> For the complete documentation index, see [llms.txt](https://docs.vibetrading.dev/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.vibetrading.dev/library/backtest-analysis.md).

# Backtest Analysis

Use an LLM to evaluate backtest results — get a performance score (1-10), strengths, weaknesses, risk assessment, and actionable improvement suggestions.

## Quick Usage

```python
import vibetrading.strategy
import vibetrading.backtest

results = vibetrading.backtest.run(strategy_code, interval="1h", data=data)

report = vibetrading.strategy.analyze(
    results,
    strategy_code=strategy_code,
    model="gpt-4o",
)

print(f"Score: {report.score}/10")
print(report.summary)
```

## `BacktestAnalysisResult`

The analysis returns a `BacktestAnalysisResult` with the following fields:

| Field              | Type       | Description                            |
| ------------------ | ---------- | -------------------------------------- |
| score              | int        | Overall score (1-10)                   |
| summary            | str        | 2-3 sentence assessment                |
| strengths          | list\[str] | What the strategy does well            |
| weaknesses         | list\[str] | Problems to address                    |
| risk\_assessment   | str        | Risk evaluation                        |
| suggestions        | list\[str] | Actionable improvement recommendations |
| detailed\_analysis | str        | Multi-paragraph deep analysis          |
| raw\_metrics       | dict       | The metrics dict that was analyzed     |

### Scoring Guidelines

| Score | Meaning     | Characteristics                                      |
| ----- | ----------- | ---------------------------------------------------- |
| 9-10  | Exceptional | Sharpe > 2.0, drawdown < 10%, strong win rate        |
| 7-8   | Good        | Positive risk-adjusted returns, manageable drawdowns |
| 5-6   | Mediocre    | Marginal returns or concerning risk metrics          |
| 3-4   | Poor        | Negative returns or extreme drawdowns                |
| 1-2   | Failing     | Liquidation, massive losses, or non-functional       |

## Reading the Report

```python
report = vibetrading.strategy.analyze(results, strategy_code=code)

# Score & summary
print(f"Score: {report.score}/10")
print(report.summary)

# Strengths
for s in report.strengths:
    print(f"  + {s}")

# Weaknesses
for w in report.weaknesses:
    print(f"  - {w}")

# Risk assessment
print(report.risk_assessment)

# Actionable suggestions
for i, s in enumerate(report.suggestions, 1):
    print(f"  {i}. {s}")

# Full analysis text
print(report.detailed_analysis)
```

## Feeding Analysis Back to the Generator

The analysis result includes `format_for_llm()` — a method that converts the report into structured feedback suitable for a follow-up generation call:

```python
import vibetrading.strategy

# First generate and backtest
code = vibetrading.strategy.generate("BTC momentum with RSI", model="gpt-4o")
results = vibetrading.backtest.run(code, interval="1h", data=data)

# Analyze
report = vibetrading.strategy.analyze(results, strategy_code=code)

# Feed back into generator for improvement
feedback = report.format_for_llm()
improved_code = vibetrading.strategy.generate(
    "BTC momentum with RSI",
    model="gpt-4o",
    feedback=feedback,
)
```

Or use `vibetrading.strategy.generate()` and `vibetrading.strategy.analyze()` in a loop to iterate manually.

## Detail Levels

Control how much data is sent to the LLM:

```python
# Metrics only (fast, cheap)
report = vibetrading.strategy.analyze(results, detail_level="brief")

# Metrics + trade summary (default)
report = vibetrading.strategy.analyze(results, detail_level="standard")

# Metrics + trades + equity curve (most thorough)
report = vibetrading.strategy.analyze(results, detail_level="detailed")
```

## Using `BacktestAnalyzer` Directly

For repeated analysis or custom configuration:

```python
from vibetrading.strategy import BacktestAnalyzer

analyzer = BacktestAnalyzer(
    model="gpt-4o",
    temperature=0.3,
)

report1 = analyzer.analyze(results1, strategy_code=code1)
report2 = analyzer.analyze(results2, strategy_code=code2)
```


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