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learnfinance-models-ppo
hajirazin/learnfinance-models-ppo
learnfinance-models-ppo is a reinforcement learning model from hajirazin. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product.
Proximal Policy Optimization (PPO) portfolio allocation agent using dual forecasts (LSTM + PatchTST) as features.
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.pt120 KB · 94%
At a glance
- Task
- Reinforcement Learning
- Access
- Public
- Created
- Jan 30, 2026
- Updated
- Mar 3, 2026
- SHA
- 2cc81548
Model card
From the Hugging Face model README
LearnFinance PPO Model - v2026-02-27-e5345ed2cf47
Proximal Policy Optimization (PPO) portfolio allocation agent using dual forecasts (LSTM + PatchTST) as features.
Model Details
- Version: v2026-02-27-e5345ed2cf47
- Model Type: PPO (Proximal Policy Optimization) with dual forecasts
- Training Window: 2016-01-01 to 2026-02-27
- Symbols: 15 stocks
Components
weights.pt- PPO actor-critic network weightsscaler.pkl- PortfolioScaler for state normalizationsymbol_order.json- Ordered list of portfolio symbols
Metrics
- Policy Loss: -0.008436506443942449
- Value Loss: 47129.908203125
- Avg Episode Return: 0.17079946008368832
- Avg Episode Sharpe: 0.1145480840508518
- Eval Sharpe: 1.2736954447449413
- Eval CAGR: 0.3237192191683296
- Eval Max Drawdown: 0.14662845878963948
Usage
from brain_api.storage.ppo import PPOHuggingFaceModelStorage
storage = PPOHuggingFaceModelStorage(repo_id="hajirazin/learnfinance-models-ppo")
artifacts = storage.download_model(version="v2026-02-27-e5345ed2cf47")
- Task
- Reinforcement Learning
- Created
- Jan 30, 2026
- Updated
- Mar 3, 2026