mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
9d47ee942d
* 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
29 lines
1.3 KiB
Python
29 lines
1.3 KiB
Python
from reporting.wandb import send_report_to_wandb
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import pandas as pd
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from utils.helpers import weighted_average
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from config.types import Config
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from training.types import WeightsSeries, Stats
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def report_results(directional_stats: Stats, output_stats: Stats, output_weights: WeightsSeries, config: Config, wandb, sweep: bool):
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# Only send the results of the final model to wandb
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send_report_to_wandb(output_stats, wandb)
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pd.Series(output_stats).to_csv('output/results.csv')
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output_weights.rename(config.target_asset[1]).to_csv('output/predictions.csv')
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print("\n--------\n")
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print("Benchmark buy-and-hold sharpe: ", output_stats['benchmark_sharpe'])
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print("Level-1: Number of samples evaluated: ", directional_stats['no_of_samples'])
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print("Mean Sharpe ratio for Level-1 models: ", round(directional_stats['sharpe'], 3))
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print("Mean Probabilistic Sharpe ratio for Level-1 models: ", round(directional_stats['prob_sharpe'], 3))
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print("Level-2 (Ensemble): Number of samples evaluated: ", output_stats['no_of_samples'])
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print("Mean Sharpe ratio for Level-2 (Ensemble) models: ", output_stats['sharpe'])
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print("Mean Probabilistic Sharpe ratio for Level-2 (Ensemble) models: ", output_stats['prob_sharpe'])
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if sweep:
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if wandb.run is not None:
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wandb.finish() |