What Four Years of Building Trading Systems Actually Taught Me

Four years ago I knew basically nothing about coding, machine learning, or quantitative finance. I had some basic market knowledge from manual trading and a gut feeling that systematic approaches were better than discretionary ones.

Now I have a production-ready trading engine with 43 integrated modules, validated over 7.5 years of data, with a 0.08% probability of breaching FTMO limits. The journey from point A to point B was nothing like I expected.

Year 1: The Dunning-Kruger Peak

I thought I would have a working system in 6 months. Maybe 3 if I really focused. I was reading Medium articles about algorithmic trading and thinking "this does not look that hard." Just throw some moving averages at it and add machine learning right?

Wrong. So wrong.

First I had to learn Python basics. Then data handling with Pandas. Then I needed to understand what a feature actually is in ML context. Then I needed to understand why my features were terrible. Then I needed to learn about stationarity and why raw price data is useless for ML.

Six months in I had a system that was worse than random. Literally. It would have been more profitable to flip a coin.

Year 2: The Valley of Despair

This is when I almost quit. I had spent a year learning and building and my results were garbage. Every strategy I built looked great in backtesting and died in forward testing. Classic overfitting but I did not even have the vocabulary to identify the problem yet.

The turning point was discovering PBO (Probability of Backtest Overfitting). For the first time I had a mathematical framework for understanding why my backtests were lying to me. It was like getting glasses after years of blurry vision.

I also started using AI tools seriously around this time. ChatGPT had just come out and I realized I could accelerate my learning AND implementation by like 10x if I used it properly.

Year 3: The Architecture Emerges

This is when V7's 3-layer architecture crystallized. Not because I designed it all at once. It evolved through trial and error:

Each layer exists because I hit a wall that required a new solution. The S-series modules grew out of the same organic process. Every module solves a specific problem I encountered.

Year 4: Validation and Production

This year was about proving the system works and making it production-ready. Walk-forward validation, Monte Carlo simulations, FTMO compliance testing, hash-verified model deployment.

The unsexy but crucial work. No new features, no exciting experiments. Just rigorous validation and hardening.

What I Actually Learned

The technical skills are obvious. Python, ML, statistics, finance. But the real lessons were:

Patience is the actual edge. Markets reward patience at every level. Patient in building systems. Patient in waiting for valid signals. Patient in letting trades work. Most people give up in year 1 or 2.

Simplicity beats complexity. My early systems had hundreds of features and complex logic. V7 uses 38 features. The simpler system won because it generalizes better.

Risk management IS the strategy. I spent years optimizing entry signals. Then I realized that the difference between a winning and losing system is often just position sizing and drawdown management.

Domain knowledge > code quality. AI can write perfect code. But it cannot tell you why your strategy is wrong. Understanding markets deeply is what matters.

Compounding effort is real. Every module I built taught me something that made the next module easier. Year 4 me can build in a week what year 1 me would have spent months on.

Where I Am At Now

V7 is production-ready. 59.2% win rate, 0.08% breach probability, FTMO compliant. The system works. Not perfectly, not in all market conditions, but consistently enough to be profitable.

Am I done? Not even close. There is always another module to add, another edge case to handle, another validation to run. But the foundation is solid and thats what matters.

If you are in year 1 or 2 of this journey, keep going. It gets better. Much better. Just do not skip the validation.

What I Would Tell Year-One Me

Looking back I notice something interesting about my trajectory. The years where I learned the most were the years where I was most wrong. Year 1 and 2 felt like failure at the time. But those "failures" were actually my highest-bandwidth learning periods. The better question was never "am I succeeding?" It was "am I seeing more clearly than I did yesterday?" By that metric, even the worst months were productive. The system I have now exists because I was willing to be wrong hundreds of times about the system I thought I wanted. That willingness is not optional. It is the whole game.