firmai/machine-learning-asset-management

Covers 15+ trading strategy implementations spanning algorithmic execution (CTA), reinforcement learning agents, and quantamental approaches, alongside seven portfolio optimization methods including hierarchical risk parity, deep learning networks, and deterministic policy gradients. The collection integrates supervised, unsupervised, and reinforcement learning frameworks with practical financial data sources and research papers, targeting the full asset management workflow from alpha factor design through position sizing to strategy backtesting.

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