hajirazin/learnfinance-models-sac

LearnFinance SAC Model - v2026-05-08_af8e8546

Soft Actor-Critic (SAC) portfolio allocation agent using dual forecasts (LSTM + PatchTST) as features.

Model Details

• Version: v2026-05-08_af8e8546
• Model Type: SAC (Soft Actor-Critic) with dual forecasts
• Training Window: 2016-01-01 to 2026-05-08
• Symbols: 15 stocks

Components

• actor.pt - Gaussian policy network
• critic.pt - Twin Q-value networks
• critic_target.pt - Target Q-value networks
• log_alpha.pt - Entropy temperature coefficient
• scaler.pkl - PortfolioScaler for state normalization
• symbol_order.json - Ordered list of portfolio symbols

Metrics

• Actor Loss: 0.23356868057614508
• Critic Loss: 0.0
• Avg Episode Return: 0.2215855169356661
• Avg Episode Sharpe: 0.17108438529616588
• Eval Sharpe: 1.304194063957088
• Eval CAGR: 0.29702645044596276
• Eval Max Drawdown: 0.21236569737348215

Usage

from brain_api.storage.sac import SACHuggingFaceModelStorage
from brain_api.storage.sac.local import SACHalalFilteredModelStorage

storage = SACHuggingFaceModelStorage(
    repo_id="hajirazin/learnfinance-models-sac",
    local_cache=SACHalalFilteredModelStorage(),
)
artifacts = storage.download_model(version="v2026-05-08_af8e8546")