From 16957176a9033d2caf8f3628b0da3c787cdc9fce Mon Sep 17 00:00:00 2001
From: you-n-g <you-n-g@users.noreply.github.com>
Date: Fri, 5 Nov 2021 13:00:32 +0800
Subject: [PATCH] Update README.md

Update  benchmark link
---
 README.md | 35 ++++++++++++++++++-----------------
 1 file changed, 18 insertions(+), 17 deletions(-)

diff --git a/README.md b/README.md
index f38e8949f..f47ee5bc1 100644
--- a/README.md
+++ b/README.md
@@ -11,6 +11,7 @@
 Recent released features
 | Feature | Status |
 | --                      | ------    |
+| TCN  model | [Released](https://github.com/microsoft/qlib/pull/668) on Nov 4, 2021 |
 |Temporal Routing Adaptor (TRA) | [Released](https://github.com/microsoft/qlib/pull/531) on July 30, 2021 |
 | Transformer & Localformer | [Released](https://github.com/microsoft/qlib/pull/508) on July 22, 2021 |
 | Release Qlib v0.7.0 | [Released](https://github.com/microsoft/qlib/releases/tag/v0.7.0) on July 12, 2021 |
@@ -278,23 +279,23 @@ The automatic workflow may not suit the research workflow of all Quant researche
 # [Quant Model (Paper) Zoo](examples/benchmarks)
 
 Here is a list of models built on `Qlib`.
-- [GBDT based on XGBoost (Tianqi Chen, et al. KDD 2016)](qlib/contrib/model/xgboost.py)
-- [GBDT based on LightGBM (Guolin Ke, et al. NIPS 2017)](qlib/contrib/model/gbdt.py)
-- [GBDT based on Catboost (Liudmila Prokhorenkova, et al. NIPS 2018)](qlib/contrib/model/catboost_model.py)
-- [MLP based on pytorch](qlib/contrib/model/pytorch_nn.py)
-- [LSTM based on pytorch (Sepp Hochreiter, et al. Neural omputation 1997)](qlib/contrib/model/pytorch_lstm.py)
-- [GRU based on pytorch (Kyunghyun Cho, et al. 2014)](qlib/contrib/model/pytorch_gru.py)
-- [ALSTM based on pytorch (Yao Qin, et al. IJCAI 2017)](qlib/contrib/model/pytorch_alstm.py)
-- [GATs based on pytorch (Petar Velickovic, et al. 2017)](qlib/contrib/model/pytorch_gats.py)
-- [SFM based on pytorch (Liheng Zhang, et al. KDD 2017)](qlib/contrib/model/pytorch_sfm.py)
-- [TFT based on tensorflow (Bryan Lim, et al. International Journal of Forecasting 2019)](examples/benchmarks/TFT/tft.py)
-- [TabNet based on pytorch (Sercan O. Arik, et al. AAAI 2019)](qlib/contrib/model/pytorch_tabnet.py)
-- [DoubleEnsemble based on LightGBM (Chuheng Zhang, et al. ICDM 2020)](qlib/contrib/model/double_ensemble.py)
-- [TCTS based on pytorch (Xueqing Wu, et al. ICML 2021)](qlib/contrib/model/pytorch_tcts.py)
-- [Transformer based on pytorch (Ashish Vaswani, et al. NeurIPS 2017)](qlib/contrib/model/pytorch_transformer.py)
-- [Localformer based on pytorch (Juyong Jiang, et al.)](qlib/contrib/model/pytorch_localformer.py)
-- [TRA based on pytorch (Hengxu, Dong, et al. KDD 2021)](qlib/contrib/model/pytorch_tra.py)
-- [TCN based on pytorch (Shaojie Bai, et al. 2018)](qlib/contrib/model/pytorch_tcn.py)
+- [GBDT based on XGBoost (Tianqi Chen, et al. KDD 2016)](examples/benchmarks/XGBoost/)
+- [GBDT based on LightGBM (Guolin Ke, et al. NIPS 2017)](examples/benchmarks/LightGBM/)
+- [GBDT based on Catboost (Liudmila Prokhorenkova, et al. NIPS 2018)](examples/benchmarks/CatBoost/)
+- [MLP based on pytorch](examples/benchmarks/MLP/)
+- [LSTM based on pytorch (Sepp Hochreiter, et al. Neural omputation 1997)](examples/benchmarks/LSTM/)
+- [GRU based on pytorch (Kyunghyun Cho, et al. 2014)](examples/benchmarks/GRU/)
+- [ALSTM based on pytorch (Yao Qin, et al. IJCAI 2017)](examples/benchmarks/ALSTM)
+- [GATs based on pytorch (Petar Velickovic, et al. 2017)](examples/benchmarks/GATs/)
+- [SFM based on pytorch (Liheng Zhang, et al. KDD 2017)](examples/benchmarks/SFM/)
+- [TFT based on tensorflow (Bryan Lim, et al. International Journal of Forecasting 2019)](examples/benchmarks/TFT/)
+- [TabNet based on pytorch (Sercan O. Arik, et al. AAAI 2019)](examples/benchmarks/TabNet/)
+- [DoubleEnsemble based on LightGBM (Chuheng Zhang, et al. ICDM 2020)](examples/benchmarks/DoubleEnsemble/)
+- [TCTS based on pytorch (Xueqing Wu, et al. ICML 2021)](examples/benchmarks/TCTS/)
+- [Transformer based on pytorch (Ashish Vaswani, et al. NeurIPS 2017)](examples/benchmarks/Transformer/)
+- [Localformer based on pytorch (Juyong Jiang, et al.)](examples/benchmarks/Localformer/)
+- [TRA based on pytorch (Hengxu, Dong, et al. KDD 2021)](examples/benchmarks/TRA/)
+- [TCN based on pytorch (Shaojie Bai, et al. 2018)](examples/benchmarks/TCN/)
 
 Your PR of new Quant models is highly welcomed.