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https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
b656f790f5
* feat(Data): resample exogenous/other datasource when their frequency is different * fix(Linter): ran * fix(Data): resampling done properly
51 lines
1.4 KiB
Python
51 lines
1.4 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(
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directional_stats: Stats,
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output_stats: Stats,
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output_weights: WeightsSeries,
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config: Config,
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wandb,
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sweep: bool,
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):
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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.file_name).to_csv(
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"output/predictions.csv"
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)
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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(
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"Mean Sharpe ratio for Level-1 models: ", round(directional_stats["sharpe"], 3)
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)
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print(
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"Mean Probabilistic Sharpe ratio for Level-1 models: ",
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round(directional_stats["prob_sharpe"], 3),
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)
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print(
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"Level-2 (Ensemble): Number of samples evaluated: ",
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output_stats["no_of_samples"],
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)
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print("Mean Sharpe ratio for Level-2 (Ensemble) models: ", output_stats["sharpe"])
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print(
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"Mean Probabilistic Sharpe ratio for Level-2 (Ensemble) models: ",
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output_stats["prob_sharpe"],
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)
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if sweep:
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if wandb.run is not None:
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wandb.finish()
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