- MQL5: Implement 1-second caching for environment API calls (trade allowed checks) to reduce IPC overhead.
- Python: Optimize dashboard I/O using os.stat() and cache HTML template as a constant.
- Updated .jules/bolt.md with MQL5 IPC caching learnings.
This commit implements a caching mechanism for the web dashboard's Markdown content.
Previously, README.md and VERIFICATION.md were read and rendered on every request.
Now, they are cached and only re-rendered if the file modification time (mtime) changes.
This significantly reduces CPU and I/O overhead for dashboard requests.
Key changes:
- Added MarkdownCache class to scripts/web_dashboard.py
- Moved file path definitions to module level
- Updated health_check route to use cached HTML
- Verified logic with standalone test script (mocked)
- Updated .jules/bolt.md journal with this learning
# Vercel Web Analytics Implementation
## Summary
Successfully implemented Vercel Web Analytics for the MQL5 Trading Automation dashboard project. The implementation follows Vercel's recommended HTML/plain JavaScript approach, which is ideal for this Flask-based application.
## Changes Made
### Modified Files:
1. **dashboard/index.html**
- Added Vercel Web Analytics script tags before the closing `</body>` tag
- Implemented the standard HTML analytics tracking code that initializes the `window.va` function
- Added deferred loading of the Vercel insights script from `/_vercel/insights/script.js`
2. **scripts/web_dashboard.py**
- Updated the Flask application's HTML template to include Vercel Web Analytics
- Added the same analytics script tags to the dynamically generated HTML
- Ensures analytics tracking works on both the static and Flask-rendered pages
### Created Files:
3. **vercel.json**
- Created Vercel deployment configuration file
- Configured builds for both the Python Flask app and static dashboard files
- Set up routing to properly serve the Flask app and static assets
- Added production environment variables for Flask
## Implementation Details
The implementation uses Vercel's HTML/JavaScript approach for web analytics, which:
- Requires no package installation (no @vercel/analytics npm package needed)
- Works seamlessly with Flask and static HTML pages
- Automatically tracks page views and visitor metrics
- Uses deferred script loading for optimal performance
- Will be activated once the project is deployed to Vercel with Web Analytics enabled
## Next Steps for Deployment
To complete the Vercel Web Analytics setup:
1. **Enable Web Analytics in Vercel Dashboard:**
- Go to your Vercel project dashboard
- Click the "Analytics" tab
- Click "Enable" to activate Web Analytics
- This will make the `/_vercel/insights/*` routes available
2. **Deploy to Vercel:**
- Run `vercel deploy` or push to your connected Git repository
- The analytics will automatically start tracking after deployment
3. **Verify Installation:**
- After deployment, visit your site
- Open browser DevTools > Network tab
- Look for a request to `/_vercel/insights/view` to confirm tracking is active
## Technical Notes
- No changes to requirements.txt were needed (Python-only dependencies)
- No package manager operations required (npm/yarn/pnpm)
- The implementation is framework-agnostic and works with plain HTML/JavaScript
- Analytics tracking is non-blocking and won't affect page load performance
- The solution supports both the static HTML dashboard and Flask-rendered pages
Co-authored-by: Vercel <vercel[bot]@users.noreply.github.com>
- Implemented a Flask-based web dashboard in `scripts/web_dashboard.py` to serve project status and documentation.
- Integrated the web dashboard into `config/startup_config.json` as a managed component.
- Updated `requirements.txt` with `flask` and `markdown` dependencies.
- Added `/health` endpoint for cloud platform health checks.
- Verified system integrity with existing validation and testing scripts.
