time-causal-vae 0.1.1

Time-causal financial generative models: refactored TC-VAE baselines with causal VQ/RVQ tokenizers, token priors, S&P500/VIX, Hawkes/SVMHJD, multi-dimensional benchmarks, and path-risk diagnostics.

TimeCausalVAE

Time-causal financial generative models: refactored TC-VAE baselines with causal VQ/RVQ tokenizers, token priors, S&P500/VIX, Hawkes/SVMHJD, multi-dimensional benchmarks, and path-risk diagnostics.

time-causal-vae is a research package for time-causal financial generative modelling across synthetic and empirical market time series.

The Python distribution is time-causal-vae; the import package is time_causal_vae. The GitHub repository remains TimeCausalVQVAE because it also hosts the discrete VQ extension work.

Release notes: 0.1.1.

Discrete time-causal VQ-VAE architecture. The diagram shows the S&P 500/VIX input window, causal convolutional encoder and decoder stacks, vector quantization, the VIX conditioning branch, and the receptive-field structure used to preserve no-anticipation behaviour.

Installation

Install the package from PyPI:

pip install time-causal-vae

Wheel installs include the runtime package only. From a source checkout, use Poetry groups for development tools, local empirical data access, notebooks, and optional tracking:

poetry install --only main
poetry install --with dev
poetry install --with notebooks
poetry install --with data
poetry install --with tracking

The docs URL currently points to the repository documentation directory. No hosted Sphinx documentation is published yet.

Quickstart

Check the installed package:

python - <<'PY'
import time_causal_vae

print(time_causal_vae.__version__)
PY

Inspect installed command-line entry points:

tcvae-train --help
tcvae-train-tokenizer --help
tcvae-train-token-prior --help
tcvae-evaluate --help
tcvae-select-model --help

Repository examples use configs, scripts, and registry files from the source tree. Clone the repository when running the public workflows:

git clone https://github.com/GVourvachakis/TimeCausalVQVAE.git
cd TimeCausalVQVAE
poetry install --with dev,data

Inspect the public S&P500/VIX registry entry:

poetry run python scripts/select_registered_model.py \
  --experiment sp500_vix \
  --family discrete

Run a dry-run continuous S&P500/VIX smoke command:

poetry run tcvae-train \
  --config configs/experiments/sp500_vix_beta_cvae.yaml \
  --output-dir outputs/sp500_vix_continuous \
  --epochs 1 \
  --no-wandb \
  --dry-run

Remove --dry-run only when you intentionally want to train locally.

Public Status

S&P500/VIX is the stable public default one-dimensional workflow. Hawkes/SVMHJD is an optional research benchmark with research-candidate metadata. Multidimensional benchmarks are experimental infrastructure, and no multidimensional model is selected in trained_models/model_registry.yaml. Experimental multidimensional profile metadata is kept in trained_models/multidim_profiles.yaml.

No downloaded data, trained weights, checkpoints, token tensors, generated paths, W&B runs, notebooks with outputs, or local result summaries are shipped with the package.

Stable Benchmarks

Benchmark	Role	Public status
S&P500/VIX	Empirical one-dimensional market workflow with VIX conditioning and a local processed data convention.	Public default. Uses local-only processed data and selected continuous/discrete registry metadata.
Black-Scholes	Synthetic geometric Brownian motion baseline for smoke tests and one-dimensional generation checks.	Stable baseline config and registry metadata.
Heston	Synthetic stochastic-volatility baseline with a latent variance channel.	Stable baseline config and registry metadata.
Path-dependent volatility	Conditional synthetic volatility baseline with a prefix volatility feature.	Stable baseline config and registry metadata.

The selected public S&P500/VIX discrete baseline is a standard causal VQ tokenizer plus an additive scalar-conditioned causal autoregressive token prior:

configs/experiments/sp500_vix_causal_vq_tokenizer.yaml
configs/experiments/sp500_vix_causal_token_prior_additive.yaml

Optional Research Benchmark

Benchmark	Description	Public status
Hawkes/SVMHJD	Marked Hawkes jump-diffusion benchmark with Ogata event simulation and fixed-grid observation.	Optional rare-event research benchmark with public_default: false. No weights or generated outputs are committed.

Experimental Benchmarks

Benchmark	Description	Public status
Multifactor market	50-dimensional low-rank factor market with sector structure and optional common/sector jumps.	Experimental infrastructure for shape, covariance, and no-leakage checks.
S&P500 50-stock panel	Local-only yfinance/Yahoo-backed daily 50-stock equity panel.	Experimental infrastructure. Downloaded Yahoo-backed data must remain local and is not redistributed.

The benchmark notes live under docs/benchmarks. They document the synthetic SDE or simulator specification, empirical data source conventions, tensor and condition layouts, preprocessing rules, and local-data boundaries for each workflow.

Benchmark Data Conventions

Benchmark	Data convention
S&P500/VIX	Local processed benchmark data is expected at data/processed/sp500vix/sp500vix_normalized.npy.
Hawkes/SVMHJD	Synthetic paths are generated locally from the marked Hawkes jump-diffusion simulator.
Multifactor market	Synthetic 50D panels are generated locally from the low-rank sector-factor simulator.
S&P500 50-stock panel	Daily panels are downloaded locally through optional yfinance access and must not be redistributed.

Models And Features

Area	Included	Release status
Continuous TC-VAE	No-anticipation continuous VAE baseline, RealNVP-compatible prior paths, and financial dataset conventions.	Stable baseline surface.
Causal VQ tokenizers	Causal convolutional tokenizers with vector-quantized latent codes.	Public S&P500/VIX discrete baseline.
RVQ and multi-code tokenizers	Residual and multi-code tokenizer infrastructure.	Experimental. No multidimensional model is registry-selected.
Token priors	Additive autoregressive priors and causal conv-transformer research variants.	Additive prior is the public S&P500/VIX default; conv-transformer variants are research candidates.
Registry metadata	Selected configs, local checkpoint conventions, metrics, caveats, and no-leakage status.	Metadata only. It does not contain weights.
Notebook demos	Output-stripped notebooks that print guarded commands and read local outputs when available.	Demonstration only. They should not train or evaluate by default.

Executed notebook previews are available on the docs/executed-notebook-previews branch. The main branch keeps notebooks output-stripped for reproducibility and package size. Preview outputs depend on local artefacts and checkpoints and are not the package source of truth.

Diagnostics

Diagnostic family	Examples	Notes
Distributional distances	MMD, sliced Wasserstein, terminal and volatility Wasserstein distances.	Used for registry summaries and model comparison.
Path-risk summaries	Drawdown, return autocorrelation, squared-return autocorrelation, VaR, and ES.	Intended for generated-vs-real path checks, not investment advice.
Conditional checks	VIX-bucket summaries and prefix-safe condition handling.	Used by the public S&P500/VIX workflow.
Token diagnostics	Codebook usage, active codes, token perplexity, transition summaries, and latent geometry.	Used to inspect discrete-token behaviour.
Jump diagnostics	Jump count, inter-arrival, jump-size, and lower-tail summaries.	Used by the optional Hawkes/SVMHJD benchmark.
Cross-sectional checks	Covariance, correlation, eigenspectrum, sector-block, and portfolio-risk summaries.	Experimental multidimensional infrastructure.

Local Data Policy

The package does not redistribute empirical market data. The S&P500/VIX data file is expected locally at:

data/processed/sp500vix/sp500vix_normalized.npy

The S&P500 50-stock panel downloader uses optional yfinance access and writes local raw and processed files under data/raw/ and data/processed/. Yahoo-backed data is subject to Yahoo's terms and must not be redistributed or committed.

Generated artefacts belong under local paths such as outputs/, wandb/, or data/processed/. They are intentionally excluded from the public repository and package.

Repository Layout

Path	Purpose
src/time_causal_vae	Importable package source.
configs/experiments	Repository workflow configs used by scripts and notebooks.
scripts	Inspection, extraction, evaluation, no-leakage, and smoke helpers.
trained_models	Lightweight registry metadata and model cards only.
docs/benchmarks	Public benchmark notes.
assets/figures	Small curated README figures generated from local runs.
notebooks	Output-stripped demos and report-facing notebooks.

Background

TimeCausalVAE keeps the no-anticipation contract from upstream TC-VAE: at time t, encoders, tokenizers, priors, and diagnostics should only use observations and conditions available up to that point. The public branch preserves the continuous TC-VAE baseline and adds a discrete two-stage path: causal tokenizer first, causal token prior second.

The package is research software for generative modelling diagnostics. It is not a calibrated pricing library, a trading system, or a source of financial advice.

Citation And Acknowledgement

This repository refactors selected parts of the original Time-Causal VAE code and extends the public workflow with causal VQ-style discrete latent models. Please cite or acknowledge the relevant upstream work when using the package:

• Time-Causal VAE: Robust Financial Time Series Generator - Beatrice Acciaio, Stephan Eckstein, and Songyan Hou. DOI: 10.48550/arXiv.2411.02947; code: justinhou95/TimeCausalVAE.
• Neural Discrete Representation Learning - Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu. DOI: 10.48550/arXiv.1711.00937.
• Vector Quantized Time Series Generation with a Bidirectional Prior Model - Daesoo Lee, Sara Malacarne, and Erlend Aune. DOI: 10.48550/arXiv.2303.04743; code: ML4ITS/TimeVQVAE.
• vector-quantize-pytorch - lucidrains. Repository: lucidrains/vector-quantize-pytorch.

License

This project is released under the GNU General Public License v3. See LICENSE.