AI-Powered Trading Strategies

Deploy machine learning models with real-time inference. Reinforcement learning, supervised models, and ensemble methods with GPU acceleration for adaptive algorithmic trading.

GPU Accelerated
Real-Time Inference
Adaptive Learning

ML Frameworks

Support for leading machine learning platforms

TensorFlow

Production-ready ML deployment

  • TensorFlow Serving
  • GPU acceleration
  • Model versioning
  • A/B testing support

PyTorch

Research and production models

  • TorchServe integration
  • Dynamic computation
  • Custom layers
  • ONNX export

Reinforcement Learning

Adaptive trading agents

  • Custom RL environments
  • Policy optimization
  • Multi-agent support
  • Continuous learning

Strategy Types

ML-powered trading approaches

Predictive Models

Price prediction using deep learning. LSTM and transformer models for time series forecasting with market microstructure features.

Reinforcement Learning Agents

Agents that learn optimal trading policies through interaction with markets. Adaptive strategies that improve over time.

Sentiment Analysis

NLP models analyzing news, social media, and alternative data sources. Real-time sentiment scoring for trading signals.

Ensemble Methods

Combine multiple models for robust predictions. Weighted voting, stacking, and boosting for improved accuracy.

Deploy AI-Powered Strategies

Access ML infrastructure with Zi-HFT Professional

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