dashboard-pnl-visualization

P&L visualization breaks down realized and unrealized profit into separate components integrated with your broker's equity data. Use profit-tracker overlays to display live P&L on your Python dashboard, pulling historical data from Alpaca to support both current monitoring and backtest analysis.

Win-rate overlays and R-multiple visualization are integrated into the dashboard configuration to show position performance metrics automatically. These overlays layer on top of your existing P&L data without requiring separate analysis or manual chart updates.

Alpaca integration pulls live equity data and historical trade records into your Python dashboard to fuel P&L calculations. The Skill handles cost accounting and configurable display rules, so you configure broker credentials and dashboard connection settings once.

Configurable costs and display rules let you customize P&L breakdowns, win-rate thresholds, and R-multiple scaling to match your trading strategy. Apply regime-aware performance metrics to pivot how the dashboard highlights results based on market conditions.

The Skill supports both live monitoring and historical analysis, so you can use end-of-day Alpaca snapshots or real-time feeds depending on your infrastructure. Realized and unrealized P&L split adapts to whichever data cadence you choose.

The Skill handles cost accounting and visualization configuration but depends on your Python dashboard's rendering capacity and Alpaca query limits. Ensure your dashboard framework supports the volume of historical trades and overlay layers you intend to display.