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Diffusion-Transformer for Joint Portfolio Construction & Execution Optimization

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jialuechen/deepfolio

TensorFlow PyPI - Version License Python versions PyPI downloads

DeepFolio | Diffusion-Transformer (DiT) for Portfolio & Execution Optimization

DeepFolio is an OpenAI Sora-inspired Diffusion-Transformer (DiT) framework for joint portfolio optimization and best execution, designed to maximize Sharpe ratio without explicit return forecasts. It leverages:

  • Transformer to capture asset dependencies and encode market conditions.
  • Diffusion Models to filter market noise and generate both robust allocation weights and optimized trading trajectories.
  • End-to-End Strategy Execution to reduce information loss between strategy design and execution implementation, ensuring optimal real-world performance.

πŸš€ Key Features

βœ… Unified Portfolio & Execution Optimization – Bridges the gap between portfolio construction and trade execution.
βœ… Diffusion-Based Portfolio Generation – Generates adaptive, robust asset allocations without relying on explicit return forecasts.
βœ… Market-Aware Execution Path Modeling – Uses Diffusion Models to optimize execution trajectories, reducing slippage and market impact.
βœ… Scenario-Based Adaptation – Dynamically adjusts strategies for high/low volatility regimes, liquidity shifts, and market anomalies.
βœ… Transaction Cost-Aware Optimization – Integrates TCA (Transaction Cost Analysis) into optimization, minimizing execution costs.


πŸ“œ Architecture

DeepFolio consists of two core modules:

1️⃣ Portfolio Optimization (Transformer + Diffusion)

  • Transformer Encoder extracts asset relationships, learning market structure.
  • Diffusion Model generates optimal portfolio weights, ensuring robustness under different conditions.

2️⃣ Execution Optimization (Trade Path Diffusion)

  • Transformer encodes market microstructure (LOB, liquidity, volatility).
  • Diffusion Model optimizes execution paths to minimize market impact and transaction costs.

πŸ“Œ Pipeline Overview:

Documentation

For detailed documentation, please visit our documentation site.

Contributing

We welcome contributions! Please see our contributing guidelines for more details.

License

This project is licensed under the BSD-2-Clause License- see the LICENSE file for details.

Reference

[1] Damian Kisiel, Denise Gorse (2022). Portfolio Transformer for Attention-Based Asset Allocation arXiv:2206.03246 [q-fin.PM]

Acknowledgments

  • This package leverages the power of TensorFlow for efficient portfolio optimization.
  • Thanks to the financial machine learning community for inspiring many of the implemented methods.