TPTBusiness e1e86e1bd3 feat: Add advanced ML models (Transformer, TCN, PatchTST, CNN+LSTM) ...

New models in models/local/:
- transformer_factor.py: Transformer with self-attention
- tcn_factor.py: Temporal Convolutional Network (multi-scale)
- patchtst_factor.py: PatchTST (SOTA for time-series)
- cnn_lstm_hybrid.py: CNN+LSTM with attention

Features:
- All models support sequence and tabular data
- Automatic device selection (CPU/GPU)
- Training with Adam optimizer + LR scheduler
- Save/load functionality
- Production-ready code

Dependencies installed:
- xgboost
- lightgbm
- torch (PyTorch)

Usage:
  from rdagent.components.model_loader import load_model
  model = load_model('transformer_factor')  # Auto-loads your local version!

2026-04-02 22:44:05 +02:00

..

backtesting

test: Add backtesting tests with 98.77% coverage

2026-04-02 19:24:38 +02:00

finetune

feat: Add advanced ML models (Transformer, TCN, PatchTST, CNN+LSTM)

2026-04-02 22:44:05 +02:00

notebook

fix: model/factor experiment filtering in Qlib proposals (#1257)

2025-10-10 22:49:46 +08:00

oai

feat: add LLM-finetune scenario (#1314)

2026-03-02 19:04:10 +08:00

qlib

fix: model/factor experiment filtering in Qlib proposals (#1257)

2025-10-10 22:49:46 +08:00

rl

feat(rl): add AutoRL-Bench framework and benchmark integrations (#1348)

2026-03-17 15:49:30 +08:00

utils

feat(rl): add AutoRL-Bench framework and benchmark integrations (#1348)

2026-03-17 15:49:30 +08:00