Under the technical leadership and direction of Professor , the BeaconAI Quant System has been built as a fully integrated, AI-driven quantitative trading architecture — seamlessly connecting data acquisition, modeling, execution, and risk management.
Algorithmic Synergy
DeepSense (Powered by DeepSeek):
Utilizes convolutional neural networks (CNNs) to extract high-dimensional features from microstructure data — such as tick-by-tick trades and order books — allowing precise detection of market sentiment and liquidity conditions.
Natural Language Understanding (GPT-4):
Leverages large-scale pretrained language models to parse global financial news, corporate disclosures, and social media sentiment in real time, seamlessly integrating emotional factors into quantitative signals.
Reinforcement Learning (Based on DeepMind):
Adopts an AlphaZero-style self-play reinforcement learning framework to autonomously explore optimal trading strategies and dynamically adapt buy/sell decisions.
Synergistic Effect:
Combined, these three core algorithms enhance signal accuracy by an average of 15% and improve drawdown control by up to 20%.
Adaptive Real-Time Calibration
Stream-Processing Pipeline:
Built on Apache Kafka and Flink, enabling millisecond-level data ingestion and continuous real-time updates.
Parameter Optimization Engine:
Employs online Bayesian optimization and transfer learning to auto-tune model parameters within 15 minutes, ensuring high responsiveness to evolving market conditions.
Model Decay Monitoring:
Tracks key rolling performance indicators (e.g., Sharpe ratio and win rate), and automatically triggers model retraining if deviation exceeds 10% from baseline.
Ultra-Low-Latency Execution Engine
Sub-Millisecond Matching:
The in-house execution engine achieves order routing within 0.8 milliseconds, supporting six algorithmic execution strategies including TWAP, VWAP, and iceberg orders.
Intelligent Order Slicing:
Predicts real-time volume and order book depth to dynamically optimize slice size and timing, reducing market impact cost by up to 30%.
Explainable Quant Intelligence (XAI)
Factor Visualization:
Interactive dashboards display the top five contributing factors behind each trade signal, explaining over 85% of signal variance.
Decision Path Audit Trail:
Automatically generates a full-chain audit log — from data ingestion to execution — facilitating historical analysis, backtesting, and regulatory compliance.
Multi-Layered Risk Control
Real-Time VaR & ES Monitoring:
Continuously calculates Value at Risk (VaR) and Expected Shortfall (ES) for portfolios; automatically adjusts positions when risk thresholds are breached.
Tail-Risk Hedging Module:
Integrates built-in hedging mechanisms using options and index futures, keeping maximum drawdown under 5%, even during extreme market conditions.
Performance Highlights — Past 12 Months
Hedge Arbitrage Strategy:
Achieved an average monthly return of 9.3%, consistently capturing cross-market arbitrage opportunities.
Resilience During Volatility:
Recorded a maximum single-month drawdown of just 3.2%, even under extreme market turbulence.
Institutional Adoption:
Currently serves over 100 institutional clients, with total assets under management (AUM) exceeding USD 1.2 billion.
