> For the complete documentation index, see [llms.txt](https://docs.synthdata.co/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.synthdata.co/endpoints-overview-1/predictions-best.md).

# Predictions Best

```
GET /v2/prediction/best
```

## Parameters

| Parameter        | Type    | Required | Description                                 |
| ---------------- | ------- | -------- | ------------------------------------------- |
| `asset`          | string  | Yes      | `BTC`, `ETH`, `SOL`, `XAU`, `SPYX`, `NVDAX` |
| `time_increment` | integer | Yes      | `300` (5min intervals)                      |
| `time_length`    | integer | Yes      | `86400` (24h forecast)                      |

## Request Example

{% code title="curl" %}

```bash
curl "https://api.synthdata.co/v2/prediction/best?asset=BTC&time_increment=300&time_length=86400" \
  -H "Authorization: Apikey YOUR_API_KEY"
```

{% endcode %}

## Response

```json
[
  {
    "miner_uid": 2,
    "start_time": 1769097780,
    "predictions": [
      [88975.00, 89012.45, 88934.21, ..., 87633.98],
      [88975.00, 89102.33, 89245.67, ..., 90256.12],
      ...
    ]
  }
]
```

| Field         | Description                                                               |
| ------------- | ------------------------------------------------------------------------- |
| `miner_uid`   | ID of the top-performing miner                                            |
| `start_time`  | Unix timestamp when prediction was generated                              |
| `predictions` | Array of 1,000 price paths, each with 289 points (24h at 5-min intervals) |

## Example: Quick Price Forecast

{% code title="example.py" %}

```python
import requests
import numpy as np

response = requests.get(
    "https://api.synthdata.co/v2/prediction/best",
    headers={"Authorization": "Apikey YOUR_API_KEY"},
    params={"asset": "BTC", "time_increment": 300, "time_length": 86400}
)

data = response.json()[0]
paths = data['predictions']
current_price = paths[0][0]
final_prices = [path[-1] for path in paths]

print(f"Current: ${current_price:,.0f}")
print(f"24h Median: ${np.median(final_prices):,.0f}")
print(f"24h 5th percentile: ${np.percentile(final_prices, 5):,.0f}")
print(f"24h 95th percentile: ${np.percentile(final_prices, 95):,.0f}")
print(f"Prob up: {sum(1 for p in final_prices if p > current_price) / len(final_prices):.1%}")
```

{% endcode %}