Module pinkfish.fetch

Fetch time series data.

"""
Fetch time series data.
"""

import datetime
import os
import sys
import warnings

import pandas as pd
from pandas_datareader._utils import RemoteDataError
import pandas_datareader.data as pdr
import yfinance as yf

from pinkfish.pfstatistics import (
    select_trading_days
)
from pinkfish.stock_market_calendar import (
    stock_market_calendar
)
import pinkfish.utility as utility


# Override pandas_datareader with yfinance
yf.pdr_override()


########################################################################
# TIMESERIES (fetch, select, finalize)

def _get_cache_dir(dir_name):
    """
    Get the data dir path.

    Parameters
    ----------
    dir_name : str
        The leaf data dir name.

    Returns
    -------
    str
        Path to the data dir.
    """
    base_dir = ''
    try:
        conf = utility.read_config()
        base_dir = conf['base_dir']
    except Exception as e:
        pass
    finally:
        dir_name = os.path.join(base_dir, dir_name)

    if not os.path.exists(dir_name):
        os.makedirs(dir_name)
    return dir_name


def _adj_column_names(ts):
    """
    Make all column names lower case.

    ta-lib expects columns to be lower case. To be consistent,
    make date index lowercase also.  Replace spaces with underscores.

    Parameters
    ----------
    ts : pd.DataFrame
        The timeseries of a symbol.

    Returns
    -------
    pd.DataFrame
        The timeseries with adjusted column names.
    """
    ts.columns = [col.lower().replace(' ','_') for col in ts.columns]
    ts.index.names = ['date']
    return ts


def fetch_timeseries(symbol, dir_name='data', use_cache=True, from_year=None):
    """
    Read time series data.

    Use cached version if it exists and use_cache is True, otherwise
    retrive, cache, then read.

    Parameters
    ----------
    symbol : str
        The symbol for a security.
    dir_name : str, optional
        The leaf data dir name (default is 'data').
    use_cache: bool, optional
        True to use data cache.  False to retrieve from the internet 
        (default is True).
    from_year: int, optional
        The start year for timeseries retrieval (default is None,
        which implies that all the available data is retrieved).

    Returns
    -------
    pd.DataFrame
        The timeseries of a symbol.
    """
    if from_year is None:
        from_year = 1900 if not sys.platform.startswith('win') else 1971

    # Yahoo finance uses '-' where '.' is used in symbol names.
    symbol = symbol.replace('.', '-')
    symbol = symbol.upper()

    # pinkfish allows the use of a suffix starting with a '_',
    # like SPY_SHRT, so extract the symbol.
    symbol = symbol.split('_')[0]

    timeseries_cache = os.path.join(_get_cache_dir(dir_name), symbol + '.csv')

    if os.path.isfile(timeseries_cache) and use_cache:
        pass
    else:
        try:
            ts = pdr.get_data_yahoo(symbol, start=datetime.datetime(from_year, 1, 1), progress=False)
        except RemoteDataError as e:
            print(f'\n{e}')
        except Exception as e:
            print(f'\n{e}')
        else:
            ts.to_csv(timeseries_cache, encoding='utf-8')

    ts = pd.read_csv(timeseries_cache, index_col='Date', parse_dates=True)
    ts = _adj_column_names(ts)

    # Remove rows that have duplicated index.
    ts = ts[~ts.index.duplicated(keep='first')]
    return ts


def _adj_prices(ts):
    """
    Back adjust prices relative to adj_close for dividends and splits.

    Parameters
    ----------
    ts : pd.DataFrame
        The timeseries of a symbol.

    Returns
    -------
    pd.DataFrame
        The timeseries with adjusted prices.
    """
    ts['open'] = ts['open'] * ts['adj_close'] / ts['close']
    ts['high'] = ts['high'] * ts['adj_close'] / ts['close']
    ts['low'] = ts['low'] * ts['adj_close'] / ts['close']
    ts['close'] = ts['close'] * ts['adj_close'] / ts['close']
    return ts


def select_tradeperiod(ts, start, end, use_adj=False,
                       use_continuous_calendar=False,
                       force_stock_market_calendar=False,
                       check_fields=['close']):
    """
    Select the trade period.

    First, remove rows that have zero values in price columns. Then,
    select a time slice of the data to trade from ts.  Back date a year
    to allow time for long term indicators, e.g. 200sma is become valid.

    Parameters
    ----------
    ts : pd.DataFrame
        The timeseries of a symbol.
    start : datetime.datetime
        The desired start date for the strategy.
    end : datetime.datetime
        The desired end date for the strategy.
    use_adj : bool, optional
        True to adjust prices for dividends and splits
        (default is False).
    use_continuous_calendar: bool, optional
        True if your timeseries has data for all seven days a week,
        and you want to backtest trading every day, including weekends.
        If this value is True, then `force_stock_market_calendar`
        is set to False (default is False).
    force_stock_market_calendar : bool, optional
        True forces use of stock market calendar on timeseries.
        Normally, you don't need to do this.  This setting is intended
        to transform a continuous timeseries into a weekday timeseries.
        If this value is True, then `use_continuous_calendar` is set
        to False (default is False).
    check_fields : list of str, optional {'high', 'low', 'open',
        'close', 'adj_close'}
        Fields to check for for NaN values.  If a NaN value is found
        for one of these fields, that row is dropped
        (default is ['close']).

    Returns
    -------
    pd.DataFrame
        The timeseries for specified start:end, optionally with prices
        adjusted.

    Notes
    -----
    You should only set one of `use_continuous_calendar`=True or
    `force_stock_market_calendar`=True for a continuous timeseries.
    You should set neither of these to True if your timeseries is based
    on the stock market.
    """
    columns = ['high', 'low', 'open', 'close']
    if use_adj:
        columns.append('adj_close')

    # Replace 0 value columns with NaN.
    ts[columns] = ts[ts[columns] > 0][columns]

    if use_continuous_calendar:
        force_stock_market_calendar = False
    if force_stock_market_calendar:
        use_continuous_calendar = False

    if use_continuous_calendar:
        select_trading_days(use_stock_market_calendar=False)

    if force_stock_market_calendar:
        index = pd.to_datetime(stock_market_calendar)
        ts = ts.reindex(index=index)

    ts.dropna(subset=check_fields, inplace=True)

    if use_adj:
        _adj_prices(ts)

    if start < ts.index[0]:
        start = ts.index[0]
    if end > ts.index[-1]:
        end = ts.index[-1]
    ts = ts[start - datetime.timedelta(365):end]

    return ts


def finalize_timeseries(ts, start, dropna=False, drop_columns=None):
    """
    Finalize timeseries.

    Drop all rows that have nan column values.  Set timeseries to begin
    at start.

    Parameters
    ----------
    ts : pd.DataFrame
        The timeseries of a symbol.
    start : datetime.datetime
        The start date for backtest.
    dropna : bool, optional
        Drop rows that have a NaN value in one of it's columns
        (default is False).
    drop_columns : list of str, optional
        List of columns to drop from `ts` (default is None, which
        implies that no columns should be dropped).

    Returns
    -------
    datetime.datetime
        The start date.
    pd.DataFrame
        The timeseries of a symbol.
    """
    if drop_columns:
        ts.drop(columns=drop_columns, inplace=True)
    if dropna:
        ts.dropna(inplace=True)
    elif ts.isnull().values.any():
        warnings.warn("NaN value(s) detected in timeseries")
    ts = ts[start:]
    start = ts.index[0]
    return ts, start


#####################################################################
# CACHE SYMBOLS (remove, update, get_symbol_metadata)

def _difference_in_years(start, end):
    """
    Calculate the number of years between two dates.

    Parameters
    ----------
    start : datetime.datetime
        The start date.
    end : datetime.datetime
        The end date.

    Returns
    -------
    float
        The difference in years between start and end dates.
    """
    diff = end - start
    diff_in_years = (diff.days + diff.seconds/86400)/365.2425
    return diff_in_years


def remove_cache_symbols(symbols=None, dir_name='data'):
    """
    Remove cached timeseries for list of symbols.

    Filter out any symbols prefixed with '__'.

    Parameters
    ----------
    symbols : str or list of str, optional
        The symbol(s) for which to remove cached timeseries (default
        is None, which imples remove timeseries for all symbols).
    dir_name : str, optional
        The leaf data dir name (default is 'data').

    Returns
    -------
    None
    """
    cache_dir = _get_cache_dir(dir_name)

    if symbols:
        # If symbols is not a list, cast it to a list.
        if not isinstance(symbols, list):
            symbols = [symbols]
        filenames = [symbol.upper() + '.csv' for symbol in symbols]
    else:
        filenames = [f for f in os.listdir(cache_dir) if f.endswith('.csv')]

    # Filter out any filename prefixed with '__'.
    filenames = [f for f in filenames if not f.startswith('__')]

    print('removing symbols:')
    for i, f in enumerate(filenames):
        symbol = os.path.splitext(f)[0]
        print(symbol + ' ', end='')
        if i % 10 == 0 and i != 0: print()

        filepath = os.path.join(cache_dir, f)
        if os.path.exists(filepath):
            os.remove(filepath)
        else:
            print(f'\n({f} not found)')
    print()


def update_cache_symbols(symbols=None, dir_name='data', from_year=None):
    """
    Update cached timeseries for list of symbols.

    Filter out any filename prefixed with '__'.

    Parameters
    ----------
    symbols : str or list, optional
        The symbol(s) for which to update cached timeseries (default
        is None, which imples update timeseries for all symbols).
    dir_name : str, optional
        The leaf data dir name (default is 'data).
    from_year: int, optional
        The start year for timeseries retrieval (default is None,
        which implies that all the available data is retrieved).

    Returns
    -------
    None
    """
    cache_dir = _get_cache_dir(dir_name)

    if symbols:
        # If symbols is not a list, cast it to a list.
        if not isinstance(symbols, list):
            symbols = [symbols]
    else:
        filenames = ([f for f in os.listdir(cache_dir)
                      if f.endswith('.csv') and not f.startswith('__')])
        symbols = [os.path.splitext(filename)[0] for filename in filenames]

    # Make symbol names uppercase.
    symbols = [symbol.upper() for symbol in symbols]

    print('updating symbols:')
    for i, symbol in enumerate(symbols):
        print(symbol + ' ', end='')
        if i % 10 == 0 and i != 0:
            print()

        try:
            fetch_timeseries(symbol, dir_name=dir_name, use_cache=False,
                             from_year=from_year)
        except RemoteDataError as e:
            print(f'\n({e})')
        except Exception as e:
            print(f'\n({e})')
    print()


def get_symbol_metadata(symbols=None, dir_name='data', from_year=None):
    """
    Get symbol metadata for list of symbols.

    Filter out any filename prefixed with '__'.

    Parameters
    ----------
    symbols : str or list, optional
        The symbol(s) for which to get symbol metadata (default
        is None, which imples get symbol metadata for all symbols).
    dir_name : str, optional
        The leaf data dir name (default is 'data).
    from_year: int, optional
        The start year for timeseries retrieval (default is None,
        which implies that all the available data is retrieved).

    Returns
    -------
    pd.DataFrame
        Each row contains metadata for a symbol.
    """
    cache_dir = _get_cache_dir(dir_name)

    if symbols:
        # If symbols is not a list, cast it to a list.
        if not isinstance(symbols, list):
            symbols = [symbols]
    else:
        filenames = ([f for f in os.listdir(cache_dir)
                     if f.endswith('.csv') and not f.startswith('__')])
        symbols = [os.path.splitext(filename)[0] for filename in filenames]

    # Make symbol names uppercase.
    symbols = [symbol.upper() for symbol in symbols]

    l = []
    for i, symbol in enumerate(symbols):
        try:
            ts = fetch_timeseries(symbol, dir_name=dir_name, use_cache=True,
                                  from_year=from_year) 
            start = ts.index[0].to_pydatetime()
            end = ts.index[-1].to_pydatetime()
            num_years = _difference_in_years(start, end)
            start = start.strftime('%Y-%m-%d')
            end = end.strftime('%Y-%m-%d')
            t = (symbol, start, end, num_years)
            l.append(t)
        except RemoteDataError as e:
            print(f'\n({e})')
        except Exception as e:
            print('\n({})'.format(e))
    columns = ['symbol', 'start_date', 'end_date', 'num_years']
    df = pd.DataFrame(l, columns=columns)
    return df

Functions

def fetch_timeseries(symbol, dir_name='data', use_cache=True, from_year=None)

Read time series data.

Use cached version if it exists and use_cache is True, otherwise retrive, cache, then read.

Parameters

symbol : str

The symbol for a security.

dir_name : str, optional

The leaf data dir name (default is 'data').

use_cache : bool, optional

True to use data cache. False to retrieve from the internet (default is True).

from_year : int, optional

The start year for timeseries retrieval (default is None, which implies that all the available data is retrieved).

Returns

pd.DataFrame

The timeseries of a symbol.

Expand source code

def fetch_timeseries(symbol, dir_name='data', use_cache=True, from_year=None):
    """
    Read time series data.

    Use cached version if it exists and use_cache is True, otherwise
    retrive, cache, then read.

    Parameters
    ----------
    symbol : str
        The symbol for a security.
    dir_name : str, optional
        The leaf data dir name (default is 'data').
    use_cache: bool, optional
        True to use data cache.  False to retrieve from the internet 
        (default is True).
    from_year: int, optional
        The start year for timeseries retrieval (default is None,
        which implies that all the available data is retrieved).

    Returns
    -------
    pd.DataFrame
        The timeseries of a symbol.
    """
    if from_year is None:
        from_year = 1900 if not sys.platform.startswith('win') else 1971

    # Yahoo finance uses '-' where '.' is used in symbol names.
    symbol = symbol.replace('.', '-')
    symbol = symbol.upper()

    # pinkfish allows the use of a suffix starting with a '_',
    # like SPY_SHRT, so extract the symbol.
    symbol = symbol.split('_')[0]

    timeseries_cache = os.path.join(_get_cache_dir(dir_name), symbol + '.csv')

    if os.path.isfile(timeseries_cache) and use_cache:
        pass
    else:
        try:
            ts = pdr.get_data_yahoo(symbol, start=datetime.datetime(from_year, 1, 1), progress=False)
        except RemoteDataError as e:
            print(f'\n{e}')
        except Exception as e:
            print(f'\n{e}')
        else:
            ts.to_csv(timeseries_cache, encoding='utf-8')

    ts = pd.read_csv(timeseries_cache, index_col='Date', parse_dates=True)
    ts = _adj_column_names(ts)

    # Remove rows that have duplicated index.
    ts = ts[~ts.index.duplicated(keep='first')]
    return ts

def finalize_timeseries(ts, start, dropna=False, drop_columns=None)

Finalize timeseries.

Drop all rows that have nan column values. Set timeseries to begin at start.

Parameters

ts : pd.DataFrame

The timeseries of a symbol.

start : datetime.datetime

The start date for backtest.

dropna : bool, optional

Drop rows that have a NaN value in one of it's columns (default is False).

drop_columns : list of str, optional

List of columns to drop from ts (default is None, which implies that no columns should be dropped).

Returns

datetime.datetime

The start date.

pd.DataFrame

The timeseries of a symbol.

Expand source code

def finalize_timeseries(ts, start, dropna=False, drop_columns=None):
    """
    Finalize timeseries.

    Drop all rows that have nan column values.  Set timeseries to begin
    at start.

    Parameters
    ----------
    ts : pd.DataFrame
        The timeseries of a symbol.
    start : datetime.datetime
        The start date for backtest.
    dropna : bool, optional
        Drop rows that have a NaN value in one of it's columns
        (default is False).
    drop_columns : list of str, optional
        List of columns to drop from `ts` (default is None, which
        implies that no columns should be dropped).

    Returns
    -------
    datetime.datetime
        The start date.
    pd.DataFrame
        The timeseries of a symbol.
    """
    if drop_columns:
        ts.drop(columns=drop_columns, inplace=True)
    if dropna:
        ts.dropna(inplace=True)
    elif ts.isnull().values.any():
        warnings.warn("NaN value(s) detected in timeseries")
    ts = ts[start:]
    start = ts.index[0]
    return ts, start

def get_symbol_metadata(symbols=None, dir_name='data', from_year=None)

Get symbol metadata for list of symbols.

Filter out any filename prefixed with '__'.

Parameters

symbols : str or list, optional

The symbol(s) for which to get symbol metadata (default is None, which imples get symbol metadata for all symbols).

dir_name : str, optional

The leaf data dir name (default is 'data).

from_year : int, optional

The start year for timeseries retrieval (default is None, which implies that all the available data is retrieved).

Returns

pd.DataFrame

Each row contains metadata for a symbol.

Expand source code

def get_symbol_metadata(symbols=None, dir_name='data', from_year=None):
    """
    Get symbol metadata for list of symbols.

    Filter out any filename prefixed with '__'.

    Parameters
    ----------
    symbols : str or list, optional
        The symbol(s) for which to get symbol metadata (default
        is None, which imples get symbol metadata for all symbols).
    dir_name : str, optional
        The leaf data dir name (default is 'data).
    from_year: int, optional
        The start year for timeseries retrieval (default is None,
        which implies that all the available data is retrieved).

    Returns
    -------
    pd.DataFrame
        Each row contains metadata for a symbol.
    """
    cache_dir = _get_cache_dir(dir_name)

    if symbols:
        # If symbols is not a list, cast it to a list.
        if not isinstance(symbols, list):
            symbols = [symbols]
    else:
        filenames = ([f for f in os.listdir(cache_dir)
                     if f.endswith('.csv') and not f.startswith('__')])
        symbols = [os.path.splitext(filename)[0] for filename in filenames]

    # Make symbol names uppercase.
    symbols = [symbol.upper() for symbol in symbols]

    l = []
    for i, symbol in enumerate(symbols):
        try:
            ts = fetch_timeseries(symbol, dir_name=dir_name, use_cache=True,
                                  from_year=from_year) 
            start = ts.index[0].to_pydatetime()
            end = ts.index[-1].to_pydatetime()
            num_years = _difference_in_years(start, end)
            start = start.strftime('%Y-%m-%d')
            end = end.strftime('%Y-%m-%d')
            t = (symbol, start, end, num_years)
            l.append(t)
        except RemoteDataError as e:
            print(f'\n({e})')
        except Exception as e:
            print('\n({})'.format(e))
    columns = ['symbol', 'start_date', 'end_date', 'num_years']
    df = pd.DataFrame(l, columns=columns)
    return df

def remove_cache_symbols(symbols=None, dir_name='data')

Remove cached timeseries for list of symbols.

Filter out any symbols prefixed with '__'.

Parameters

symbols : str or list of str, optional

The symbol(s) for which to remove cached timeseries (default is None, which imples remove timeseries for all symbols).

dir_name : str, optional

The leaf data dir name (default is 'data').

Returns

None

 

Expand source code

def remove_cache_symbols(symbols=None, dir_name='data'):
    """
    Remove cached timeseries for list of symbols.

    Filter out any symbols prefixed with '__'.

    Parameters
    ----------
    symbols : str or list of str, optional
        The symbol(s) for which to remove cached timeseries (default
        is None, which imples remove timeseries for all symbols).
    dir_name : str, optional
        The leaf data dir name (default is 'data').

    Returns
    -------
    None
    """
    cache_dir = _get_cache_dir(dir_name)

    if symbols:
        # If symbols is not a list, cast it to a list.
        if not isinstance(symbols, list):
            symbols = [symbols]
        filenames = [symbol.upper() + '.csv' for symbol in symbols]
    else:
        filenames = [f for f in os.listdir(cache_dir) if f.endswith('.csv')]

    # Filter out any filename prefixed with '__'.
    filenames = [f for f in filenames if not f.startswith('__')]

    print('removing symbols:')
    for i, f in enumerate(filenames):
        symbol = os.path.splitext(f)[0]
        print(symbol + ' ', end='')
        if i % 10 == 0 and i != 0: print()

        filepath = os.path.join(cache_dir, f)
        if os.path.exists(filepath):
            os.remove(filepath)
        else:
            print(f'\n({f} not found)')
    print()

def select_tradeperiod(ts, start, end, use_adj=False, use_continuous_calendar=False, force_stock_market_calendar=False, check_fields=['close'])

Select the trade period.

First, remove rows that have zero values in price columns. Then, select a time slice of the data to trade from ts. Back date a year to allow time for long term indicators, e.g. 200sma is become valid.

Parameters

ts : pd.DataFrame

The timeseries of a symbol.

start : datetime.datetime

The desired start date for the strategy.

end : datetime.datetime

The desired end date for the strategy.

use_adj : bool, optional

True to adjust prices for dividends and splits (default is False).

use_continuous_calendar : bool, optional

True if your timeseries has data for all seven days a week, and you want to backtest trading every day, including weekends. If this value is True, then force_stock_market_calendar is set to False (default is False).

force_stock_market_calendar : bool, optional

True forces use of stock market calendar on timeseries. Normally, you don't need to do this. This setting is intended to transform a continuous timeseries into a weekday timeseries. If this value is True, then use_continuous_calendar is set to False (default is False).

check_fields : list of str, optional {'high', 'low', 'open',

'close', 'adj_close'} Fields to check for for NaN values. If a NaN value is found for one of these fields, that row is dropped (default is ['close']).

Returns

pd.DataFrame

The timeseries for specified start:end, optionally with prices adjusted.

Notes

You should only set one of use_continuous_calendar=True or force_stock_market_calendar=True for a continuous timeseries. You should set neither of these to True if your timeseries is based on the stock market.

Expand source code

def select_tradeperiod(ts, start, end, use_adj=False,
                       use_continuous_calendar=False,
                       force_stock_market_calendar=False,
                       check_fields=['close']):
    """
    Select the trade period.

    First, remove rows that have zero values in price columns. Then,
    select a time slice of the data to trade from ts.  Back date a year
    to allow time for long term indicators, e.g. 200sma is become valid.

    Parameters
    ----------
    ts : pd.DataFrame
        The timeseries of a symbol.
    start : datetime.datetime
        The desired start date for the strategy.
    end : datetime.datetime
        The desired end date for the strategy.
    use_adj : bool, optional
        True to adjust prices for dividends and splits
        (default is False).
    use_continuous_calendar: bool, optional
        True if your timeseries has data for all seven days a week,
        and you want to backtest trading every day, including weekends.
        If this value is True, then `force_stock_market_calendar`
        is set to False (default is False).
    force_stock_market_calendar : bool, optional
        True forces use of stock market calendar on timeseries.
        Normally, you don't need to do this.  This setting is intended
        to transform a continuous timeseries into a weekday timeseries.
        If this value is True, then `use_continuous_calendar` is set
        to False (default is False).
    check_fields : list of str, optional {'high', 'low', 'open',
        'close', 'adj_close'}
        Fields to check for for NaN values.  If a NaN value is found
        for one of these fields, that row is dropped
        (default is ['close']).

    Returns
    -------
    pd.DataFrame
        The timeseries for specified start:end, optionally with prices
        adjusted.

    Notes
    -----
    You should only set one of `use_continuous_calendar`=True or
    `force_stock_market_calendar`=True for a continuous timeseries.
    You should set neither of these to True if your timeseries is based
    on the stock market.
    """
    columns = ['high', 'low', 'open', 'close']
    if use_adj:
        columns.append('adj_close')

    # Replace 0 value columns with NaN.
    ts[columns] = ts[ts[columns] > 0][columns]

    if use_continuous_calendar:
        force_stock_market_calendar = False
    if force_stock_market_calendar:
        use_continuous_calendar = False

    if use_continuous_calendar:
        select_trading_days(use_stock_market_calendar=False)

    if force_stock_market_calendar:
        index = pd.to_datetime(stock_market_calendar)
        ts = ts.reindex(index=index)

    ts.dropna(subset=check_fields, inplace=True)

    if use_adj:
        _adj_prices(ts)

    if start < ts.index[0]:
        start = ts.index[0]
    if end > ts.index[-1]:
        end = ts.index[-1]
    ts = ts[start - datetime.timedelta(365):end]

    return ts

def update_cache_symbols(symbols=None, dir_name='data', from_year=None)

Update cached timeseries for list of symbols.

Filter out any filename prefixed with '__'.

Parameters

symbols : str or list, optional

The symbol(s) for which to update cached timeseries (default is None, which imples update timeseries for all symbols).

dir_name : str, optional

The leaf data dir name (default is 'data).

from_year : int, optional

The start year for timeseries retrieval (default is None, which implies that all the available data is retrieved).

Returns

None

 

Expand source code

def update_cache_symbols(symbols=None, dir_name='data', from_year=None):
    """
    Update cached timeseries for list of symbols.

    Filter out any filename prefixed with '__'.

    Parameters
    ----------
    symbols : str or list, optional
        The symbol(s) for which to update cached timeseries (default
        is None, which imples update timeseries for all symbols).
    dir_name : str, optional
        The leaf data dir name (default is 'data).
    from_year: int, optional
        The start year for timeseries retrieval (default is None,
        which implies that all the available data is retrieved).

    Returns
    -------
    None
    """
    cache_dir = _get_cache_dir(dir_name)

    if symbols:
        # If symbols is not a list, cast it to a list.
        if not isinstance(symbols, list):
            symbols = [symbols]
    else:
        filenames = ([f for f in os.listdir(cache_dir)
                      if f.endswith('.csv') and not f.startswith('__')])
        symbols = [os.path.splitext(filename)[0] for filename in filenames]

    # Make symbol names uppercase.
    symbols = [symbol.upper() for symbol in symbols]

    print('updating symbols:')
    for i, symbol in enumerate(symbols):
        print(symbol + ' ', end='')
        if i % 10 == 0 and i != 0:
            print()

        try:
            fetch_timeseries(symbol, dir_name=dir_name, use_cache=False,
                             from_year=from_year)
        except RemoteDataError as e:
            print(f'\n({e})')
        except Exception as e:
            print(f'\n({e})')
    print()