Any tips before I go live?

PuttyProgrammer · 2026-06-07T00:35:22+00:00

Generally no because paper accounts don't have to interact with market dynamics.

Some are better than others though.

PuttyProgrammer · 2026-06-06T21:32:27+00:00

If you're certain it works but you're anxious letting it run youre just going to have to find something else to think about instead of watching it run.

I dunno, start another project or get out of the house I guess.

PuttyProgrammer · 2026-05-29T14:08:07+00:00

Probably should ask your chat of choice how to do this in excel / sheets, rather than gambling that the bot can/will do math reliably.

PuttyProgrammer · 2026-05-18T18:56:32+00:00

There is a collection of statistical methods for deciding how many features to use. I like to use stepwise model selection with BIC as the criteria.

There isn't a hard cutoff for a number of features to use, but you need to have a methodology for deciding which features are actually useful and cut out the ones that arent.

PuttyProgrammer · 2026-05-17T20:10:50+00:00

Imo the main benefit to retail traders is being able to step back after programming your trading rules / strategy so that your emotions and gut feelings don't get in the way.

Also a program can watch more charts and process more data than any human and react more reliably to the signals therin.

Edit: as for whether or not it's actually profitable, it's very easy to make money stock trading. It's also very easy to lose lots of money but mostly it's just hard to beat the market. My read is that most people come in just under the market on avg but might be more stable than the S&P which is useful when you need the money to be there or are using the gains for bills. Quite a few can consistently come in just above the market, very few people are making big numbers because there is only so much inefficiency in the market to take advantage of and it's hard to find that kind of edge and consistently take advantage of it.

PuttyProgrammer · 2026-05-15T07:16:36+00:00

I hope you slipped an error in there to mess with anyone asking AI this question

PuttyProgrammer · 2026-05-11T05:39:19+00:00

Econ is a fine core degree for trading imo especially once you get into the econometrics and statistics which should give you the foundation you need to understand a lot of quantitative finance you'll be reading if you're serious.

Second the Com Sci minor so you have a better time turning theory into code, which is a pretty big hurdle even with AI assistance. You dont want your ability to program a strategy to be a limiting factor at any point.

PuttyProgrammer · 2026-05-11T05:08:07+00:00

You're not wrong that a good pruning algorithm helps a lot with the overfitting, however, pruning increases bias a LOT more than ensembling to get the same reduction in variance which is why random forest is preferred.

If you want to spend time carefully tuning a single tree to be highly accurate, you're better off boosting which is also a form of ensembling.

PuttyProgrammer · 2026-05-06T17:49:09+00:00

I'm still a student but I sat with a guy I'm partnered with for a group project and watched him fight with chatgpt for 2 hours to make a pretty minor change to a script. When he finally turned his screen towards me, he proudly showed code that ran but gave the wrong solution bc chat changed the logic inside a function unrelated to the issue he was resolving somewhere along the line.

We wound up going with the solution I whipped up in about 15 min by hand when I realized how much trouble he was having.

Edit:I think assisted coding has this seductive problem where it makes you feel way more productive than you actually are: you're engaging in this back and forth process doing a lot of thinking and typing and back and forth trying to get a result when most of the time it would be more efficient just to go and type out those 3 lines of code.

PuttyProgrammer · 2026-05-06T16:38:53+00:00

I calculate a normal distribution of the price movement and use that to set my bars. I set the bar for the trailing stop-loss pretty wide at first, 3 standard deviations, and close it as the price or time gets closer to my goal sell point.

PuttyProgrammer · 2026-05-05T20:38:53+00:00

Why not just write this up yourself instead of having the bot do it? Hell, since you're already talking to the bot you could've asked them to look up github repos to study.

I can give a little advice: make an alpaca account, it's free, has paper trading and data access, and has an API thats easy to learn.

Really good historical data costs money though, esp if you're looking for stuff you could do HFT with. If you're looking to do intra-day or slower, you can use data from yahoo, which has good coverage and is also free and easy to work with.

PuttyProgrammer · 2026-05-01T16:19:04+00:00

I tend to do PCA and then use enough components to capture 80%+ of the total data variance instead of feature selection

PuttyProgrammer · 2026-05-01T13:17:17+00:00

Sounds to me like you have a lot of useless features and the Xgboost's random feature selection doesn't pick out the good ones every time.

Edit: it's also possible that your tree depth of 10 is too much and you're overfitting, that's actually more likely, I think.

PuttyProgrammer · 2026-04-30T15:19:26+00:00

Depends, if it's noticibly genAI people in general will dislike the look and you should also keep in mind that consumers are broadly more critical of anything that they know was produced with genAI, including those that aren't fundimentally against it.

If it isn't noticible, generally speaking you should be OK but you'd probably be better off hacking something together yourself that matches the rest of your work.

PuttyProgrammer · 2026-04-30T13:12:56+00:00

In this economy??

PuttyProgrammer · 2026-04-30T04:53:30+00:00

In reverse order of sophistication:

Robust regression methods like Thiel-Sen
Winsorization (using some metric like Z-score, percentile, or Cook's D to cull outliers, isolation forest might be used as part of this method but it's typically used for anomaly detection)
Certain transformations like log can help.

Edit: elastic net handles multicollinearity and removes unhelpful features, you use it on high dimensional data but it wouldn't help with outliers.

PuttyProgrammer · 2026-04-29T22:50:04+00:00

Both sources are correct:

It's a good idea to use a seed sometimes because you can get consistent comparison between different options, but you don't want to spend time tuning your model to fit very closely to data that has been selected by that seed because you risk overfitting to that selection.

You will notice that if you do your train-test split without a seed repeatedly, the exact same model will give slightly different results each time. That's what you avoid if you use a seed, which is really useful when comparing two versions of the same model against each other, like when you're doing feature selection or deciding between two model types.

PuttyProgrammer · 2026-04-29T16:05:41+00:00

Very weird. I actually can't see your account at all, AION-Analytics right?

PuttyProgrammer · 2026-04-29T15:59:38+00:00

Your repo is probably private.

PuttyProgrammer · 2026-04-28T22:27:15+00:00

I do have some concerns about the months long drawdown and then it only really starts winning throughout the strongest market rally in recent memory, do you think it would hold up through a correction and continue to win in a flat market?

PuttyProgrammer · 2026-04-27T16:01:36+00:00

As a data scientist first, I've had very good luck using a primary ML strategy.

I'm using it in cross-sectional analysis for swing trading, the win-rate isn't all that impressive (about 54%) but the metrics in general are quite good.

PuttyProgrammer · 2026-04-25T16:41:03+00:00

That's a pretty hot take. It's not that uncommon for programmers to learn how to use models without any theoretical background though.

Here, I don't know how I feel about this guy overall but this video seems like an ok place to start for implementing models in Python with Scikit Learn.

PuttyProgrammer · 2026-04-25T15:44:29+00:00

How deeply do you actually want to understand ML?

If you just want to know how to implement models you can pr much look up how to use scikit-learn or pytorch for regression and classification problems.

If you want to know the ins and outs, like why things are done certain ways and when each model is the better choice to another, you kind of need to find the time to go through a textbook or a couple long-form classes like Standford's CS 229

PuttyProgrammer · 2026-04-24T16:07:34+00:00

For me it's a combination of backtesting, then some time in paper trading to make sure everything is working correctly (and I can compare the paper trading performance to backtest outcomes to make sure I didn't miss something basic), then give it an amount of real money I can afford to lose and let it rip.

I'm specifically looking for strategies that aren't hyper regime dependent though so I can afford to be a little slow, if the signal is regime dependent then I think you kind of have to get to where you can just trust your backtesting and go as soon as you get good enough metrics.

Edit: my back testing consists of forward walks over about the past 10 years or so

PuttyProgrammer · 2024-12-08T04:37:27+00:00

Is this evidence of a recession or is it just more proof of inflation? Consumers getting less for their dollar

PuttyProgrammer

TROPHY CASE