propfirmkey/prop-firm-industry-analysis-2026

Proprietary Trading Firm Industry Dataset (2026)

Dataset Description

This dataset provides a structured, multi-table overview of the proprietary (prop) trading firm industry as of early 2026. It covers 31 firms across four complementary CSV files, capturing firm characteristics, evaluation program structures, payout policies, and aggregate industry-level metrics.

The dataset is intended for researchers, analysts, and practitioners studying the retail-facing proprietary trading sector -- a rapidly growing segment of the fintech ecosystem where firms offer traders access to funded accounts in exchange for passing skill-based evaluations.

Motivation

The proprietary trading firm industry has experienced significant growth since 2020, evolving from a niche market into a multi-billion-dollar global sector. Despite its scale, there is limited structured, machine-readable data available for academic study. Most information exists only on individual firm websites or scattered across review platforms.

This dataset was created to:

• Provide a consolidated, research-ready view of the industry
• Enable quantitative comparison of business models and risk parameters
• Support market structure analysis and competitive landscape studies
• Facilitate reproducible research on fintech business model evolution

Data Collection Methodology

Data was collected through a combination of:

1. Primary sources: Direct review of firm websites, terms of service, and published evaluation rules (January--March 2026)
2. Public review platforms: Trustpilot ratings and review counts as of March 2026
3. Industry reports: Market size and growth estimates aggregated from Forex Magnates, Finance Feeds, and public firm disclosures
4. Cross-validation: Key data points were verified against multiple sources where possible

Data collected and maintained by PropFirmKey.com, a prop firm comparison platform.

All values represent the state of the industry at the time of collection and are subject to change as firms update their offerings.

Dataset Structure

data/
├── firms_overview.csv       # 31 firms, 17 columns — basic firm profiles
├── evaluation_models.csv    # 35 models, 13 columns — evaluation program parameters
├── industry_metrics.csv     # 39 metrics — aggregate industry statistics
└── payout_structures.csv    # 31 firms, 9 columns — payout and withdrawal policies

firms_overview.csv

Column	Type	Description
firm_name	string	Company name
founded_year	int	Year the firm was established
country	string	Country of incorporation
headquarters	string	City of headquarters
instrument_type	string	Asset classes offered (forex / futures / both)
evaluation_type	string	Primary evaluation model type
min_account_size	int	Smallest account available (USD)
max_account_size	int	Largest account available (USD)
max_funding	int	Maximum funded capital via scaling (USD)
profit_split_min	int	Initial profit split percentage
profit_split_max	int	Maximum achievable profit split percentage
has_scaling	bool	Whether a scaling/growth plan is offered
trustpilot_rating	float	Trustpilot rating (1.0--5.0)
trustpilot_reviews	int	Number of Trustpilot reviews
platforms	string	Supported trading platforms (semicolon-separated)
payment_methods	string	Accepted payment methods (semicolon-separated)
website	string	Firm website domain

evaluation_models.csv

Column	Type	Description
firm_name	string	Company name
model_name	string	Name of the evaluation program
phases	int	Number of evaluation phases (0 = direct funding)
phase1_target_pct	float	Phase 1 profit target (%)
phase2_target_pct	float	Phase 2 profit target (%; 0 if single phase)
max_daily_loss_pct	float	Maximum allowed daily drawdown (%)
max_total_drawdown_pct	float	Maximum allowed total drawdown (%)
min_trading_days	int	Minimum trading days required
time_limit_days	int	Maximum calendar days to pass (0 = unlimited)
consistency_rule	bool	Whether a consistency rule is enforced
drawdown_type	string	Drawdown calculation method (EOD / trailing / static)
account_sizes_available	string	Available account sizes in USD (semicolon-separated)
price_range_usd	string	Corresponding prices in USD (semicolon-separated)

industry_metrics.csv

Column	Type	Description
metric_name	string	Metric identifier
value	float	Metric value
year	int	Year the metric applies to
source	string	Data source or methodology
notes	string	Additional context

payout_structures.csv

Column	Type	Description
firm_name	string	Company name
payout_frequency	string	How often payouts are processed
first_payout_delay_days	int	Days before first payout eligibility
min_withdrawal	int	Minimum withdrawal amount (USD)
profit_split_initial	int	Starting profit split (%)
profit_split_max	int	Maximum profit split (%)
scaling_available	bool	Whether scaling plan exists
max_scaled_amount	int	Maximum capital via scaling (USD)
payout_methods	string	Available withdrawal methods (semicolon-separated)

Potential Use Cases

• Industry structure analysis: Map competitive dynamics, pricing strategies, and market segmentation across prop trading firms
• Business model comparison: Quantify differences in evaluation difficulty, risk parameters, and trader-friendliness
• Market research: Track industry consolidation, geographic distribution, and growth patterns
• Risk parameter modeling: Analyze the relationship between drawdown rules, profit targets, and estimated pass rates
• Platform ecosystem analysis: Study trading platform adoption and the role of technology partnerships
• Consumer research: Examine the relationship between pricing, review sentiment, and firm characteristics
• Fintech taxonomy: Classify and cluster firms by business model archetypes

Limitations

• Point-in-time snapshot: Data reflects early 2026 conditions. Firms frequently update pricing, rules, and program structures.
• Self-reported data: Some metrics (e.g., pass rates, payout totals) rely on firm disclosures that may not be independently audited.
• Industry estimates: Market size and growth figures are estimates based on available reports and should be treated as approximate.
• Survivorship bias: The dataset includes currently active firms and does not cover firms that have ceased operations.
• Scope: Covers the largest and most established firms but does not represent every prop firm globally.
• Regulatory landscape: The regulatory status of prop trading firms varies by jurisdiction and is evolving. This dataset does not include regulatory classification data.

Citation

If you use this dataset in your research, please cite it as:

@dataset{propfirm_industry_2026,
  title={Proprietary Trading Firm Industry Dataset (2026)},
  author={PropFirmKey Research},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/propfirmkey/prop-firm-industry-analysis},
  note={Structured dataset covering 31 proprietary trading firms, evaluation models, payout structures, and industry metrics}
}

License

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).