Tradeify Co-Founders: “Difficult to Enter” Futures Prop; Subscription Ditch “Barely Moved Revenue”

Not financial advice. Past performance is not indicative of future results. Trading involves substantial risk of loss. Do your own research before making any investment decisions. See our Editorial Policy for details on how we test and rate AI trading bots and algorithmic platforms.

The futures prop trading space has become one of the most competitive corners of retail trading in 2026, and Tradeify sits near its center. When we reviewed this firm through the lens of our algorithmic trading platform testing program—evaluating how automated strategies interact with prop firm evaluation models—the co-founders' recent interview with Finance Magnates raised several flags worth unpacking. Brett Simberkoff and Vinan Mistry, who launched Tradeify in 2024 after pivoting from a planned CFD prop firm, have built a business that now claims over 100,000 active traders and a sevenfold user growth rate year-over-year. But the details beneath that headline growth reveal a firm operating in a regulatory gray zone, making strategic bets on prediction markets and introducing broker models, all while claiming a subscription fee change "didn't really impact the business."

We benchmarked Tradeify's evaluation structure against the Ellington AI trading platform in our 2026 review cycle, specifically testing how strategy execution differs between simulated prop environments and live brokerage execution. What we found raises important questions for any trader considering automated or algorithmic strategies on prop firm accounts.

What does Tradeify actually offer traders?

Tradeify is a futures prop trading firm that provides simulated funded accounts with a stated path from evaluation to funded account to payout. The firm does not offer live brokerage services directly—that's where the new introducing broker arm, Slay Markets, comes in, expected to go fully online in early July 2026 after a beta testing period (Finance Magnates, 2026). The core product is an evaluation account that traders must pass to qualify for a simulated funded account, with profit splits on any gains generated.

The firm launched a crypto prop offering earlier this year using perpetual futures pricing data sourced from Binance, which Mistry described as a first step toward multi-asset expansion into forex and CFDs (Finance Magnates, 2026). Tradeify also recently launched a free-entry prediction market tournament tied to the football World Cup knockout rounds, offering $250,000 in cash prizes split across the top 500 participants, using Polymarket's API. Within days of launch, the tournament attracted close to 20,000 registrations (Finance Magnates, 2026).

For algorithmic traders evaluating whether to run automated strategies on Tradeify's platform, the key question is how the simulated environment maps to real execution conditions. Our testing methodology treats prop firm evaluations as a distinct asset class—the rules of the evaluation game matter as much as the strategy itself.

How accurate are the backtests, really?

Here's where Tradeify's model creates a structural challenge for algorithmic traders. The firm operates as a "data analytics company," according to Mistry, constantly fine-tuning models and offerings based on customer surveys, community Discord feedback, competitor benchmarking, and internal data analysis (Finance Magnates, 2026). But the firm does not publish backtest data, live performance metrics, win rates, or drawdown bands for its evaluation structure.

When we cross-referenced Tradeify's evaluation rules against our 2026 algorithmic testing framework, we found that the lack of published performance data makes it impossible to model how a given strategy would behave under Tradeify's specific evaluation constraints. This is a meaningful gap. In our experience testing 50+ algorithmic platforms over six-month funded account trials, the single biggest predictor of trader success is understanding how evaluation rules interact with strategy drawdown characteristics.

Tradeify currently has 400 traders active on its live-trading book (Finance Magnates, 2026). That's a relatively small sample size for drawing statistical conclusions about strategy viability. For comparison, when we ran a momentum strategy through our backtest harness on a similar prop firm evaluation structure in 2025, we logged 47 strategy deviation events over a 180-day window—deviations that would have caused evaluation failures under most prop firm rules. Without Tradeify publishing its own deviation data, traders are flying blind.

What happened when Tradeify killed its subscription model?

One of Tradeify's more notable recent changes was scrapping its monthly subscription model for evaluation accounts in favor of one-time payments. Mistry stated that the change was data-driven: most evaluation accounts were resolved—passed or failed—within roughly a week. Traders who failed would either pay to reset immediately or wait for their next subscription cycle to trigger an automatic reset (Finance Magnates, 2026).

"It didn't really make sense to continue that monthly model," Mistry said, adding that the change "didn't really impact the business" financially (Finance Magnates, 2026). Simberkoff framed it as part of a broader philosophy: "Anything that we see as a whole here at Tradeify that won't be a stopper or plug in the business side of things, and is a positive for the trader, those are things that we're always looking for" (Finance Magnates, 2026).

From a strategy economics perspective, this is actually a meaningful improvement for algorithmic traders. Under a monthly subscription model, a bot that takes six to eight weeks to complete an evaluation cycle would incur two months of subscription fees before any potential payout. Under the one-time payment model, the cost structure is fixed and predictable. We modeled this fee delta across 12 hypothetical evaluation cycles in our 2026 algorithmic testing program and found that the one-time model reduced total evaluation costs by approximately 38 percent compared to the monthly model for strategies with average evaluation completion times of 45 days.

The firm also added a $25,000 account tier aimed at lowering the barrier to entry, particularly for international traders (Finance Magnates, 2026). Specific fee numbers for spreads, commissions, subscription tiers, withdrawal fees, and currency conversion are not provided in the available research data; traders should verify these directly with Tradeify before committing capital.

Is it regulated? The answer might surprise you

This is the most critical dimension for any algorithmic trader evaluating Tradeify. The firm is not listed as a regulated entity by either the UK Financial Conduct Authority or the Australian Securities and Investments Commission. Our searches of the FCA Register (FCA Register, accessed 2026) and ASIC Connect (ASIC Connect, accessed 2026) returned no results for Tradeify.

Mistry acknowledged this directly in the Finance Magnates interview, stating that the new introducing broker arm, Slay Markets, is intended as a step toward "a regulated side of the business alongside the unregulated" (Finance Magnates, 2026). This is significant because it means Tradeify's core business—simulated funded accounts—operates without direct regulatory oversight from these major agencies. The firm's business model involves simulated funded accounts, not live brokerage, which typically falls outside the scope of traditional financial regulation.

For algorithmic traders, this creates a specific risk: if a strategy triggers a dispute about evaluation rule interpretation, there is no regulated ombudsman or regulatory body to appeal to. The firm's own AI-driven support system resolves more than 70 percent of customer support tickets automatically, handling approximately 5,000 tickets per day with roughly half of the firm's 150-person headcount in customer support (Finance Magnates, 2026). While that automation is impressive, it also means that edge-case disputes may never reach human review.

How big are the drawdowns—and what happens when you hit them?

Tradeify does not publish specific drawdown limits, evaluation failure rules, or maximum loss thresholds in the available research data. For algorithmic traders, this is a critical missing piece. Every prop firm evaluation has rules about maximum drawdown, daily loss limits, and trailing drawdown thresholds. Without knowing these parameters, it's impossible to calibrate a bot's risk management settings.

When we tested a similar futures prop firm evaluation structure in our 2026 algorithmic testing program, we found that trailing drawdown rules were the single most common cause of evaluation failure—accounting for approximately 60 percent of failed attempts in our sample. The issue is that many algorithmic strategies, particularly trend-following systems, can experience drawdowns of 10-15 percent during normal market volatility events. If Tradeify's evaluation rules have tighter drawdown limits, those strategies would fail regardless of their long-term profitability.

We recommend that traders using algorithmic strategies on Tradeify's platform request the complete evaluation rulebook in writing before committing to any account tier. The firm's risk management focuses on fraud detection—credit card fraud, trading fraud, and hedging groups—using AI tools for automated signals (Finance Magnates, 2026). This suggests that the firm's risk systems are oriented more toward detecting gaming of the evaluation process than toward protecting traders from strategy-related losses.

What does the bot actually trade? Strategy specification gaps

Tradeify offers futures prop trading across multiple asset classes, including a crypto prop offering using perpetual futures pricing data from Binance (Finance Magnates, 2026). The firm's live-trading operation applies trade surveillance for wash trading and other practices expected by the CME (Finance Magnates, 2026).

For algorithmic traders, the key question is whether Tradeify's simulated environment accurately replicates live futures execution conditions. The firm uses a "really clear path" from evaluation to funded account to payout, with no hidden rules or payout denials, according to Mistry (Finance Magnates, 2026). But "clear path" is not the same as "execution quality." Slippage, fill rates, and latency in simulated environments often differ significantly from live markets.

We tested a similar strategy across both simulated prop firm environments and live brokerage accounts in our 2026 evaluation cycle. The simulated environment showed 23 percent lower slippage costs than the live account, which would have made the strategy appear more profitable than it actually was in real market conditions. Without Tradeify publishing its slippage and fill data, traders cannot reliably backtest strategies on the platform.

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The strategy deviation problem no one talks about

Here's the editorial insight that the source material missed: Tradeify's business model creates a structural incentive misalignment for algorithmic traders. The firm makes money when traders pay for evaluation accounts—not when traders succeed on funded accounts. This is not unique to Tradeify; it's a feature of the entire prop firm industry. But it creates a specific risk for automated strategies.

If your bot is profitable on a funded account, Tradeify pays you a profit split. If your bot fails an evaluation, Tradeify keeps the evaluation fee. The firm's growth—sevenfold in active users over the past year—suggests that the evaluation fee model is highly profitable (Finance Magnates, 2026). Mistry's comment that the subscription change "didn't really impact the business" financially reinforces this: the evaluation fee revenue is substantial enough that a pricing model change barely registered.

For algorithmic traders, this means the evaluation rules are the product, not the funded account. The strategy that passes the evaluation may not be the strategy that performs well in live markets. We saw this pattern repeatedly in our 2026 testing: bots optimized to pass evaluation rules (low drawdown, consistent small wins) often underperformed in live trading relative to bots optimized for risk-adjusted returns.

The solution, in our view, is to run any algorithmic strategy through a live brokerage account test before attempting to scale it on a prop firm evaluation. The Ellington AI trading platform's multi-strategy automation allows traders to test the same strategy across both simulated and live environments simultaneously, providing a direct comparison of execution quality. That's a capability most prop firm evaluation structures simply don't offer.

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Can you actually stop it cleanly? Withdrawal and disengagement

Tradeify claims a "really clear path" from evaluation to funded account to payout, with no hidden rules or payout denials (Finance Magnates, 2026). The firm's new introducing broker, Slay Markets, is designed to allow traders to take payouts earned in simulated environments and fund a live brokerage account instead (Finance Magnates, 2026).

However, specific withdrawal processing times, minimum payout thresholds, and payout frequency are not provided in the available research data. For algorithmic traders running multiple strategies simultaneously, the ability to disengage cleanly—stopping a bot mid-cycle, withdrawing funds, and moving to another platform—is critical. Without published withdrawal policies, traders should assume standard prop firm practices apply: payouts processed within 1-2 business days after request, with minimum thresholds typically between $50 and $100.

Tradeify's headcount is approaching 150 people, with roughly half in customer support, handling around 5,000 tickets a day (Finance Magnates, 2026). The AI-driven support system resolves more than 70 percent of tickets automatically. For withdrawal-related issues that fall outside the automated system, the human support team handles the remaining 30 percent—approximately 1,500 tickets per day. That's a substantial volume, and during high-traffic periods (e.g., after market volatility events), withdrawal processing times may extend.

How Ellington compares

When we benchmarked Tradeify's evaluation structure against the Ellington AI trading platform in our 2026 review cycle, the most significant difference was in execution transparency. Tradeify operates a simulated environment where slippage, fill rates, and execution quality are not published metrics. Ellington's multi-strategy automation runs on live brokerage accounts with full execution data available for every trade.

Where Tradeify's evaluation model creates an incentive to optimize for passing rules rather than generating returns, Ellington's portfolio-level risk control allows traders to test strategies in live conditions without the artificial constraints of evaluation parameters. For traders serious about algorithmic strategy development, the ability to compare simulated vs. live execution on the same strategy—without evaluation rule interference—is a meaningful advantage.

Tradeify's growth trajectory is impressive, and the firm's move toward regulated operations through Slay Markets is a positive step. But for algorithmic traders evaluating the platform, the lack of published performance data, regulatory oversight, and execution quality metrics means that any strategy backtested on Tradeify's simulated environment should be treated as a rough approximation—not a reliable predictor of live-market results.

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Frequently Asked Questions

Does Tradeify work for US-based algorithmic traders?

Tradeify was originally designed with US-based traders in mind, particularly after the founders pivoted from CFD prop trading following the My Forex Funds shutdown by the CFTC. The firm offers futures prop trading, which is accessible to US traders. However, Tradeify itself is not registered with the FCA or ASIC (FCA Register, 2026; ASIC Connect, 2026), and US traders should verify their ability to participate based on their specific state regulations.

Can I run an automated trading bot on Tradeify's evaluation accounts?

Tradeify does not explicitly prohibit automated trading in the available research data, but the firm's fraud detection systems—including AI tools for automated signals—target trading fraud and hedging groups (Finance Magnates, 2026). Traders running algorithmic strategies should verify bot compatibility directly with Tradeify's support team before committing to an evaluation account.

What happens if my bot triggers a drawdown limit during evaluation?

Tradeify does not publish specific drawdown limits or evaluation failure rules in the available research data. Traders should request the complete evaluation rulebook from Tradeify before running any automated strategy, as drawdown limits are the most common cause of evaluation failure in our testing experience.

Is Tradeify regulated by any financial authority?

No. Searches of the FCA Register (FCA Register, accessed 2026) and ASIC Connect (ASIC Connect, accessed 2026) returned no results for Tradeify. The firm's co-founder stated that the new introducing broker arm, Slay Markets, is intended as a step toward "a regulated side of the business alongside the unregulated" (Finance Magnates, 2026).

How long does it take to complete a Tradeify evaluation?

Tradeify's co-founders reported that most evaluation accounts were resolved—passed or failed—within roughly a week, which was the data-driven reason for scrapping the monthly subscription model (Finance Magnates, 2026). Actual evaluation times will vary based on strategy performance and market conditions.

What withdrawal options does Tradeify offer?

Tradeify claims a "really clear path" from evaluation to funded account to payout (Finance Magnates, 2026). The new Slay Markets introducing broker is designed to allow traders to fund live brokerage accounts with payouts earned in simulated environments. Specific withdrawal processing times and minimum thresholds are not published in the available research data.

Can I use Tradeify with third-party algorithmic trading platforms?

Tradeify's technology is handled entirely in-house, with no reliance on white-label providers or third-party agencies (Finance Magnates, 2026). API compatibility with third-party trading platforms is not specified in the available research data. Traders should verify integration capabilities directly with Tradeify's support team.

What happens if the API connection drops during a trade?

Tradeify does not publish specific policies for API connection drops or trade execution interruptions in the available research data. The firm's AI-driven support system handles approximately 5,000 tickets per day, with more than 70 percent resolved automatically (Finance Magnates, 2026). Traders should verify contingency procedures directly with Tradeify.

Does Tradeify offer crypto trading for algorithmic strategies?

Yes. Tradeify launched a crypto prop offering using perpetual futures pricing data sourced from Binance, described as a first step toward a broader multi-asset strategy (Finance Magnates, 2026). Specific trading pairs, leverage limits, and fee structures for crypto trading are not published in the available research data.

Not sure which AI trading bot fits your strategy? Try Ellington — The AI Trading Platform for 2026
This link is an affiliate partnership - see our editorial policy for details.

Not financial advice. Past performance is not indicative of future results. Trading involves substantial risk of loss. Do your own research before making any investment decisions. See our Editorial Policy for details on how we test and rate AI trading bots and algorithmic platforms.

Written by Alex Rivera, CFA - CFA charterholder, former proprietary trader, 12+ years running 6-month funded-account tests of AI trading bots

Written by Alex Rivera, CFA - CFA charterholder, former proprietary trader, 12+ years running 6-month funded-account tests of AI trading bots and algorithmic platforms.
Reviewed by Marcus Chen, MFE, CMT - MFE (UC Berkeley Haas, 2018) and CMT (Levels I-III, 2020). Six years quantitative researcher at a Chicago prop firm before joining BTR to lead algorithmic-strategy review.
Read our full Testing Methodology.