Free PDF field guide
How I build an AI trading agent that learns from every trade.
And how you can too, even with zero coding experience.
- Why the next indicator, signal service or course was never going to fix it
- The 60% win-rate trap, and the everyday habit behind it
- Why a plain trading algorithm won't work, and what an agent does differently
- The one rule I hard-coded before the agent ever touched an order
- How to build a system that checks every rule and learns from every outcome
Research cited inside
Inside the guide
Seventeen pages. No hype, no signals, no shortcuts.
The honest story of 18 years spent searching for the right indicator, signal and strategy, and the agentic system that finally ended the search.
p. 06
The 60% win-rate trap
Six winners, four losers, and the account still goes down. The everyday habit that quietly flips the math, and why a better entry signal won't save you.
Fictional illustration from the guide. Same win rate, different habits.
p. 07
The one rule I hard-coded before the agent ever touched an order
And why automation without it doesn't fix your trading. It just helps you fail faster.
p. 11
The three layers every agent I'd trust is built on
Why a perfect signal still isn't allowed to trade on its own, and which layer gets the final say.
p. 12
Learning, with boundaries
How the agent turns wins and losses into tested improvements, and the one thing it must never be allowed to change.
p. 15
The five-minute process audit
Seven questions that show exactly where your own process leaks, before you write a single line of code.
All 17 pages, free.
The core idea
My trading plan was never the problem. I was.
For years I blamed my tools. A better indicator, a sharper signal, one more course. When that didn't work, I blamed myself. Closed a winner too early? Be more patient. Held a loser too long? Be more disciplined. None of it survived the next stressful week.
The fix wasn't more willpower. It was an architecture where no single part, including me, can break the rules, and an agent that learns from every outcome without ever being allowed to touch its own limits. Page 11 shows the whole thing.
Illustrative sequence. Not a record of actual trades or results.
The plan, not the mood
It enters only when every condition is met. Boredom, FOMO and revenge simply aren't inputs.
Limits with teeth
The risk layer can veto any trade, and the agent can't raise its own limits to win a loss back.
Runs while you work
Routine decisions stop depending on your constant attention. You supervise instead of babysitting charts.
Learns from every trade
Wins and losses become tested, reversible improvements. Never a gut-feeling rewrite after a bad week.
Who wrote this
“For years, I couldn't make trading fit my life. Until I rethought the way I traded.”
I'm Hendrik. I read The 4-Hour Workweek at 18, and one idea never left me: build something that keeps running when you step away. That was 18 years ago. I've tried almost everything since, and nothing tested me like trading.
I tried every angle of it. Expert advisors. Signal services. Indicators by the dozen. Trading software, courses, a shelf full of books. I even won a BDSwiss trading challenge against more than 500 traders.1 And I still couldn't make trading fit into my life. It always ended up as a second job.
When AI arrived, I used it to build my own trading systems. Faster code, faster backtests, same problem. Only when I built an agentic workflow, an agent that tests, validates and learns inside limits it can't touch, did trading finally work the way I'd wanted for 18 years. The guide is the honest version: what worked, what didn't, and what is still my job.
1 Individual result in a single competition. Not typical and no indication of future results.
Read it tonight. Implement it tomorrow.
Short on time? The quotes, the three-layer framework and the five-minute audit give you the core in ten minutes.