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Multi-perspective AI council for designing, validating, and improving trading strategies with 5 specialized agents.
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Baseado na classificação ocupacional SOC
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