Data Fetching

fetch

Data fetching utilities using yfinance.

Function

fetch_ohlc( ticker, interval="1d", lookback: str | None = None, start: str | None = None, end: str | None = None, auto_adjust: bool = False, )

Parameter semantics

• interval: Aggregation interval for candles ('1d', '1wk', '1mo').
• lookback: Relative period for history breadth ('5d','1mo','3mo','6mo','1y','2y','5y','10y','ytd','max').
• start/end: Explicit date range (YYYY-MM-DD). If both provided they override lookback. Passing values like '3mo' to start/end is invalid and will be ignored.

We guard against accidentally sending a lookback string where a date is required by validating format.

Returns a pandas DataFrame with columns: Open, High, Low, Close, Volume

fetch_ohlc(ticker, interval='1d', lookback=None, start=None, end=None, auto_adjust=False)

Fetch OHLC data for a single ticker.

Parameters:

Name	Type	Description	Default
ticker	str	Stock symbol to download.	required
interval	str	Aggregation interval ('1d', '1wk', '1mo').	'1d'
lookback	str | None	Relative period for history ('1y', '5y', 'max', etc.).	None
start	str | None	Start date YYYY-MM-DD (overrides lookback if end also provided).	None
end	str | None	End date YYYY-MM-DD.	None
auto_adjust	bool	Whether to adjust OHLC for splits/dividends.	False

Returns:

Type	Description
DataFrame	DataFrame with columns [Open, High, Low, Close, Volume].

Raises:

Type	Description
ValueError	If data is empty or missing required columns.

Note

• If both start and end are valid dates they override lookback.
• If either start/end is invalid (e.g. '3mo'), it is ignored.
• If nothing specified, default lookback = '1y'.

Source code in src/stockcharts/data/fetch.py

def fetch_ohlc(
    ticker: str,
    interval: str = "1d",
    lookback: str | None = None,
    start: str | None = None,
    end: str | None = None,
    auto_adjust: bool = False,
) -> pd.DataFrame:
    """Fetch OHLC data for a single ticker.

    Args:
        ticker: Stock symbol to download.
        interval: Aggregation interval ('1d', '1wk', '1mo').
        lookback: Relative period for history ('1y', '5y', 'max', etc.).
        start: Start date YYYY-MM-DD (overrides lookback if end also provided).
        end: End date YYYY-MM-DD.
        auto_adjust: Whether to adjust OHLC for splits/dividends.

    Returns:
        DataFrame with columns [Open, High, Low, Close, Volume].

    Raises:
        ValueError: If data is empty or missing required columns.

    Note:
        - If both start and end are valid dates they override lookback.
        - If either start/end is invalid (e.g. '3mo'), it is ignored.
        - If nothing specified, default lookback = '1y'.
    """
    download_kwargs = _validate_and_build_download_kwargs(
        interval, lookback, start, end, auto_adjust
    )

    df = yf.download(ticker, **download_kwargs)
    return _normalize_single_ticker_df(df, ticker)

fetch_ohlc_batch(tickers, interval='1d', lookback=None, start=None, end=None, auto_adjust=False, threads=True, progress=False)

Fetch OHLC data for multiple tickers in a single batch request.

Uses yfinance's built-in threading for parallel downloads, which is significantly faster than sequential single-ticker downloads.

Parameters:

Name	Type	Description	Default
tickers	list[str]	List of stock symbols to download.	required
interval	str	Aggregation interval ('1d', '1wk', '1mo').	'1d'
lookback	str | None	Relative period for history ('1y', '5y', 'max', etc.).	None
start	str | None	Start date YYYY-MM-DD (overrides lookback if end also provided).	None
end	str | None	End date YYYY-MM-DD.	None
auto_adjust	bool	Whether to adjust OHLC for splits/dividends.	False
threads	bool	Use multi-threading for faster downloads (default: True).	True
progress	bool	Show download progress bar (default: False).	False

Returns:

Type	Description
dict[str, DataFrame]	Dictionary mapping ticker symbols to their OHLC DataFrames.
dict[str, DataFrame]	Failed downloads are silently omitted from results.

Source code in src/stockcharts/data/fetch.py

def fetch_ohlc_batch(
    tickers: list[str],
    interval: str = "1d",
    lookback: str | None = None,
    start: str | None = None,
    end: str | None = None,
    auto_adjust: bool = False,
    threads: bool = True,
    progress: bool = False,
) -> dict[str, pd.DataFrame]:
    """Fetch OHLC data for multiple tickers in a single batch request.

    Uses yfinance's built-in threading for parallel downloads, which is
    significantly faster than sequential single-ticker downloads.

    Args:
        tickers: List of stock symbols to download.
        interval: Aggregation interval ('1d', '1wk', '1mo').
        lookback: Relative period for history ('1y', '5y', 'max', etc.).
        start: Start date YYYY-MM-DD (overrides lookback if end also provided).
        end: End date YYYY-MM-DD.
        auto_adjust: Whether to adjust OHLC for splits/dividends.
        threads: Use multi-threading for faster downloads (default: True).
        progress: Show download progress bar (default: False).

    Returns:
        Dictionary mapping ticker symbols to their OHLC DataFrames.
        Failed downloads are silently omitted from results.
    """
    if not tickers:
        return {}

    download_kwargs = _validate_and_build_download_kwargs(
        interval, lookback, start, end, auto_adjust
    )
    download_kwargs["progress"] = progress
    download_kwargs["threads"] = threads
    download_kwargs["group_by"] = "ticker"

    # yfinance accepts list or space-separated string
    batch_df = yf.download(tickers, **download_kwargs)

    results: dict[str, pd.DataFrame] = {}

    if batch_df.empty:
        return results

    # Handle single vs multiple ticker return format
    if len(tickers) == 1:
        # Single ticker: columns are just OHLCV
        ticker = tickers[0]
        try:
            df = _normalize_single_ticker_df(batch_df.copy(), ticker)
            results[ticker] = df
        except ValueError:
            pass  # Skip failed ticker
    else:
        # Multiple tickers: MultiIndex columns (ticker, OHLCV)
        for ticker in tickers:
            try:
                if ticker not in batch_df.columns.get_level_values(0):
                    continue

                ticker_df = batch_df[ticker].copy()

                # Drop rows where all values are NaN (no data for this date)
                ticker_df = ticker_df.dropna(how="all")

                if ticker_df.empty:
                    continue

                # Standardize columns
                ticker_df = ticker_df.reset_index().set_index(ticker_df.index.names[0])

                needed = ["Open", "High", "Low", "Close", "Volume"]
                missing = [c for c in needed if c not in ticker_df.columns]
                if missing:
                    continue

                results[ticker] = ticker_df[needed].copy()
            except Exception:
                # Skip any ticker that fails processing
                continue

    return results