Last Updated 09/20/26
Disclosure: This post may contain affiliate or referral links. If you sign up for a platform or tool through our links, we may earn a commission at no extra cost to you.
Table of Contents
Seven posts in. A fully redesigned trading system. Real money on the line. Lessons learned the hard way, most of them more than once. If I could go back to day one and start building a trading bot over, knowing everything the last several months have taught me, here is what I would change.
None of this is theory. Every item on this list is something I actually got wrong, felt the consequences of, and eventually fixed. The version of the bot running today looks almost nothing like the one I started with, and the gap between them is basically this list. So think of it less as advice and more as a letter to the person I was on day one, who was confident, underinformed, and about to learn a lot of things the slow way.
I’d Start With More Stocks Immediately
My original watchlist was a small, hand-picked set of stocks. Names I knew. Companies I followed. Tickers I could recognize without looking them up. It felt like a reasonable, responsible starting point, the kind of thing a careful person would do.
It wasn’t. It was just my bias in disguise, dressed up as caution.
The problem is simple once you see it. A trading bot lives or dies on how many opportunities it gets to evaluate. My bot couldn’t find good setups because the pool was too small. On any given day, maybe one or two names on that short list had anything interesting happening. Most days, none of them did. I had built a system designed to be selective and then handed it almost nothing to select from. It was like opening a restaurant and only letting four people know.
When I expanded to over a thousand stocks the difference was immediate and obvious. More candidates meant the bot could actually be selective instead of desperate. It could wait for a genuinely good setup instead of settling for the least-bad option among five names I happened to like. Better inputs, better timing, better results, and all it took was letting the machine look at the whole field instead of my personal favorites.
If I started over I would skip the hand-picked phase entirely and go straight to a broad universe from day one. The instinct to start small and familiar feels safe, but in this case familiar was the enemy. The bot does not need me to pre-approve the list. That is the entire point of building the bot.
I’d Focus on Exits Before Entries
I spent most of my early energy on the buy logic. What signals to use, what thresholds to set, how to score and rank candidates. The entry was the exciting part, the moment the bot finds something promising and pulls the trigger. It felt like the strategy, because it is the part that looks like a decision.
But exits are where you actually make or lose money.
This took me embarrassingly long to internalize. A mediocre entry paired with a disciplined exit strategy will outperform a brilliant entry paired with sloppy exits, every single time, over any meaningful sample. The stop-loss system, the trailing logic, knowing when to take profit and when to cut a loser before it becomes a disaster, that is where the real edge lives. The entry only decides which game you are playing. The exit decides whether you walk away with the winnings.
The reason people obsess over entries anyway is that entries are satisfying and exits are stressful. Buying feels like opportunity. Selling feels like admitting something, either that you were wrong or that the fun part is over. So the natural pull is to lavish attention on the entry and treat the exit as an afterthought, which is exactly backwards.
If I started over I would build the exit system first, before a single line of entry logic, and I would spend at least twice as long on it. The entry gets you into the trade. The exit is the trade.
There is a practical version of this too. When you build exits first, you are forced to answer the hard questions before you get seduced by a shiny entry signal. How much am I willing to lose on any one position? What does the bot do when a trade goes right, and how does it lock in the gain without strangling it early? What happens when a position gaps against me overnight and blows straight through the stop I thought was safe? Answer those first and your entries get more disciplined automatically, because now every buy has to justify itself against a plan for how it ends. Build entries first and you tend to answer none of those questions until a losing trade forces you to, which is the most expensive classroom there is.
I’d Automate the Boring Stuff Earlier
For the first few weeks I was the monitoring system. I manually checked logs. I manually reviewed positions. I manually watched the server to make sure nothing had quietly fallen over. It worked, in the sense that a person frantically juggling works, right up until they drop something.
Eventually I built the unglamorous infrastructure that should have been there from the start. Automated reports that land in my inbox. Health checks that run on their own. Monitoring that flags a problem while it is still small instead of after it has become an emergency. Processes that restart themselves if something crashes at 3 AM, without needing me awake to notice. Some of the worst surprises I have written about, like the ones in Here’s What Went Wrong With My Trading Bot, would have been caught far earlier if this layer had existed sooner.
That infrastructure isn’t glamorous. It doesn’t feel like progress while you’re building it, because it doesn’t add a feature or improve a return. It just quietly removes ways for the system to hurt you. But it is the entire difference between a bot that needs constant babysitting and a bot that actually runs on its own, which is the whole reason you build a bot in the first place.
If I started over I would build all of that from day one and treat it as part of the core system, not a chore to bolt on once things break.
I’d Use Smaller Position Sizes for Longer
I started with deliberately small positions, which was already the conservative move and one of the few things I got right early. But I was tempted, constantly, to size up the moment things seemed to be working. A few good days and the little voice starts whispering that you have figured it out and you are leaving money on the table.
The little voice is not your friend in month one.
The right move was to stay small until the system had proven itself across genuinely different market conditions. Up markets, down markets, choppy sideways markets, dead quiet markets. Each one stresses the system in a different way and surfaces a different class of problem. A bot that looks like a genius in a calm uptrend can quietly fall apart the first time volatility spikes, and you desperately want to discover that with small money, not big money.
If I started over I would commit to small positions for at least a full month regardless of how good the early results looked, and honestly probably longer. The goal at the beginning is not to make money. The goal is to find the bugs, and bugs are much cheaper to find when the stakes are small. Early profits are a trap if they talk you into scaling up a system you have not actually tested yet.
I’d Track Everything From Day One
Every trade. Every signal. Every scan. What the bot saw, what it decided, what it did, and what happened afterward. The full picture, written down and kept.
I did not set up proper tracking early enough, and I paid for it. When I later went back to analyze my earliest trades, some of the data was simply incomplete. I could see what the bot bought and sold, but I couldn’t always reconstruct why it made those decisions at that exact moment, which is the one thing you actually need in order to learn anything. This is the same lesson that shows up in my Part 1 write-up of the mistakes, where a logging gap turned a night of analysis into a dead end.
Now every scan logs the entire story. What scored, what qualified, what got filtered out and why. That data is invaluable, because it is the only honest referee for the question that matters most: what is actually working versus what I merely think is working. Without it, you are just telling yourself flattering stories about your own system.
If I started over, the very first thing I would build, before the buy logic, before the sell logic, before anything that places a single order, would be the logging system. It is not the exciting part. It is the foundation everything else stands on.
I’d Expect to Rebuild the Whole Thing
The bot I’m running today shares almost nothing with the one I started with. The entry logic is completely different. The exit system was torn down and redesigned from scratch. The scoring was overhauled more than once. The watchlist expanded by orders of magnitude. If you lined up version one and the current version side by side, you would not guess they were built by the same person, let alone that one grew out of the other.
At the time, every rebuild felt like failure. Like I was standing up in front of myself and admitting the last design was wrong, that I had wasted the effort, that a smarter person would have gotten it right the first time.
That framing was the actual mistake. None of those versions were wrong. Each one was a starting point that taught me the thing I needed to know to build the next one. The first bot taught me what a trading bot even needs to do. The second taught me how signals actually behave once real money and real market noise are involved. The current version is built on the accumulated wreckage of everything the earlier ones got wrong, and it is better precisely because those earlier ones existed to be wrong first.
If I started over I would plan for that from the beginning. Do not try to build the perfect system on attempt one. Build something that works well enough to teach you something, then rebuild it on purpose, again and again, as the lessons pile up. The rebuilds are not detours off the path. They are the path.
I’d Stop Reading and Start Building a Trading Bot Sooner
Before I wrote a line of code, I spent weeks reading. Trading strategies, technical analysis, algorithm design, backtesting frameworks. I was trying to learn enough to build it right the first time, which, as the previous section should make clear, was never going to happen anyway. Today I would spend more of that time testing ideas than reading about them, and TrendSpider for trading bots makes that possible without writing code first.
Most of that reading didn’t help. Not because the information was bad, but because trading theory and trading practice are genuinely different things that live in different parts of your brain. You can read about moving averages and oscillators and mean reversion for months and still not understand them, because understanding them means watching how they behave on live charts, with real money moving, when you are the one who has to decide what to do about it. The book version and the live version share vocabulary and almost nothing else.
The best way to learn how to build a trading bot is to build a trading bot. Start simple, start small, and let the live market teach you the things no article can. Reading is comfortable because it feels productive without any risk of being wrong. Building is uncomfortable for exactly the same reason it works: it forces the market to grade your assumptions, honestly and immediately.
That is not an argument against learning. It is an argument for learning in the right order. A little reading to get oriented, then straight into building, then reading again once you have real questions that the theory can finally answer. If you are ready to make the jump, here is exactly how I set mine up from scratch.
The Thread Running Through All Seven
Read the list back and a single theme runs through every item. Start broad. Build the safety and the record-keeping first. Stay small while you learn. Expect to be wrong and rebuild. Get your hands dirty instead of over-preparing. Underneath all of it is the same idea: the point of the early phase is not to make money or to look smart. It is to learn as fast and as cheaply as possible.
Almost every mistake on this list came from optimizing for the wrong thing at the wrong time. I optimized for feeling careful (the small watchlist), for the exciting part (entries over exits), for looking successful (sizing up too soon), for feeling prepared (reading instead of building). Every one of those is a way of protecting your ego at the expense of your education, and the market charges interest on that trade.
The bots that survive are not the ones that were brilliant on day one. They are the ones whose builders set things up so that being wrong was survivable and informative instead of catastrophic. That is the meta-lesson behind the other seven, and it is the one I would tattoo on day-one me if I could.
The Honest Summary
If I started this project over today, I would build it faster, track it better, focus on exits over entries, size small for longer, and plan from the outset to rebuild the whole thing at least once. I would treat the boring infrastructure as core work and the reading as a warm-up rather than the main event.
I would also give myself permission to be wrong more often, and earlier, and out loud. Every mistake turned into a lesson. Every lesson made the system better. The path from the original bot to what is running today was never a straight line. It was a long series of problems followed by fixes, each one landing a little closer to something that actually works. If you want the unflattering version of some of those problems, they are in Part 1 and Part 2 of the bumps-and-bruises series.
That is still the process today, and I suspect it always will be. Building a trading bot is iterative. You learn by shipping, not by planning, and the shipping never really stops.
Up Next
Performance numbers are coming. The system is generating real trades now, and I am building a data set large enough to actually say something meaningful instead of cherry-picking a good week. When I share those numbers they will come with full context, not just what the bot returned, but why, and what it says about what is working and what still needs to change.
If you want to follow along, the article library has the whole build in order. The interesting part is just getting started.
Steve is the founder of Bot and Bull. He reviews and evaluates the tools featured on this site and only recommends what he genuinely believes in. No press releases, no paid placements.
Found Bot and Bull useful? A quick review helps others decide whether this site is worth their time, and it takes about a minute. Review us on SmartCustomer →
Disclaimer: Everything in this post is shared for educational and informational purposes only and is not financial, investment, or trading advice. I am not a licensed financial advisor, broker, or tax professional. Automated trading carries real risk, including the possible loss of your capital, and the tools or platforms mentioned may not be suitable for your situation. Do your own research, and consider speaking with a licensed professional, before making any financial decision.