He Stopped Coding His Own Trading Strategies After Realizing They Weren’t Profitable
He Started as a Manual Trader, Then Turned to Algorithms
Ramon began trading manually in 2014. Four years later, his background in computer engineering pushed him toward algorithmic trading. The transition was not simply about convenience. He says he had not been profitable as a manual trader and saw automation as another way to approach the market. But having programming skills did not immediately solve the problem. When Ramon coded the strategies he had learned from other traders, the historical results often showed that those systems were not profitable over time.
The Moment He Stopped Coding Every Strategy Himself
Ramon eventually realized that being able to program a strategy was not the same as having a profitable strategy. Instead of continuing to code every idea manually, he began using StrategyQuant to generate and test potential systems. His process starts by choosing an instrument, timeframe, indicators or market behaviors, and minimum performance criteria. The software then searches for possible edges, but Ramon is clear that this is only the beginning. The generated strategy still needs testing and validation before it can be trusted with real capital.
Why He Prefers Markets That Actually Trend
Ramon believes instrument selection matters before the strategy itself. He prefers markets that spend meaningful periods moving in one direction rather than constantly chopping sideways. That has led him toward gold, Nasdaq and USD/JPY. When evaluating systems, he keeps the metrics relatively simple. Risk versus reward is his primary KPI, followed by win rate. He says he generally wants strategies above a 40% win rate, but he also cares deeply about how much drawdown was required to generate the return.
One Strategy Wasn’t Enough
Another major shift came when Ramon stopped relying on a single strategy or account. He began running several strategies together so one system, instrument or market condition would not determine the entire year. He eventually extended the same thinking to multiple accounts. Ramon describes trading as a business and argues that traders can become too emotionally attached to their first account. His goal is not to protect one account at all costs. It is to build a broader operation capable of surviving mistakes, drawdowns and changing market conditions.
A Single Setting Cost Him 10% of an Account
Automation did not eliminate human error. Ramon explains how one incorrect default lot setting caused a gold robot to open a one-lot position when it should have been dramatically smaller. Because the system used an ATR-based stop and volatility was high, that position quickly cost the account roughly 10%. The mistake pushed him down from a $1 million allocation level to $500,000. Since then, he has reduced risk substantially, using around 0.5% or less per trade while continuing to test replacement strategies in demo before moving them live.
Trading Strategy/ Mindset
Ramon’s Algorithmic Trading Strategy
Ramon’s process is built around diversification, testing and statistical validation rather than searching for one perfect robot.
He starts by selecting an instrument and timeframe, then defines the indicators or market behaviors he wants the system to explore. StrategyQuant generates possible strategies that meet his criteria, including minimum win-rate and profitability requirements. Ramon treats those outputs as candidates, not finished systems.
He prefers instruments that show sustained trends. Gold, Nasdaq and USD/JPY have been among his main markets because he sees directional movement as easier for his systems to exploit than prolonged sideways conditions.
His main performance metric is return relative to drawdown. A strategy that earns $10,000 with a $2,000 drawdown is more attractive to him than one earning the same amount with a $5,000 drawdown. He generally wants win rates above 40%, but he does not evaluate a strategy on win rate alone.
Ramon also does not rely on one system. He may run three, six or even twenty strategies in the same account. If one approaches its historical maximum drawdown, he can remove it and replace it with a strategy that has already been running in demo. He wants at least a month of demo performance, and in some cases he prefers systems that have survived six months to a year before going live.
His risk is currently much lower than it was during his climb through the funding stages. He says he previously risked roughly 1% to 1.5% per trade but now trades around 0.5% or less.