Reproducibility

The project is designed so that the reported results can be reproduced from the source code, configuration files, and tracked experiment summaries.

Environment

Create a Python virtual environment and install the project dependencies from the repository root:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
pip install -e . --no-build-isolation

The Sphinx documentation has a small additional dependency set:

pip install -r requirements-docs.txt

Final Experiment

The final reported neural-network configuration can be reproduced with:

python scripts/run_experiment.py \
  --n-samples 100000 \
  --max-epochs 200 \
  --batch-size 1024 \
  --mc-n-paths 50000 \
  --mc-evaluation-samples 512 \
  --feature-set with_moneyness \
  --activation silu \
  --seed 42 \
  --data-dir data/final_improved \
  --output-dir outputs/final_improved

The compact tracked summary of this run is stored in results/final/. The larger generated artifacts remain reproducible but untracked:

• data/final_improved/ contains the generated dataset;

• outputs/final_improved/ contains model checkpoints, scalers, and full run outputs.

Tracked Results

The following tracked directories contain the selected experiment summaries used by the report:

• results/final/ for the final neural-network experiment;

• results/experiments/moneyness_feature/ for the moneyness comparison;

• results/experiments/activation_functions/ for the activation-function comparison;

• results/experiments/combined_feature_activation/ for the combined moneyness and SiLU experiment;

• results/experiments/runtime_benchmark/ for the pricing-time comparison;

• results/experiments/svr_benchmark/ for the clean-target SVR baseline;

• results/experiments/noisy_targets/ for the noisy-target neural-network robustness experiment;

• results/experiments/noisy_svr_benchmark/ for the noisy-target SVR robustness benchmark.

Validation Commands

Run the automated tests with:

pytest

Build the documentation in strict mode with:

sphinx-build -W -b html docs/source docs/build/html

The report is built with XeLaTeX:

cd report
latexmk -xelatex main.tex

The printable report variant is generated with:

cd report
latexmk -xelatex -jobname=main_print \
  -usepretex='\def\printlinks{1}' main.tex

The Beamer presentation is also built with XeLaTeX:

cd presentation
latexmk -xelatex main.tex

Reproducibility Notes

Random seeds are fixed in the final experiment and in the reduced-scale benchmarks. Monte Carlo evaluation is performed on a deterministic subset of test options, which makes the reported comparison reproducible while keeping the benchmark computationally bounded.

Generated datasets and checkpoints are intentionally not tracked by Git. They are outputs of the experiment pipeline, while the source code, commands, configuration snapshots, metrics, figures, and verification notes are tracked.