Why is aggregated crypto data better?

Some time ago we wrote blog posts about crypto data filtering and crypto data standardization – now it’s time to tell you more about crypto data aggregation. A great process that allows traders to have a better overview of the crypto market and make even better decisions and trades. What is it exactly? What are its pros and cons? How’s CoinAPI’s Market Data API dealing with it? Let’s dig into it!

What is data aggregation?

Simply put, it’s collecting and mixing data from many places. This creates a big picture that’s easy to understand. As a result, it helps with making decisions and creating reports.

How does data aggregation work?

First, we gather data from various sources. Next, we clean it up to make sure it’s accurate. Then, we combine all the data into one set. After that, we organize it for easy use. Finally, we sum it up to give useful insights.

Why is aggregated data better than single-source data?

• Makes analysis easier: Summarized data helps identify trends, patterns, and anomalies across many data sources. Consequently, it’s less likely to be false.
• Improves decision-making: Aggregated data provides a complete view, thus aiding informed decisions. Furthermore, it allows you to see the overview of the whole market. In contrast, a single data source could give out wrong data. Therefore, using aggregated data is safer for most.
• Enhances reporting: Aggregated data is perfect for reports and dashboards, thereby giving stakeholders a clear overview of key metrics.
• Increases efficiency: Managing a single, consolidated dataset is more efficient than dealing with multiple disparate sources.

Crypto data aggregation

In crypto trading, we collect and organize data from many exchanges. As a result, this helps traders make informed choices based on solid information.

Let us show you how crypto data is aggregated using our Market Data API as an example.

CoinAPI’s Market Data API aggregation process

1. Data Collection: Firstly, our Market Data API connects to multiple cryptocurrency exchanges to collect market data. Subsequently, the raw market data is continuously ingested, including trades, quotes, and order book updates, from these exchanges in real time.

2. Data Normalization: Secondly, the API converts raw data into a single, standardized format. This involves mapping different data structures and terminologies to a common schema. Then, it removes inconsistencies, duplicates, or errors to ensure accuracy and reliability.

3. Timestamp Alignment: Thirdly, Market Data API aligns timestamps from different exchanges to a common time standard (e.g., UTC) for accurate comparison and aggregation.

4. Data Aggregation: Data Aggregation: Next, the API computes aggregated metrics like average prices and total volumes. After that, it generates Open, High, Low, Close, and Volume (OHLCV) data for different time intervals.

Some of the data types our Market Data API provides are:
-Trades: Individual transactions on the exchange.
-Order Books: Current buy and sell orders.
-OHLCV: Data showing the open, high, low, and close prices, and volume for a given period.
-Exchange Rates: Rates between different asset pairs.

5. Data Storage: Furthermore, we store aggregated and normalized data in a structured database for efficient retrieval. Additionally, we don’t forget about historical data which is maintained as a comprehensive historical database to support backtesting and analysis.

6. Data Distribution: Market Data API provides access to aggregated data through various API endpoints (RESTful, WebSocket, FIX). We offer real-time streaming of market data for live updates, as well.

💡 Batch Downloads: Our Market Data API allows batch downloads of historical data for extensive analysis.

7. Quality Assurance: We constantly monitor data collection and aggregation processes to ensure data quality. Of course, we implemented robust error handling and recovery mechanisms to prevent any issues.

Pros and cons of crypto data aggregation

Pros

1. Consistency: Aggregating data from multiple sources and normalizing it ensures consistency and reliability.
2. Error reduction: Data cleaning processes remove inconsistencies, duplicates, and errors, leading to higher-quality data.
3. Wide coverage: Aggregating data from various exchanges provides a more comprehensive view of the market, capturing a broader range of trading activities.
4. Detailed insights: High-resolution data (e.g., tick-by-tick) offers detailed insights into market trends and behaviors.
5. Time-saving: Aggregated data saves users the time and effort required to collect and process data from multiple sources.
6. Simplified integration: Standardized data formats and protocols make it easier to integrate aggregated data into existing systems and workflows.
7. Custom metrics: Users can define custom aggregation metrics and time intervals, allowing for tailored analysis and insights.
8. Historical data: Access to comprehensive historical data supports backtesting, trend analysis, and other research activities.
9. Live data: Real-time streaming of aggregated data ensures that users have access to the most up-to-date market information.
10. Immediate action: Real-time data enables immediate decision-making and action, which is crucial for trading and other time-sensitive applications.

Cons

1. Data integration: Aggregating data from multiple sources can be complex, requiring sophisticated data integration and normalization processes.
2. Technical challenges: Ensuring data quality, consistency, and synchronization across different sources can be technically challenging.
3. Processing time: The process of aggregating and normalizing data can introduce some latency, which may affect the timeliness of the data.
4. Real-time constraints: While real-time streaming is available, the initial aggregation process may still introduce slight delays.
5. Infrastructure: Maintaining the infrastructure required for data aggregation, storage, and distribution can be costly.
6. Subscription fees: Accessing high-quality aggregated data from providers like CoinAPI may involve subscription fees.
7. Volume: Aggregating data from multiple sources can result in large volumes of data, which may be overwhelming for some users.
8. Filtering: Users may need to implement filtering mechanisms to extract relevant information from the aggregated data.
9. Reliability: Users are dependent on the reliability and accuracy of the data provided by the aggregation service.
10. Vendor lock-in: Switching providers can be challenging due to differences in data formats, protocols, and integration processes.

In short, data aggregation has some challenges. However, it’s very helpful for crypto traders. Want to learn more? Visit our website or talk to our team!