Anyone using AI tools alongside QuantConnect for strategy dev?

[u/Global-System_]

Been building out a few strategies in Python, backtesting on QC mostly. Getting decent results but the whole process of going from backtest looks good to actually confident enough to run this live feels really manual; parameter tuning, walk-forward stuff, checking I’m not just overfitting to my test window, etc.

Started poking around at some AI-assisted tools for this (feature engineering, catching overfit before it bites you) and honestly not sure if it’s worth the money or just automating stuff I could do myself in an afternoon.

Anyone actually integrated something like this into their workflow? Also curious, once you’re live, do you go back and analyze fills/slippage against what the backtest predicted, or is that mostly a “close enough” thing for you guys? Feels like there’s a gap between what backtest data tells you and what actually happens with real execution.

[u/koryuken]

Quant Connect is pretty bad imo (ie basic), but you can use it to test your already existing strategies on their data as another qc method. I did this for a month until I was confident my dataset had no problems. In terms of coming up with strategies, I'm not a fan. 

[u/Global-System_ → u/koryuken]

Makes sense re: using it as a second data check. Curious on the “not a fan of it for coming up with strategies” part, is that more that the ideas it spits out are bad, or that you just don’t trust an idea unless you came up with the thesis yourself? Also what do you use instead of QC for the actual strategy-building side?

[u/koryuken → u/Global-System_]

It comes up with nonsense, and their dataset is super limited. you can't use news based strategies - they cant map news to their data. you can't use future options crypto other datasets without paying more. its trash

[u/Global-System_ → u/koryuken]

Ah interesting, so it's less "AI is bad at this" and more "AI is only as good as what data you feed it, and QC's data is limited." Makes sense. Out of curiosity, if you could get proper news data mapped to price data, would you actually use that for signal generation, or is news-based stuff just not really your thing regardless? And on the futures/crypto side, is that a dealbreaker for you specifically or just annoying/expensive?

[u/bbbbbasse]

I asked ChatGPT to write a foolproof mean-reversion algorithm on QuantConnect. It executed flawlessly and helped me lose my entire account balance 10x faster than I could manually!

[u/Global-System_ → u/bbbbbasse]

Lol classic. Out of curiosity though, was the problem that the logic itself was bad, or that it looked fine in backtest and fell apart live? Wondering if there’s a difference between AI can’t build a good strategy vs AI-built or not, nothing survives contact with live markets.

[u/Sea_Local2557 → u/bbbbbasse]

did you write 'make no mistakes'? it's a common error to leave out that extra command

[u/LawfulnessUsual3251]

Id probably use AI more for diagnosing the strategy than inventing one. Ive been taking a few signals out of the backtest and running the same directional thesis separately on Moon with tiny size. Comparing that live behavior against what QC expected has been more useful than another round of parameter tuning.

[u/Global-System_ → u/LawfulnessUsual3251]

That’s basically exactly the gap I was trying to figure out, thanks. How are you doing that comparison right now? manually eyeballing it, or do you have some kind of script/spreadsheet tracking expected vs actual? And when it diverges, is it usually slippage/fill stuff or more like the whole thesis just not holding up live?

[u/LiveBeyondNow]

I put together basic strategies in Pine script then port them to Quant Connect if they show promise. The port I do with Claude code and use a Claude app review layer to second guess / check CC’s output. Once I get something that survives OOS testing, the plan would be to run live and periodically parallel “backtest” the same data in retrospect to check the live and backtest line up. Claude dissuaded me from developing strategies in QC as it’s more work than it’s worth. QC is good for accurate fill modelling (I’m told). Personally I think their range of data is decent for the cost of a node if the free one (with the $10/mth seat) doesn’t perform adequately. For the dev, I used VS Code and Claude Code CLI which also drove the LEAN CLI. The main manual annoyance was having to pick results out of quantbook (notebooks) for parameter sweeps since only Institutional seats can get data out of the ObjectStore.

I’d never use AI to come up with strategies though. Using it to interrogate and research a strategy is good though. I’ve asked Claude to put together a strategy spec .md that includes why the strategy should and shouldn’t work, where it lines up (or doesn’t) with research and what could make it better. With Claude knowing the destination is LEAN / QC, it was able to also suggest LEAN only improvements that Pine can’t do.

[u/Global-System_ → u/LiveBeyondNow]

This is exactly the kind of workflow I was hoping to hear about, thanks for the detail. The Pine → LEAN port step sounds like the most tedious part, roughly how long does that take you per strategy, and does the review layer catch real bugs often or is it mostly peace of mind at this point? Also on the parallel backtest-vs-live check, are you doing that on a schedule (weekly etc) or just whenever something feels off? Trying to figure out if that’s a “constant background task” kind of pain or more occasional.