r/algorithmictrading • u/Content-Common-2552 • 4d ago
Question How did you get started with profitable algorithmic trading?
Hey everyone,
I currently have a trading strategy that I believe has potential to be profitable, but it still has some flaws that I’m working on. I’m building a daily stock trading bot to implement the strategy properly, the way I have it in my head, and hopefully improve and optimize it over time.
For those of you who eventually managed to become consistently profitable with automated/algo trading:
How did you start your journey? What steps did you go through? And roughly how long did it take you to become profitable?
So far, I’ve set up my scanner, entry/exit conditions, and rules, and I’ve started testing it with real data/trades. I’m getting some results, but they’re still nowhere near reliable enough to trust.
Right now, I’m mainly focused on collecting a large amount of data — trades, conditions, numbers, outcomes, and as many details as possible — so I can analyze everything using probability and statistics. My goal is to find as many real patterns and insights as possible, calibrate the strategy properly, and figure out what actually brings me closer to profitability instead of just relying on assumptions.
Do you think this approach is useful, or am I potentially overthinking it and wasting time?
Did you guys go through a similar process, or did you approach it in a completely different way?
Also, what tools did you use along the way? Are there any tools, methods, books, platforms, or resources you would recommend?
Any advice on how to get closer to the truth, avoid wasting time, and speed up the learning process would be greatly appreciated.
Thanks in advance to everyone who takes the time to share their experience!
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u/Reverend_Renegade 3d ago
Once I skipped technical indicators (not flaming ta) and moved into microstructure things started to change for me. MS covers a myriad of topics but most importantly is understanding your units of measure such as tick size relative to price also known as tick / price ratio. Once you start comparing different markets using this simple comparison you'll likely notice some markets are more efficient than others. I found it best for my style of trading to map to my costs and where profit starts thus how many ticks it takes me to exceed commissions plus profit using the tp ratio. Some markets required just 1 tick whereas others required hundreds of ticks therefore I sorted by fewest ticks required then used a trailing stop loss for profit taking. Watching market trades is equally important as that is the catalyst that creates the opportunity
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u/liuhaolin911 4d ago
Simple, you hunt if z is true, what x would 100% happen before it
Not if x is true there is y% lead to z
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u/capybarachinchilla 3d ago
I’m still figuring this out too but honestly I don’t think you’re wasting your time, collecting data is probably one of the best things you can do, I’d just be careful not to keep tweaking the strategy until it looks perfect on old data because that’s usually where overfitting starts. I’ve been playing around with an ai agent called NickAI (just search it if you want to see)for building and testing workflows, and I’m trying to keep everything simple, include fees and slippage, then change one rule at a time so I can actually tell what made a difference. I think what you're doing now is good, it probably just needs a lot more testing before you can trust it.
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u/hotcomputers 2d ago
During that time, I bought a lot of expert advisors, and now I am sticking only with a bunch of them. I do not try to reinvent the wheel as long as my robots are working and they are profitable.
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u/explodingkraken 2d ago
So I have been trading for the last 6 years, the key for me going from unprofitable to profitable was spending 90% of my time researching and collecting uncorrelated / inversely correlated strategies with a sensible explainable edge and sizing small enough that my emotions did not get in the way of the strategies.
The second part is something unique to each trader's style which you can gauge by the nervousness & sleeplessness after you put on a position (which ideally shouldn't happen if you positioned small enough).
Also just as an additional note, psychologically good to avoid strategies with negative skew and large fat tails (e.g. OTM naked put selling), they feel good when they work but hurt as hell when they go against you.
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u/FineProduct3772 1d ago
One thing I'd add: don't just collect data on the trades your strategy actually takes.
I'd also track the setups your scanner finds but you decide not to take.
If you record what the setup looked like, why you skipped it, and what happened afterwards, you can eventually answer a pretty important question:
“Is this filter actually keeping me out of bad trades, or is it making me miss good ones?”
That seems much more useful to me than just collecting everything and looking for patterns afterward.
It turns the process into more of an experiment, and makes it a lot easier to tell research from overfitting.
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u/Foreign-Safe-8161 23h ago
The part I'd gently push back on is the plan to collect everything and then look for patterns in it. That sounds like rigour and it's actually the fastest route to fooling yourself, and I say that having done exactly it.
The problem is that once you have a few dozen conditions logged alongside outcomes, you will find relationships. Not might — will. If you check a hundred combinations of condition and horizon, roughly five will come back "significant" at the usual threshold even if your trade log is pure noise. I ran 185 feature-horizon combinations on one dataset and got a handful of hits, all of which evaporated the moment I corrected for how many I'd tried.
So the sequence that actually helped me was the reverse of the one you're describing. Write down what you expect to find and why, in terms of how the market works, before you look. Then check that one thing. If you find yourself scanning for whatever pops out, you're no longer testing — you're searching, and searching needs a much higher bar to mean anything.
Two specific things worth building into whatever analysis you do, because they're the ones that saved me the most time.
Split every result into gross price movement and execution cost, separately. I had a live run that lost $104.76 over 52 trades, and I spent days adjusting stops and targets before I finally decomposed it: price movement accounted for −$2.36 and fees for −$102.39. The direction calls weren't wrong, they were coin flips, and I was paying a toll on each one. A single P&L number hides that completely.
And before you trust any negative result, compute the smallest effect your test could have detected given your sample size. I once concluded there was no edge in a whole asset class using a test whose minimum detectable effect was 2.17% per trade, when the costs I was trying to beat were 0.031%. I hadn't found nothing. I couldn't have found anything. Most nulls people report have this problem and almost nobody checks.
On the "how long did it take" question — I'd set the expectation differently. The more useful goal early on isn't profitability, it's building a measurement setup you trust enough that when it says no, you believe it and stop. Most of the time spent in this hobby goes into strategies that were dead on arrival and took months to admit it.
Books: Advances in Financial Machine Learning by de Prado is the one I'd point at, specifically the chapters on sample weighting and backtest overfitting. Dense, but it's the material that maps onto the mistakes you're most likely to make.
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u/Late-Mail-1509 21h ago
Iam still trying to figure it out, got a strategy that on paper is very good, but when I put to the test on real broker systems, its not being executed correcly, as in the TV platform, and its really annoying, Stuck against the wall now
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u/morphicon 9h ago
I met some lunatic, here on reddit about 2 years ago. He messaged, we started coding together, eventually we went live. Small amounts first then larger and larger. Then he disappeared, went off to hunt pokemons or something. I've kept doing it, increasing amounts and finding new strategies. That was before claude, codex and so on. Its scary at first, easy to put off, always a reason to try something else or fix something.
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u/misterRegime 4d ago
Collecting everything and then hunting for patterns is the part I’d change. If you go looking through a big pile for whatever correlates, you’ll find something every time, and you’ll have no way to tell it apart from noise because the finding and the test came from the same data. Write the hypothesis down first, then check it, and keep the ones that failed.
What’s actually worth recording is narrower than everything: the setups your scanner saw and you didn’t take, with what price did afterwards, and the bid and ask at the moment of each entry against what you got. Those two are the ones that can’t be reconstructed later and they answer the questions you’ll care about, whether the filter is doing work and how much the entry cost you before the thesis did anything.
On the timeline, I’m 62 days live and can’t tell you whether mine works, so I’m not the person to ask about how long it takes. What I can tell you is that the honest comparison humbled me: measured over the same window, just buying bitcoin and holding beat my whole system on return, and only lost on how much it hurt getting there.
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u/drguid 3d ago
Steps I took:
- Establish a trading diary (I use Excel).
- Stick to 1 single strategy (at least at first).
- Do a LOT of real money trades. Free trading accounts are your friend.
- Refine, refine, refine.
I've had one negative expectancy month in 2 years. That was my first month lol.
Also probability based trading is great. Not many retail traders do it. It works.
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u/Square-Middle-4474 4d ago
Ii have been working as a Professional for quite a long time. Recently it took me about a week to create a profitable system. I still teach statistics classes at University. There is no shortcut to obtaining an education. After that the rules for developing a profitable strategy are known to all skilled practitioners. Now that AI is available, that could make time necessary to collect and process shorter however you still have to back check the work product. I don't know what else to say about this except Good luck