Software Engineer Builds a Trading System That Earned $86K with FundedNext
How a Software Engineer Turned Coding Into a Trading Edge
Sylvanus Adam isn't a full-time trader. By day, he works as a software engineer in Tanzania, and by choice, he has continued balancing that career with trading rather than leaving one for the other. Instead of separating his two worlds, he combined them. His engineering background became the foundation of his trading strategy, allowing him to automate nearly every part of his decision-making process. Rather than relying on emotion or subjective chart reading, Sylvanus designed a rules-based system that analyzes market conditions using statistical concepts, custom indicators, and coded alerts. The result is a disciplined approach that helped him generate more than $86,000 in profits while trading with FundedNext.
Years of Failure Before Finding a Repeatable Process
Sylvanus first discovered forex trading in 2016 after hearing stories of young traders making life-changing money. Like many beginners, he quickly became overwhelmed by conflicting advice on YouTube and eventually walked away. A year later he tried again, this time trusting a friend to manage his account, but that also failed. After burning multiple personal accounts, he finally committed to learning the markets himself. Although he traded on and off for several years, it wasn't until 2022 that he became consistently active. Looking back, he believes seeing other traders succeed kept him motivated during the years when he was losing far more often than winning.
Building a Trading Strategy Around Data Instead of Emotion
Rather than copying popular trading styles, Sylvanus built a strategy that reflects the way software engineers solve problems. His system combines moving averages, Bollinger Bands, custom indicators, statistical analysis, and coded TradingView alerts to reduce discretionary decision-making. Instead of immediately buying or selling every overbought or oversold signal, the software waits for additional confirmation based on predefined patterns before allowing a trade. Position sizing, stop losses, take profits, and volatility measurements are all calculated through objective rules. The goal isn't to remove the trader completely, but to eliminate as much emotional interference as possible while still allowing manual execution.
Why a 55% Win Rate Is More Than Enough
One of the most refreshing parts of Sylvanus' interview is his honesty about expectations. His strategy doesn't win 90% of the time, and he doesn't believe it needs to. Through extensive backtesting using Pine Script and large amounts of historical data, he found that a win rate around 55% was enough to remain profitable over the long run with disciplined execution. His strategy typically uses a one-to-one risk-to-reward ratio while risking roughly 1% to 1.5% per trade. He understands that consistency comes from following a proven edge repeatedly rather than trying to find a system that never loses.
The Biggest Mistakes New Traders Make
After nearly a decade in the markets, Sylvanus believes new traders struggle most with unrealistic expectations. Too many people expect to turn a few hundred dollars into life-changing money within weeks because of what they see on social media. He also sees traders constantly jumping from one strategy to another before giving any system enough time to prove itself. His advice is simple: develop a strategy with a measurable edge, test it across different market conditions, and commit to following it consistently. Trading, he says, is both science and art. Data matters, but so does the discipline to trust your process.
Trading Strategy/ Mindset
Sylvanus' Automated Trading Strategy with FundedNext
Sylvanus has built a hybrid trading system that combines software engineering with discretionary execution. Instead of manually analyzing every chart from scratch, he uses custom-coded indicators that merge moving averages, Bollinger Bands, and statistical analysis into a single workflow. The system automatically identifies overbought and oversold conditions, generates TradingView alerts, and highlights potential opportunities before he even opens the chart.
Once an alert appears, the real analysis begins. Rather than entering immediately, Sylvanus waits for price to confirm the signal using predefined chart patterns such as double tops, double bottoms, and ABC wave structures. Only after price reaches a confirmation level does he execute the trade manually.
Risk management is equally systematic. Stop losses and profit targets are calculated using average market volatility from the previous quarter, allowing every trade to adapt to changing market conditions. He typically risks between 1% and 1.5% per trade while targeting a one-to-one risk-to-reward ratio. Although that ratio appears conservative, extensive backtesting showed that a win rate around 55% provides a positive long-term expectancy.
Perhaps the biggest takeaway is his philosophy that trading is neither pure science nor pure art. Statistical analysis provides the framework, but experience, pattern recognition, and disciplined execution complete the process. Rather than trying to eliminate the human element entirely, Sylvanus uses automation to remove repetitive decisions while keeping himself responsible for the final execution.