Health Check

Strategy Health Check

Surface key performance metrics and stress test results to help understand a strategy's characteristics.

Prerequisites

• TradeBlocks MCP server must be running
• At least one block with trade data loaded

Process

Step 1: Select Strategy

List available blocks and help the user choose what to analyze.

Use list_blocks to show available options.

Ask clarifying questions:

• "Which backtest would you like to analyze?"
• "Do you want to analyze the full portfolio or a specific strategy within it?"

If analyzing a specific strategy, note it for filtering in subsequent steps.

Shortcut for multi-strategy portfolios: If the user wants a comprehensive portfolio-level health check, consider using portfolio_health_check (see Step 6) instead of running Steps 2-5 individually. It combines correlation, tail risk, Monte Carlo, walk-forward, and profile-aware dimensions in one call.

Step 2: Gather Basic Metrics

Run get_statistics for the selected block (with strategy filter if specified).

Present key metrics with context:

| Metric | What It Measures | |--------|------------------| | Sharpe Ratio | Risk-adjusted return (higher = better return per unit risk) | | Sortino Ratio | Downside risk-adjusted return (focuses only on losses) | | Max Drawdown | Largest peak-to-trough decline (lower = less historical pain) | | Win Rate | Percentage of trades that were profitable | | Profit Factor | Gross wins / gross losses (>1 means profitable overall) | | Net P&L | Total profit after commissions |

Key insight: A strategy can have low win rate but high profit factor if average wins exceed average losses significantly. Neither metric alone tells the full story.

Step 3: Stress Testing

Run stress_test to see how the strategy performed during named historical market stress scenarios (COVID crash, 2022 bear, VIX spikes, etc.).

Key parameters:

• blockId: Block folder name
• scenarios: Optional list of specific scenario names (omit to run all built-in scenarios)
• customScenarios: User-defined scenarios with custom date ranges
• includeEmpty: Include scenarios with no trades (default: false)

Tool returns per scenario:

• Scenario name and date range
• Trade count, win rate, net P&L during that period
• Profit factor and max drawdown during the stress period

Present a stress test summary table:

| Scenario | Dates | Trades | Win Rate | Net P&L | Max DD | |----------|-------|--------|----------|---------|--------| | COVID Crash | | | | | | | 2022 Bear | | | | | | | VIX Spike Events | | | | | |

Flag any scenario where the strategy had significant losses or drawdown.

Step 4: Monte Carlo Projections

Run run_monte_carlo to project performance under uncertainty.

Key parameters to understand:

• resampleMethod: "trades" resamples individual trade P&L (default)
• includeWorstCase: Injects synthetic worst-case scenarios (default: true)
• worstCasePercentage: How much of simulation is worst-case (default: 5%)

Focus on these outputs:

• 5th percentile outcome: What the data suggests in a bad scenario (1 in 20 chance of worse)
• Probability of profit: How often simulations ended profitable
• Mean max drawdown: Typical drawdown across simulations

Present these as "what the historical data suggests could happen" - not predictions.

Step 5: Drawdown Attribution

Run drawdown_attribution to identify which strategies contributed most to losses during the portfolio's maximum drawdown period.

Key parameters:

• blockId: Block folder name
• strategy: Optional filter to specific strategy
• topN: Number of top contributors to return (default: 5)

Tool returns:

• Drawdown period (peak date to trough date)
• Peak and trough equity values
• Per-strategy P&L attribution during the drawdown
• Each strategy's percentage contribution to the total loss

Present which strategies drove the worst drawdown. This is critical for understanding concentrated risk.

Step 6: Portfolio Health Check (Multi-Strategy Blocks)

For multi-strategy blocks, run portfolio_health_check to get a comprehensive one-call assessment.

Tool returns a layered report:

• Verdict: Overall status (HEALTHY / MODERATECONCERNS / SIGNIFICANTCONCERNS)
• Grades: A-F across 9 dimensions (diversification, tail risk, robustness, consistency, regime coverage, day coverage, concentration risk, correlation risk, scaling alignment)
• Flags: Specific warnings and passes with details
• Key numbers: Sharpe, Sortino, max drawdown, avg correlation, avg tail dependence, MC probability of profit, walk-forward efficiency

Surface the verdict, any warning flags, and the dimension grades. Focus attention on any grade below B.

Step 7: Summary

Synthesize findings into a clear picture of what the data shows:

Metrics Summary:

• Sharpe Ratio: [value] - [context: >1.0 considered acceptable by many, >2.0 considered excellent]
• Max Drawdown: [value] - [context: 40% significant]
• Profit Factor: [value] - [context: >1.5 considered good, >2.0 excellent]

Stress Test Insights:

• [Scenario results — which stress periods hurt, which held up]
• [Any scenarios with zero trades (no exposure during that period)]

Monte Carlo Projections:

• 5th percentile scenario: [value]
• Probability of profit: [value]

Drawdown Attribution:

• Worst drawdown period: [peak date] to [trough date]
• Primary contributor: [strategy] ([percentage] of total loss)

What stands out:

• [Highlight any notably strong or weak metrics]
• [Note any warnings from the tools]
• [Stress scenarios that caused outsized damage]

Let the user draw their own conclusions about whether this fits their risk tolerance.

Interpretation Reference

For detailed explanations of each metric, see [references/metrics.md](references/metrics.md).

Related Skills

After health check, the user may want to:

• /tradeblocks:wfa - Test if optimized parameters hold up on unseen data
• /tradeblocks:risk - Deep dive into position sizing and tail risk analysis
• /tradeblocks:portfolio - Portfolio-level correlation and diversification

Notes

• Always use trade-based calculations when filtering by strategy (daily logs represent full portfolio)
• Historical performance doesn't guarantee future results
• Stress tests only cover known historical scenarios — unknown risks aren't captured

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: davidromeo
• Source: davidromeo/tradeblocks-skills
• License: MIT
• Homepage: https://tradeblocks.io

Install and usage instructions live in the source repository linked above.