# $LITE Social Sentiment & Intelligence — 2026-08-09 16:20 UTC

> **Asset:** $LITE  
> **Momentum Status:** Fading Quickly  
> **Timestamp:** 2026-08-09 16:20 UTC (2026-08-09T16:20:00Z)  
> **Canonical URL:** https://cryptitalk.com/2026-08-09-16-20/crypto/LITE  
> **Overview Brief:** https://cryptitalk.com/2026-08-09-16-20/crypto.md  

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## 10-Minute Social Metrics
- **Posts Analyzed:** 1
- **Total Impressions:** 220
- **Likes:** 0
- **Retweets:** 0
- **Comments:** 0

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## Momentum & Sentiment Analysis
Total Engagement - Comments: 0, Retweets: 0, Likes: 0, Impressions: 220


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## Cited Community Posts & Evidence


### Post #1 by @reddit
> **Author:** [@reddit](https://x.com/reddit)  
> **Metrics:** 1 likes · 0 retweets · 0 comments · 100 views  


> **Source Link:** [https://x.com/reddit/status/7f8db5eb4c3c537227f426492405161d6d455367f9e02d61abcbe22d25f4560e](https://x.com/reddit/status/7f8db5eb4c3c537227f426492405161d6d455367f9e02d61abcbe22d25f4560e)  


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> "Programmatic trendline detection: how do you handle the line-selection problem?

Programmatic trendline detection: how do you handle the line-selection problem? I'm building a system that detects support/resistance trendlines from OHLCV data (crypto daily/hourly, \~6 years of history, a few hundred symbols).
Levels are straightforward — cluster swing highs/lows by price and count members. Breakouts and retests fall out of that easily. Trendlines are where I'm stuck. With N swing lows there are N(N−1)/2 candidate lines. A human draws one by eye. A program has to pick, and I don't want a hand-tuned rule that only works on the charts I looked at.
The approach I'm considering: draw every pairwise line, extend it forward, and score it by touches (price approached within X% and reversed) minus violations (price closed through it). Keep the top-scoring lines. The idea is to let price history tell me which lines the market actually respected, instead of me choosing."




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## Contributing Accounts

- `@MarketProphit` (https://x.com/MarketProphit)