by Steve
Last Updated 07/02/26
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When you build something and put real money behind it, you find trading bot problems fast.
Not theoretical problems. Not “what if” scenarios. Real problems that cost real money — or almost do — and force you to fix things the same day.
The first few weeks of live trading taught me more about my bot than all the testing before it. In this post I’m going to walk through what went wrong, what I learned, and what changed because of it. I know I mentioned results were coming in this post, but I am going to push that back. The bot isn’t ready.
The Bot Killed a Winning Trade
This one hurt.
The bot found a legitimate breakout. Volume was there, the trend was there, everything lined up. It bought in and the stock ran — up sharply in about an hour. A textbook trade.
Then it sold.
The stock paused for a few minutes. Nothing dramatic — just a normal consolidation after a move. Buyers taking some profit, price drifting sideways. The kind of thing that happens in every breakout.
But the bot had an exit rule that was too sensitive. It saw a small dip in one of the momentum indicators and interpreted it as weakness. Emergency exit. Trade closed.
The stock was still up. The breakout was still intact. Volume was still elevated. The bot just panicked over normal price action.
It locked in a tiny gain on a trade that had plenty of room to run.
The Fix
The exit rule was designed as an emergency valve — a last resort if momentum truly collapsed. But the threshold was set so close to normal trading behavior that it was triggering on routine pullbacks.
I widened the threshold so it only fires on actual weakness, not consolidation. The trailing stop handles normal exits. The emergency valve is for emergencies.
Stops Weren’t Moving Fast Enough
That same trade revealed another issue. The stock ran up sharply, but the stop was still sitting well below the entry price. If the stock had reversed hard, the bot would have given back the entire gain plus more.
The stop management system was updating on a slow, fixed schedule. That seemed reasonable when I designed it. But breakouts don’t move on a fixed schedule. A stock can run several percent in minutes and reverse just as fast.
Thirty minutes is a long time to wait when a winning trade could evaporate.
The Fix
I changed the stop updater to run far more frequently. Now the system ratchets the stop up quickly after a trade starts moving. The first priority is always the same: get the stop to breakeven as fast as possible. Protect capital first, then let winners run.
It’s a small change on paper. In practice it means the difference between locking in a gain and watching it disappear.
The Scoring System Had a Blind Spot
This took longer to figure out because the scoring system looked like it was working. Stocks with high scores were genuinely showing strong momentum signals. The math was right.
But the timing was wrong.
The system was scoring stocks based on momentum factors — how much they’d moved, how fast, how much volume was behind them. Higher score, better candidate. Sounds logical.
The problem: by the time a stock has a massive move with heavy volume and stretched momentum, the best part of the move is often already over. The bot was scoring the aftermath of a breakout, not the beginning.
Some of the best setups of the week scored modestly — because they were early. They hadn’t exploded yet. That’s exactly when you want to buy, and the scoring system was ranking them below stocks that had already run.
The Fix
I started removing factors that rewarded late entries and over-weighted pure momentum without context. The goal shifted from “find what just moved the most” to “find what’s about to move.”
That’s an ongoing process. The scoring system is still evolving, and it probably always will be. But the principle is clear: you want to catch the move early, not chase it late.
The Temptation to Lower the Bar
During quiet stretches — days where the bot scanned everything and found nothing — the urge to tweak things was constant.
Maybe the criteria are too strict. Maybe I should loosen one threshold. Maybe if I just adjust this one parameter the bot will start finding setups again.
That’s a trap. Loosening the criteria to generate trades during unfavorable conditions is called overfitting. You optimize the system for what just happened, and the moment conditions change you’re worse off than before.
The bot wasn’t broken during quiet periods. The market just wasn’t offering what the system was looking for. The correct response was to wait. I left the criteria alone.
When conditions shifted and momentum returned, the bot found real setups again — better setups than anything it would have taken during the dry spell.
Sometimes the Human Has to Step In
One day I had a position up enormously in a single session. A parabolic move — the kind that makes great screenshots but terrible overnight holds.
The trailing stop system would have held it through the close. If the stock gapped down at the open, the stop would have caught it at a lower level. If it gapped up, the ride continues.
I sold manually.
That wasn’t a failure of the system. It was recognizing that an enormous single-day move is outside what the system was designed to handle. The trailing stop tiers were built for normal trends, not vertical spikes. Sometimes the operator sees context that the algorithm can’t, and that’s okay.
The best automated systems still have a human layer that knows when to engage.
What These Trading Bot Problems Added Up To
Every problem followed the same pattern: a rule that made sense in theory didn’t hold up in live conditions. The fix was never to throw the rule away — it was to calibrate it against reality.
Exit signals needed more room. Stops needed more speed. Scoring needed better timing. The operator needed to know when to step in and when to stay out.
None of these were catastrophic. The system protected capital through all of them because the risk management was solid underneath. But each one made the bot meaningfully better.
That’s the rhythm of building something like this. You deploy it, you find the gaps, you fix them, and you deploy again. The bot I’m running today is substantially different from the one I launched with — and the one I’m running next month will be different again.
Performance numbers are coming once there’s enough data to say something meaningful. Right now the focus is on getting the system right — because the numbers don’t matter if the process is broken.
Every one of these trading bot problems made the system tougher and the rules smarter. If you are just getting started, learn from these trading bot problems early — here is exactly how I would set up a Robinhood trading bot from scratch.
Up Next
In my next post I’m stepping back from the technical side to talk about what I’d actually do differently if I were starting this project from scratch today. What I’d skip, what I’d do sooner, and what I wish someone had told me before I started.
If you haven’t already, sign up for Robinhood with my link and we’ll both get a free stock 🎁
Steve is the founder of Bot and Bull. He personally uses every app and tool reviewed on this site and only recommends what he genuinely believes in — no press releases, no paid placements.
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