DEV Community

Igor Ganapolsky
Igor Ganapolsky

Posted on • Originally published at github.com

AI Trading: Lesson Learned #115: PAL MCP for Adversarial Trade Validation

#trading #ai #machinelearning #python

Lesson Learned #115: PAL MCP for Adversarial Trade Validation

Date: January 7, 2026
Category: Risk Management / Quality Control
Severity: Medium
Status: Active

Summary

We have PAL MCP Server configured but were not actively using the challenge tool for adversarial validation of trading decisions. This creates a gap in our verification pipeline.

What PAL MCP Provides

PAL (Provider Abstraction Layer) MCP Server includes:

  1. challenge tool - Uses a separate model to critically evaluate decisions
  2. debug tool - Systematic investigation of issues
  3. chat tool - Multi-model coordination

When to Use PAL Challenge Tool

BEFORE Trading Decisions

When Claude (primary) recommends a trade, use PAL to get adversarial perspective:

[Primary Claude]: Recommending CSP on F at $10 strike
[PAL Challenge]: Let me challenge this decision...
- What if F drops below $10?
- What's the max loss scenario?
- Is there a better risk/reward option?
Enter fullscreen mode Exit fullscreen mode

AFTER Losses

Any trade that results in loss should be challenged:

[Post-Trade]: Lost $5 on SPY put
[PAL Challenge]: Why did this fail?
- Was entry timing wrong?
- Was stop-loss set correctly?
- What should we learn?
Enter fullscreen mode Exit fullscreen mode

For New Strategy Implementation

Before deploying any new trading strategy:

[Strategy Proposal]: Implement iron condor strategy
[PAL Challenge]: Adversarial review...
- What market conditions break this?
- What's the tail risk?
- Have we backtested all scenarios?
Enter fullscreen mode Exit fullscreen mode

How to Invoke PAL Challenge

In Claude Code with PAL MCP configured:

# The PAL challenge tool is available via MCP
# Use it for critical decisions:
mcp__pal__challenge "Should we enter a CSP on F at $10 strike given current market conditions?"
Enter fullscreen mode Exit fullscreen mode

Integration Points

  1. Pre-trade: Challenge tool before any position entry
  2. Position management: Challenge before adjusting stops
  3. Exit decisions: Challenge before closing positions
  4. Strategy changes: Challenge before modifying core strategy

Configuration (Already in mcp_config.json) 

"pal": {
  "command": "npx",
  "args": ["-y", "@anthropic/pal-mcp-server"],
  "env": {
    "OPENROUTER_API_KEY": "${OPENROUTER_API_KEY}",
    "PAL_ENABLED_TOOLS": "challenge,debug,chat",
    "PAL_DEFAULT_MODEL": "openrouter/anthropic/claude-sonnet-4"
  }
}
Enter fullscreen mode Exit fullscreen mode

Action Items

  1. Add PAL challenge to pre-trade checklist
  2. Document adversarial validation in trading workflow
  3. Log challenge results in trade records

Related Files

  • .claude/mcp_config.json - PAL configuration
  • scripts/autonomous_trader.py - Should invoke challenge before trades
  • data/system_state.json - Log challenge outcomes

Tags

risk-management #adversarial-validation #multi-model #pal-mcp

This lesson was auto-published from our AI Trading repository.

More lessons: rag_knowledge/lessons_learned

Top comments (0)