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Prediction Market Arbitrage Project Launch and Technology Selection
Quantitative Finance
👤 Readers interested in prediction markets, high-frequency trading, Rust programming, or technical project development
This article describes the prediction market arbitrage project launched on February 8, 2026, which falls under the high-frequency trading (HFT) category and has extremely high requirements for execution efficiency. The technology selection decision is to use Rust language to build a low-latency trading execution system to cope with the rapid elimination of arbitrage opportunities. The team's current technology stack is limited, and they plan to advance the project through vibe coding, taking this opportunity to deeply learn the Rust ecosystem and toolchain in preparation for future projects. The article also mentions that the team previously had basic Rust experience with Solana smart contracts but not in-depth, and they look forward to embracing challenges through this project.
- ✨ Prediction market arbitrage project launch, belonging to the high-frequency trading (HFT) category
- ✨ Technology selection adopts Rust language to achieve a low-latency trading execution system
- ✨ Team technology stack is limited, planning to advance the project through vibe coding
- ✨ Take this opportunity to learn the Rust ecosystem and toolchain for future preparation
- ✨ The project has high requirements for execution efficiency to quickly capture arbitrage opportunities
📅 2026-02-08 · 178 words · ~1 min read
EA Project Introduction: AI-Driven Priority Fund for Quantitative Trading
Quantitative Finance
👤 Investors interested in blockchain investments, quantitative trading, and stable returns, particularly those seeking low-risk, principal-protected priority fund participation.
EA (Earnby.AI) is a priority fund project deployed on the BSC chain, settled in USDC, offering stable returns to investors through AI-driven quantitative trading strategies. The project uses a priority/subordinated capital structure, where priority capital enjoys principal protection, and subordinated capital is borne by the project's own funds to assume risks. The management team consists of professionals in quantitative trading and blockchain, including 5 co-founders. The project offers floating returns, currently with an annualized yield of 12%, and investors can redeem at any time. Strategies include directional portfolio strategies and delta-neutral strategies, with historical performance showing a cumulative return of 39.22% and an annualized return of approximately 22%. The project has no management fees, flexible lock-up periods, and aims to provide low-risk, sustainable returns for investors.
- ✨ EA is a priority fund project deployed on the BSC chain, settled in USDC
- ✨ Utilizes AI-driven quantitative trading strategies, including directional portfolio and delta-neutral strategies
- ✨ Capital is divided into priority and subordinated tiers, with priority capital enjoying principal protection
- ✨ The management team consists of 5 professionals in quantitative trading and blockchain
- ✨ Offers floating returns, currently with an annualized yield of 12%, and investors can redeem at any time
📅 2025-11-01 · 1,525 words · ~7 min read
Full Spectrum Analysis: The Optimal Method for Information Monetization
Quantitative Finance
👤 Quantitative traders, investment strategy developers, financial engineers, and advanced investors interested in the Kelly Criterion and leverage optimization.
This article proposes Full Spectrum Analysis (FSA), an investment trading strategy framework optimized based on the Kelly Criterion. It first analyzes the limitations of the traditional Kelly formula in investment applications, such as lack of leverage and short-selling considerations, and liquidation timing issues. Then, FSA constructs a systematic trading decision model by defining outcome spaces, calculating optimal leverage and compound returns. The article elaborates on the mathematical principles of FSA, including the calculation of expected returns and compound returns, as well as the algorithm for solving optimal leverage using Newton's iteration method. Additionally, it introduces historical backtesting methods (e.g., Gross Profit Margin GPM calculation), considerations for live trading modules, and measures to address black swan events. The core advantage of FSA lies in its ability to utilize imperfect probability information to maximize long-term returns by optimizing leverage decisions, reducing the high requirements for information quality.
- ✨ Full Spectrum Analysis (FSA) is based on the Kelly Criterion, optimizing investment leverage to maximize compound growth rate
- ✨ Define outcome spaces, probability distributions, and returns to calculate optimal leverage and compound returns
- ✨ Use Newton's iteration method to solve for optimal leverage, handling feasible regions and convergence issues
- ✨ Introduce Gross Profit Margin (GPM) for historical backtesting to evaluate strategy profitability and capacity
- ✨ Incorporate symmetric black swan event probabilities to limit leverage and prevent abuse and extreme risks
📅 2025-08-10 · 2,810 words · ~13 min read