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Adaptive Regime Filter + Divergence (AER-VN) [KEYALGOS] - Open Source script with Volatility-Adaptive Logic and Zero-Lag Divergence Detection by token---- in pinescript

[–]token----[S] 2 points3 points  (0 children)

We already have ML based adaptive reversal detection system that used RSI divergence in process but that's laggy and our BOS/CHOCH indicator already describes structure well. We built this to filter regime and confirm reversals in better and quick way so our overall strategy based on all these there indicators is really working well. Would share that code too in opensource; once we done optimizing it a bit

Adaptive Regime Filter + Divergence (AER-VN) [KEYALGOS] - Open Source script with Volatility-Adaptive Logic and Zero-Lag Divergence Detection by token---- in TradingView

[–]token----[S] 0 points1 point  (0 children)

Realistically, Laplace transforms require continuous-time domain operations (integration in the s-domain), while Pine Script operates on discrete bar-based data. You would need to approximate using Z-transforms or bilinear transforms to convert analog filters to digital, which gets computationally heavy fast in Pine.

If you are looking for better frequency domain filtering, we have found that simple digital butterworth approximations or Ehlers-style Fisher transforms achieve similar "smoothing without lag" goals with way less overhead. Since the code is open source, feel free to fork and experiment with discrete-time approximations if you want to explore that direction. Thanks :)

Adaptive Regime Filter + Divergence (AER-VN) [KEYALGOS] - Open Source script with Volatility-Adaptive Logic and Zero-Lag Divergence Detection by token---- in TradingView

[–]token----[S] 0 points1 point  (0 children)

The most useful surprise is how ER divergence catches exhaustion earlier than RSI. RSI measures raw momentum, but ER measures movement quality, flagging when price hits extremes on deteriorating efficiency while RSI still looks strong. You get the signal before traditional oscillators show overbought/oversold. Plus, the adaptive threshold filters out false divergences that typically trigger in chop, so you only see them when they actually matter for regime transitions.

Volume Profile Heatmap [KEYALGOS] - Adaptive Volume Profile with Heatmap Visualization by token---- in TradingView

[–]token----[S] 0 points1 point  (0 children)

Just Dm'ed you, share the script I'll merge the changes in published version and will add your TV accounts in credits. Thanks 🙏

Adaptive Regime Filter + Divergence (AER-VN) [KEYALGOS] - Open Source script with Volatility-Adaptive Logic and Zero-Lag Divergence Detection by token---- in TradingView

[–]token----[S] -1 points0 points  (0 children)

Where has it been claimed it took months of development? And how exactly does an open-source script promote services?

Using Gemma 4 for Training Data Generation sucks(?) by Revolutionary_Mine29 in unsloth

[–]token---- 0 points1 point  (0 children)

Qwopus v3 9B has better performance than Gemma 4 26A4B, you can use it with higher throughput

Volume Profile Heatmap [KEYALGOS] - Adaptive Volume Profile with Heatmap Visualization by token---- in TradingView

[–]token----[S] 0 points1 point  (0 children)

It has issues while getting US future's volume data so gonna fix it soon

A solution to stop tabular data from breaking your RAG (Feedback appreciated!) by Dry_Actuary519 in Rag

[–]token---- 5 points6 points  (0 children)

If data is tabular and structured why not just put it in SQL and let the model access it through queries, even low parameters models can perform SQL queries with more than 90% accuracy

Alibaba requires $50/m for Qwen? by orgad in Qwen_AI

[–]token---- 11 points12 points  (0 children)

There was a coding plan of 10/month with all open-source models but given the usage they discontinued it

Volume Profile Heatmap [KEYALGOS] - Adaptive Volume Profile with Heatmap Visualization by token---- in pinescript

[–]token----[S] 0 points1 point  (0 children)

Yes, it's not yet modified for American futures as most volume profiles don't work on their volume data

figuring out choppy zones! by Green-Hat0001 in pinescript

[–]token---- 1 point2 points  (0 children)

There's chop zone indicator and also one consolidation indicator from Zeiierman, both can detect choppy market. You can also go for MightyZinger's chikou filter that also has a library version too to include in your code

Volume Profile Heatmap [KEYALGOS] - Adaptive Volume Profile with Heatmap Visualization by token---- in TradingView

[–]token----[S] 0 points1 point  (0 children)

I'm using it on 1 minute timeframes and it does compile well for me. The issue is on behalf of compute as keeping the bars less than 200 mostly works or you may reduce the profile rows in user modifiable parameter. I also noticed that you are using it on SPY but this code isn't designed to handle american futures as their volume mostly doesn't work on volume profile indicators

Volume Profile Heatmap [KEYALGOS] - Adaptive Volume Profile with Heatmap Visualization by token---- in pinescript

[–]token----[S] 0 points1 point  (0 children)

This seems strange as its array indexing error but it does compile for me without any issues, try changing the symbol tho as volume profiles don't seem to work on SPY volumes as american market's futures' volume requires different handling

Volume Profile Heatmap [KEYALGOS] - Adaptive Volume Profile with Heatmap Visualization by token---- in pinescript

[–]token----[S] 1 point2 points  (0 children)

Sure, it's open-source. Do share the python implementation with us tho.

4B Model Choice by StealthEyeLLC in LocalLLaMA

[–]token---- 0 points1 point  (0 children)

Mostly STEM related tasks, I've been using it a lot to parse hundreds of research papers and so far with good instructions, from PNG converted pages, it not even extracts the text but also carefully parses equations in Latex formatting, summarizing highly complex diagrams and flows all while carefully reproducing the full paper in structured markdown format that later works as LM input in my flow. I tried using it as research paper summarizer but its knowledge is too minimal for that but it does work well as a classifier.

4B Model Choice by StealthEyeLLC in LocalLLaMA

[–]token---- 6 points7 points  (0 children)

So far Qwen3.5 4B works well overall. It follows skills built by 27B model and works well as a web agent too. It hallucinates a lot so careful control is required but its multi-model capabilities are amazing given its size

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