analysis becomes the key to competition

The launch of the Hong Kong digital asset ETF brings new development momentum and investment opportunities to the digital asset market. Over the past month, mainstream digital assets such as Bitcoin and Ethereum have experienced significant volatility, indicating that a new bull market may be about to start. This has not only attracted the attention of investors but also raised higher technical requirements for trading platforms.

The cryptocurrency trading market has unique characteristics: 24/7 trading, massive data, and extreme price volatility. Over 10TB of market data is generated daily, and it continues to grow. The data volume for different cryptocurrencies is extremely unbalanced, with top assets accounting for the majority. There are significant differences in market depth, ranging from a dozen levels to thousands. Price fluctuations are rapid, and there are very high requirements for system latency.

In the face of these challenges, time-series databases have become the ideal solution. They are specifically designed to handle time-series data, capable of efficiently storing and querying massive amounts of data, quickly processing a large number of read and write requests to meet real-time demands. Time-series databases can also effectively compress data to reduce storage costs and support complex time-series analysis. They are currently widely used in the traditional financial sector, providing the foundation for stable platform operations.

In cryptocurrency trading, technical analysis is an important part. By analyzing charts and data to predict price trends, it aids trading decisions. This article will demonstrate how to utilize high-performance real-time computing to implement 9 commonly used technical indicators and build a visual trading dashboard. These indicators can help identify market trends, observe price fluctuations, explore market structures, and provide comprehensive references for decision-making.

Common technical indicators include:

1. Moving Average Price ( MA ): calculates the average price over a certain period, used to identify trend reversal points, support levels, and resistance levels.

2. K-line chart: shows the opening, closing, highest, and lowest prices, reflecting price trends.

3. Relative Strength Index ( RSI ): Measures the speed and magnitude of price movements to identify overbought and oversold trends.

4. Smooth Moving Average ( MACD ): Use short-term and long-term moving averages to determine buying and selling opportunities, applicable to volatile markets.

5. Bollinger Bands: Show the price volatility range and trends through the middle band and the upper and lower standard deviation lines.

6. Correlation of Trading Pairs: Analyze the degree of correlation between price fluctuations of different trading pairs.

7. Real-time trading table: displays the latest transaction data, reflecting the market situation.

8. Real-time transaction volume: statistics by buying and selling direction, indicating market activity and capital scale.

These indicators can be efficiently calculated and visualized through time-series databases, helping investors grasp market dynamics comprehensively. As digital assets enter the institutional era, time-series databases will play an important role in recording transactions, analyzing historical data, and gaining insights into market trends, providing data support for investment decisions. Institutions can use these tools to develop more timely trading strategies and gain an advantage in competition.