His Automated Trading Software Helped Him Reach 5 Payouts in One Month
He Doesn't Think of Himself as a Trader
Danielle is a software developer who approaches trading like a programming problem. Rather than spending the day watching charts and making discretionary decisions, he builds Expert Advisors that execute his strategies automatically. He says he normally spends less than an hour a day checking his systems, yet recently reached five payouts in just over a month.
He Stopped Looking for the Perfect Equity Curve
Like many system developers, Danielle originally wanted to create a strategy with a nearly perfect backtest. Eventually he realized that was not realistic. Instead, he began thinking in probabilities. Some accounts will fail, others will reach payouts, and the results only make sense when viewed across a larger group of accounts and over a longer period of time.
He Treats Trading Like a Business, Not a Single Account
Danielle reinvests part of his payouts into additional accounts and expects both winning and losing periods. That makes individual account failures much less emotional. His focus is whether the overall system remains profitable across enough trades and enough accounts, rather than whether every individual account survives.
His Software Trades Gold While He Sleeps
Danielle primarily focuses on gold and says understanding the behavior of the instrument is an important part of developing an automated system. Because he lives in Europe, he often prefers the Asian session, when many major news events affecting gold are still hours away. His Expert Advisors can monitor and trade that session while he is asleep.
Five Payouts Still Haven't Made Him Quit His Job
Despite his recent results, Danielle still works his regular job and has no immediate plan to become a full-time discretionary trader. He understands trading deeply enough to build systems around it, but he is also aware that manually executing trades introduces an emotional element he does not enjoy. Instead of forcing himself to trade differently, he built a process around his strengths.
Trading Strategy/ Mindset
Danielle's Automated Trading Strategy
Danielle approaches trading more like a software engineer than a discretionary trader.
His strategies are executed through Expert Advisors, commonly called EAs. Rather than making every decision manually, he develops a rules-based system, backtests it and allows the software to execute those rules.
One of the biggest changes in his thinking was giving up on the idea that a successful strategy should produce a perfectly smooth equity curve.
He expects losing periods. He also expects some accounts to fail. That is built into the model.
Instead of evaluating the system based on one account, Danielle looks at results across multiple accounts. He has operated as many as eight simultaneously, with accounts potentially sitting at different stages of the challenge or funded process.
His basic framework is built around probabilities. If a payout produces enough capital to purchase several additional accounts, he can reinvest part of that payout and allow the same strategy to continue operating across a larger pool.
That does not mean every account wins. Danielle estimates that he has lost roughly 30 prop accounts over the course of developing the system.
The question for him is whether the winners outweigh those failures over time.
Automation also solves another problem.
Danielle realized early in his trading journey that his emotions made discretionary trading difficult. Instead of trying to force himself to become a different type of trader, he used his background as a developer to remove himself from much of the execution process.
His systems can trade while he works or sleeps. He generally spends about an hour in the evening checking results, making sure the software is configured properly and preparing everything for the next session.
He has also narrowed his focus primarily to gold. Danielle believes automated strategies still need to be designed around the characteristics of the market being traded. He is skeptical of systems marketed as working equally well across currencies, commodities and indexes without adjustment.
The result is less about finding a perfect trading robot and more about building a repeatable process where probability, diversification and risk can work over a large enough sample.