High-frequency DeFi

High-frequency DeFi is the future of finance

“High-frequency DeFi” refers to trading strategies that use algorithmic trading bots to execute large numbers of trades at a very high frequency in the decentralized finance (DeFi) market. These bots use complex mathematical models to analyze market data and make trading decisions in real-time.

The New York Stock Exchange (NYSE) reported an average daily trading volume of approximately 2.2 billion shares in 2020, which is equivalent to about 27,500 trades per second assuming an average trade size of 80 shares. If we assume that all of these trades were to be settled on a blockchain-based system, we would need a TPS capacity of at least 27,500.

Other blockchains face several technical challenges that make it difficult to support high-frequency trading in the DeFi market.

Challenges

High throughput

High TPS is crucial for high-frequency DeFi trading because it enables traders to execute a large number of trades quickly and efficiently. In high-frequency trading, speed is critical, and even small delays can lead to massive loses

Lower fees

Every transaction carries a cost, and these costs can add up quickly when executing a large number of trades. In a high-frequency trading strategy, even small differences in fees can have a significant impact on profitability.

Low latency

Low latency is important for high-frequency DeFi trading because it can help ensure that trades are executed as quickly as possible.

Security

The blockchain should have strong security measures in place to protect against hacking, fraud, and other forms of attack

Arcology makes high-frequency DeFi possible

High Performance

Lowest Fees

Arcology offers the lowest fee in the industry making it the only platform for resource intensive applications.

Full EVM Compatibility

Ethereum developers can use the tools and programming languages they are already familiar with, making it easier to develop and deploy on Arcology.

Cluster Deployment

Cluster deployment allows a node to automatically scale horizontally by adding more machines to the node cluster as needed making it possible to handle more workload.