AI-powered global opportunity discovery
Systematic opportunity discovery across global markets using cutting-edge artificial intelligence
Introduction
Deep Learning-Powered Quantitative Trading System
Complete quantitative trading system built on deep mathematical research and large-scale deep learning technologies. Core technologies include proprietary multi-level rule-based trading models, Agent MCP RAG intelligent decision system, and large model fine-tuning capabilities. Deep learning technologies span neural network architecture design, time series forecasting, pattern recognition, reinforcement learning across multiple dimensions, forming a full-chain AI solution from data processing to strategy execution. Mathematical modeling capabilities extend deep into probability theory, stochastic processes, optimization theory and other core fields, providing solid theoretical foundation for algorithmic trading.
About Us
/ Who We Are
Quantitative investment company specializing in artificial intelligence research and application.
Core team consists of leading AI researchers, machine learning engineers, computer scientists, and financial technology experts, dedicated to transforming cutting-edge AI technologies into practical investment applications.
Core Technologies
01.
Multi-Agent Trading System
02.
Knowledge-Enhanced Reasoning System
03.
AI-Driven Multi-Factor Models
04.
Macro Trend Prediction
05.
Machine Learning Model Library
06.
Algorithm Optimization Technology
Feature
/ Works We Have Done
We’re here to give you effective ideas.
Pro Team
We engineer an explainable, replayable and self-evolving decision backbone. Focus areas: deep representation learning, probabilistic reasoning, adaptive filtering, uncertainty stratification, and regime drift resilience.
Great Experience
Self-learning filtration: failure replay → rule synthesis
Uncertainty surfaces: confidence + expected payoff + tail risk
Continuous calibration: outcome → retrain → compress
Human–AI transparency: every decision traceable
Not a black box—an interpretable evolving agent.
Projects
/ Works We Have Done
Intelligent Trading System Architecture: From Chaos to Order
Our core philosophy views financial markets as Complex Adaptive Systems, constructing multi-scale, multi-modal intelligent architectures to achieve full-spectrum perception and decision-making from market microstructure to macroscopic dynamics.
I. Theoretical Foundation & System Philosophy
1.1 Statistical Physics & Market Dynamics Integration
Modeling markets as open systems far from equilibrium based on non-equilibrium statistical mechanics. Using Fokker-Planck equations to describe price probability density evolution, combined with Lévy flight models to capture tail events, building predictive frameworks with Self-Organized Criticality awareness.
1.2 Information Theory-Driven Decision Paradigm
Employing Maximum Entropy Principle and Mutual Information optimization for optimal feature space encoding. Dynamically assessing prediction-market distribution divergence through Kullback-Leibler divergence, forming adaptive Bayesian update mechanisms.
II. Core Technical Architecture
2.1 Multi-Scale Deep Cognitive Network
- Spatiotemporal Attention: Integrating Temporal Fusion Transformers with Graph Attention Networks for cross-temporal (millisecond to monthly) and cross-asset global attention modeling
- Hierarchical Representation: Building latent space representations via Variational Autoencoders, combined with Contrastive Learning for unsupervised market regime discovery
- Generative Scenario Simulation: Using Diffusion Models to generate extreme market scenarios for tail risk assessment
2.2 Quantum-Inspired Probabilistic Framework
Leveraging quantum superposition and measurement collapse principles to construct market state “probability cloud” models. Each trading opportunity represented as quantum state vectors, converting probability amplitudes to classical decisions through measurement operators.
2.3 Self-Organizing Risk Management System
- Dynamic Phase Transition Detection: Real-time monitoring of order-to-disorder transitions via Ising model analogies
- Cascade Failure Protection: Multi-layered risk contagion blocking mechanisms based on complex network theory
- Adaptive Elasticity Control: Dynamic risk exposure management through control theory negative feedback loops
III. Engineering Implementation
3.1 Distributed Intelligence Engine
- Heterogeneous Computing: CPU+GPU+TPU hybrid deployment for hardware-accelerated inference
- Stream Processing: Apache Flink-based real-time data processing with microsecond latency
- Elastic Architecture: Kubernetes-native deployment supporting dynamic resource allocation
3.2 Knowledge Graph & Causal Reasoning
Constructing dynamic financial market knowledge graphs, identifying true market drivers through Causal Inference while avoiding spurious correlations. Implementing Pearl’s causal hierarchy from association to intervention to counterfactual reasoning.
3.3 Evolutionary Algorithms & Meta-Learning
- Genetic Programming: Automated strategy component combination and evolution
- Meta-Learning Adaptation: Model-Agnostic Meta-Learning (MAML) for rapid adaptation to new markets
- Neural Architecture Search: Automated discovery of optimal network structures
Future Frontiers
Exploring cutting-edge directions including:
- Neuromorphic Computing: Spiking neural network trading systems mimicking brain neurons
- Topological Data Analysis: Discovering high-dimensional market structures via persistent homology
- Quantum Computing: Portfolio optimization on real quantum processors
- Swarm Intelligence: Distributed decision-making via multi-agent reinforcement learning
View more project
