AI-powered global opportunity discovery

Introduction


Deep Learning-Powered Quantitative Trading System

About Us


Quantitative investment company specializing in artificial intelligence research and application.

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

We’re here to give you effective ideas.

Pro Team

Great Experience

Uncertainty surfaces: confidence + expected payoff + tail risk

Continuous calibration: outcome → retrain → compress

Projects


Intelligent Trading System Architecture: From Chaos to Order

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

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