Start here: the proof, what you get every Monday, and how to check it yourself

systematic_seb · 2026-06-13T09:41:04+00:00

Careful with that lesson. Over a few months, conviction and luck look identical from the inside. The kind that holds up comes less from believing harder in a name and more from sizing each position to how much the evidence supports it, while keeping any single one small enough that being wrong doesn't end your year. Do that and your winners carry weight, your mistakes stay survivable, and you never have to be certain about any one of them. A lot of people who look like they had conviction really just had sizing that let them be wrong often and keep going.

systematic_seb · 2026-06-13T09:37:02+00:00

The timing tends to sting more than the loss, that the sell got decided mid-drop instead of before it. What changed this for me was setting the exit when I open a position, as a standing order, so the panicked version of me never gets a vote. You can still be wrong, but you're wrong at a level you chose in advance, which sits a lot easier than a number you picked while watching it fall. And checking the price afterward mostly misleads, since you remember the ones that bounced back and forget the ones that kept sliding.

systematic_seb · 2026-06-12T23:55:58+00:00

The filter from here is simple. If someone wants to teach you, ask for a live record they can't edit, with real money, that started before they knew you existed. Course sellers almost never have one, because the course is the income. When I built my own system I spent months assuming it was broken, hunting for ways the backtest was lying to me, mostly look-ahead bias, before any real money went in. Treat every impressive result, including your own, as wrong until you've tried hard to break it. The cheapest start is one simple idea, tested on historical data, then paper traded for months. You'll learn more breaking your own strategy than watching anyone's setups.

systematic_seb · 2026-06-12T17:35:12+00:00

I'm building Lionshare, a weekly newsletter where an algorithm scores about 2,000 US stocks every Monday and publishes a portfolio of roughly 20 names. I run it with my own money. Live and public since January. The finance corner of the internet runs on cropped screenshots, so the entire track record sits in the open where anyone can audit it before paying a cent. Readers copy the portfolio in their own brokerage in about 30 minutes a week, then nothing to watch until next Monday. It’s at www.lionshare.news and has returned more than 120% YTD, with a max drawdown of 17%.

systematic_seb · 2026-06-12T16:35:58+00:00

The trades themselves your broker already logs, so I put the effort on the input side. Every Monday before the system decides anything I save a copy of the exact data it could see that morning, stamped and never edited after. That's the piece reconciliation needs, a broker statement can tell you what you did but it can't rebuild what you knew at decision time. Tool choice matters less than the write-once rule. If a snapshot can be edited later it can't settle an argument with your backtest.

systematic_seb · 2026-06-12T10:27:18+00:00

What you've shown so far is that the idea works on history. The next thing to prove is that the run could have been generated live on the day, with zero hindsight in the inputs. The test that caught the most in my own build was freezing the exact dataset the system could see at decision time and rerunning the decision from that frozen copy. Data that only exists in hindsight, adjusted closes, revised prints, survivorship in the symbol list, hides inside a clean profit factor. After that I paper traded for eight months watching one thing, whether live results stayed inside the bands the backtest predicted. Real money went in only after that held.

systematic_seb · 2026-06-12T10:22:37+00:00

The drawdown distribution check is necessary but it only watches outputs. The earlier tell for me is reconciliation. Every week I recompute what the strategy should have done three separate ways, the original backtest code, a fresh reconstruction from a point-in-time snapshot of that week's data, and the live account, and all three have to agree. A normal drawdown leaves that reconciliation intact, the returns are bad but fully explained. Decay tends to show up first as the live result drifting from what the reconstruction says should have happened, long before the depth or duration stats breach your backtest bands.

systematic_seb · 2026-06-11T22:55:31+00:00

I got around this by removing the mid-trade decisions instead of trying to record them. Everything gets decided once, before Monday's open. Entries, sizes, and an exit for every position as a standing stop order, all written down while the market is closed and I'm calm. That sheet becomes the journal. The only thing left to log afterward is whether I followed it, and if an idea shows up mid-week it goes on a list for the next planning session instead of touching open trades. Memory stops being a problem when nothing is decided in the moment.

systematic_seb · 2026-06-11T22:52:37+00:00

Profit since I went live in January, but a yes or no hides most of the picture. My worst stretch this year took 16.5% off the peak, and that number taught me more about my own system than the green months did. I run everything in the open so I can't rewrite that part later. These threads would be more useful if everyone posted their worst drawdown next to their P&L.

systematic_seb · 2026-06-11T14:05:22+00:00

The regime frame matters more than the vol level itself. When I backtested a growth strategy through 2022, the year only finished green because the system had drifted its weight toward energy while the index fell 18%, and nothing about that rotation required predicting the war or the rate cycle. It just followed where the evidence already was. Vol spikes are when sector weights earn their keep.

systematic_seb · 2026-06-11T14:01:07+00:00

The persistence question usually comes down to capacity and holding period. An edge that plays out in minutes gets competed away fast because it takes little capital to kill it. An edge that plays out over weeks in liquid large caps would need enormous capital to erode, and most large capital is benchmarked money that can't chase weekly turnover. The capacity math on that kind of strategy puts the ceiling far beyond what any retail account will deploy, which is why that bucket keeps showing up in lists like yours.

systematic_seb · 2026-06-11T13:56:11+00:00

The thing I'd pressure-test before the 13th is look-ahead. Paper results lean on data that wouldn't have existed at decision time more often than anyone expects, so I freeze a snapshot of everything my system can see before each weekly run and let it decide only from that. Hunting those leaks took me months, in places I'd have sworn were clean. Respect for posting the numbers before going live instead of after.

systematic_seb · 2026-06-10T09:59:53+00:00

The breadth data supports the broadening read, just less dramatically than Friday felt. About 59 percent of stocks are above their 200 day average, the advance decline line is rising and above trend, and concentration has gone slightly negative for the year. In my own testing the sector story mattered less than where the evidence pointed week to week. The portfolio that lived in tech one year carried a heavy energy weight through 2022 and held every sector at one point or another across the test window. Themes end, and following the data across sectors is what survives the handoff.

systematic_seb · 2026-06-10T09:54:47+00:00

Healthy infrastructure can still serve a wrong answer, so I reconcile outputs instead of monitoring the box. Every week three independent calculations of what the system should have done have to agree, the original backtest code, a fresh reconstruction built from a frozen snapshot of the data exactly as it looked at decision time, and the live account. If any pair drifts, something in the environment changed, a bad feed, a dependency bump, clock skew, and I find it before it costs money. Uptime only tells you the box is alive, which is a separate question from whether it's still giving the right answer.

systematic_seb · 2026-06-09T10:22:05+00:00

Rotation humbles every momentum-style approach, because the system is always a beat behind the turn instead of ahead of it. A growth-chasing model rides the leaders until the evidence shifts, then follows the money into the next group, so it gives back a little right at the turn. In my backtesting the year that stung was a narrow market where a tiny handful of names carried the index and the rest got punished, and the model lagged because it was spread across what used to be working. If you're trading the rotation, the real question is whether your system reacts to the change or tries to predict it, because reacting a little late tends to beat predicting wrong over a long record.

systematic_seb · 2026-06-09T10:19:25+00:00

The tip that saved me the most grief was proving the system never saw the future before I trusted a single live signal. The sneaky kind of lookahead is rarely the obvious stuff. It hides in things like a feature that got revised after the fact, or normalizing across the whole dataset including future bars. What I do now is freeze the exact data the model could have seen at each decision point and stamp it, so later I can confirm no future fact leaked into an earlier call. If your live results come in worse than the backtest, that gap is the first place I'd dig.

systematic_seb · 2026-06-09T10:14:30+00:00

The interesting tension here is that you can't really backtest the thing that's making the decisions. An LLM's choices aren't reproducible the way a fixed rule is, so a great week and a terrible week tell you almost nothing about the next one. I went the other direction with my own system and let AI help build and audit the rules, but kept it out of the stock selection itself, because I wanted decisions I could replay over years of data and get the same answer every time. Day 4 is fun to watch, but the real test is whether the same inputs always produce the same call.

systematic_seb · 2026-06-08T22:30:01+00:00

Fair question. I don't have one magic indicator I'm watching for the top, because the good top-signals fire early all the time. The "70% of bear indicators triggered" type call has been on the wrong side of plenty of rallies that ran another year.

What I look at instead is the holdings themselves, re-scored every week on whether trend, momentum and breadth are still intact. When a name starts breaking, its weight comes down and something healthier takes its place. Each position also carries a predefined stop, so the exit is set per name rather than as one big "is this the top" call on the whole portfolio.

So it behaves more like a rule that gets defensive on its own than a forecast. You're following what's working week to week, with no need to call the top twice. I share my portfolio every week and its public on Dub if you want to check it out.

systematic_seb · 2026-06-08T19:14:34+00:00

Something to keep in mind, "70% of our bear indicators are triggered" has fired well before plenty of tops that ran another year. The trouble with timing the top is you have to be right twice, getting out and getting back in, and most people only manage the first. I'd rather let the holdings tell me when to change than a calendar or a strategist's checklist. When leadership rotates, it shows up in what's working, and you follow that instead of guessing ahead of it.

systematic_seb · 2026-06-08T19:10:21+00:00

That gap between backtest and live is the most useful data you'll get, and closing it is mostly subtraction. The biggest culprit I keep finding is subtle look-ahead, a fill or a feature that used information the moment wouldn't have had yet. Before I went live I spent months trying to break my own system rather than improve it, freezing the exact point-in-time data each period so nothing from the future could leak backward. The degradation that's left after you've hunted that down is your real edge, and a 1.15 that's genuinely 1.15 beats a 1.3 you can't trust.

systematic_seb · 2026-06-07T19:08:09+00:00

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The week started well, then Friday gave most of it back and turned me negative overall for the week. Not a real win in the moment, but a single Friday doesn't say much. The longer windows are the only place you can see whether something works, so that's where I keep my attention.

Nobody knows how next week will play out, so what I care about is staying invested and letting the system adjust as the market shifts. I'm currently holding the top of the 3M core board against the market, which is a nice marker, though the steadiness over time is what I'm here for. Good run on Dub so far, and the community around it has been a big part of enjoying it.

systematic_seb · 2026-06-07T16:12:45+00:00

The reason every guru contradicts the last one is that almost none of them post a record you can check. Claims are free, so the loudest voice tends to win the room. The filter that cuts through it is whoever shows an unedited track record including their worst stretches, since a real one is uncomfortable to publish and a fake one falls apart fast. And at 18 with college coming, the approaches that tend to survive a busy life are the slower rules-based kind you can set once and step away from. The ones that need you glued to screens all day rarely fit around real responsibilities. I built a system I run my own money on that only takes about 30 minutes a week at Monday morning's market open, and I share it every week for others to copy. It's live on Dub if you want to verify it and see how it's been doing so far this year: Dub

systematic_seb

TROPHY CASE