What is dashboard-symbol-discovery?

This Claude skill automates the synchronization of trading dashboard symbols with loaded Reinforcement Learning (RL) models. It eliminates manual configuration by dynamically discovering active symbols from model files, ensuring that monitoring interfaces accurately reflect the live trading environment and preventing data mismatches or 'NO_MODEL' errors.

When should I use dashboard-symbol-discovery?

dashboard-symbol-discovery is useful in the following scenarios: • Case 1: Automatically updating dashboard symbols when new RL models are added to the system without requiring manual configuration changes. • Case 2: Resolving symbol mismatches between live trading processes and monitoring dashboards by passing active symbols through CLI arguments. • Case 3: Eliminating 'NO_MODEL' display errors in trading UIs by dynamically populating the dashboard based on successfully loaded predictors. • Case 4: Maintaining a consistent symbol universe across decoupled system components like traders and monitors through automated discovery logic.

name	dashboard-symbol-discovery
description	"Auto-discover dashboard symbols from loaded RL models. Trigger when: (1) dashboard shows old/wrong symbols, (2) symbols mismatch between live trader and dashboard, (3) adding new models to system, (4) dashboard shows NO_MODEL for all symbols."
author	Claude Code
date	2024-12-24

Dashboard Symbol Auto-Discovery

Experiment Overview

Item	Details
Date	2024-12-24
Goal	Fix dashboard showing stale symbols that don't match trained models
Environment	scripts/monitor_dashboard.py, scripts/live_trader.py
Status	Success

Context

The dashboard was showing hardcoded symbols (SPY, QQQ, AAPL, MSFT, NVDA, GOOGL, TSLA, BTCUSD, ETHUSD) even when:

No models existed for those symbols
Different models were trained (e.g., TMO, PANW, AMZN, etc.)
Live trader selected different symbols via universe selection

Root cause: DashboardConfig.symbols had hardcoded defaults, and the live trader didn't pass --symbols to the dashboard subprocess.

Verified Workflow

Problem Pattern

# WRONG: Hardcoded defaults in DashboardConfig
@dataclass
class DashboardConfig:
    symbols: List[str] = field(default_factory=lambda: [
        "SPY", "QQQ", "AAPL", "MSFT", "NVDA", "GOOGL", "TSLA", "BTCUSD", "ETHUSD"
    ])

Solution: Auto-Discovery

# CORRECT: Empty default, auto-discover from models
@dataclass
class DashboardConfig:
    # Symbols to monitor - auto-discovered from models if not specified
    symbols: List[str] = field(default_factory=list)

# In run_dashboard(), after loading predictors:
if not cfg.symbols:
    if predictors:
        cfg.symbols = list(predictors.keys())
        logger.info(f"Auto-discovered {len(cfg.symbols)} symbols from models: {cfg.symbols}")
    else:
        # Fallback if no models found
        cfg.symbols = ["SPY", "QQQ"]
        logger.warning("No models found - using fallback: SPY, QQQ")

Solution: Pass Symbols from Live Trader

# In live_trader.py, when launching dashboard subprocess:
dashboard_cmd = [
    sys.executable, "-m", "scripts.monitor_dashboard",
    "--paper", str(args.paper),
    "--interval", str(args.dashboard_interval),
    "--keys-file", args.keys_file,
    "--symbols", ",".join(symbols),  # Pass active symbols
    "--show"
]
logger.info(f"Dashboard symbols: {symbols}")

Priority Order

CLI --symbols argument (highest priority) - Explicitly passed symbols
Auto-discovery from models - If no CLI symbols, use loaded predictors
Fallback (lowest priority) - SPY, QQQ if nothing else available

Failed Attempts (Critical)

Attempt	Why it Failed	Lesson Learned
Hardcoded symbol defaults	Symbols became stale when models changed	Use empty default + auto-discovery
Dashboard subprocess without --symbols	Live trader and dashboard had different symbol lists	Always pass symbols to subprocess
Only auto-discovery	Would ignore explicit user preferences	Support CLI override as highest priority
No fallback	Crashes when no models exist	Always have a minimal fallback

Key Insights

Architecture: Two Separate Symbol Flows

The live trader and dashboard have independent symbol selection:

live_trader.py:
  1. Discovers models from disk (*.pt files)
  2. Runs universe selection (constrained to symbols WITH models)
  3. Launches dashboard subprocess

monitor_dashboard.py (OLD - BROKEN):
  1. Uses hardcoded defaults
  2. Ignores loaded predictors for symbol list
  3. Shows NO_MODEL for symbols without models

monitor_dashboard.py (NEW - FIXED):
  1. If --symbols passed, use those
  2. Else auto-discover from loaded predictors
  3. Else use minimal fallback

Model Discovery Pattern

Both live_trader and dashboard discover models the same way:

model_dir = Path("models/rl_symbols")
model_files = list(model_dir.glob("*.pt"))
for model_file in model_files:
    # Parse SYMBOL_TIMEFRAME.pt
    stem = model_file.stem  # e.g., "AAPL_1Hour"
    parts = stem.rsplit("_", 1)
    symbol, timeframe = parts[0], parts[1]

Dashboard --symbols Argument

Already existed but wasn't being used:

parser.add_argument('--symbols', type=str, default=None,
                   help='Comma-separated symbols to monitor')

# Parsing:
if args.symbols:
    cfg.symbols = [s.strip() for s in args.symbols.split(',') if s.strip()]

Files Modified

scripts/monitor_dashboard.py:
  - Line 297-299: Changed default symbols to empty list
  - Line 961-969: Added auto-discovery logic

scripts/live_trader.py:
  - Line 2033: Added --symbols argument to dashboard command
  - Line 2037: Added logging for dashboard symbols

References

scripts/monitor_dashboard.py: Lines 297-299 (config), 961-969 (auto-discovery)
scripts/live_trader.py: Lines 2028-2037 (subprocess launch)
models/rl_symbols/*.pt: Model files for discovery

dashboard-symbol-discovery

When & Why to Use This Skill

Use Cases