sf-backtester 0.1.1

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sf-backtester

SLURM-based parallel backtesting for quantitative finance. Distributes MVO optimization across compute nodes, processing one year per task.

Installation

pip install sf-backtester

Usage

CLI

# Run backtest
sf_backtester run config.yml

# Preview sbatch script without submitting
sf_backtester run config.yml --dry-run

Python API

from sf_backtester import BacktestRunner, BacktestConfig

slurm_config = SlurmConfig(
    n_cpus=8,
    mem="32G",
    time="03:00:00",
    mail_type="BEGIN,END,FAIL",
    max_concurrent_jobs=30,
)

config = BacktestConfig(
    signal_name="momentum",
    gamma=50,
    data_path="/path/to/alphas.parquet",
    project_root="/path/to/project",
    byu_email="[e-mail]",
    constraints=["ZeroBeta", "ZeroInvestment"],
    slurm=slurm_config,
)

runner = BacktestRunner(config)

# Preview the sbatch script
print(runner.dry_run())

# Submit to SLURM
runner.submit()

Or load from YAML:

from sf_backtester import BacktestRunner

runner = BacktestRunner.from_yaml("config.yml")

runner.submit()

You can also pass a DataFrame directly:

from sf_backtester import BacktestRunner
import polars as pl

runner = BacktestRunner.from_yaml("config.yml")

data = pl.read_parquet("alphas.parquet")

runner.submit(data=data)

Configuration

YAML format

signal_name: momentum
gamma: 500
data_path: /path/to/alphas.parquet
project_root: /path/to/project
email: [e-mail]

constraints:
  - ZeroBeta
  - ZeroInvestment

slurm:
  n_cpus: 8
  mem: 32G
  time: "03:00:00"
  mail_type: BEGIN,END,FAIL

Data format

Input parquet must have columns:

• date: Date column
• barrid: Asset identifier
• alpha: Alpha signal values
• predicted_beta: Predicted beta values

Output is one parquet per year in output_dir/{year}.parquet containing portfolio weights.

Publishing

1. Bump the version

uv version v*.*.*

2. Add changes (it can be just the version change)

git add .
git commit -m "Bumped version."

3. Tag the branch

git tag v*.*.*

4. Push to origin

git push origin/main --tags