After dedicating four years to its development and witnessing over three years of consistently positive results, we are thrilled to introduce R Factor to the MQL5 community. Our guiding principle has always been to ensure that a strategy proves its worth in the hands of its creator before sharing it with others. This commitment not only underscores our belief in the strategy but also ensures its continual refinement.

For anyone familiar with the world of trading, you’ve likely encountered a common frustration. You painstakingly craft or acquire a strategy, rigorously testing it using methods like robustness analysis, randomness checks, Walk Forward Analysis, and more. But as soon as you apply it to your real trading account, it seems to hit a rough patch – a prolonged drawdown or an unexpected market condition it wasn’t designed for. This doesn’t necessarily mean the strategy has failed; it’s just encountering a challenging phase in the market. However, the emotional toll it takes can be significant. Market cycles can persist for months or even longer, and enduring extended periods of losses can be mentally draining. Meanwhile, another strategy or asset outside your portfolio might be thriving, intensifying the frustration of choosing the wrong strategy at the wrong time.

Recognizing that markets indeed move in cycles, with certain assets performing better or worse during these cycles, we have developed a suite of strategies tailored for night scalping and breakout trading. Additionally, we’ve implemented a proprietary dynamic portfolio balancing algorithm, inspired by the principles of Kelly Criterion management. This algorithm automatically assigns more weight to winning pairs, thereby diminishing the impact of losses from underperforming pairs within the portfolio during a given period.

Consequently, under the R Factor algorithm, successful pairs gain autonomy within the portfolio, assuming greater responsibility and influence over its overall performance. This dynamic allocation amplifies both current and future potential gains. Simultaneously, underperforming pairs have their significance and impact on profits minimized. While this approach naturally increases portfolio volatility, the potential for profits tips the equation significantly in favor of taking calculated risks, ultimately leading to greater gains.