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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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reporting/reporting.py
+7 -9
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@@ -4,7 +4,7 @@ from utils.helpers import weighted_average
from config.types import Config
from training.types import WeightsSeries, Stats
def report_results(directional_stats: list[Stats], output_stats: Stats, output_weights: WeightsSeries, config: Config, wandb, sweep: bool):
def report_results(directional_stats: Stats, output_stats: Stats, output_weights: WeightsSeries, config: Config, wandb, sweep: bool):
# Only send the results of the final model to wandb
send_report_to_wandb(output_stats, wandb)
@@ -13,18 +13,16 @@ def report_results(directional_stats: list[Stats], output_stats: Stats, output_w
output_weights.rename(config.target_asset[1]).to_csv('output/predictions.csv')
print("\n--------\n")
directional_avg_stats = weighted_average(pd.concat([pd.Series(stat) for stat in directional_stats], axis = 1), 'no_of_samples')
print("Benchmark buy-and-hold sharpe: ", output_stats['benchmark_sharpe'])
print("Level-1: Number of samples evaluated: ", directional_avg_stats.loc['no_of_samples'].sum())
print("Mean Sharpe ratio for Level-1 models: ", round(directional_avg_stats.loc['sharpe'], 3))
print("Mean Probabilistic Sharpe ratio for Level-1 models: ", round(directional_avg_stats.loc['prob_sharpe'].mean(), 3))
print("Level-1: Number of samples evaluated: ", directional_stats['no_of_samples'])
print("Mean Sharpe ratio for Level-1 models: ", round(directional_stats['sharpe'], 3))
print("Mean Probabilistic Sharpe ratio for Level-1 models: ", round(directional_stats['prob_sharpe'], 3))
if len(config.meta_models) > 0:
print("Level-2 (Ensemble): Number of samples evaluated: ", output_stats['no_of_samples'])
print("Mean Sharpe ratio for Level-2 (Ensemble) models: ", output_stats['sharpe'])
print("Mean Probabilistic Sharpe ratio for Level-2 (Ensemble) models: ", output_stats['prob_sharpe'])
print("Level-2 (Ensemble): Number of samples evaluated: ", output_stats['no_of_samples'])
print("Mean Sharpe ratio for Level-2 (Ensemble) models: ", output_stats['sharpe'])
print("Mean Probabilistic Sharpe ratio for Level-2 (Ensemble) models: ", output_stats['prob_sharpe'])
if sweep:
if wandb.run is not None: