mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-29 03:37:45 +00:00
Mark Aron Szulyovszky
3eb3ea94e3
* refactor(Training): added InferenceResult & TrainedModel types * refactor(Pipeline): introduced TrainingOutcome, BetSizingWithMetaOutcome, etc. * fix(Pipeline): getting it to compile * refactor(WalkForward): separate preprocessing step * feat(Pipeline): separate out transformations processing step * refactor(Pipeline): use the Directional model terminology, put bet_sizing into pipeline instead of hiding it in a step * refactor(WalkForward): moved functions to separate folder * fix(WalkForward): use sparse array to store models, process transformations in parallel (lot faster) * fix(Tests): and evaluation * fix(Tests): for realz * fix(Inference): preloading everything now, renamed primary models to directional models * fix(BetSizing): was running transformations on the wrong data, oops * fix(BetSizing): concatenated on the wrong axis accidentally * fix(Reporting): able to use the new Stats type * fix(BetSizing): renamed int column names * fix(Portfolio): name the column properly * fix(Reporting): rename the correct Series, lol * fix(Inference): walk_forwad_inference() can deal with models not being aligned with the starting index * fix(WalkForward): accidentally using the wrong index * fix(WalkForward): use the correct indicies to fetch last model/transformations * fix(CI): changed the name of the results
31 lines
1.5 KiB
Python
31 lines
1.5 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: list[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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directional_avg_stats = weighted_average(pd.concat([pd.Series(stat) for stat in directional_stats], axis = 1), 'no_of_samples')
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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_avg_stats.loc['no_of_samples'].sum())
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print("Mean Sharpe ratio for Level-1 models: ", round(directional_avg_stats.loc['sharpe'], 3))
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print("Mean Probabilistic Sharpe ratio for Level-1 models: ", round(directional_avg_stats.loc['prob_sharpe'].mean(), 3))
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if len(config.meta_models) > 0:
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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() |