compare

Performance Comparison

Explore differences between strategies, execution modes, or time periods.

Prerequisites

• TradeBlocks MCP server running
• At least one block with trade data loaded
• For backtest vs actual: Both trade log and reporting log for the same strategy
• For strategy vs strategy: Multi-strategy block or two blocks
• For block vs block: Two or more blocks

Process

Step 1: Identify Comparison Type

Ask the user what they want to compare:

Type	Use Case	Primary Tool
Backtest vs Actual	Explore theoretical vs live execution	compare_backtest_to_actual
Strategy vs Strategy (same block)	Compare strategies within a portfolio	get_strategy_comparison
Block vs Block	Compare separate portfolios side-by-side	compare_blocks / block_diff
Period vs Period	Analyze same strategy across time ranges	get_period_returns

Ask: "What would you like to compare?"

Step 2a: Backtest vs Actual Comparison

Use compare_backtest_to_actual to explore how theoretical performance compares to live execution.

Key parameters:

• blockId: Block folder name
• scaling: How to compare P&L fairly (see below)
• strategy: Optional filter to specific strategy
• dateRange: Optional date filter
• matchedOnly: Only include trades where both backtest and actual exist
• groupBy: Group results by "none", "strategy", "date", "week", or "month"
• detailLevel: "summary" (aggregate by date+strategy) or "trades" (individual trade comparison with field-by-field differences)
• outliersOnly: Only return high-slippage outliers (z-score threshold)
• outliersThreshold: Z-score threshold for outlier detection (default: 2)

Scaling modes (see references/scaling.md):

Mode	What It Does	Use When
raw	Shows P&L as-is	Contract sizes match between backtest and actual
perContract	Divides each P&L by contract count	Comparing per-lot performance regardless of size
toReported	Scales backtest DOWN to match actual contract count	Backtest uses more contracts than actual

Recommended approach:

1. Start with groupBy: "strategy" and scaling: "perContract" for an overview
2. Drill into specific strategies with groupBy: "month" to spot trends
3. Use outliersOnly: true to find the worst slippage trades
4. Use detailLevel: "trades" for field-by-field comparison on outliers

Tool returns:

• Per-date/strategy comparison with backtest vs actual P&L
• Slippage calculation (actual minus backtest)
• Match status (whether both sides exist for each date)
• Summary totals and average slippage percentage
• Outlier detection with z-scores

Present findings from the data:

• Total backtest P&L vs Total actual P&L (at selected scaling)
• Matched trade count (how many dates have both)
• Average slippage (percentage deviation from backtest)
• Unmatched trades (missed fills or extra trades)
• Outliers (trades with unusually high slippage)

Step 2b: Strategy vs Strategy Comparison (Same Block)

For comparing strategies within the same block, use get_strategy_comparison:

Key parameters:

• blockId: Block folder name
• sortBy: "netPl", "winRate", "trades", "profitFactor", "name"
• sortOrder: "asc" or "desc"
• minTrades: Minimum trades per strategy to include
• startDate / endDate: Optional date filters

Tool returns per strategy:

• Trade count, win rate, net P&L
• Average win, average loss
• Profit factor

Present as a ranked table:

Strategy	Trades	Win Rate	Net P&L	Profit Factor
...	...	...	...	...

For deeper comparison between two specific strategies, also run:

• get_correlation_matrix to understand how they move together
• get_statistics on each (with strategy filter) for full metric suites

Correlation context:

• Very low (<0.2): Strategies move independently
• Low (0.2-0.4): Some independence
• Moderate (0.4-0.6): Shared behavior
• High (>0.6): Similar movements, less diversification

Step 2c: Block vs Block Comparison

For comparing separate portfolio blocks:

Option 1: Side-by-side metrics via compare_blocks:

• blockIds: Array of block IDs (max 5)
• metrics: Optional filter to specific metrics (e.g., ["sharpeRatio", "maxDrawdown", "profitFactor"])
• sortBy: Sort by any metric

Option 2: Strategy overlap analysis via block_diff:

• blockIdA: First block (baseline)
• blockIdB: Second block (comparison target)
• Shows shared vs unique strategies between blocks
• Calculates performance deltas for shared strategies

Use compare_blocks for a quick overview, then block_diff when blocks share strategies and you want to understand what changed.

Step 2d: Period vs Period Comparison

For analyzing performance across time:

Use get_period_returns with period type and optional date filters.

Key parameters:

• period: "monthly", "weekly", or "daily"
• dateRange: Optional filter to specific time range
• normalizeTo1Lot: Normalize for fair comparison across different position sizes
• strategy: Optional strategy filter

Present period breakdown:

• Performance by month/quarter/year
• Best and worst periods
• Trends or regime changes

Questions to explore:

• "How does recent performance compare to earlier results?"
• "Are there periods that stand out?"
• "Does performance vary by time of year?"

Step 3: Present Findings

Synthesize the data into what stands out:

Comparison Summary:

• What was compared: [backtest vs actual / strategy A vs B / block X vs Y / period X vs Y]
• Key observation: [Most notable difference from the data]
• Magnitude: [Size of the divergence]

What the data shows:

• [Notable finding 1 from tool output]
• [Notable finding 2 from tool output]
• [Any patterns or anomalies]

Context for interpretation:

• [Possible explanations for observed differences]
• [Factors that may affect the comparison]

Present these as observations from the historical data. The user can decide what meaning to draw from the findings.

Interpretation Reference

For detailed explanation of scaling modes, see references/scaling.md.

Related Skills

After comparison analysis:

• /tradeblocks:health-check - Deep dive into either strategy
• /tradeblocks:portfolio - Explore correlation and diversification
• /tradeblocks:wfa - Test parameter robustness

Notes

• Backtest results typically look better than live (ideal fills, no slippage)
• Some degradation from backtest to live is expected
• High correlation between strategies means they may draw down together
• Short comparison periods have more noise than signal
• Scaling mode choice affects what story the data tells