# 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](https://docs.vibetrading.dev/library/backtest-analysis) 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()
```