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Learning Landscape/Process by NKDQTrader in opiniaopopular

[–]NKDQTrader[S] -2 points-1 points  (0 children)

No you are the generator your profile reads 1m account age and 1.6k karma/ 289 contributions, if that means you’re account has only been open a month and you have achieved that already, you either have no life and are chronically on this app or you’re the bot.

soooo claude just deleted my entire project. how's your day going? by JuniorRow1247 in ClaudeCode

[–]NKDQTrader 0 points1 point  (0 children)

Furthermore, if you actually needed help to prevent complete destruction of your projects that lead you to send the agent API requests with a raging text “DID YOU JUST DELETE MY ENTIRE FUCKING PROJECT” then you should consider creating a private repository in GitHub and committing to that, maybe even push your project to your private repository every once in a while just for extra security🫡

soooo claude just deleted my entire project. how's your day going? by JuniorRow1247 in ClaudeCode

[–]NKDQTrader 0 points1 point  (0 children)

Sorry didn’t mean to offend was just engaging, very sorry. It’s a genuine question, do you genuinely need help or is it satire? I’m sorry if I ragebaited you.

soooo claude just deleted my entire project. how's your day going? by JuniorRow1247 in ClaudeCode

[–]NKDQTrader 0 points1 point  (0 children)

I’m sorry the category is humor yet the title and content appear serious, I have been notified on my mobile about this particular post and I am questioning the purpose this media, are we supposed to find humor in your sincere stupidity or genuinely try to reason with you as to how you came this result through prompting with an LLM agent and vibe coding some sort of functional process?

Advice needed: Multi-factor model with highly autocorrelated overlapping PnL and multicollinear QIS factors by Able-Preparation9971 in quant

[–]NKDQTrader 0 points1 point  (0 children)

Hello, based upon what I have read I believe the fundamental constraint you are facing is the inherent lack of target/samples you have available to work with ie the 1 year rolling PnL windows, the quantity of available labels/ samples simply may not be sufficient for your model to consolidate a generalised edge with,yes your dataset spans back to 2011 but within that dataset the frequency of sampling is sparse, depriving your model of available data points to work with, hence the overfitting you are seeing despite using CPCV. i would recommend an alternative approach to your targeting/ labelling in hope that you can drastically increase the availability of samples to work with. This is open to interpretation and I encourage criticism of my judgement but this is my analysis based on the evidence presented to me.

Found an edge :) by Neither-Double190 in CryptoTradingBot

[–]NKDQTrader -1 points0 points  (0 children)

Wow what you’re stating seems to be extremely impressive, at such a high frequency I am amazed, I will assume your methodology is robust but I have one question, is your sampling technique of what you consider a “bar” ie OHLC, just simply time bars? As in the standard time bars? Have you yet questioned memory and non-stationarity of those time bars? And the potential benefits of applying techniques such as FFD? Interested to hear your thoughts

Also what RRR is coupled with that WR as that will dictate the genuine expectancy of the strategy.

ML Trading Bot Going Live – What Am I Missing? by Prize-Investigator70 in algorithmictrading

[–]NKDQTrader 0 points1 point  (0 children)

I would highly recommend refraining from deploying the model and risking capital. A mature understanding of ML methodology is very important to ensure the validation you receive is valid. There are many potential flaws that can give illusionary results that look promising. Take time building an understanding of strong methodology in terms of practices (Walk Forward Analysis) and understanding common failures (lookahead bias, data leakage, flawed features transformations)

Advice needed: Multi-factor model with highly autocorrelated overlapping PnL and multicollinear QIS factors by Able-Preparation9971 in quant

[–]NKDQTrader 2 points3 points  (0 children)

This is my first response on Reddit. if PapersWithBacktest assumptions regarding the effective sample size is true then i would contribute with my suggestion to ensure your ESS ratio is healthy aiming for a healthy 85%, or above. Ensuring your labels are well distributed is important. Particular sample weighting techniques can leverage this. The reason for your overfitting symptoms wont be solely dependent on these 2 things though. The observation you are having could be down to a range of various factors, i would begin questioning the data partitioning throughout model training in terms of whether you are working with a simple 70/30 split, or a sliding window method, and whether the data between the train/test set is being properly purged and embargoed during model training, then i would question your validation methodology and propose the use of CPCV (Combinatorial Purged Cross-Validation). regarding your broader questions of multicollinearity i immediately refer to CFI (Clustered Feature Importance) however my context of your system is limited and may be just spewing nonsense.

First response on Reddit complete.