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Update benchmark performance #97

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merged 4 commits into from
Dec 17, 2020
Merged

Update benchmark performance #97

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d642c7b
Update benchmark performance
Derek-Wds Dec 11, 2020
c10955d
Update tft
Derek-Wds Dec 11, 2020
4b4cd38
Update benchmark results
Derek-Wds Dec 17, 2020
7372a3a
Merge branch 'main' into main
Derek-Wds Dec 17, 2020
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1 change: 0 additions & 1 deletion .github/workflows/test.yml
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Expand Up @@ -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
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10 changes: 5 additions & 5 deletions examples/benchmarks/README.md
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Expand Up @@ -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 |
| 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 |
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@you-n-g you-n-g Dec 11, 2020 •
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Why isTFT‘s annual return so stable while it's information ratio so variable？

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Wendi calculated the information ratio's std in a wrong way. It should be 0.08 instead of 0.8. I have just updated it.

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I calculated the mean of two 5-runs and typed by hand, and missed a "0"...

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@wendili-cs make sure your rank IC. TFT has high IC & annual return while it's IC is low.
Please double check it though such results ares possible.

| 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 |