jankrepl/deepdow

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1181 stars · 169 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

deepdow is a Python library built on PyTorch that connects deep learning with portfolio optimization, allowing networks that output asset weight allocations in a single forward pass. It merges market forecasting and allocation layers into a fully differentiable pipeline trainable with gradient descent.

Use cases

• train neural networks to allocate portfolio weights end-to-end
• research differentiable portfolio optimization layers
• optimize networks with sharpe ratio or maximum drawdown losses
• experiment with clustering-based allocation algorithms
• backtest buy-and-hold allocation strategies learned from time series

When to choose

• you want to jointly learn forecasting and allocation with gradient descent
• you need differentiable convex optimization inside a PyTorch model
• you are researching deep learning approaches to asset allocation

When to avoid

• you need active trading strategies with transaction cost modeling
• you want a reinforcement learning trading framework
• you need a production-ready trading system rather than a research framework

Facets

library · maturity maintenance

machine-learning deep-learning trading math fintech machine-learning deep-learning time-series python portfolio-optimization pytorch markowitz asset-allocation differentiable-optimization finance gpu

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markdown · JSON · MCP: product_card(name="jankrepl/deepdow")