¶ 01Context & Design Rationale

MLOps-style batch job in Python with a focus on reproducibility, observability, and robustness. Reads OHLCV market data, computes rolling averages on closing prices, generates binary buy/sell signals, and writes structured output to metrics.json and a run.log. Configuration is fully externalised via config.yaml, and the job handles malformed rows, empty files, and missing config gracefully. Docker-ready with no hardcoded paths. Engineered with strict architectural boundaries, deterministic memory models, and zero extraneous runtime dependencies.