Futures Real-time Monitoring System

Problem and Background

Futures trading spans 6 major exchanges with over 90 main contracts, requiring real-time market monitoring for strategy and risk management. Existing tools often depend on external software or cloud services, leading to complex deployment, high latency, and data privacy concerns. Developers or traders need a lightweight, localized solution that can be quickly launched to provide comprehensive market data and analysis features for rapid decision-making.

Core Capabilities and Workflow

This skill delivers a locally running futures monitoring dashboard, with core capabilities built on a pure tech stack:

• Data Coverage and Acquisition: Automatically fetches market data via the WorkBuddy finance-data API, covering 90+ main contracts across 6 exchanges; the backend uses the http.server standard library with no additional dependencies.
• Interactive Analysis Interface: The frontend integrates ECharts 5.5 to generate interactive charts like candlestick and minute charts, supporting zoom, tooltips, and shortcuts (e.g., R to refresh, Esc to close popups).
• Real-time Feature Modules: Includes price alerts (set breakout/breakdown rules with popup notifications), a profit/loss calculator (input entry prices for automatic return calculation), market overview, gain/loss rankings, and more, served via API endpoints (e.g., /api/daily, /api/detail).
• Performance Optimization: Employs LRU caching (adjustable limit) and ThreadPoolExecutor for concurrent processing with exponential backoff retries, ensuring responsiveness under high loads.

The workflow involves three deployment steps: first, copy the server.py and index.html template files to a local directory; second, run the Python script to start the service (default port 8765, customizable via -p); third, the browser auto-opens the dashboard, allowing immediate use of all features with data auto-refreshing every 30 seconds to 5 minutes.

Applicability and Considerations

• Dependencies and Limits: Data relies on an external API, requiring stable network access; the API may have call frequency limits, causing update delays. Chart rendering depends on browser performance, demanding certain client-side capabilities.
• Deployment Scope: The skill supports only local single-machine deployment, unsuitable for multi-user sharing; manual parameter adjustment (e.g., --no-browser) is needed for port conflicts.
• Feature Boundaries: The current version focuses on main contracts, potentially excluding all next-month contracts or options data; alert rules persist to alert_rules.json and require reloading after service restart.
• Extensibility: API endpoints (e.g., /api/alerts/add, /api/pnl) allow basic extensions, but customization requires source code modification; thread count and cache size are tunable via startup parameters (e.g., --workers, --cache-size).

Overall, this skill suits individual traders, developers, or small teams for quick local monitoring setup, but performance and dependency risks should be evaluated in multi-user or high-frequency trading scenarios.