Reproducible research software for studying cyber-financial resilience in crypto markets using high-frequency public data, Little's Law-inspired observability proxies, and a roadmap toward Bayesian sequence modeling.
This repository is designed as an audit-grade scientific artifact. Phase 1 is complete and focuses on data collection, exploratory analysis, and reproducible run management. Later phases extend the project toward Bayesian LSTM modeling and resilience-oriented inference under market stress and non-stationarity.
- Domain: cyber-financial resilience and public-market observability
- Data source: Binance public API
- Current artifact phase: Phase 1 baseline complete
- Latest release: v0.1.1 — Citable Artifact & DOI Registration
- DOI: 10.5281/zenodo.18275035
- Documentation: reproducibility notes, data rationale, threat model, and model card in
docs/
- a reproducible pipeline for collecting high-frequency public market data
- a run-based artifact system with manifests, checksums, and deterministic traceability
- an exploratory analysis layer for volatility, stress, and flow-intensity proxies
- the software foundation for future Bayesian LSTM work on cyber-financial resilience
Crypto-financial markets behave like complex socio-technical systems:
- they are non-stationary
- they exhibit burstiness and clustered volatility
- they show regime shifts and stress propagation
- they offer only partial observability through public market data
This project uses queueing intuition inspired by Little's Law to construct proxy-observable signals while keeping strict epistemic guardrails: the outputs are descriptive and reproducible, not causal claims disguised as certainty.
- Phase 1: data pipeline and EDA complete
- Phase 2: Bayesian LSTM modeling planned
- Phase 3: resilience metrics and stress propagation planned
- immutable run directories under
runs/<RUN_ID>/ - machine-readable manifests and SHA-256 checksums
- generated figures for volatility, drawdowns, trade intensity, and inter-arrival behavior
- multilingual scientific documentation for reproducibility and modeling scope
- Exchange: Binance public API
- Instrument: BTC/USDT
- Granularity: 1-minute OHLCV plus public trades
- Access model: public REST only
The repository does not use proprietary, private, or user-identifiable data.
git clone https://github.com/ulissesflores/cyberfinancial-resilience-lrblstm.git
cd cyberfinancial-resilience-lrblstm
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txtpython scripts/make_run.py --note "phase-1 baseline"Use the generated run_id printed by the script in the next steps.
python scripts/collect_data.py \
--run_id <RUN_ID> \
--exchange binance \
--symbol BTC/USDT \
--timeframe 1m \
--ohlcv_days 90 \
--with_trades \
--trades_days 14python scripts/eda_generate_figures.py --run_id <RUN_ID>All generated artifacts are written under runs/<RUN_ID>/.
- queue-inspired variables are proxy observables, not direct queue measurements
- public exchange APIs provide partial visibility
- results are period- and regime-dependent
- the repository makes descriptive and methodological claims, not direct causal claims
Additional documentation is also available in pt-BR and es.
- Citation metadata: CITATION.cff
- Machine-readable metadata: codemeta.json
- Latest release: v0.1.1 — Citable Artifact & DOI Registration
- DOI: 10.5281/zenodo.18275035
If you use this work, cite the DOI-backed archival release rather than an unversioned repository snapshot.
configs/ run and data configuration
docs/ scientific documentation in multiple languages
schema/ formal schemas for datasets and manifests
scripts/ reproducible data collection and EDA pipelines
runs/ immutable run outputs created during execution