Crypto arbitrage: real-time data when it matters most

Automated Marketing

Case study on our automated arbitrage solution 

Cryptocurrency has been a very interesting topic over the past few years. Many investors have been burned by the recent downturn (myself included) – but as international tensions rise and inflation continues to soar, the promise of crypto has never looked better. 

As this young industry continues to grow, there are natural inefficiencies that arise in the market. For example, different exchanges will get out of sync with each other from time to time, which causes a variation in price between exchanges. This variance can be exploited for profit, but traders must move quickly to capture the opportunities.

Last year, we worked with a crypto trading firm to build a solution for this exact use case. The goal was to design and implement a real-time data stream with the ability to feed data into an automated trading bot about market opportunities.

Case Study

See below for a case study that summarizes our implementation of a data streaming & trading bot solution for cryptocurrency arbitrage.


Create a cloud environment that can support 100+ web scrapers at a time, aggregate and compare data in real-time from several data sources, implement an API for the trading bots, and enable autoscaling to handle high-volume periods.

Current Issues

Previous attempts to build a real-time data feed were unsuccessful because there was too much latency between the data feed and trading bots. Data was unreliable, there were many false positives, and trading bots ended up missing opportunities becuase of the system latency.


For this project, our team recommended: Kubernetes for its autoscaling capabilities, Redis for in-memory data storage, and Python for data collection and the decision engine.

Our solution enabled anyone in the organization to have access to “live” data within a moment’s notice. Markets with different naming conventions were merged together using a matching engine, and historical archives were able to capture a treasure trove of information for predictive use.

One of the largest benefits from this solution: market conditions were fed into trading bots every half second, enabling rapid decisions to be made by our arbitrage model. By leveraging the power of high-quality, rapid data ingestion – this implementation enabled a trading bot to operate and learn at a much faster pace than any manual trader. 

Understanding the proper tools needed for this use case allowed this client to achieve levels of efficiency with their data that were previously unattainable. Trading bot profitability increased around 63%, and overall efficiency improved dramatically (efficiency in terms of profitable trades).

Curious about data streaming applications or financial bots? Please get in touch with for more detailed information or questions about how this can apply to your organization.


Technology used

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