# Quick Start

## Setup

Install and set your LLM API key:

```bash
pip install vibetrading
export OPENAI_API_KEY=sk-...   # or ANTHROPIC_API_KEY, GEMINI_API_KEY, etc.
```

## Generate a Strategy

```python
import vibetrading.strategy

code = vibetrading.strategy.generate(
    "BTC momentum strategy: RSI(14) oversold entry, SMA crossover confirmation, "
    "3x leverage, 10% position size, 8% take-profit, 4% stop-loss",
    model="gpt-4o",
)

print(code)
```

## Backtest

```python
import vibetrading.backtest
import vibetrading.tools

data = vibetrading.tools.download_data(["BTC"], exchange="binance", interval="1h")

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

if results:
    metrics = results["metrics"]
    print(f"Return: {metrics['total_return']:.2%}")
    print(f"Sharpe: {metrics['sharpe_ratio']:.2f}")
    print(f"Max Drawdown: {metrics['max_drawdown']:.2%}")
    print(f"Win Rate: {metrics['win_rate']:.2%}")
```

## Analyze Results

Use an LLM to evaluate backtest performance:

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

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

for s in report.suggestions:
    print(f"  → {s}")
```

See [Backtest Analysis](/library/backtest-analysis.md) for full details.

## Use the Prompt Template with Any LLM

Don't want to use the built-in generator? Use the prompt template directly with any LLM client:

### OpenAI

```python
import openai
import vibetrading.strategy

messages = vibetrading.strategy.build_generation_prompt(
    "BTC grid strategy with 0.25% spacing, 72 levels per side, 5x leverage",
    assets=["BTC"],
    market_type="perp",
    max_leverage=5,
)

response = openai.chat.completions.create(model="gpt-4o", messages=messages)
strategy_code = response.choices[0].message.content
```

### Anthropic

```python
import anthropic
import vibetrading.strategy

messages = vibetrading.strategy.build_generation_prompt("SOL scalping with VWAP and RSI")

client = anthropic.Anthropic()
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=4096,
    system=messages[0]["content"],
    messages=[{"role": "user", "content": messages[1]["content"]}],
)
strategy_code = response.content[0].text
```

## Validate Generated Code

Check generated strategy code for common errors before running:

```python
import vibetrading.strategy

result = vibetrading.strategy.validate(strategy_code)

if result.is_valid:
    print("Strategy passed validation")
else:
    print(result)
    feedback = result.format_for_llm()
```


---

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Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.vibetrading.dev/getting-started/quick-start.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.