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feat(Project): use SKLearn models directly, removed custom ensembling, use 5 minute data, batch inference, numba cusum filter (#192)

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* feat(Project): use 5 minute data, running training in parallel, sped up cusum filter by 10x with numba

* fix(WalkForward): inference mini-batch parallelization

* fix(WalkForward): don't use the parallel version of any of the functions

* feat(CI): download the data required

* fix(Project): 5min_crypto folder added

* fix(Evaluate): make sure we have numerical stability in returns

* feat(Models): use SKLearn models directly to enable composability

* feat(Inference): batched inference now working, added forecasting_horizon

* fix(Inference): works again

* fix(Inference)

* chore(Models): remove unused Ensemble model

* fix(Labeller): don't just forward shift returns, also take the sum of the data happened until then

* Update test.yml
This commit is contained in:
Mark Aron Szulyovszky
2022-02-17 16:36:35 +01:00
committed by GitHub
parent 5c94af8b01
commit 9d47ee942d
52 changed files with 470 additions and 628 deletions
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utils/evaluate.py
+1 -1
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@@ -49,7 +49,7 @@ def evaluate_predictions(
return len(series[series != 0])
no_of_samples = count_non_zero(df.y_pred)
scorecard['no_of_samples'] = no_of_samples
sharpe = sharpe_ratio(df.result)
sharpe = sharpe_ratio(df.result + 1e-20)
scorecard['sharpe'] = sharpe
benchmark_sharpe = sharpe_ratio(df.forward_returns)
scorecard['benchmark_sharpe'] = benchmark_sharpe