Import extra_fields

[takumi-miyajima]

Is your feature request related to a problem? Please describe.
Cannot use extra_fields functionality as documented.

Describe the solution you'd like
I would like to be able to use extra_fields when working with CSV data, allowing access to additional columns beyond the standard OHLCV data.

"""
@pyne
"""

from pynecore.lib import ta, Series, close, extra_fields

def main():
    some_price: Series[float] = extra_fields["some_price"]
    realtime_some_price = some_price[0]
    confirmed_some_price = some_price[1]
    some_price_ma = ta.sma(some_price, 7)

    some_flag: Series[bool] = extra_fields["some_flag"]
    ...

Describe alternatives you've considered
Currently using the volume column as a workaround (though it's limited to integers float only).

Additional context
Thank you for creating such an excellent library.
This feature would greatly enhance the flexibility of CSV data processing workflows.

[wallneradam]

Thank you we are thinking about this, because it would be a great addition. Our binary .ohlcv format is only for OHLCV data which is enough for Pine Scripts. But PyneCore is more then Pine Script, so you are right it would be great to have this feature.

Anyway volume is float too.

[wallneradam]

Related: #26 (comment) describes a similar need from a different angle — programmatically injecting arbitrary external data (e.g., economic events) into a running script via the ScriptRunner, rather than reading it from CSV extra columns.

Both requests share the same core requirement: accessing custom data beyond standard OHLCV inside a Pyne script. #25 covers the file-based approach (CSV extra fields), while the #26 comment describes a runtime/programmatic injection approach.

A complete solution might address both: CSV extra columns for file-based workflows, and a programmatic API for embedding scenarios.

[wallneradam]

This feature is now implemented and available in v6.3.6.

How to use

If your CSV contains extra columns beyond standard OHLCV, they are automatically available via extra_fields:

"""
@pyne
"""
from pynecore import Series
from pynecore.lib import script, ta, close, extra_fields, plot


@script.indicator(title="Extra Fields Example")
def main():
    rsi: Series[float] = extra_fields["rsi"]
    signal: Series[str] = extra_fields["signal"]

    # Series indexing works
    prev_rsi = rsi[1]

    # Works with built-in functions
    rsi_sma = ta.sma(rsi, 14)

    if signal[0] == "buy":
        plot(close, "Buy Signal", linewidth=2)

How it works

• When CSV is converted to binary OHLCV, extra columns are saved to a .extra.csv sidecar file automatically
• The extra_fields dict is updated each bar with the current row's values
• Annotating with Series[float] or Series[str] gives you full Series functionality (history indexing, use with ta.* functions, etc.)

Documentation

See the full documentation: https://pynecore.org/docs/advanced/extra-fields/