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Multi-perspective AI council for designing, validating, and improving trading strategies with 5 specialized agents.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Pre-trade risk gates and post-trade audit actions for institutional compliance.
Institutional-grade monitoring of alpha exhaustion, execution capacity, and regime drift.
Institutional-grade strategy validation, walk-forward analysis, and regime-aware testing.
Institutional-grade data cleaning, tick physics auditing, and gap reconstruction.
Minimizing slippage, benchmarking fills, and detecting toxic flow.
Institutional-grade management of systemic risk, hidden correlations, and black-swan stress testing.
| name | Strategy Council |
| description | Multi-perspective AI council for designing, validating, and improving trading strategies with 5 specialized agents. |
A multi-agent discussion framework for rigorous strategy evaluation. Each "agent" represents a specialized perspective that challenges and validates trading strategies.
Use the /council workflow to initiate a council session.
from scripts.strategy_council import StrategyCouncil
council = StrategyCouncil()
result = council.evaluate_strategy(
symbol="GOLD",
strategy_type="breakout",
strategy_params={"atr_mult": 2, "timeframe": "H1"}
)
print(result["verdict"]) # APPROVED, CONDITIONAL, REJECTED
Each session produces:
The council uses real market intelligence from:
titan_system/core/comprehensive_intel.py - Live spread/ATR dataconfig/alpha_registry.json - Historical validated edgesdata/market_intelligence_export.json - Symbol profiles