Faster parsing of NATURALGAS option and futures tickers: index slicing beats regex at scale

Parse NATURALGAS .MCX option and futures tickers at scale: extract date, strike, and type with fixed-offset index slicing vs regex for faster pandas workflows.

Parsing option and futures tickers at scale sounds trivial until you hit edge cases and performance ceilings. A typical filename like NATURALGAS21FEB25270CE.MCX embeds date, price and type, but the date can be either DDMMMYY or DDMMM. When you need to process hundreds of CSVs with tens of thousands of rows each, the question becomes practical: regex, or fixed-offset slicing? The goal here is to extract the date, the three-digit price, and the option type (CE, PE, or FUT) efficiently and in a way that won’t collapse under real-world volume.

Baseline: a straightforward regex-first implementation

A common starting point is a vectorized regex extraction in pandas. It works and is easy to read, but the concern is runtime when scaled across large datasets.

import pandas as pd

# Columns: symbol contains values like NATURALGAS21FEB25270CE.MCX
# Extract option type (CE, PE, or FUT)
tbl["opt_kind"] = tbl["symbol"].str.extract(r'(PE|CE|FUT)', expand=False)

# Extract strike, originally written to allow 2-5 digits; price is expected to be 3 digits
# and the date part can be DDMMM or DDMMMYY
tbl['strike_val'] = tbl['symbol'].str.extract(
    r'NATURALGAS\d{2}[A-Z]{3}(?:\d{2})?(\d{2,5})(?=CE|PE)', expand=False)

tbl['strike_val'] = pd.to_numeric(tbl['strike_val'], errors='coerce')

# Extract date only for CE/PE rows
tbl['exp_date'] = None
sel_opts = tbl['opt_kind'].isin(['PE', 'CE'])
tbl.loc[sel_opts, 'exp_date'] = tbl.loc[sel_opts, 'symbol'].str.extract(
    r'NATURALGAS(\d{2}[A-Z]{3}(?:\d{2})?)(?=\d{2,5}(?:CE|PE))')[0]

What actually makes this hard

The filenames are rigidly structured: they begin with NATURALGAS, then a date that can be DDMMMYY or DDMMM, then a three-digit price, and then the option type CE or PE or FUT, followed by .MCX. The format looks regular, but the variable-length date section introduces ambiguity for naive slicing and motivates regex. At the same time, regex isn’t the most efficient tool when the layout is predictable and the string surface is simple. That’s where position-based slicing comes in.

There is a reliable disambiguation rule that follows from the filenames’ total length. If the filename length is 26 or 27 characters, the date uses DDMMMYY; otherwise it’s DDMMM. Another property also holds: if the total length is odd, the option type is FUT (three letters), otherwise it’s CE or PE (two letters). Given these two invariants and the constant NATURALGAS prefix, you can compute offsets deterministically and extract the parts with simple substring operations.

A faster approach: index slicing with fixed offsets

When formats are predictable, substring extraction by index is typically faster than regex. The logic here uses the filename length to decide whether the date is five or seven characters long, and whether the option type consumes two or three characters.

def split_parts(file_name):
    # Default assumptions: DDMMM (5) and two-letter type (CE/PE)
    date_width = 5
    type_width = 2

    # Decide DDMMMYY vs DDMMM based on total length
    if len(file_name) >= 26:
        date_width = 7

    # Decide FUT vs CE/PE based on total length parity
    if len(file_name) % 2 == 1:
        type_width = 3

    # NATURALGAS has length 10, so the date starts at index 10
    date_val = file_name[10:10 + date_width]
    price_val = file_name[10 + date_width:13 + date_width]
    kind_val = file_name[13 + date_width:13 + date_width + type_width]

    return date_val, price_val, kind_val

# Examples
x_date, x_price, x_kind = split_parts("NATURALGAS21FEB25270CE.MCX")
print(x_date)
print(x_price)
print(x_kind)

x_date, x_price, x_kind = split_parts("NATURALGAS21FEB270CE.MCX")
print(x_date)
print(x_price)
print(x_kind)

x_date, x_price, x_kind = split_parts("NATURALGAS21FEB25270FUT.MCX")
print(x_date)
print(x_price)
print(x_kind)

x_date, x_price, x_kind = split_parts("NATURALGAS21FEB270FUT.MCX")
print(x_date)
print(x_price)
print(x_kind)

Why this works and when to use it

The approach leverages a stable naming convention. NATURALGAS is fixed at the start. The date length can be inferred from the entire string length. The option type width is inferred from parity. With those two decisions made, every other substring position becomes deterministic. There’s no pattern backtracking and no scanning, so it avoids the overhead associated with regex engines. For the scale described—hundreds of files and tens of thousands of rows—index slicing is a sound choice when the schema is consistent.

There’s also a maintainability angle. For formats as regular as these filenames, explicit slicing makes intent obvious: you see exactly what byte ranges are read. If your filenames evolve or new variants appear, update the small set of conditions that compute lengths. If you’re unsure where the time goes in your pipeline, measure before changing anything. It’s not worth micro-optimizing if parsing isn’t your bottleneck.

Why it matters

In large-scale data processing, a tiny inefficiency multiplied by millions of rows becomes real money and time. Regex is a great tool, but if the string layout is known upfront, index arithmetic usually wins in simplicity and speed. Equally important is removing ambiguity early: choosing rules that map every filename to a single interpretation keeps downstream logic predictable.

Conclusion

If you own a predictable ticker schema like NATURALGASDDMMM[YY]PPP[CE|PE|FUT].MCX, favor direct substring extraction over regex. Derive the date width and option type width from the filename length, then slice once. Keep the baseline regex approach around if you need quick experiments, but decide with measurements, not guesses. Profile the end-to-end job, confirm that parsing is a hotspot, and only then switch tactics. Stable formats reward simple code; let the structure of your data drive the choice.