Stocks

With SPY's average ~5% return rate, does it make sense to invest in companies like SJT, MPW, PXD, etc that have over 10% dividends? Companies like ORC which have like 18% dividend, but the underlying stock price continues to fall more that 18% are traps sure, but SJT, MPW, PXD and others that have a decent track record of not losing much (or even gaining) value seem like good investments.

Am I missing something?

cross-posted from: https://lemmy.world/post/704145

Yes, live.

​

I've looked for this for a while and didn't find much. Being a stubborn ass (The Boondocks voice) I kept looking until I got it down. There are trackers for the options you've sold, call and put screeners, calculators to avoid CSP risk, holdings, and a singular location for cost basis so you don't do something dumb like Idk sell SPCE CCs at $20 when your cost basis is $22 but you thought it was $18. I definitely didn't do that.

​

Google Sheet

You'll need to add a custom script:

1. Tools
2. Script Editor
3. Add file
4. Name it SAMPLE
5. Paste this into it:

The code is thanks to tanaike

Code source: https://stackoverflow.com/questions/64437503/importxml-not-producing-correct-values

function SAMPLE(url) {
  const res = UrlFetchApp.fetch(url, {muteHttpExceptions: true});
  const tables = [...res.getContentText().matchAll(/(<table[\w\s\S]+?<\/table>)/g)];
  if (tables.length < 2) return "No tables. Please confirm URL again.";
  const values = tables.reduce((ar, [,table]) => {
    if (table) {
      const root = XmlService.parse(table).getRootElement();
      const temp = root.getChild("tbody", root.getNamespace()).getChildren().map(e => e.getChildren().map(f => isNaN(f.getValue()) ? f.getValue() : Number(f.getValue())));
      ar = ar.concat(temp);
    }
    return ar;
  }, []);
  return values[0].map((_, i) => values.map(r => r[i]));
}

The result: It returns a table, so you need to use INDEX with it in order to point to a specific row/column. The method itself is SAMPLE, which takes a URL and returns a table. So you'll need to use Concatenate in order to make up the URL for a Yahoo link.

Google Sheet function:

=INDEX(SAMPLE(CONCATENATE("https://finance.yahoo.com/quote/", $A7, RIGHT(YEAR($E7), 2), TEXT(MONTH($E7), "00"), TEXT(DAY($E7),"00"), IF(B7 = "PUT", "P", "C"), SUBSTITUTE(TEXT($M7,"00000.000"), ".", ""), "?p=", $A7, RIGHT(YEAR($E7), 2), TEXT(MONTH($E7), "00"), TEXT(DAY($E7),"00"), "C", SUBSTITUTE(TEXT($M7,"00000.000"), ".", ""))), 2, 3) 

​

Known issues:

1. Loading from mobile is not reliable. You may have to erase the cell and undo, or reload, or get on a laptop/desktop.
2. Sorting recalculates the numbers and sometimes it stops working. I just don't sort anymore, but it'll eventually fix itself.
3. It doesn't calculate if you change values. Just erase the cell and undo, it'll do it correctly.
4. Not all options are available on Yahoo, especially if you're looking 30+ days.

​

Full page, two words: ~~Fuck you~~ good luck!

​

P.S. Yahoo dev: If you're seeing this, please let me be. This was already hard enough to put together. Plz.

cross-posted from: https://lemmit.online/post/11065

This is an automated archive made by the Lemmit Bot.

The original was posted on /r/technology by /u/Sorin61 on 2023-06-23 10:41:54+00:00.

cross-posted from: https://radiation.party/post/22211

[ comments | sourced from HackerNews ]

cross-posted from: https://lemmy.world/post/226506

Microsoft's investment in AI, notably through OpenAI's ChatGPT, has led to predictions of a $10 billion revenue increase in the coming years, driving shares to an all-time high.

Record High Stocks and AI Growth: Microsoft shares have reached a record high due to its growth prospects in artificial intelligence.

• The company's stocks rose 3.2%, closing at $348.10, largely fueled by AI, particularly with Microsoft's investment in OpenAI.

Microsoft and OpenAI Partnership: The partnership with OpenAI is pivotal to Microsoft's AI success.

• Microsoft heavily invested in OpenAI and provides underlying computing power for its projects.
• Microsoft has an exclusive license on OpenAI’s models, like the GPT-4 language model.
• The integration of OpenAI tools into Microsoft's services like Bing and Windows boosts their offerings.

Financial Prospects and Investor Interest: Microsoft's AI ventures have raised investor interest and revenue expectations.

• Microsoft’s finance chief Amy Hood forecasts Azure cloud's growth at 26-27% YoY, with 1% coming from AI services.
• Hood mentioned that “the next generation AI business will be the fastest-growing $10 billion business in our history.”
• This prospect has lifted the interest of investors who are keen on the company's earnings and revenue.

Future Predictions and Market Response: Microsoft’s recent successes have led to optimistic market predictions.

• JPMorgan analysts raised their price target from $315 to $350.
• Despite challenges like cloud growth and a shrinking PC market, Microsoft's AI investments, such as OpenAI/ChatGPT, signal long-term success.
• Microsoft’s shares have recovered from their 2022 losses, indicating a positive market response.

AI and Market Trends: AI has emerged as a leading factor in tech market trends.

• AI has been a trending topic after the release of the ChatGPT chatbot.
• Tech companies have adopted AI technologies in their products to drive cost savings amid recession concerns.
• The widespread adoption of AI, backed by companies like Microsoft, has sparked optimism in the tech sector, reviving bullish market sentiments.

Source (CNBC)

PS: I run a ML-powered news aggregator that summarizes with an AI the best tech news from 40+ media (TheVerge, TechCrunch…). If you liked this analysis, you’ll love the content you’ll receive from this tool!

cross-posted from: https://lemmy.world/post/136900

cross-posted from: https://lemmy.world/post/136244

cross-posted from: https://lemmy.world/post/135600

For anyone following the AI space of technology - this is pretty cool - especially since AMD has fallen behind its NVIDIA CUDA competitors.

I wish we had a new hardware announcement or benchmark/spec sheet to pair with the announcement, but I'll take what I can get.

Curious to see what AMD can muster in terms of AI computation. It's going to be hard to beat NVIDIA's Grace Hopper Superchip, but I'm all for the competition!

(full article for convenience)

Hugging Face and AMD partner on accelerating state-of-the-art models for CPU and GPU platforms

Whether language models, large language models, or foundation models, transformers require significant computation for pre-training, fine-tuning, and inference. To help developers and organizations get the most performance bang for their infrastructure bucks, Hugging Face has long been working with hardware companies to leverage acceleration features present on their respective chips.

Today, we're happy to announce that AMD has officially joined our Hardware Partner Program. Our CEO Clement Delangue gave a keynote at AMD's Data Center and AI Technology Premiere in San Francisco to launch this exciting new collaboration.

AMD and Hugging Face work together to deliver state-of-the-art transformer performance on AMD CPUs and GPUs. This partnership is excellent news for the Hugging Face community at large, which will soon benefit from the latest AMD platforms for training and inference.

The selection of deep learning hardware has been limited for years, and prices and supply are growing concerns. This new partnership will do more than match the competition and help alleviate market dynamics: it should also set new cost-performance standards.

Supported hardware platforms

On the GPU side, AMD and Hugging Face will first collaborate on the enterprise-grade Instinct MI2xx and MI3xx families, then on the customer-grade Radeon Navi3x family. In initial testing, AMD recently reported that the MI250 trains BERT-Large 1.2x faster and GPT2-Large 1.4x faster than its direct competitor.

On the CPU side, the two companies will work on optimizing inference for both the client Ryzen and server EPYC CPUs. As discussed in several previous posts, CPUs can be an excellent option for transformer inference, especially with model compression techniques like quantization.

Lastly, the collaboration will include the Alveo V70 AI accelerator, which can deliver incredible performance with lower power requirements.

Supported model architectures and frameworks

We intend to support state-of-the-art transformer architectures for natural language processing, computer vision, and speech, such as BERT, DistilBERT, ROBERTA, Vision Transformer, CLIP, and Wav2Vec2. Of course, generative AI models will be available too (e.g., GPT2, GPT-NeoX, T5, OPT, LLaMA), including our own BLOOM and StarCoder models. Lastly, we will also support more traditional computer vision models, like ResNet and ResNext, and deep learning recommendation models, a first for us.

We'll do our best to test and validate these models for PyTorch, TensorFlow, and ONNX Runtime for the above platforms. Please remember that not all models may be available for training and inference for all frameworks or all hardware platforms.

The road ahead

Our initial focus will be ensuring the models most important to our community work great out of the box on AMD platforms. We will work closely with the AMD engineering team to optimize key models to deliver optimal performance thanks to the latest AMD hardware and software features. We will integrate the AMD ROCm SDK seamlessly in our open-source libraries, starting with the transformers library.

Along the way, we'll undoubtedly identify opportunities to optimize training and inference further, and we'll work closely with AMD to figure out where to best invest moving forward through this partnership. We expect this work to lead to a new Optimum library dedicated to AMD platforms to help Hugging Face users leverage them with minimal code changes, if any.

Conclusion

We're excited to work with a world-class hardware company like AMD. Open-source means the freedom to build from a wide range of software and hardware solutions. Thanks to this partnership, Hugging Face users will soon have new hardware platforms for training and inference with excellent cost-performance benefits. In the meantime, feel free to visit the AMD page on the Hugging Face hub. Stay tuned!