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name: tradeblocks-portfolio description: Portfolio analysis for trading strategies. Explores correlation, diversification, and combined performance characteristics. Use when understanding how strategies relate, exploring diversification effects, or analyzing portfolio composition.

Portfolio Analysis

Explore how strategies relate and what combining them might mean for portfolio characteristics.

What This Skill Does

Surfaces data to help understand portfolio dynamics:

• Correlation: How do strategies move relative to each other?
• Standalone metrics: What are each strategy's individual characteristics?
• Diversification context: What do the numbers suggest about combining them?

Prerequisites

• TradeBlocks MCP server running
• Multiple strategy blocks or a multi-strategy block loaded

Process

Step 1: Identify Context

Understand the current situation:

Ask:

• "Which strategies would you like to analyze together?"
• "What aspect of portfolio composition interests you?"

Use list_blocks to show available blocks.

Step 2: Correlation Analysis

Use get_correlation_matrix to understand how strategies move relative to each other.

Key parameters:

• blockId: Block folder name
• method: "kendall" (robust, rank-based, default), "spearman" (rank), "pearson" (linear)
• alignment: "shared" (only days both traded) or "zero-pad" (fill missing with 0)
• timePeriod: "daily", "weekly", or "monthly" aggregation
• normalization: "raw" (absolute P&L), "margin" (P&L/margin), "notional" (P&L/notional)
• minSamples: Minimum shared periods for valid calculation (default: 10)

Tool returns:

• Correlation matrix between all strategies
• Sample sizes for each pair
• Analytics (average correlation, strongest/weakest pairs)
• Highly correlated pairs above threshold

Interpreting correlation values:

See references/correlation.md for why Kendall's tau is often more informative than Pearson for trading returns.

Step 3: Standalone Strategy Metrics

Use get_statistics on each strategy to understand individual characteristics.

Key metrics to surface:

• Sharpe Ratio (risk-adjusted return)
• Profit Factor (gross wins / gross losses)
• Max Drawdown (worst peak-to-trough decline)
• Win Rate (percentage of profitable trades)
• Trade count (sample size for confidence)

Present each strategy's profile:

Step 4: Tail Risk Analysis (Optional)

For deeper understanding, use get_tail_risk to explore extreme co-movement.

Key parameters:

• tailThreshold: What defines "extreme" (0.1 = worst 10% of days)
• varianceThreshold: For effective factors calculation (0.8 = 80% variance explained)

Tool returns:

• Joint tail risk matrix (do strategies fail together in extremes?)
• Effective factors (how many independent risk sources exist)
• Copula correlation (statistical dependency structure)

Risk level context:

• Average joint tail risk <0.3: Lower shared extreme risk
• Average joint tail risk 0.3-0.5: Moderate shared extreme risk
• Average joint tail risk >0.5: Higher shared extreme risk

See references/diversification.md for why tail correlation often exceeds normal correlation.

Step 5: Present Findings

Synthesize the data into what the numbers reveal:

Correlation Findings:

• Average correlation across pairs: [value]
• Highest correlation pair: [pair] at [value]
• Lowest correlation pair: [pair] at [value]
• Sample sizes: [range or note any insufficient data]

Strategy Profiles:

• [Strategy A]: [key characteristic - e.g., "highest Sharpe but also highest drawdown"]
• [Strategy B]: [key characteristic]
• [Note any strategies with limited trade counts]

Tail Risk (if analyzed):

• Average joint tail risk: [value] ([context])
• Effective factors: [X] of [Y] strategies
• [Note any pairs with high extreme co-movement]

What stands out:

• [Notable pattern 1]
• [Notable pattern 2]
• [Any data quality considerations]

Present these as insights from the historical data. The user can decide what fits their risk tolerance and portfolio goals.

Interpretation References

• references/correlation.md - Understanding correlation methods
• references/diversification.md - Diversification concepts and tail risks

Related Skills

After portfolio analysis:

• /tradeblocks-compare - Deep comparison of specific strategy pairs
• /tradeblocks-risk - Position sizing and Kelly analysis
• /tradeblocks-health-check - Full metrics on any strategy

Common Scenarios

"How do my strategies relate to each other?"

1. Run get_correlation_matrix to see pairwise relationships
2. Note the average correlation and any high-correlation pairs
3. Consider sample sizes - low overlap means less reliable correlation

"What would happen if these strategies draw down together?"

1. Run get_tail_risk to explore extreme co-movement
2. Check joint tail risk matrix for pairs that fail together
3. Effective factors < strategy count suggests shared risk sources

"I'm comparing two similar strategies"

1. Check correlation - if >0.7, they behave similarly
2. Compare standalone metrics to see performance differences
3. High correlation means drawdowns likely compound, not diversify

Notes

• Correlation is measured on aggregated returns, not trade-by-trade
• Past correlation patterns may not persist in future market conditions
• Tail correlation (crisis behavior) is often higher than normal correlation
• Low correlation doesn't guarantee protection - both can lose for different reasons
• Sample size matters - 10 shared data points is minimum, more is better