If I Had to Start Over With $1,000 on Prop Firms, I’d Do This

Summary

The presenter’s proposed approach is to test a prop-firm trading plan before spending money on evaluations, then scale only after results show that payouts exceed all costs. A positive trading equity curve or a high evaluation pass rate is not enough: the relevant outcome is cash actually paid out, net of evaluation fees, resets, and other costs.

Suggested testing and tracking framework

• Simulate 50 complete evaluation attempts under each firm’s actual rules, including trailing or adjusted drawdown, consistency requirements, winning-day rules, payout limits, and withdrawal eligibility.
• Continue each simulation through funded-account payouts or failures. Do not count account profits that were never withdrawn.
• Track:
  • Evaluation fees, resets, and other costs
  • Evaluations passed and the approval rate
  • Funded-account payout rate
  • Actual payouts and average payout per funded account
  • Net profit after costs
• Judge the plan by net payouts versus total costs, not by win rate or approval rate alone. The presenter says a higher pass rate can reduce the cost of acquiring funded accounts, but it is not itself the ultimate measure of profitability.
• If simulations lose money, test changes to risk management, risk/reward, strategy, and the approach used for evaluations versus funded accounts. The presenter says these stages have different objectives: passing evaluations versus generating payouts.
• Repeat the test using the rules for each account size or firm; do not assume a strategy optimized for one account size will transfer unchanged to another.
• Once live, follow the tested plan, request payouts as soon as eligible, protect the cash received, and compare actual results with the simulations.

Firms, accounts, and figures mentioned

• Lucid Flex 25K: The presenter recommends this as a starting choice for a $1,000 budget, citing his simulations and a goal of minimizing risk of ruin. He says an account costs about $50 with an activated daily loss limit (DLL) and that $1,000 would buy 20 evaluations at that price.
• Personal results cited for Lucid Flex 25K: 37% approval rate, 35% payout rate, and 4.2× return on investment. These are the presenter’s reported results, not a guarantee or independently verified figures.
• Scaling checkpoints: Build a bankroll of about $3,000 before moving to 50K accounts, then about $15,000 before moving to 150K accounts.
• The presenter refers to Lucid, Topstep, and Funded Next as firms that have paid him. He also says he has received over $2 million in payments from funding companies overall.
• Account-rule examples cited:
  • The 25K account is described as having a 1:1.25 profit-target-to-maximum-loss ratio.
  • The 50K example is described as 1:1.5.
  • For a 150K account, he cites a $9,000 profit target and $4,500 maximum loss, or 2:1.
  • He says account-size changes can alter the optimal risk/reward approach.

Risk and probability discussion

• The presenter illustrates payout probability as the probability of passing an evaluation multiplied by the probability of receiving a payout after funding.
• Using assumed rates of 30% for passing and 33% for receiving a payout, the chance of a payout per evaluation is about 9.9%, leaving about a 90% chance of no payout on an individual attempt. If attempts were independent, the chance of no payout across 20 attempts would be roughly 12% (about (0.9^{20})), rather than 90%.
• He frames payout size and payout probability as a trade-off: targeting smaller payouts may raise the chance of receiving one, while targeting larger payouts may lower that chance. He recommends estimating these probabilities from one’s own tests.
• He cautions against starting with less than $1,000, arguing that a smaller budget buys too few attempts and increases the risk of running out of capital. This is his personal recommendation, not a universal rule.

Disclosures and cautions

The presenter says the content is information, not financial advice. He cautions that even a profitable simulation can be followed by bad luck in live trading and stresses adhering to firm rules and verifying results through actual tracking.

Presenter and source

JJ Simon is the presenter and source of the strategies, firm references, and performance figures summarized above.