# Historical Data Export for Backtesting

Export historical data with local caching for backtesting trading strategies.

```python
import pandas as pd
from pathlib import Path
from datetime import datetime, timedelta

def fetch_with_cache(
    dataset: str,
    start: str,
    end: str,
    cache_dir: str = ".backtest_cache",
    **kwargs
) -> pd.DataFrame:
    """Fetch data with local CSV caching to avoid repeated API calls."""
    cache_path = Path(cache_dir)
    cache_path.mkdir(exist_ok=True)

    # Create unique cache key
    params_str = "_".join(f"{k}={v}" for k, v in sorted(kwargs.items()))
    cache_file = cache_path / f"{dataset}_{start}_{end}_{hash(params_str)}.csv"

    if cache_file.exists():
        print(f"Loading from cache: {cache_file.name}")
        return pd.read_csv(cache_file)

    print(f"Fetching {dataset} from {start} to {end}...")
    df = client.get_dataset(dataset, start=start, end=end, **kwargs)

    if len(df) > 0:
        df.to_csv(cache_file, index=False)
        print(f"Cached {len(df)} rows to {cache_file.name}")

    return df

# Example: Export one week of hourly hub prices for backtesting
df = fetch_with_cache(
    "ercot_lmp_by_settlement_point",
    start="2026-01-01",
    end="2026-01-08",
    filter_column="location",
    filter_value="HB_HOUSTON",
    resample="1 hour",
    resample_function="mean",
    timezone="America/Chicago"
)

print(f"\nExported {len(df)} hourly price observations")
print(f"Date range: {df['interval_start_local'].min()} to {df['interval_start_local'].max()}")
print(f"Price range: ${df['lmp'].min():.2f} to ${df['lmp'].max():.2f}")
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

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

```
GET https://docs.gridstatus.io/developers/concepts/recipes/historical-data-export.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.