From d642c7b6eac4445424f69e3362719ebb6a46835b Mon Sep 17 00:00:00 2001 From: Jactus Date: Fri, 11 Dec 2020 09:55:37 +0800 Subject: [PATCH 1/3] Update benchmark performance --- .github/workflows/test.yml | 1 - examples/benchmarks/README.md | 10 +++++----- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index be6f6b75d..64ff99dfe 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -26,7 +26,6 @@ jobs: - name: Install dependencies run: | if [ "$RUNNER_OS" == "Windows" ]; then - $CONDA\\python.exe -m pip install --upgrade cython $CONDA\\python.exe -m pip install --upgrade cython $CONDA\\python.exe -m pip install numpy jupyter jupyter_contrib_nbextensions $CONDA\\python.exe -m pip install -U scipy scikit-learn # installing without this line will cause errors on GitHub Actions, while instsalling locally won't diff --git a/examples/benchmarks/README.md b/examples/benchmarks/README.md index c561906d6..37677a99e 100644 --- a/examples/benchmarks/README.md +++ b/examples/benchmarks/README.md @@ -25,8 +25,8 @@ The numbers shown below demonstrate the performance of the entire `workflow` of | XGBoost | Alpha158 | 0.0481±0.00 | 0.3659±0.00| 0.0495±0.00 | 0.4033±0.00 | 0.1111±0.00 | 1.2915±0.00| -0.0893±0.00 | | LightGBM | Alpha158 | 0.0475±0.00 | 0.3979±0.00| 0.0485±0.00 | 0.4123±0.00 | 0.1143±0.00 | 1.2744±0.00| -0.0800±0.00 | | MLP | Alpha158 | 0.0363±0.00 | 0.2770±0.02| 0.0421±0.00 | 0.3167±0.01 | 0.0856±0.01 | 1.0397±0.12| -0.1134±0.01 | -| TFT | Alpha158 (with selected 20 features) | 0.0335±0.00 | 0.2009±0.01| 0.0090±0.00 | 0.0553±0.01 | 0.0605±0.01 | 0.5438±0.12| -0.1772±0.03 | -| GRU | Alpha158 (with selected 20 features) | 0.0313±0.00 | 0.2427±0.01 | 0.0416±0.00 | 0.3370±0.01 | 0.0335±0.01 | 0.4808±0.22 | -0.1112±0.03 | -| LSTM | Alpha158 (with selected 20 features) | 0.0337±0.01 | 0.2562±0.05 | 0.0427±0.01 | 0.3392±0.04 | 0.0269±0.06 | 0.3385±0.74 | -0.1285±0.04 | -| ALSTM | Alpha158 (with selected 20 features) | 0.0366±0.00 | 0.2803±0.04 | 0.0478±0.00 | 0.3770±0.02 | 0.0520±0.03 | 0.7115±0.30 | -0.0986±0.01 | -| GATs | Alpha158 (with selected 20 features) | 0.0355±0.00 | 0.2576±0.02 | 0.0465±0.00 | 0.3585±0.00 | 0.0509±0.02 | 0.7212±0.22 | -0.0821±0.01 | \ No newline at end of file +| TFT | Alpha158 (with selected 20 features) | 0.0344±0.00 | 0.2071±0.02| 0.0103±0.00 | 0.0632±0.01 | 0.0638±0.00 | 0.5845±0.8| -0.1754±0.02 | +| GRU | Alpha158 (with selected 20 features) | 0.0302±0.00 | 0.2353±0.03| 0.0411±0.00 | 0.3309±0.03 | 0.0302±0.02 | 0.4353±0.28| -0.1140±0.02 | +| LSTM | Alpha158 (with selected 20 features) | 0.0359±0.01 | 0.2774±0.06| 0.0448±0.01 | 0.3597±0.05 | 0.0402±0.03 | 0.5743±0.41| -0.1152±0.03 | +| ALSTM | Alpha158 (with selected 20 features) | 0.0329±0.01 | 0.2465±0.07| 0.0450±0.01 | 0.3485±0.06 | 0.0288±0.04 | 0.4163±0.50| -0.1269±0.04 | +| GATs | Alpha158 (with selected 20 features) | 0.0349±0.00 | 0.2526±0.01| 0.0454±0.00 | 0.3531±0.01 | 0.0561±0.01 | 0.7992±0.19| -0.0751±0.02 | \ No newline at end of file From c10955d0263686b740401714292ee7ca19c032f7 Mon Sep 17 00:00:00 2001 From: Jactus Date: Fri, 11 Dec 2020 14:33:16 +0800 Subject: [PATCH 2/3] Update tft --- examples/benchmarks/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/benchmarks/README.md b/examples/benchmarks/README.md index 37677a99e..2557eb9b3 100644 --- a/examples/benchmarks/README.md +++ b/examples/benchmarks/README.md @@ -25,7 +25,7 @@ The numbers shown below demonstrate the performance of the entire `workflow` of | XGBoost | Alpha158 | 0.0481±0.00 | 0.3659±0.00| 0.0495±0.00 | 0.4033±0.00 | 0.1111±0.00 | 1.2915±0.00| -0.0893±0.00 | | LightGBM | Alpha158 | 0.0475±0.00 | 0.3979±0.00| 0.0485±0.00 | 0.4123±0.00 | 0.1143±0.00 | 1.2744±0.00| -0.0800±0.00 | | MLP | Alpha158 | 0.0363±0.00 | 0.2770±0.02| 0.0421±0.00 | 0.3167±0.01 | 0.0856±0.01 | 1.0397±0.12| -0.1134±0.01 | -| TFT | Alpha158 (with selected 20 features) | 0.0344±0.00 | 0.2071±0.02| 0.0103±0.00 | 0.0632±0.01 | 0.0638±0.00 | 0.5845±0.8| -0.1754±0.02 | +| TFT | Alpha158 (with selected 20 features) | 0.0344±0.00 | 0.2071±0.02| 0.0103±0.00 | 0.0632±0.01 | 0.0638±0.00 | 0.5845±0.08| -0.1754±0.02 | | GRU | Alpha158 (with selected 20 features) | 0.0302±0.00 | 0.2353±0.03| 0.0411±0.00 | 0.3309±0.03 | 0.0302±0.02 | 0.4353±0.28| -0.1140±0.02 | | LSTM | Alpha158 (with selected 20 features) | 0.0359±0.01 | 0.2774±0.06| 0.0448±0.01 | 0.3597±0.05 | 0.0402±0.03 | 0.5743±0.41| -0.1152±0.03 | | ALSTM | Alpha158 (with selected 20 features) | 0.0329±0.01 | 0.2465±0.07| 0.0450±0.01 | 0.3485±0.06 | 0.0288±0.04 | 0.4163±0.50| -0.1269±0.04 | From 4b4cd38ca61f6f9848e8f1493a8af18add847612 Mon Sep 17 00:00:00 2001 From: Jactus Date: Thu, 17 Dec 2020 14:41:12 +0800 Subject: [PATCH 3/3] Update benchmark results --- examples/benchmarks/README.md | 38 +++++++++++++++++------------------ 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/examples/benchmarks/README.md b/examples/benchmarks/README.md index 2557eb9b3..3f9b1f55b 100644 --- a/examples/benchmarks/README.md +++ b/examples/benchmarks/README.md @@ -1,32 +1,32 @@ # Benchmarks Performance -Here are the results of each benchmark model running on Qlib's `Alpha360` and `Alpha158` dataset with China's A shared-stock & CSI300 data respectively. The values of each metric are the mean and std calculated based on 10 runs. +Here are the results of each benchmark model running on Qlib's `Alpha360` and `Alpha158` dataset with China's A shared-stock & CSI300 data respectively. The values of each metric are the mean and std calculated based on 20 runs. The numbers shown below demonstrate the performance of the entire `workflow` of each model. We will update the `workflow` as well as models in the near future for better results. ## Alpha360 dataset | Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown | |---|---|---|---|---|---|---|---|---| -| Linear | Alpha360 | 0.0150±0.00 | 0.1049±0.00| 0.0284±0.00 | 0.1970±0.00 | -0.0655±0.00 | -0.6985±0.00| -0.2961±0.00 | -| CatBoost | Alpha360 | 0.0397±0.00 | 0.2878±0.00| 0.0470±0.00 | 0.3703±0.00 | 0.0342±0.00 | 0.4092±0.00| -0.1057±0.00 | -| XGBoost | Alpha360 | 0.0400±0.00 | 0.3031±0.00| 0.0461±0.00 | 0.3862±0.00 | 0.0528±0.00 | 0.6307±0.00| -0.1113±0.00 | -| LightGBM | Alpha360 | 0.0399±0.00 | 0.3075±0.00| 0.0492±0.00 | 0.4019±0.00 | 0.0323±0.00 | 0.4370±0.00| -0.0917±0.00 | -| MLP | Alpha360 | 0.0253±0.01 | 0.1954±0.05| 0.0329±0.00 | 0.2687±0.04 | 0.0161±0.01 | 0.1989±0.19| -0.1275±0.03 | -| GRU | Alpha360 | 0.0503±0.01 | 0.3946±0.06| 0.0588±0.00 | 0.4737±0.05 | 0.0799±0.02 | 1.0940±0.26| -0.0810±0.03 | -| LSTM | Alpha360 | 0.0466±0.01 | 0.3644±0.06| 0.0555±0.00 | 0.4451±0.04 | 0.0783±0.05 | 1.0539±0.65| -0.0844±0.03 | -| ALSTM | Alpha360 | 0.0472±0.00 | 0.3558±0.04| 0.0577±0.00 | 0.4522±0.04 | 0.0522±0.02 | 0.7090±0.32| -0.1059±0.03 | -| GATs | Alpha360 | 0.0480±0.00 | 0.3555±0.02| 0.0598±0.00 | 0.4616±0.01 | 0.0857±0.03 | 1.1317±0.42| -0.0917±0.01 | +| Linear | Alpha360 | 0.0150±0.00 | 0.1049±0.00| 0.0284±0.00 | 0.1970±0.00 | -0.0659±0.00 | -0.7072±0.00| -0.2955±0.00 | +| CatBoost (Liudmila Prokhorenkova, et al.) | Alpha360 | 0.0397±0.00 | 0.2878±0.00| 0.0470±0.00 | 0.3703±0.00 | 0.0342±0.00 | 0.4092±0.00| -0.1057±0.00 | +| XGBoost (Tianqi Chen, et al.) | Alpha360 | 0.0400±0.00 | 0.3031±0.00| 0.0461±0.00 | 0.3862±0.00 | 0.0528±0.00 | 0.6307±0.00| -0.1113±0.00 | +| LightGBM (Guolin Ke, et al.) | Alpha360 | 0.0399±0.00 | 0.3075±0.00| 0.0492±0.00 | 0.4019±0.00 | 0.0323±0.00 | 0.4370±0.00| -0.0917±0.00 | +| MLP | Alpha360 | 0.0285±0.00 | 0.1981±0.02| 0.0402±0.00 | 0.2993±0.02 | 0.0073±0.02 | 0.0880±0.22| -0.1446±0.03 | +| GRU (Kyunghyun Cho, et al.) | Alpha360 | 0.0490±0.01 | 0.3787±0.05| 0.0581±0.00 | 0.4664±0.04 | 0.0726±0.02 | 0.9817±0.34| -0.0902±0.03 | +| LSTM (Sepp Hochreiter, et al.) | Alpha360 | 0.0443±0.01 | 0.3401±0.05| 0.0536±0.01 | 0.4248±0.05 | 0.0627±0.03 | 0.8441±0.48| -0.0882±0.03 | +| ALSTM (Yao Qin, et al.) | Alpha360 | 0.0493±0.01 | 0.3778±0.06| 0.0585±0.00 | 0.4606±0.04 | 0.0513±0.03 | 0.6727±0.38| -0.1085±0.02 | +| GATs (Petar Velickovic, et al.) | Alpha360 | 0.0475±0.00 | 0.3515±0.02| 0.0592±0.00 | 0.4585±0.01 | 0.0876±0.02 | 1.1513±0.27| -0.0795±0.02 | ## Alpha158 dataset | Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown | |---|---|---|---|---|---|---|---|---| | Linear | Alpha158 | 0.0393±0.00 | 0.2980±0.00| 0.0475±0.00 | 0.3546±0.00 | 0.0795±0.00 | 1.0712±0.00| -0.1449±0.00 | -| CatBoost | Alpha158 | 0.0503±0.00 | 0.3586±0.00| 0.0483±0.00 | 0.3667±0.00 | 0.1080±0.00 | 1.1567±0.00| -0.0787±0.00 | -| XGBoost | Alpha158 | 0.0481±0.00 | 0.3659±0.00| 0.0495±0.00 | 0.4033±0.00 | 0.1111±0.00 | 1.2915±0.00| -0.0893±0.00 | -| LightGBM | Alpha158 | 0.0475±0.00 | 0.3979±0.00| 0.0485±0.00 | 0.4123±0.00 | 0.1143±0.00 | 1.2744±0.00| -0.0800±0.00 | -| MLP | Alpha158 | 0.0363±0.00 | 0.2770±0.02| 0.0421±0.00 | 0.3167±0.01 | 0.0856±0.01 | 1.0397±0.12| -0.1134±0.01 | -| TFT | Alpha158 (with selected 20 features) | 0.0344±0.00 | 0.2071±0.02| 0.0103±0.00 | 0.0632±0.01 | 0.0638±0.00 | 0.5845±0.08| -0.1754±0.02 | -| GRU | Alpha158 (with selected 20 features) | 0.0302±0.00 | 0.2353±0.03| 0.0411±0.00 | 0.3309±0.03 | 0.0302±0.02 | 0.4353±0.28| -0.1140±0.02 | -| LSTM | Alpha158 (with selected 20 features) | 0.0359±0.01 | 0.2774±0.06| 0.0448±0.01 | 0.3597±0.05 | 0.0402±0.03 | 0.5743±0.41| -0.1152±0.03 | -| ALSTM | Alpha158 (with selected 20 features) | 0.0329±0.01 | 0.2465±0.07| 0.0450±0.01 | 0.3485±0.06 | 0.0288±0.04 | 0.4163±0.50| -0.1269±0.04 | -| GATs | Alpha158 (with selected 20 features) | 0.0349±0.00 | 0.2526±0.01| 0.0454±0.00 | 0.3531±0.01 | 0.0561±0.01 | 0.7992±0.19| -0.0751±0.02 | \ No newline at end of file +| CatBoost (Liudmila Prokhorenkova, et al.) | Alpha158 | 0.0503±0.00 | 0.3586±0.00| 0.0483±0.00 | 0.3667±0.00 | 0.1080±0.00 | 1.1561±0.00| -0.0787±0.00 | +| XGBoost (Tianqi Chen, et al.) | Alpha158 | 0.0481±0.00 | 0.3659±0.00| 0.0495±0.00 | 0.4033±0.00 | 0.1111±0.00 | 1.2915±0.00| -0.0893±0.00 | +| LightGBM (Guolin Ke, et al.) | Alpha158 | 0.0475±0.00 | 0.3979±0.00| 0.0485±0.00 | 0.4123±0.00 | 0.1143±0.00 | 1.2744±0.00| -0.0800±0.00 | +| MLP | Alpha158 | 0.0358±0.00 | 0.2738±0.03| 0.0425±0.00 | 0.3221±0.01 | 0.0836±0.02 | 1.0323±0.25| -0.1127±0.02 | +| TFT (Bryan Lim, et al.) | Alpha158 (with selected 20 features) | 0.0343±0.00 | 0.2071±0.02| 0.0107±0.00 | 0.0660±0.02 | 0.0623±0.02 | 0.5818±0.20| -0.1762±0.01 | +| GRU (Kyunghyun Cho, et al.) | Alpha158 (with selected 20 features) | 0.0311±0.00 | 0.2418±0.04| 0.0425±0.00 | 0.3434±0.02 | 0.0330±0.02 | 0.4805±0.30| -0.1021±0.02 | +| LSTM (Sepp Hochreiter, et al.) | Alpha158 (with selected 20 features) | 0.0312±0.00 | 0.2394±0.04| 0.0418±0.00 | 0.3324±0.03 | 0.0298±0.02 | 0.4198±0.33| -0.1348±0.03 | +| ALSTM (Yao Qin, et al.) | Alpha158 (with selected 20 features) | 0.0385±0.01 | 0.3022±0.06| 0.0478±0.00 | 0.3874±0.04 | 0.0486±0.03 | 0.7141±0.45| -0.1088±0.03 | +| GATs (Petar Velickovic, et al.) | Alpha158 (with selected 20 features) | 0.0349±0.00 | 0.2511±0.01| 0.0457±0.00 | 0.3537±0.01 | 0.0578±0.02 | 0.8221±0.25| -0.0824±0.02 | \ No newline at end of file