Backtest Analysis Skill

Triggering Contexts

• •User shares TradingView strategy tester results (pasted or file)
• •User asks "Is this strategy good?" or "Should I trade this?"
• •User wants to compare strategy versions
• •After implementing changes from coding-pinescript

Input Handling

Accept Any Format

Users may provide backtest data as:

1. •Raw paste from TradingView Performance Summary (messy but parseable)
2. •Excel export from TradingView (complete multi-sheet data)
3. •CSV trade list (individual trade data)
4. •Screenshot (extract visually)

Semantic Parsing Rules

Do NOT rely on exact formatting. Look for these concepts flexibly:

Value Extraction

After finding a metric name, extract:

• •Number with $ or USD → Dollar value
• •Number with % → Percentage
• •Plain number → Raw value

Fallback

If parsing fails, ask user to confirm key metrics directly.

Core Analysis Framework

Step 1: Extract & Validate Metrics

Must Have (required for analysis):

• •Net Profit ($ and %)
• •Total Trades
• •Win Rate (%)
• •Profit Factor
• •Max Drawdown ($ and %)
• •Avg Win / Avg Loss

Nice to Have:

• •Sharpe Ratio
• •Sortino Ratio
• •CAGR
• •Avg Bars in Trade

Step 2: Sanity Checks

Flag if any of these are true:

• • Profit Factor < 0 or > 10 → Parsing error or suspicious
• • Win Rate > 100% or < 0% → Parsing error
• • Total Trades < 30 → Statistically insignificant
• • Max DD > Net Profit → Strategy may not be viable

Step 3: Quality Assessment

Statistical Significance

Profit Factor Interpretation

Drawdown Analysis

Expectancy Check

Expectancy = (Win% × Avg Win) - (Loss% × Avg Loss)

Must be positive. Calculate per-trade expected value.

Step 4: Red Flag Detection

Overfitting Indicators:

• • Very high PF (> 3.0) with low trade count (< 100)
• • Win rate extremely high (> 80%) for trend strategy
• • Tested on single asset only
• • No out-of-sample validation mentioned

Survivorship Bias:

• • Only tested on assets that "worked"
• • No mention of failed variations

Curve Fitting:

• • Many optimized parameters
• • Strategy logic too complex for trade count

Step 5: Probing Questions

Always ask:

1. •"Was this tested on out-of-sample data (walk-forward or separate period)?"
2. •"How many parameters were optimized?"
3. •"Does this logic make sense fundamentally, or is it just pattern-matched?"

Output Format

Summary Card

📊 BACKTEST ANALYSIS: [Strategy Name]

Edge Quality: [STRONG/MODERATE/WEAK/NONE]
Trade Count: [N] trades → [Significance assessment]
Profit Factor: [X] → [Interpretation]
Max Drawdown: [X%] → [Risk assessment]
Expectancy: $[X] per trade

⚠️ WARNINGS:
- [List any red flags]

✅ STRENGTHS:
- [List positive aspects]

📋 RECOMMENDATIONS:
1. [Actionable next steps]

Verdict Categories

Cross-References

• •After analysis, if improvements needed → coding-pinescript
• •If strategy needs redesign → planning-trading-systems
• •Track changes between versions → iterating-strategies