Trader Payout vs OptionSpace

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Trader Payout compared with OptionSpace
Detail	Trader Payout	OptionSpace
What it does	Independent verification of prop firm payouts and trading costs	Volatility data and research tools powered by a market-maker-grade fitting and pricing engine
Stage	Established	Not given
Founded	Not given	Not given
Based in	Not given	US
Pricing model	Free	Freemium
Pricing	Not given	Free sample plan with no card required. Retail subscription available for individual personal use in the United States with daily limits on API credits. Institutional plans available at contact pricing for any business use with annual licenses invoiced monthly via ACH or wire. Higher-frequency data
Key features	Payout archive with public source links Firm reviews with rules and fees Trading guides from funded traders Position sizing tools	Fitted volatility surfaces with bid-ask-weighted curves across every expiration Accounts for American-style options with early exercise, discrete dividends, and implied borrow rates One-minute granularity historical data across 140+ symbols spanning 10 years Realized volatility calculations from 385 intraday returns per day Vol Screener, Vol Premium, Skew Scanner, and Earnings Vol analysis tools Historical simulation using actual bid-ask prices and fitted vol surfaces Python package for research workflows Interactive visual dashboards for surface inspection and event studies Model Context Protocol (MCP) integration for AI agent workflows Greeks and constant-maturity metrics via API and dashboards
What makes it different	Only publishes payouts from public sources with live links to evidence No firm pays to be listed or rated Corrections published openly, never silent rewrites Human editors sign every page, AI never publishes alone Independent media with no capital or guaranteed returns	Arbitrage-free fitted volatility wings extending beyond quoted book using Lee-moment discipline Accessible to quantitative researchers and AI workflows through Python, API, and MCP without requiring market-maker infrastructure 10 years of consistent historical data at one-minute granularity across 140+ symbols for rigorous backtesting
Community votes	0	0
DR (Domain Rating by Ahrefs)	13 −1 (down) since last check	Not checked yet no change since last check
Trust Flow (Majestic)	11	0
Citation Flow (Majestic)	24	0
Referring domains (Majestic)	16	0

4 details filled in for both companies.

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