Why 1:1 RR Beats 3:1 RR for Retail Traders

Summary

The presenter argues that a higher-win-rate strategy with a tighter risk/reward ratio, such as 1:1, may be easier for many retail traders to execute than a lower-win-rate 3:1 strategy. The case focuses on losing streaks, trading psychology, drawdowns, and prop-firm challenge limits—not just theoretical returns.

Strategies compared

• Sarah: 1:1 risk/reward
  • Risks $100 to make $100.
  • Assumed win rate: 55%.
  • The video describes her losses as generally shorter and her equity curve as relatively steady.
• Mike: 3:1 risk/reward
  • Risks $100 to make $300.
  • Assumed win rate: 35%.
  • The presenter says a 3:1 setup has a break-even win rate of 25%. The video describes frequent losses, potentially long losing streaks, and a more volatile equity curve.

The broader point is that an edge must be executed consistently. A strategy that looks attractive on paper may be difficult to follow through its normal drawdowns.

Main arguments and framework

• Assess losing-streak tolerance: Repeated losses can undermine confidence, prompt revenge trading, or cause traders to abandon a valid strategy.
• Consider equity-curve volatility: The video contrasts a steadier, staircase-like curve with a more volatile curve featuring drawdowns and occasional large gains.
• Account for trading constraints: In a hypothetical prop-firm challenge, the presenter uses a $100,000 demo account, a 10% maximum drawdown, and $1,000 risk per trade. Eight consecutive losses would result in an $8,000 drawdown.
• Consider trade frequency and learning: The presenter estimates 20–30 potential monthly setups for the 1:1 approach, compared with 8–12 for the 3:1 approach. More trades may provide a larger sample for evaluating a strategy.
• Test rather than follow a fixed rule: The video recommends evaluating a strategy over hundreds of trades and across multiple market conditions, including its actual win rate, maximum drawdown, and personal psychological tolerability.

Figures and claims cited

• The presenter cites a University of California study that reportedly found traders with five or more consecutive losses performed 23% worse on subsequent trades.
• The presenter refers to MIT research on stress responses to trading losses and research on reward prediction error and decision-making. The subtitles provide no study titles or publication details.
• A Monte Carlo simulation is said to show prop-challenge pass rates of 67% for the 1:1/55% system and 41% for the 3:1/35% system. The simulation assumptions are not specified.
• The presenter claims that, in backtests of systems with identical expected value, the lower-win-rate approach can produce ulcer-index scores two to three times higher. Details are not provided.
• The video estimates 8–12 setups per month for 3:1 strategies and 20–30 for 1:1 strategies. Over six months, it gives corresponding samples of 48–72 and 120–180 trades.

Qualification and numerical caveat

The video does not argue that 3:1 is always wrong. Wider targets may suit higher-timeframe position trades, strong trends, or thoroughly tested strategies. They may also fit traders who can tolerate longer losing streaks and larger drawdowns. The recommendation is to choose an approach based on context, testing, capital, and execution ability—not to pursue 1:1 automatically.

The example systems are not equal in expected value under the stated assumptions. Per $100 risk:

• The 1:1 system at a 55% win rate has an expected result of +$10 per trade: (0.55 \times \$100 - 0.45 \times \$100)

• The 3:1 system at a 35% win rate has an expected result of +$40 per trade: (0.35 \times \$300 - 0.65 \times \$100)

This conflicts with the video’s later characterization of the systems as having identical expected value.

Assets, instruments, and disclosures

• Specific tradable assets or tickers: None mentioned.
• Trading contexts: Generic risk/reward setups, equity curves, and simulated prop-firm challenges.
• Financial-advice disclosure: No explicit disclaimer appears in the subtitles.

Presenter and sources mentioned

Presenter: The Zen Trader.

The presenter cites the University of California, MIT, Martin Seligman, John Coates, SMB Capital, Jane Street, and DRW Trading. These references are reported as presented in the video; the subtitles do not provide enough detail to verify the underlying studies or firm practices.