Fix Feeder Cattle (GF) tick size in symbol-properties-database

[AlexCatarino]

Description

Corrects the minimum_price_variation for CME Feeder Cattle (GF) futures in Data/symbol-properties/symbol-properties-database.csv from 0.025 to 0.00025:

-cme,GF,future,Feeder Cattle Futures,USD,50000.0,0.025,1.0,,1,100
+cme,GF,future,Feeder Cattle Futures,USD,50000.0,0.00025,1.0,,1,100

This aligns GF with the per-pound dollar convention used by the other livestock contracts (LE, HE) and with the official CME contract specifications.

Related Issue

Fixes #9492

Motivation and Context

The Feeder Cattle row was using cents-per-pound (0.025) while every other livestock row uses dollars-per-pound (0.00025 for LE / HE). The CME contract specs define the minimum price fluctuation as $0.00025 per pound and the tick value as $12.50 on the 50,000 lb contract.

With price_magnifier = 100 correctly set, the prior 0.025 value produced an effective tick value of 0.025 × 50,000 = $1,250 per contract, vs. the actual exchange tick value of $12.50 — off by a factor of 100. Any algorithm that rounds target / stop / limit prices to Symbol.Properties.MinimumPriceVariation on GF landed on a price grid 100× coarser than the exchange grid, producing orders at unreachable prices or broker rejections. Issue surfaced in TradeStation live trading; sibling contracts (LE, HE, JPY futures) behaved correctly.

References:

• CME Feeder Cattle contract spec: https://www.cmegroup.com/markets/agriculture/livestock/feeder-cattle.contractSpecs.html
• CME Rulebook Chapter 102 (Feeder Cattle Futures): https://www.cmegroup.com/rulebook/CME/II/100/102/102.pdf

Requires Documentation Change

No.

How Has This Been Tested?

Reproduction (against master prior to this fix):

from QuantConnect import *
from QuantConnect.Securities.Future import Futures

algo = QCAlgorithm()
gf = algo.add_future(Futures.Meats.FEEDER_CATTLE).symbol
le = algo.add_future(Futures.Meats.LIVE_CATTLE).symbol
he = algo.add_future(Futures.Meats.LEAN_HOGS).symbol
for s in (gf, le, he):
    p = algo.securities[s].symbol_properties
    print(s.id.symbol, p.minimum_price_variation, p.minimum_price_variation * p.contract_multiplier)
# GF 0.025  1250.00   <-- wrong
# LE 0.00025  10.00
# HE 0.00025  10.00

After the fix, GF reports 0.00025 and a per-contract tick value of $12.50, matching the exchange.

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• Refactor (non-breaking change which improves implementation)
• Performance (non-breaking change which improves performance. Please add associated performance test and results)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Non-functional change (xml comments/documentation/etc)

Checklist:

• My code follows the code style of this project.
• I have read the CONTRIBUTING document.
• I have added tests to cover my changes.
• All new and existing tests passed.
• My branch follows the naming convention bug-<issue#>-<description>