> 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/backtesting.md).

# Backtesting

## Run a Backtest

```python
from datetime import datetime, timezone

import vibetrading.backtest
import vibetrading.tools

start = datetime(2025, 1, 1, tzinfo=timezone.utc)
end = datetime(2025, 7, 1, tzinfo=timezone.utc)

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

engine = vibetrading.backtest.BacktestEngine(
    start_time=start,
    end_time=end,
    interval="1h",
    exchange="binance",
    initial_balances={"USDC": 10000},
    data=data,
)

results = engine.run(strategy_code)

print(results["metrics"])
```

## Quick Backtest with `run()`

For a simpler interface, use the `run()` shortcut:

```python
results = vibetrading.backtest.run(
    code,
    start_time=start,
    end_time=end,
    interval="1h",
    data=data,
)
```

## Backtest Results

`engine.run()` returns a dictionary containing:

```python
results["metrics"]          # Performance metrics dict
results["trades"]           # List of all executed trades
results["final_balances"]   # Final asset balances
results["results"]          # Time-series DataFrame of portfolio values
results["simulation_info"]  # Metadata (steps, time range, liquidation status)
```

## Metrics

| Metric                          | Description                            |
| ------------------------------- | -------------------------------------- |
| total\_return                   | Total portfolio return (decimal)       |
| max\_drawdown                   | Maximum peak-to-trough drawdown        |
| sharpe\_ratio                   | Annualized Sharpe ratio                |
| win\_rate                       | Percentage of profitable closed trades |
| number\_of\_trades              | Total number of trades executed        |
| funding\_revenue                | Net funding payments received/paid     |
| total\_tx\_fees                 | Total transaction fees paid            |
| average\_trade\_duration\_hours | Mean holding period                    |

## Supported Intervals

`1s`, `1m`, `5m`, `15m`, `30m`, `1h`, `6h`, `1d`

## Supported Exchanges

Data is fetched from exchanges via CCXT. Download data first, then pass it to the backtest engine:

```python
import vibetrading.tools

data = vibetrading.tools.download_data(["BTC", "ETH"], exchange="binance", ...)
data = vibetrading.tools.download_data(["BTC"], exchange="bybit", ...)
data = vibetrading.tools.download_data(["BTC"], exchange="okx", ...)
```

## Next Steps

After backtesting, you can:

* [**Analyze results**](/library/backtest-analysis.md) — Use an LLM to score performance and get actionable improvement suggestions.
* **Iterate manually** — Use `vibetrading.strategy.generate()` and `vibetrading.strategy.analyze()` in a loop to refine based on feedback.


---

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