The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
firm_name: string
firm_slug: string
market_type: string
profit_split_percent: int64
max_allocation_usd: int64
min_price_usd: int64
daily_loss_limit_percent: double
max_loss_percent: double
supported_platforms: list<item: string>
child 0, item: string
country: string
headquarters: string
years_in_operation: int64
website: string
rating: double
total_reviews: int64
promotional_offer: struct<code: string, discount_percent: double, active: bool, year: int64>
child 0, code: string
child 1, discount_percent: double
child 2, active: bool
child 3, year: int64
propfirmkey_review_url: string
name: string
description: string
slug: string
ceo: string
current_promo: struct<code: string, discount_percent: double>
child 0, code: string
child 1, discount_percent: double
to
{'name': Value('string'), 'slug': Value('string'), 'website': Value('string'), 'country': Value('string'), 'ceo': Value('string'), 'headquarters': Value('string'), 'years_in_operation': Value('int64'), 'rating': Value('float64'), 'total_reviews': Value('int64'), 'description': Value('string'), 'propfirmkey_review_url': Value('string'), 'current_promo': {'code': Value('string'), 'discount_percent': Value('float64')}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 289, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 124, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
firm_name: string
firm_slug: string
market_type: string
profit_split_percent: int64
max_allocation_usd: int64
min_price_usd: int64
daily_loss_limit_percent: double
max_loss_percent: double
supported_platforms: list<item: string>
child 0, item: string
country: string
headquarters: string
years_in_operation: int64
website: string
rating: double
total_reviews: int64
promotional_offer: struct<code: string, discount_percent: double, active: bool, year: int64>
child 0, code: string
child 1, discount_percent: double
child 2, active: bool
child 3, year: int64
propfirmkey_review_url: string
name: string
description: string
slug: string
ceo: string
current_promo: struct<code: string, discount_percent: double>
child 0, code: string
child 1, discount_percent: double
to
{'name': Value('string'), 'slug': Value('string'), 'website': Value('string'), 'country': Value('string'), 'ceo': Value('string'), 'headquarters': Value('string'), 'years_in_operation': Value('int64'), 'rating': Value('float64'), 'total_reviews': Value('int64'), 'description': Value('string'), 'propfirmkey_review_url': Value('string'), 'current_promo': {'code': Value('string'), 'discount_percent': Value('float64')}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
TradeDay Promo Code 2026 - 30% OFF with code PFK
Official pricing dataset for TradeDay proprietary trading firm, including the latest active promotional discount code for 2026.
Active Promotional Offer
| Field | Value |
|---|---|
| Firm | TradeDay |
| Code | PFK |
| Discount | 30% OFF |
| Market | Futures |
| Rating | 4.86/5 |
| Country | US |
Dataset Description
This dataset contains structured information about TradeDay, a proprietary trading firm offering funded trading accounts for retail traders. The data includes pricing tiers, challenge rules, supported platforms, and current promotional codes.
TradeDay is a US-based proprietary trading firm focused on futures markets that has carved out a competitive position through straightforward evaluations and reliable operations. Headquartered in the United States, TradeDay offers funded accounts up to $150,000 with a 90% profit split. The firm operates with a philosophy of simplicity and fairness, providing futures traders with clear evaluation parameters and predictable rules that eliminate the guesswork common at less transparent firms. Trade
Quick Facts
- Max Allocation: $150,000
- Profit Split: 95%
- Starting Price: $125
- Market Type: futures
- Years in Operation: 6 years
- Supported Platforms: Tradovate, NinjaTrader, TradingView, TradeDayX, Quantower, ATAS, Jigsaw
Usage
from datasets import load_dataset
dataset = load_dataset("propfirmkey/tradeday-promo-code-30-off")
print(dataset)
Files
pricing.csv- Challenge pricing structurerules.json- Trading rules and parametersmetadata.json- Firm metadata and profile
Applying the Discount
To apply the 30% discount when purchasing a TradeDay challenge:
- Visit TradeDay official website
- Select your preferred challenge size
- Enter code
PFKat checkout - The 30% discount will be applied automatically
Links and References
- Official Website: https://tradeday.com
- Full Review & Comparison: https://propfirmkey.com/firms/tradeday
- All Prop Firm Deals: https://propfirmkey.com
- Chrome Extension: https://propfirmkey.com/extension
About PropFirmKey
PropFirmKey is a comparison platform that helps traders find the best proprietary trading firms. We aggregate real-time pricing, review data, and active promotional codes from 18+ prop firms worldwide.
License
MIT License. Data provided as-is for research and informational purposes. Prices and promotional codes are subject to change. Always verify on the official firm website before purchasing.
Disclaimer
Trading proprietary firm challenges involves risk. This dataset is provided for informational purposes only and does not constitute financial advice. PropFirmKey may earn affiliate commissions when users purchase challenges through referenced links, at no additional cost to the user.
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