MicroStrategy's Bitcoin holdings have generated unrealized profits of over $10 billion; Ethereum's market value surpassed Johnson & Johnson to rank 33rd in the global asset rankings; CryptoPunks and BAYC are the only two NFT series that have continued to rank in the top 10 by monthly average market value since 2022; this week's NFT transaction volume increased by 14.99% month-on-month to US$95.72 million, but the number of buyers fell by more than 90% month-on-month.MicroStrategy's Bitcoin holdings have generated unrealized profits of over $10 billion; Ethereum's market value surpassed Johnson & Johnson to rank 33rd in the global asset rankings; CryptoPunks and BAYC are the only two NFT series that have continued to rank in the top 10 by monthly average market value since 2022; this week's NFT transaction volume increased by 14.99% month-on-month to US$95.72 million, but the number of buyers fell by more than 90% month-on-month.

PA Daily | Bitcoin is approaching a new high of $80,000; Tether has issued 4 billion USDT on Ethereum in the past three days, most of which flowed into exchanges

2024/11/10 17:08

Today's news tips:

1. MicroStrategy's Bitcoin holdings have made a profit of over $10 billion

2. Tether issued 4 billion USDT on Ethereum in the past 3 days, most of which flowed into exchanges

3. Trump offers to pay Harris' $20 million campaign debt

4. Ethereum's market value surpasses Johnson & Johnson and ranks 33rd in global assets

5. Cardano founder: will work with lawmakers and the government to push for the passage of a bipartisan bill

6. CoinGecko: CryptoPunks and BAYC are the only two NFT series that have continued to rank in the top 10 by monthly average market value since 2022

7. This week, NFT transaction volume increased by 14.99% month-on-month to US$95.72 million, but the number of buyers decreased by more than 90% month-on-month

8. NEAR AI launches Alpha version, including AI assistant and research center

Regulatory News

A pyramid scheme organization that used "blockchain" and "virtual currency" as gimmicks was prosecuted and sentenced, with the amount involved reaching more than 210 million yuan

According to the official account of the Yunnan Provincial People's Procuratorate, the Shidian County Procuratorate recently filed a public prosecution against Li Moumou and 10 others for organizing and leading pyramid schemes. After trial by the court, Li Moumou and 10 other defendants were sentenced to fixed-term imprisonment ranging from six years to two years for the crime of organizing and leading pyramid schemes, and were fined between RMB 500,000 and RMB 100,000.

Since May 2021, Li has successively gathered Huang, Jin and others to use "blockchain" and "virtual currency" as gimmicks to make illegal profits. They set up 5 fund pools on the network platform on the grounds of purchasing and holding virtual digital currency A and issuing virtual digital currency B and C. Through on-site meetings, WeChat groups and other offline and online methods, they created successful people's personalities, used special professional backgrounds, and used slogans such as "one coin for one luxury house, one coin for one luxury car" and "easily earn hundreds of thousands or millions a day" to promote the reward system and profit prospects, and deceived the general public to obtain membership qualifications by purchasing, destroying, adding fund pools, etc., and completed the tasks issued, and obtained static dividends and dynamic benefits based directly or indirectly on the number of people developed and the amount of investment, forming 5 rebate levels. It has been identified that the pyramid scheme funds collected by Li and others using the network platform totaled more than RMB 210 million.

The Shidian County Procuratorate reviewed and believed that Li used virtual currency as a gimmick and colluded with the other nine defendants to use the Internet platform to defraud property and disrupt the economic and social order. The pyramid scheme funds totaled more than 210 million yuan, and the circumstances were serious. The actions of Li and the other ten people violated Article 224 of the Criminal Law of the People's Republic of China and constituted the crime of organizing and leading pyramid schemes. After the court heard the case, the above judgment was made.

Trump offers to pay Harris' $20 million campaign debt

President-elect Donald Trump has offered to pay Kamala Harris’ $20 million campaign debt, according to Watcher.Guru.

FTX Bankruptcy Group Sues Anthony Scaramucci and SkyBridge Capital to Recover Over $100 Million

According to Cointelegraph, the FTX bankruptcy group is seeking to recover more than $100 million from SkyBridge Capital and founder Anthony Scaramucci to recoup the funds spent by former FTX CEO Sam Bankman-Fried (SBF) in sponsorship and investment agreements with Scaramucci and SkyBridge since 2022.

According to legal documents from Nov. 8, prior to FTX’s collapse, Bankman-Fried made a series of investments and partnerships with SkyBridge Capital and Scaramucci — starting with a $12 million sponsorship of Scaramucci’s SALT conference in January 2022. Shortly thereafter, in March 2022, SBF directed Alameda Research to invest $10 million in the SkyBridge Coin Fund. Later, in September 2022, FTX acquired a 30% stake in the operating company that manages SkyBridge’s investment vehicle for $45 million. FTX’s lawyers argued that the investment lacked financial sense — arguing that “FTX Group could have easily purchased at a lower cost” the basket of cryptocurrencies into which the vast majority of the $45 million investment was invested.

FTX files lawsuit to recover $27 million from Alameda’s Huobi and Poloniex accounts

FTX creditor Sunil said on the X platform that FTX filed a lawsuit to recover $27 million in Huobi and Poloniex accounts held by Alameda.

Project News

BTC breaks through $79,000, up 3.98% on the day

According to the OKX market data, BTC has just broken through $79,000 and is currently trading at $79,356.10 per coin, with a daily increase of 3.98%.

CoinGecko: CryptoPunks and BAYC are the only two NFT series that have consistently ranked in the top 10 by average monthly market value since 2022

According to data disclosed in the NFT report released by CoinGecko, CryptoPunks currently dominates the NFT market, with a 30.9% share of the top series, and its top position has been consolidated since surpassing Bored Ape Yacht Club (BAYC) in May 2023. Previously, at the beginning of 2022, CryptoPunks' dominance was 24.8%, lagging behind the then leader BAYC's 29.3%. Although CryptoPunks briefly surpassed BAYC in November 2022 and then slipped to second place, due to its more elastic reserve price, this pixel art NFT successfully maintained a narrow market share gap and regained the top spot in May 2023.

Notably, CryptoPunks is the only NFT collection to see its dominance increase by 10.0 percentage points in one year, growing its market share from 23.6% to 33.6% in 2023. While CryptoPunks’ dominance has declined slightly since the beginning of the year, it remains the largest NFT collection to date, with a market share of over 29.5%, more than double the dominance of any other collection.

In addition, CryptoPunks and BAYC are the only two NFT series that have consistently ranked in the top 10 by monthly average market value since 2022. During these three years, five other series have also frequently entered the top 10 but failed to maintain continuity: Mutant Ape Yacht Club (MAYC), Azuki, Autoglyphs, Snowfro's Chromie Squiggle, and Tyler Hobbs' Fidenza.

Cardano founder: Will work with lawmakers and the government to push for a bipartisan bill to pass

According to CoinGape, Cardano founder Charles Hoskinson said in a recent speech that he would work with lawmakers and the government to promote the passage of a bipartisan bill. His company Input Output Global (IOG) will set up a separate policy department for cryptocurrency regulation. The office will focus on developing a legislative framework that incorporates the provisions of the 21st Century Financial Innovation and Technology Act (FIT21) and the Responsible Financial Innovation Act (RFIA).

NEAR AI launches Alpha version, including AI assistant and research center

NEAR AI said on the X platform that the Alpha version has been launched, which includes the NEAR AI Assistant (Alpha), which has a user-specific memory and can act on behalf of users on Web2 and Web3 by connecting to other AI agents and services. The assistant knows how to render responses and generates a custom front end when needed. In addition, it also includes the NEAR AI Research Center, which supports community-built AI research and the creation of foundational models.

Solana Co-founder: Solana’s advantage lies in execution, and its potential lies in improving its infrastructure

In a recent interview, Solana founder Anatoly Yakovenko spoke about the ecosystem’s unique position in the blockchain space. “Scalability, infrastructure focus, and transaction efficiency are Solana’s three main strengths in my opinion.” Of course, he is also aware of the challenges Solana faces in a world where blockchain technology continues to evolve and platforms further develop.

Anatoly Yakovenko compared Solana to Ethereum and various L2 solutions, highlighting the trade-offs between L1 and L2. L2 solutions typically use centralized sorters for low-latency transaction ordering. However, Yakovenko noted that these can cause the same congestion issues as L1 chains. While L2s are often seen as a short-term solution to congestion issues, they face scaling bottlenecks when multiple applications or markets use them.

Yakovenko stressed that Solana's advantage lies in execution. While Ethereum is scaling through L2, Solana's development remains focused on making its L1 perfect. He acknowledged that one day, blockchains will offer similar functionality to Solana and provide faster iterations, but for now, the pace of Solana's improvements puts it far ahead of its competitors. For Anatoly Yakovenko, the core of Solana's potential lies in perfecting its infrastructure to support fairer and more open transaction processing and achieve a truly decentralized future. This, he said, will make Solana one of the leading blockchains in the coming years.

Financing News

Former ParaFi Capital partner launches private equity fund Inversion Capital

According to The Block, Santiago Roel Santos, a former partner at blockchain venture capital firm ParaFi Capital, announced the launch of Inversion Capital, a private equity fund aimed at acquiring traditional companies and transforming their operations by adopting encryption technology. Santos believes that some companies can solve coordination problems by adopting encryption tools, which "manifest as higher operating costs, capital expenditures, or declining unit economics." It doesn't matter whether business owners are reluctant to accept cryptocurrencies, because Inversion Capital will not be an investment partner-it will directly acquire these companies.

Important data

This week, NFT transaction volume increased by 14.99% month-on-month to US$95.72 million, but the number of buyers decreased by more than 90% month-on-month

According to News.bitcoin, NFT sales this week reached $95.72 million, up 14.99% from the previous week, after weeks of declining trading volumes. However, the number of buyers this week dropped sharply by 90.21%, while the number of NFT sellers dropped by 88.80%. Ethereum was the blockchain with the highest sales in the seven days of this week, reaching $31.14 million, up 13.25%. Following closely behind, Bitcoin-centric NFT sales ranked second with $26.01 million, and Solana ranked third with sales of $12.97 million from November 2 to November 9.

The Pendle team address transferred 1.4 million PENDLE to Binance, worth about US$7.74 million

According to on-chain analyst Yu Jin, another Pendle team address transferred 1.4 million PENDLE (US$7.74 million) to Binance in the past half hour. Together with the 625,000 transferred from another team address the day before yesterday afternoon, 2.025 million PENDLE (US$11.11 million) of the team have flowed into Binance in the past day or so.

Tether issued 4 billion USDT on Ethereum in the past three days, most of which flowed into exchanges

According to Spot On Chain, Tether has issued 4 billion USDT on Ethereum in the past three days. It is worth noting that about 3.44 billion USDT have been transferred to various exchanges, including 1.75 billion USDT to Binance and 770.8 million USDT to Coinbase.

A whale who once invested $5.2 million in MOODENG and suffered losses now makes more than $3 million

According to Onchain Lens monitoring, a whale once invested $5.2 million and suffered huge losses when MOODENG's market value fell below $100 million. As MOODENG hit a new high, the whale still held MOODENG and made a profit of more than $3 million.

Ethereum's market value surpasses Johnson & Johnson and ranks 33rd in the global asset ranking

According to Infinite Market Cap data, Ethereum surpassed Johnson & Johnson in the global asset market value ranking and ranked 33rd.

MicroStrategy's Bitcoin holdings have generated over $10 billion in profits

At the current price of Bitcoin at $79,000, MicroStrategy's Bitcoin holdings have a floating profit of over $10 billion. According to previous news, as of today, MicroStrategy holds a total of 252,220 Bitcoins, with a total purchase cost of approximately $9.9 billion and an average price of approximately $39,266.

Market Opportunity
Moonveil Logo
Moonveil Price(MORE)
$0.004112
$0.004112$0.004112
+0.73%
USD
Moonveil (MORE) Live Price Chart
Disclaimer: The articles reposted on this site are sourced from public platforms and are provided for informational purposes only. They do not necessarily reflect the views of MEXC. All rights remain with the original authors. If you believe any content infringes on third-party rights, please contact service@support.mexc.com for removal. MEXC makes no guarantees regarding the accuracy, completeness, or timeliness of the content and is not responsible for any actions taken based on the information provided. The content does not constitute financial, legal, or other professional advice, nor should it be considered a recommendation or endorsement by MEXC.

You May Also Like

South Korea Launches Innovative Stablecoin Initiative

South Korea Launches Innovative Stablecoin Initiative

The post South Korea Launches Innovative Stablecoin Initiative appeared on BitcoinEthereumNews.com. South Korea has witnessed a pivotal development in its cryptocurrency landscape with BDACS introducing the nation’s first won-backed stablecoin, KRW1, built on the Avalanche network. This stablecoin is anchored by won assets stored at Woori Bank in a 1:1 ratio, ensuring high security. Continue Reading:South Korea Launches Innovative Stablecoin Initiative Source: https://en.bitcoinhaber.net/south-korea-launches-innovative-stablecoin-initiative
Share
BitcoinEthereumNews2025/09/18 17:54
Trump Cancels Tech, AI Trade Negotiations With The UK

Trump Cancels Tech, AI Trade Negotiations With The UK

The US pauses a $41B UK tech and AI deal as trade talks stall, with disputes over food standards, market access, and rules abroad.   The US has frozen a major tech
Share
LiveBitcoinNews2025/12/17 01:00
Summarize Any Stock’s Earnings Call in Seconds Using FMP API

Summarize Any Stock’s Earnings Call in Seconds Using FMP API

Turn lengthy earnings call transcripts into one-page insights using the Financial Modeling Prep APIPhoto by Bich Tran Earnings calls are packed with insights. They tell you how a company performed, what management expects in the future, and what analysts are worried about. The challenge is that these transcripts often stretch across dozens of pages, making it tough to separate the key takeaways from the noise. With the right tools, you don’t need to spend hours reading every line. By combining the Financial Modeling Prep (FMP) API with Groq’s lightning-fast LLMs, you can transform any earnings call into a concise summary in seconds. The FMP API provides reliable access to complete transcripts, while Groq handles the heavy lifting of distilling them into clear, actionable highlights. In this article, we’ll build a Python workflow that brings these two together. You’ll see how to fetch transcripts for any stock, prepare the text, and instantly generate a one-page summary. Whether you’re tracking Apple, NVIDIA, or your favorite growth stock, the process works the same — fast, accurate, and ready whenever you are. Fetching Earnings Transcripts with FMP API The first step is to pull the raw transcript data. FMP makes this simple with dedicated endpoints for earnings calls. If you want the latest transcripts across the market, you can use the stable endpoint /stable/earning-call-transcript-latest. For a specific stock, the v3 endpoint lets you request transcripts by symbol, quarter, and year using the pattern: https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={q}&year={y}&apikey=YOUR_API_KEY here’s how you can fetch NVIDIA’s transcript for a given quarter: import requestsAPI_KEY = "your_api_key"symbol = "NVDA"quarter = 2year = 2024url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={API_KEY}"response = requests.get(url)data = response.json()# Inspect the keysprint(data.keys())# Access transcript contentif "content" in data[0]: transcript_text = data[0]["content"] print(transcript_text[:500]) # preview first 500 characters The response typically includes details like the company symbol, quarter, year, and the full transcript text. If you aren’t sure which quarter to query, the “latest transcripts” endpoint is the quickest way to always stay up to date. Cleaning and Preparing Transcript Data Raw transcripts from the API often include long paragraphs, speaker tags, and formatting artifacts. Before sending them to an LLM, it helps to organize the text into a cleaner structure. Most transcripts follow a pattern: prepared remarks from executives first, followed by a Q&A session with analysts. Separating these sections gives better control when prompting the model. In Python, you can parse the transcript and strip out unnecessary characters. A simple way is to split by markers such as “Operator” or “Question-and-Answer.” Once separated, you can create two blocks — Prepared Remarks and Q&A — that will later be summarized independently. This ensures the model handles each section within context and avoids missing important details. Here’s a small example of how you might start preparing the data: import re# Example: using the transcript_text we fetched earliertext = transcript_text# Remove extra spaces and line breaksclean_text = re.sub(r'\s+', ' ', text).strip()# Split sections (this is a heuristic; real-world transcripts vary slightly)if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1)else: prepared, qna = clean_text, ""print("Prepared Remarks Preview:\n", prepared[:500])print("\nQ&A Preview:\n", qna[:500]) With the transcript cleaned and divided, you’re ready to feed it into Groq’s LLM. Chunking may be necessary if the text is very long. A good approach is to break it into segments of a few thousand tokens, summarize each part, and then merge the summaries in a final pass. Summarizing with Groq LLM Now that the transcript is clean and split into Prepared Remarks and Q&A, we’ll use Groq to generate a crisp one-pager. The idea is simple: summarize each section separately (for focus and accuracy), then synthesize a final brief. Prompt design (concise and factual) Use a short, repeatable template that pushes for neutral, investor-ready language: You are an equity research analyst. Summarize the following earnings call sectionfor {symbol} ({quarter} {year}). Be factual and concise.Return:1) TL;DR (3–5 bullets)2) Results vs. guidance (what improved/worsened)3) Forward outlook (specific statements)4) Risks / watch-outs5) Q&A takeaways (if present)Text:<<<{section_text}>>> Python: calling Groq and getting a clean summary Groq provides an OpenAI-compatible API. Set your GROQ_API_KEY and pick a fast, high-quality model (e.g., a Llama-3.1 70B variant). We’ll write a helper to summarize any text block, then run it for both sections and merge. import osimport textwrapimport requestsGROQ_API_KEY = os.environ.get("GROQ_API_KEY") or "your_groq_api_key"GROQ_BASE_URL = "https://api.groq.com/openai/v1" # OpenAI-compatibleMODEL = "llama-3.1-70b" # choose your preferred Groq modeldef call_groq(prompt, temperature=0.2, max_tokens=1200): url = f"{GROQ_BASE_URL}/chat/completions" headers = { "Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json", } payload = { "model": MODEL, "messages": [ {"role": "system", "content": "You are a precise, neutral equity research analyst."}, {"role": "user", "content": prompt}, ], "temperature": temperature, "max_tokens": max_tokens, } r = requests.post(url, headers=headers, json=payload, timeout=60) r.raise_for_status() return r.json()["choices"][0]["message"]["content"].strip()def build_prompt(section_text, symbol, quarter, year): template = """ You are an equity research analyst. Summarize the following earnings call section for {symbol} ({quarter} {year}). Be factual and concise. Return: 1) TL;DR (3–5 bullets) 2) Results vs. guidance (what improved/worsened) 3) Forward outlook (specific statements) 4) Risks / watch-outs 5) Q&A takeaways (if present) Text: <<< {section_text} >>> """ return textwrap.dedent(template).format( symbol=symbol, quarter=quarter, year=year, section_text=section_text )def summarize_section(section_text, symbol="NVDA", quarter="Q2", year="2024"): if not section_text or section_text.strip() == "": return "(No content found for this section.)" prompt = build_prompt(section_text, symbol, quarter, year) return call_groq(prompt)# Example usage with the cleaned splits from Section 3prepared_summary = summarize_section(prepared, symbol="NVDA", quarter="Q2", year="2024")qna_summary = summarize_section(qna, symbol="NVDA", quarter="Q2", year="2024")final_one_pager = f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks — Key Points{prepared_summary}## Q&A Highlights{qna_summary}""".strip()print(final_one_pager[:1200]) # preview Tips that keep quality high: Keep temperature low (≈0.2) for factual tone. If a section is extremely long, chunk at ~5–8k tokens, summarize each chunk with the same prompt, then ask the model to merge chunk summaries into one section summary before producing the final one-pager. If you also fetched headline numbers (EPS/revenue, guidance) earlier, prepend them to the prompt as brief context to help the model anchor on the right outcomes. Building the End-to-End Pipeline At this point, we have all the building blocks: the FMP API to fetch transcripts, a cleaning step to structure the data, and Groq LLM to generate concise summaries. The final step is to connect everything into a single workflow that can take any ticker and return a one-page earnings call summary. The flow looks like this: Input a stock ticker (for example, NVDA). Use FMP to fetch the latest transcript. Clean and split the text into Prepared Remarks and Q&A. Send each section to Groq for summarization. Merge the outputs into a neatly formatted earnings one-pager. Here’s how it comes together in Python: def summarize_earnings_call(symbol, quarter, year, api_key, groq_key): # Step 1: Fetch transcript from FMP url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={api_key}" resp = requests.get(url) resp.raise_for_status() data = resp.json() if not data or "content" not in data[0]: return f"No transcript found for {symbol} {quarter} {year}" text = data[0]["content"] # Step 2: Clean and split clean_text = re.sub(r'\s+', ' ', text).strip() if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1) else: prepared, qna = clean_text, "" # Step 3: Summarize with Groq prepared_summary = summarize_section(prepared, symbol, quarter, year) qna_summary = summarize_section(qna, symbol, quarter, year) # Step 4: Merge into final one-pager return f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks{prepared_summary}## Q&A Highlights{qna_summary}""".strip()# Example runprint(summarize_earnings_call("NVDA", 2, 2024, API_KEY, GROQ_API_KEY)) With this setup, generating a summary becomes as simple as calling one function with a ticker and date. You can run it inside a notebook, integrate it into a research workflow, or even schedule it to trigger after each new earnings release. Free Stock Market API and Financial Statements API... Conclusion Earnings calls no longer need to feel overwhelming. With the Financial Modeling Prep API, you can instantly access any company’s transcript, and with Groq LLM, you can turn that raw text into a sharp, actionable summary in seconds. This pipeline saves hours of reading and ensures you never miss the key results, guidance, or risks hidden in lengthy remarks. Whether you track tech giants like NVIDIA or smaller growth stocks, the process is the same — fast, reliable, and powered by the flexibility of FMP’s data. Summarize Any Stock’s Earnings Call in Seconds Using FMP API was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story
Share
Medium2025/09/18 14:40