LLM-Powered Crypto Quantitative Trading Platform

Next-generation digital asset trading infrastructure that's secure, intelligent, and autonomously controllable

Terminal v3.1.4

$ Generate trading strategy

> Analyzing market conditions...

> Detected arbitrage opportunity between Binance and FTX

> Calculating optimal execution path...

> Strategy generated with 78% expected win rate

ETH: ↑2.4%
BTC: ↑1.2%
SOL: ↓0.8%

Current Technology Landscape & User Pain Points

Account Management

Current State:

  • Mainstream platforms provide basic API trading interfaces
  • Multi-account coordination relies on script splicing

Pain Points:

  • Manual operation delays (arbitrage requires <0.1s response)
  • Fragmented risk control leading to excess losses

Fund Custody

Current State:

  • Assets rely on centralized custodians or self-managed wallets
  • Cross-chain fund transfers require manual signatures

Pain Points:

  • High trust costs with custodial risks
  • Fragmented liquidity across multiple chains

Quantitative Trading Platform

Current State:

  • Traditional platforms require manual coding skills
  • New AI platforms lack crypto-specific features

Pain Points:

  • Strategy homogenization with quick alpha decay
  • Backtest vs live trading discrepancies

Our Solution & Technical Capabilities

A three-in-one product architecture addressing crypto trading pain points

Business Module Core Technical Capabilities Pain Points Solved
Smart Account Management
  • LLM + Multi-agent risk control engine
  • Real-time API traffic analysis
 Zero latency for HFT, 90% reduction in operational risk
Cross-chain Fund Custody
  • MPC + Smart contract custody
  • Fragmented private key management
 Self-custody security, 5x cross-chain efficiency
AI Quant Platform
  • Multi-agent strategy factory
  • Automated factor generation + coding + backtesting
 200+ daily strategies, accessible to non-coders

Differentiated Technical Capabilities

Custom LLM Financial Engine

  • Fine-tuned on BloombergGPT + crypto-specific datasets
  • Supports multimodal inputs (tweets, PDFs, on-chain graphs)

Low-bit Quantitative Inference

  • BitNet 1-bit quantization technology integration
  • 4x lower energy consumption for inference

Dynamic Data Fusion

  • Off-chain (exchange) + On-chain (Gas/Mempool) + Social data
  • Real-time input for agent decision making

Business Goals & Implementation Path

Phase Goals Key Metrics
Short-term Establish technical trust benchmark
  • Custodial funds ≥$50M
  • Strategy Sharpe Ratio ≥2.5
Medium-term Build developer ecosystem
  • SDK supporting 10+ chains
  • 3rd-party strategy store GMV $1M+
Long-term Become on-chain AI trading infrastructure
  • 100+ institutional clients
  • Daily on-chain settlement $1B+

Conclusion: Technology Reshaping Financial Trust

We achieve strategy democratization through 'LLM multi-agents', solve asset control conflicts with 'MPC on-chain custody', and lower high-performance trading barriers via 'edge low-bit inference'. Our goal is not just providing tools, but building a user-controlled, strategy-evolving, fund-verifiable next-gen crypto trading ecosystem.

Case Study: After integration by a Hong Kong licensed exchange, API hijacking incidents dropped to zero, with arbitrage strategy returns rising to 45% annualized (12% max drawdown).