Digital asset ETF ignites bull run, time-series database empowers trading platform technology upgrade.

The strong debut of digital asset ETFs has injected new momentum into the market, bringing new opportunities for investors. Recently, mainstream digital assets such as BTC and ETH have experienced significant fluctuations, marking the beginning of a new bull run. This has not only attracted the attention of investors but also raised higher technical demands on trading platforms.

The digital currency trading market has its unique characteristics. The amount of market data generated daily exceeds 10TB and continues to grow, with a significant imbalance in data across different currencies. The depth of the order book varies greatly, ranging from dozens of levels to thousands. Furthermore, price fluctuations are severe, 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, efficiently storing and querying massive amounts of data to meet real-time demands. They can also effectively compress data to reduce costs and support complex time-series analysis. Therefore, they have been widely used in the traditional financial sector.

In digital asset trading, technical analysis is an important part. It predicts price trends through charts and data analysis, assisting in trading decisions. The following will showcase the real-time calculation and visualization of 9 commonly used technical indicators, constructing a digital currency trading dashboard.

The sliding average price is a commonly used indicator for identifying trend reversal points, support levels, and resistance levels. It generates a curve by calculating the average price over a certain period of time.

The K-line chart is one of the most important technical indicators, and multiple K-lines connect to form a price trend line. It is possible to generate second-level K-line charts by aggregating trading data.

Relative Strength Index ( RSI ) is used to measure the speed and magnitude of price movements, helping to identify overbought and oversold trends in the market. It is derived by calculating price changes over a certain period.

The MACD uses the difference between short-term and long-term moving averages to determine buy and sell opportunities, and it has a good application in fluctuating markets.

The Bollinger Bands display the price fluctuation range and trend by plotting the middle line ( moving average line ) and the two standard deviation lines above and below. It is used to analyze market volatility and confirm trend direction.

In addition, the correlation between different trading pairs can be calculated, and real-time trading tables and transaction volume charts can be drawn. These indicators and charts can help analyze market sentiment, determine trends, and assist in trading decisions.

Time-series databases excel in processing massive amounts of data, complex metric calculations, multi-table association queries, and real-time analysis. They can aggregate queries on billions of rows of data in milliseconds, compute pairwise correlations in seconds, efficiently handle multi-level market data, and synthesize K-line data in real-time while calculating factors.

With the approval of the ETF, digital assets are entering the "institutional era". Time-series databases will play an important role in recording every transaction and event, constructing a complete digital asset lifecycle. By analyzing historical data, market trends can be discerned, directions predicted, trading strategies developed, and strong support provided for digital asset investment management.