tradeblocks-portfolio

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