An advanced trading dashboard skill that visualizes portfolio history, splits realized vs unrealized P&L, tracks R-multiple risk, and overlays real-time win-rates.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install dashboard-pnl-visualization
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install dashboard-pnl-visualization using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Trading Dashboard P&L Visualization?
The dashboard-pnl-visualization skill is an advanced analytical framework designed for automated trading systems and dashboard applications. By integrating real-time broker equity historical logs with local profit summaries, this solution bridges the gap between gross equity tracking and granular performance auditing. It resolves common visualization issues by splitting realized and unrealized P&L, factoring in persistent infrastructure costs, and overlaying current win-rate statistics.
Built on top of Openclaw Skills, this workflow allows developer-traders to run a highly customizable Python visualization daemon. Using standard APIs like Alpaca, it pulls direct equity structures and computes multi-tiered risk indicators, such as R-multiples, ensuring traders have real-time, actionable insights into their active market risk and net performance metrics.
Trading Dashboard P&L Visualization Use Cases
- Monitoring daily realized vs. unrealized trading performance on live dashboards without relying solely on simple gross equity swings.
- Auditing the net profitability of an algorithmic trading strategy by factoring in data, cloud, and software subscription costs dynamically.
- Overlaying live win-rates and tracking active loss streaks from historic logs straight onto a unified developer interface built with Openclaw Skills.
- Calculating and color-coding R-multiples on open positions in real-time to quickly inspect risk-reward ratios and enforce exit rules.
How Trading Dashboard P&L Visualization Works
- Loads Runtime Configurations: Initializes system thresholds, API parameters, and tracking criteria from a flexible DashboardConfig dataclass.
- Extracts Equity History: Calls broker APIs (such as Alpaca) to retrieve standard historical equity paths.
- Extracts Profit Metadata: Scrapes local live session profit tracker logs to parse previous trades, calculate win-rates, and define the current loss streak.
- Calculates P&L Breakdown: Determines exact net P&L metrics by evaluating open positions and subtracting custom data or platform running costs.
- Calculates Risk Multiples: Measures real-time target versus hazard gaps (R-multiples) using precise entry, current, and stop prices.
Trading Dashboard P&L Visualization Setup
To deploy this visualization system, configure your dependencies and integrate the python visualization daemon.
1. Requirements Installation
Ensure you have the required Python modules installed:
pip install alpaca-py matplotlib
2. Configuration Setup
Create a dashboard_config.json in your project root:
{
"symbols": ["SPY", "QQQ", "AAPL"],
"lookback_days": 30,
"interval_seconds": 60,
"output_dir": "dashboards",
"cost_data": 3.33,
"cost_software": 6.67,
"risk_limit_pct": 10.0,
"profit_log_dir": "logs/live",
"save_latest": true
}
3. Execution
Initialize the dashboard monitoring script using the custom configuration parameters:
python monitor_dashboard.py --config dashboard_config.json --profit-log-dir logs/live
Trading Dashboard P&L Visualization Data Schema & Taxonomy
Config Dataclass Schema
The configuration relies on the following schema:
| Property | Type | Description |
|---|---|---|
symbols |
List[str] | Target symbols to display on the dashboard |
lookback_days |
int | Historical window for chart rendering |
cost_data |
float | Daily platform data costs subtracted from net P&L |
cost_software |
float | Daily software/VPS infrastructure costs |
risk_limit_pct |
float | Upper boundary risk tolerance limit |
profit_log_dir |
str | Relative path to local tracker logs |
Live Profit Tracker Summary Schema
The skill relies on a standardized JSON format (profit_summary.json) written to the profit_log_dir:
{
"trades": [
{ "pnl": 150.00, "symbol": "AAPL", "side": "long" },
{ "pnl": -50.00, "symbol": "QQQ", "side": "short" }
]
}
Trading Dashboard P&L Visualization Advanced Features
- Dynamic Configuration Hot-reloading: Alter target tracking assets or infrastructure cost parameters on-the-fly without restarting active monitoring tasks.
- Session Globbing Auto-discovery: Scan nested dynamic session directories automatically to aggregate local historic win rates and track multi-session loss streaks.
- Robust Boundary Calculations: Prevent division-by-zero occurrences on R-multiple calculations when stops are missing or not defined, using standard safe failbacks.
- R-Multiple Visualization Gradients: Color-code current positions instantly to determine active risk levels at a single glance.
SKILL.md
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