Can the BNB dual mining model break through the 1,000 mark? SOL ecosystem continues to launch new products
Mining is launched on the 55th issue IO.NET (IO) #io.net
Project analysis
Before June 2022, Io.net focused on developing institutional-level quantitative trading systems for the US stock market and cryptocurrency market. Its main challenge is to build a back-end trading system that can support the powerful computing power required for high-frequency trading (HFT).
High-frequency trading (HFT) is a trading method that uses powerful computer programs to trade large numbers of orders in a fraction of a second. It relies on complex algorithms to analyze multiple markets and execute orders. Io.net's trading strategy is close to HFT, which requires real-time monitoring of real-time quote data for more than 1,000 stocks and 150 cryptocurrencies, and ensuring that the system's delay from market events to customer account execution orders is kept below 200 milliseconds.
Project Development
Infrastructure Challenges and Solutions:
Infrastructure Requirements: It is necessary to dynamically backtest and adjust algorithm parameters for each asset in real time, while optimizing the trading process to support more than 30,000 individual customers trading on ETrade.com, Alpaca.markets, and Binance.com. Technical Solution: Adopt Ray.io, an open source library used by OpenAI to distribute GPT-3/4 training on more than 300,000 CPUs and GPUs. The use of Ray.io simplifies infrastructure management, allowing Io.net to build a powerful backend system in less than 60 days, instead of the originally estimated 6 months.
Cost Challenges:
High hardware costs: For example, each NVIDIA A100 card costs more than $80/day, and 50 cards are required to run such a system, with a total cost of up to $100K per month. Solution: Despite the high costs, Io.net successfully integrated Ray.io to improve the efficiency and performance of the system.
Future Outlook
Expansion and Innovation:
Multi-asset Support: Plan to expand the types and number of supported assets and further optimize the algorithms and infrastructure for high-frequency trading. Reduce costs: Explore more cost-effective computing resources and optimization technologies to reduce operating costs.
Technology Upgrade:
Improve computing power: Continue to optimize and improve computing power to meet the growing needs of AI and quantitative trading. Enhance security and reliability: Continuously upgrade the security and reliability of the system to ensure the security of transaction data and customer assets.
Market and service expansion:
Global market expansion: Expand services to more international markets and attract more institutional and individual customers. Customer service optimization: Enhance customer satisfaction and loyalty by improving customer service quality and technical support.
Summary
Io.net has successfully responded to the complex requirements and high cost challenges of high-frequency trading systems by adopting advanced technology and optimizing infrastructure. In the future, with the further upgrading of technology and the expansion of the market, Io.net is expected to occupy an important position in the quantitative trading and cryptocurrency markets, providing customers with more efficient, secure and reliable trading services.
