1.Secondary Market Quantitative Strategy Matrix

Constructing a multi-layered revenue capture network through machine learning and on-chain data analysis:

1. High-frequency Market-Making Engine

• Strategy Core: Deploying 21 types of deep learning models to analyze order book data from CEX/Dex in real-time, optimizing market-making spreads and inventory management.

• AI-Enhanced:

  • Order Book Trend Prediction Based on LSTM Network (Prediction Accuracy: 83%)

  • Reinforcement Learning for Dynamic Adjustment of Market-Making Parameters (Average Daily Basis Point Capture: 0.15%-0.3%)

• Battlefield Performance:

  • Capturing a market-making share of 12-18% on DEXs of emerging public chains such as TON and Sui.

  • In 2023, the cumulative market-making profit reached $270 million, with a Sharpe ratio of 3.8.

2. Cross-Market Arbitrage Network

• Strategy Coverage:

  • CEX/DEX Spread Arbitrage (Response Latency < 0.3ms)

  • Perpetual Contract Funding Rate Arbitrage (Annualized Return 34%)

  • NFT Floor Price and Sharded Token Arbitrage (Average Monthly Return 9%)

• AI Empowerment

  • Self-developed Artemis arbitrage signal system, monitoring over 200 trading pairs in real-time.

  • Simulating extreme market conditions using GAN (Generative Adversarial Network) to optimize risk hedging.

3.Derivatives Portfolio Strategy

• Core Strategy:

Strategy Types	Underlying Assets	AI Tools	Annualized Return
Volatility Surface Trading	BTC/ETH Options	Implied Volatility Forecasting Models	47%
Basis Convergence Strategy	Perpetual Contracts	Cointegration Relationship Dynamic Monitoring System	39%
Tail Risk Hedging	Volatility Index Derivatives	Extreme Event Early Warning Framework	Hedging efficiency increased by 62%