Project Context

Current State

The project implements a modular, efficient orderflow processing pipeline:

Stream orderflow from SQLite (DBInterpreter.stream).
Process trades and orderbook updates through modular OHLCProcessor architecture.
Exchange data with the UI via atomic JSON files (viz_io).
Render OHLC+Volume, Depth, and Metrics charts with a Dash app (app.py).

The system features a clean composition-based architecture with specialized modules for different concerns, providing OBI/CVD metrics alongside OHLC data.

Recent Work

Modular Refactoring: Extracted ohlc_processor.py into focused modules:
- level_parser.py: Orderbook level parsing utilities (85 lines)
- orderbook_manager.py: In-memory orderbook state management (90 lines)
- metrics_calculator.py: OBI and CVD metrics calculation (112 lines)
Architecture Compliance: Reduced main processor from 440 to 248 lines (250-line target achieved)
Maintained full backward compatibility and functionality
Implemented read-only, batched SQLite streaming with PRAGMA tuning.
Added robust JSON IPC with atomic writes and tolerant UI reads.
Built a responsive Dash visualization polling at 500ms.
Unified CLI using Typer, with UV for process management.

Conventions

Python 3.12+, UV for dependency and command execution.
Modular Architecture: Composition over inheritance, single-responsibility modules
File Size Limits: ≤250 lines per file, ≤50 lines per function (enforced)
Type hints throughout; concise, focused functions and classes.
Error handling with meaningful logs; avoid bare exceptions.
Prefer explicit JSON structures for IPC; keep payloads small and bounded.

Priorities

Improve configurability: database path discovery, CLI flags for paths and UI options.
Add tests for DBInterpreter.stream and OHLCProcessor (run with uv run pytest).
Performance tuning for large DBs while keeping UI responsive.
Documentation kept in sync with code; architecture reflects current design.

Roadmap (Future Work)

Enhance OBI metrics with additional derived calculations (e.g., normalized OBI).
Optional repository layer abstraction and a storage orchestrator.
Extend visualization with additional subplots and interactivity.
Strategy module for analytics and alerting on derived metrics.

Tooling

Package management and commands: UV (e.g., uv sync, uv run ...).
Visualization server: Dash on http://localhost:8050.
Linting/testing: Pytest (e.g., uv run pytest).

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