Case Study: Financial Services¶

How a quantitative trading firm prevented a $2.3M loss with ACGP

Company Profile¶

Attribute	Details
Industry	Quantitative Trading
Agent Type	Algorithmic Trading Execution
Scale	15 agents, 50,000+ trades/day
Conformance Level	Complete
ACL Tier	ACL-4 (Strict Oversight)

The Challenge¶

The firm deployed AI agents to execute trades based on market signals. During a period of high volatility, one agent began exhibiting unusual behavior:

Reasoning drift: Justifications for trades became increasingly abstract
Position concentration: Building unexpectedly large positions in a single security
Timing anomalies: Executing orders at suboptimal times

Traditional monitoring systems didn't flag these issues because each individual trade was within normal parameters. The problem was in the pattern of decisions.

The Incident (Pre-ACGP)¶

In Q2 2024, before implementing ACGP, a similar situation resulted in:

$2.3M in losses from a cascading series of poor trades
4.2 hours before the issue was detected
Manual intervention required to halt the agent
3 weeks to fully unwind positions

The Solution¶

The firm implemented ACGP with:

ACL-4 Configuration¶

steward = GovernanceSteward(
    acl_tier="ACL-4",
    conformance_level="complete",
    blueprint="trading-execution-v2"
)

Tripwires¶

tripwires:
  max_position_value: 500000
  max_daily_loss: 100000
  concentration_limit: 0.15  # Max 15% in single security
  reasoning_coherence_min: 0.7

Governance Contracts¶

contracts:
  trade_execution:
    max_latency_ms: 200
    min_ctq_score: 0.75
    require_human_above: 250000

The Result¶

When similar market conditions occurred in Q4 2024:

Timeline¶

Time	Event	ACGP Response
09:14:32	First unusual trade pattern	NUDGE logged
09:14:45	Concentration approaching limit	FLAG for review
09:15:02	Position limit exceeded	BLOCK - Trade rejected
09:15:03	Human notified	ESCALATE triggered
09:17:00	Human review completed	Agent parameters adjusted

Outcome¶

$0 in losses from the incident
45ms to first intervention
Zero manual emergency stops required
Automatic recovery within 3 minutes

Key Metrics¶

Metric	Before ACGP	After ACGP
Average time to detect	4.2 hours	45ms
Incident cost	$2.3M	$0
False positives	N/A	1.2%
Trading latency impact	N/A	+180ms

Lessons Learned¶

CTQ evaluation catches drift - Individual trades looked fine; reasoning quality revealed the problem
Tripwires prevent cascades - Hard limits stopped small issues from becoming large losses
Latency trade-off is worth it - 180ms additional latency prevented millions in losses
Complete conformance for high-risk - Cryptographic audit trails were essential for regulatory compliance

Implementation Details¶

Deployment¶

Duration: 2 weeks for Complete Conformance
Testing: 3,000+ conformance tests passed
Rollout: Shadow mode for 1 week before enforcement

Architecture¶

graph LR
    A[Trading Agent] --> B[ACGP Steward]
    B --> C{Evaluate}
    C -->|OK| D[Execute Trade]
    C -->|Block| E[Reject Trade]
    C -->|Escalate| F[Human Review]
    B --> G[Audit Log]
    G --> H[Compliance System]

Contact¶

Interested in implementing ACGP for financial services?

Implementation Guide Security Hardening