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name: tradeblocks-risk description: Risk analysis for trading strategies including Kelly criterion calculations, tail risk metrics, and Monte Carlo projections. Use when exploring position sizing, capital allocation, or understanding worst-case characteristics.

Risk Analysis

Explore risk characteristics and position sizing metrics for trading strategies.

Prerequisites

• TradeBlocks MCP server running
• Block with trade data (10+ trades minimum for meaningful metrics)

Process

Step 1: Understand User Goals

Risk analysis serves different purposes. Ask what the user wants to understand:

Goal	Primary Analysis	Also Consider
"What does Kelly suggest?"	Position sizing	Monte Carlo for drawdown context
"What are worst-case scenarios?"	Monte Carlo with worst-case	Tail risk metrics
"How correlated are my strategies?"	Tail risk, correlation	Position sizing per strategy
"How much drawdown might I see?"	Monte Carlo	Historical max drawdown from stats

Ask: "What aspect of risk would you like to explore?"

Then use list_blocks to identify the target block.

Step 2: Run Appropriate Analysis

Based on the user's goal:

For Position Sizing (Kelly Criterion):

Call get_position_sizing with the user's capital base.

Key parameters:

• capitalBase: Starting capital (required)
• kellyFraction: "full", "half" (default), or "quarter"
• maxAllocationPct: Cap per strategy (default: 25%)
• minTrades: Minimum trades for valid calculation (default: 10)

The tool returns:

• Win rate and payoff ratio (inputs to Kelly formula)
• Raw Kelly percentage (what the formula suggests)
• Adjusted allocations at full/half/quarter Kelly
• Per-strategy breakdown if multiple strategies exist
• Warnings (e.g., "Portfolio Kelly exceeds 25%", "negative Kelly")

Important context for Kelly:

• Full Kelly is mathematically optimal but assumes perfect knowledge of edge
• Half Kelly is commonly used to account for estimation uncertainty
• Negative Kelly indicates historical losses exceeded wins (Kelly formula doesn't apply)

For Worst-Case Projections:

Call run_monte_carlo with worst-case injection:

• includeWorstCase: true (default)
• worstCasePercentage: 5 (default - 5% of simulation is worst-case)
• worstCaseMode: "pool" (adds synthetic losses) or "guarantee" (ensures worst appears)

Focus on:

• 5th percentile outcome (valueAtRisk.p5)
• Probability of profit
• Mean and median max drawdown

For Tail Risk (Multi-Strategy):

Call get_tail_risk (requires 2+ strategies).

Key parameters:

• tailThreshold: Percentile for "tail" events (default: 0.1 = worst 10%)
• varianceThreshold: For effective factors calculation (default: 0.8)

The tool returns:

• Joint tail risk matrix (do strategies fail together?)
• Effective factors (how many independent risk sources exist)
• Risk level: LOW (<0.3), MODERATE (0.3-0.5), HIGH (>0.5)
• Copula correlation (statistical dependency structure)

Step 3: Cross-Reference

Risk analysis benefits from multiple perspectives:

Primary Analysis	Also Run
Position sizing	Monte Carlo to see drawdown projections
Monte Carlo	Position sizing to see Kelly metrics
Tail risk	Position sizing for per-strategy Kelly

This surfaces different facets of the same underlying data.

Step 4: Present Findings

Synthesize findings into what the data reveals:

Position Sizing Metrics:

• Win rate: [value]% | Payoff ratio: [value]
• Kelly formula suggests: [value]% (based on historical data)
• At half Kelly: [dollar amount] of [capital base]
• [Any warnings from the tool]

Monte Carlo Projections (if run):

• 5th percentile return: [value]
• Probability of profit: [value]%
• Mean max drawdown: [value]%

Tail Risk (if applicable):

• Average joint tail risk: [value] ([LOW/MODERATE/HIGH])
• Effective factors: [value] of [strategy count]
• [Note any high-risk pairs]

What stands out:

• [Highlight notable findings]
• [Surface any warnings from the tools]
• [Note relationships between metrics]

Present these as insights from the historical data, letting the user decide what fits their situation.

Interpretation References

• references/kelly-guide.md - Kelly criterion explained
• references/tail-risk.md - Understanding fat tails

Common Scenarios

"I have $100,000 - what does Kelly suggest?"

1. Run position sizing with capitalBase: 100000
2. Review the Kelly percentages and warnings
3. Surface both raw Kelly and half-Kelly figures
4. Note that Kelly assumes independent trades and known edge

"Do my strategies fail together?"

1. Run tail risk analysis
2. Look at joint tail risk matrix for high values
3. Check effective factors (closer to 1 = more correlated risk)
4. High correlation means drawdowns may compound

"What's the worst realistic outcome?"

1. Run Monte Carlo with worst-case injection
2. Focus on 5th percentile (1 in 20 scenario based on resampled history)
3. Look at max drawdown distribution
4. Note this resamples historical data - unknown risks aren't captured

Related Skills

After risk analysis:

• /tradeblocks-health-check - Full metrics overview
• /tradeblocks-wfa - Test parameter robustness

Notes

• Kelly assumes independent trades; real trades may be correlated
• Historical volatility may underestimate future extremes
• Monte Carlo resamples history - it can't predict unknown risks
• Negative Kelly means the formula doesn't apply (no positive edge in historical data)