Wfa

Walk-Forward Analysis

Test whether strategy parameters hold up when applied to data the optimizer never saw.

What is Walk-Forward Analysis?

Walk-forward analysis (WFA) tests parameter robustness by:

1. Dividing history into segments
2. In-Sample (IS): The data used to find "optimal" parameters
3. Out-of-Sample (OOS): Data the optimizer never saw, used to test those parameters
4. Rolling forward: Repeat across the entire history

|------ IS Period 1 ------|-- OOS 1 --|
              |------ IS Period 2 ------|-- OOS 2 --|
                            |------ IS Period 3 ------|-- OOS 3 --|

If OOS performance is close to IS performance, the parameters may be capturing real patterns. If OOS significantly underperforms, the parameters may be fitting to noise.

Prerequisites

• TradeBlocks MCP server running
• Block with sufficient trade history (50+ trades for meaningful analysis)

Process

Step 1: Select Strategy

Use list_blocks to show available blocks.

Ask:

• "Which backtest contains the strategy you want to analyze?"
• "Do you want to test a specific strategy or the full portfolio?"

Note the block ID and optional strategy filter for subsequent steps.

Step 2: Understand User Goals

Walk-forward analysis answers different questions:

| Goal | What to Look For | |------|------------------| | Test if parameters are robust | Overall WF efficiency, OOS vs IS degradation | | Check for potential overfitting | High IS but low OOS performance | | Evaluate consistency | How many OOS periods were profitable | | Understand parameter sensitivity | Parameter stability across windows | | Test strategy weight combinations | Use parameterRanges with strategy weights | | Test position sizing approaches | Use parameterRanges with Kelly/fraction params |

Ask: "What are you trying to understand about this strategy?"

Step 3: Run Analysis

Call run_walk_forward with the selected block.

Core parameters:

• blockId: Block folder name
• strategy: Filter to specific strategy
• isWindowCount: Number of in-sample windows (default: 5)
• oosWindowCount: Number of out-of-sample windows (default: 1)
• optimizationTarget: Metric to optimize (default: "sharpeRatio")
• Options: "netPl", "profitFactor", "sharpeRatio", "sortinoRatio", "calmarRatio", "cagr", "avgDailyPl", "winRate"
• minInSampleTrades: Minimum trades in IS period (default: 10)
• minOutOfSampleTrades: Minimum trades in OOS period (default: 3)
• normalizeTo1Lot: Normalize trades to 1-lot (useful for pctofportfolio sizing)

Explicit window sizing (overrides window counts):

• inSampleDays: Explicit IS period in days
• outOfSampleDays: Explicit OOS period in days
• stepSizeDays: Days to slide forward each period (defaults to OOS days)

Parameter ranges for grid search:

• parameterRanges: Define sweep ranges as {paramName: [min, max, step]}

| Parameter | What It Tests | Example | |-----------|--------------|---------| | kellyMultiplier | Kelly fraction scaling | [0.25, 1.0, 0.25] → tests 0.25x/0.5x/0.75x/1.0x | | fixedFractionPct | Fixed fraction sizing | [1, 4, 1] → tests 1-4% | | fixedContracts | Fixed contract count | [1, 5, 1] → tests 1-5 contracts | | maxDrawdownPct | Max drawdown constraint | [15, 25, 5] → rejects combos exceeding 15-25% | | maxDailyLossPct | Max single-day loss constraint | [2, 5, 1] | | consecutiveLossLimit | Max consecutive losers | [3, 7, 1] | | strategy:StrategyName | Per-strategy weight | [0, 1, 0.5] → tests include/exclude |

Multiple parameters create a grid search across all combinations.

Risk constraints:

• enableCorrelationConstraint: Reject highly correlated strategy combinations (default: false)
• maxCorrelationThreshold: Max allowed correlation (default: 0.7)
• enableTailRiskConstraint: Reject high tail dependence combinations (default: false)
• maxTailDependenceThreshold: Max allowed tail dependence (default: 0.5)
• minProfitFactor: Reject combinations below this profit factor
• minSharpeRatio: Reject combinations below this Sharpe
• requirePositiveNetPl: Reject combinations with losses

Strategy selection:

• selectedStrategies: Filter to specific strategies (default: all)
• tickerFilter: Filter by underlying ticker

For shorter histories ( 500 trades):

• Consider isWindowCount of 7+
• Use explicit inSampleDays and outOfSampleDays for more control

Step 4: Interpret Results

The tool returns a verdict with three components, each rated as "good", "moderate", or "concerning":

Walk-Forward Efficiency (degradationFactor):

• Measures how well IS performance transfers to OOS
• Calculated as: OOS Performance / IS Performance

| Efficiency | Rating | What It Suggests | |------------|--------|------------------| | >= 80% | Good | OOS retained most of IS performance | | 60-79% | Moderate | Some degradation, but meaningful signal may remain | | 50% variation = "concerning" (parameters sensitive to data window)

Consistency Score:

• Percentage of OOS periods that were profitable
• >= 70% = "good"
• 50-70% = "moderate" (around random chance)
• < 50% = "concerning"

Step 5: Present Findings

Synthesize the analysis into what it reveals about the strategy:

Walk-Forward Results:

• Efficiency: [value]% ([rating]) - OOS retained [value]% of IS performance
• Stability: [rating] - Parameters [were consistent / showed variation] across windows
• Consistency: [value]% of OOS periods profitable ([rating])
• Overall verdict: [good/moderate/concerning]

What this suggests:

• [If efficiency is high]: OOS performance tracked IS reasonably well
• [If efficiency is low]: Significant gap between optimized and real-world performance
• [If stability is low]: Optimal parameters varied significantly between windows
• [If consistency is low]: Many OOS periods were unprofitable

Individual period breakdown (if relevant):

• Show IS vs OOS performance for each window
• Highlight any windows with unusual behavior

Grid search results (if parameterRanges used):

• Best parameter combination found
• How stable was the "best" across windows
• Adjacent parameter performance (smooth gradient = robust, cliff = overfit)

Present these as insights about what the historical data shows, not as trading advice.

Interpretation Reference

For detailed walk-forward concepts, see [references/wfa-guide.md](references/wfa-guide.md).

Related Skills

After walk-forward analysis:

• /tradeblocks:health-check - Full metrics review
• /tradeblocks:risk - Position sizing analysis
• /tradeblocks:optimize - Parameter exploration

Common Scenarios

"Is my backtest overfit?"

1. Run basic WFA with default settings
2. If efficiency < 60%, the strategy may be fitting to noise
3. Check parameter stability — high variation means parameters aren't robust
4. Low consistency (< 50% OOS profitable) is a red flag

"Which strategy combination is most robust?"

1. Use parameterRanges with strategy:StrategyName weights
2. Enable enableCorrelationConstraint and enableTailRiskConstraint
3. The optimizer will reject high-correlation, high-tail-risk combinations
4. Check if the "winning" combination is consistent across OOS periods

"What Kelly fraction should I use?"

1. Use parameterRanges with kellyMultiplier: [0.25, 1.0, 0.25]
2. WFA tests each fraction across time windows
3. If full Kelly dominates IS but half Kelly wins OOS, that's a signal to size conservatively

"How many trades do I need?"

• 50+ trades: Reasonable for basic WFA
• 100+ trades: Can use more windows for finer resolution
• 200+ trades: Can use explicit day-based windows
• <50 trades: Reduce window count to 3, interpret with extreme caution

Common Issues

"Insufficient trades for walk-forward analysis"

• Need at least 20 trades total
• Reduce window count or expand date range

Low consistency but decent efficiency:

• Small sample size causing noise
• Consider running with more windows if data permits

Individual periods show high variance:

• May indicate regime changes during the historical period
• The tool's periods array shows per-window breakdown

WFE looks inflated due to position sizing growth:

• Use normalizeTo1Lot: true to remove sizing bias
• Especially important for pctofportfolio strategies where later trades are larger

Notes

• WFA tests parameter robustness, not profitability
• Strong WFA results don't guarantee future performance
• Weak WFA results suggest the optimization may be fitting to noise

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.