Metaplanet announced the issuance of 1.75 billion yen bonds, and the funds will be used to purchase Bitcoin; CoinShares: Digital asset investment products had a net inflow of US$2.2 billion last week; Shiba Inu marketing director: More than 45,000 companies worldwide accept SHIB payments.Metaplanet announced the issuance of 1.75 billion yen bonds, and the funds will be used to purchase Bitcoin; CoinShares: Digital asset investment products had a net inflow of US$2.2 billion last week; Shiba Inu marketing director: More than 45,000 companies worldwide accept SHIB payments.

PA Daily | Polish presidential candidate promises to adopt strategic Bitcoin reserves; Binance Pool launches Fractal Bitcoin (FB) joint mining

2024/11/18 18:56

Today's news tips:

Polish Presidential Candidate Promises to Hold Strategic Bitcoin Reserves

Binance Pool Officially Launches Fractal Bitcoin (FB) Merged Mining

Metaplanet announces issuance of 1.75 billion yen bonds, funds used to purchase Bitcoin

Tether-backed Quantoz launches MiCA-compliant stablecoins USDQ and EURQ

Bitcoin mining companies’ holdings ranking: Marathon Digital tops the list with 27,562 Bitcoins

Shiba Inu Marketing Director: More than 45,000 companies around the world accept SHIB payments

CoinShares: Digital asset investment products saw net inflows of $2.2 billion last week

XRP open interest surpassed $2 billion on Saturday, setting a new all-time record

Regulatory News

Polish Presidential Candidate Promises to Hold Strategic Bitcoin Reserves

Polish presidential candidate Sławomir Mentzen has pledged to adopt a strategic Bitcoin reserve if elected, Bitcoin Magazine reports.

Trump: Brendan Carr to serve as FCC chairman

Trump said Brendan Carr will serve as chairman of the Federal Communications Commission.

Taiwan considers taxing income from cryptocurrency trading

According to CNA, Taiwan's "legislators" today expressed their concerns about the taxation of cryptocurrency in Taiwan. Minister of Finance Zhuang Cuiyun said that the profits from related transactions will be subject to income tax, and the audit will be further studied; Director of the Taxation Bureau Song Xiuling said that the tax department currently has audit tools available to review the transaction of digital goods, and promised to study the taxation methods for cryptocurrency trading income within three months.

Project News

Tether-backed Quantoz launches MiCA-compliant stablecoins USDQ and EURQ

According to The Block, Dutch blockchain company Quantoz Payments has launched two new stablecoins, USDQ and EURQ, which comply with the EU Crypto Asset Market Act (MiCA), anchored to the US dollar and the euro respectively. These stablecoins will be listed on exchanges Bitfinex and Kraken on November 21.

Quantoz is regulated by the Dutch Central Bank and holds an Electronic Money Institution (EMI) license. It says its stablecoin is fully backed by fiat currency and "highly liquid financial instruments". Currently, Circle's EURC and Societe Generale's EURCV have occupied 67% of the euro stablecoin market, and Quantoz's EURQ may join the market competition. In addition, Tether has not yet obtained a USDT issuance license under MiCA, and Coinbase plans to remove all non-compliant stablecoins by the end of the year, which may be one of the reasons why Tether chose to invest in Quantoz.

2% of USDQ and EURQ will be retained on its balance sheet in accordance with MiCA regulations. In addition, Tether, Kraken and Fabric Ventures together invested an undisclosed amount in the company (Quantoz).

Binance Pool Officially Launches Fractal Bitcoin (FB) Merged Mining

Binance Pool officially launches Fractal Bitcoin (FB) merged mining. Participate in Bitcoin (BTC) mining and get Fractal Bitcoin (FB) rewards.

Fractal Bitcoin (FB) is not currently listed on Binance.com. Fractal Bitcoin (FB) uses the PPLNS payment model with a minimum payment of 0.1 Fractal Bitcoin (FB).

Binance adds ACT/USDC and other USDC full-margin and isolated-margin leverage trading pairs

Binance Margin adds new USDC full-margin and isolated-margin margin trading pairs: ACT/USDC, NEIRO/USDC, and PNUT/USDC.

ETHGlobal Bangkok Hackathon 10 finalists announced, covering crypto games, AI agents and more

According to The Block, the hackathon held by ETHGlobal in Bangkok, Thailand attracted 713 projects to participate, with a total prize pool of up to $750,000, a record high. After review, 10 projects were shortlisted for the finals, covering hot areas from AI agents to DAO tools and crypto games. Game projects performed well, occupying four final seats. Among them, Metaloot focuses on connecting games to the Flow chain, LootGO hunts for memecoin by "earning while walking", BubbleWars is a Telegram recommendation game based on Ethereum, and Dark Factory combines the exploration of "Dark Forest" and the automation of "Alien Factory".

DAO tools have also attracted much attention. MiniDAO integrates a wallet in Telegram and is jointly controlled by members; DAOGenie uses AI agents to realize automated execution after voting; Industry.ai uses four AI agents to collaborate to complete complex blockchain operations. In addition, Zubernetes uses zero-knowledge proof technology to ensure the security of distributed workloads, ETHPark-QR solves the problem of parking payment without a local bank account, and Cat in a Box provides users with a platform for privacy data sharing and decentralized scientific experiments.

ETHGlobal announced that the next ETHGlobal Hackathon will be held in Taipei in April 2025.

DEXX: We have applied for marking the hacker's wallet address through legal channels and requested assistance from the Solana Foundation

DEXX posted on the X platform that the team has made some progress on the DEXX asset theft incident and has applied to mark the hacker wallet address through legal channels.

DEXX said that it is marking the hacker's address and requesting the Solana Foundation to provide assistance. After being marked, the hacker will not be able to recharge into the exchange or convert into legal currency by any method. DEXX said that it is doing everything possible to track down the stolen funds. It does not have the energy and time to reply to the media and KOLs one by one. It has cooperated with many security agencies to track down the hacker's information and has also filed investigations in many places.

MicroStrategy founder Michael Saylor once again wrote a post suggesting he may continue to increase his BTC holdings

MicroStrategy founder Michael Saylor once again posted on the X platform, suggesting that he may continue to increase his holdings of BTC. Like last week, he wrote: "I think there needs to be more 'green dots' on the saylortracker website." (Note: On the website mentioned by Michael Saylor, a green dot will be marked at the time when MicroStrategy purchases Bitcoin.)

Metaplanet announces issuance of 1.75 billion yen bonds, funds used to purchase Bitcoin

Metaplanet Inc. announced on its official X platform that it will issue ordinary bonds (secured) totaling 1.75 billion yen (about 11.3 million U.S. dollars) with an annual interest rate of 0.36% and a term of one year. All the funds raised will be used to purchase Bitcoin.

Binance Futures to Launch BAN and AKT 1-75x USDT Perpetual Contracts

According to the official announcement, Binance Futures will launch BANUSDT and AKTUSDT perpetual contracts at 19:30 (ET8) on November 18, 2024, with a maximum leverage of 75 times.

Shiba Inu Marketing Director: More than 45,000 companies around the world accept SHIB payments

According to Watcher.Guru, Shiba Inu's anonymous marketing director Lucie shared details about Shiba Inu and future/existing projects that the token actively participates in on X, including the decentralized exchange ShibaSwap, the ecosystem game collection Shib Games, the platform ShibaHub to promote community participation and expand the Shib brand, the metaverse Shiba, and the privacy-centric dApp "Shy Mode". In addition, Lucie said: "Currently, more than 45,000 businesses around the world accept SHIB payments through BitPay and Flexa. These include well-known companies such as GameStop, Petco, and Regal Cinemas."

Mining News

Bitcoin mining companies’ holdings ranking: Marathon Digital tops the list with 27,562 Bitcoins

According to HODL15Capital statistics, as of November 15, 2024, among the publicly listed Bitcoin mining companies, Marathon Digital (MARA) ranked first with 27,562 Bitcoins. Riot Platforms (RIOT) and Hut 8 Mining (HUT) held 10,928 and 9,110 Bitcoins, ranking second and third respectively. Among other major mining companies, CleanSpark (CLSK) held 8,701 Bitcoins, HIVE Blockchain held 2,624 Bitcoins, Cipher Mining (CIFR) and Bitfarms (BITF) held 1,428 and 1,188 Bitcoins respectively. DMG Blockchain ranked last with 590 Bitcoins.

Important data

100 Bitcoins were transferred out of MtGox-related wallet addresses, worth about $9.1 million

According to iChainfo, in the past three hours, the wallet address 17cKtYm5NnUVfQ7dNYvd27etPCCecjtZBD associated with MtGox transferred 100 bitcoins (worth about $9.1 million) to two addresses bc1q6wenhtl0pf5g09hvculkdeehwy88nv66jcfqnz and bc1qwcp8duz34e08fwmq4u3uan7fgys3nt00pcrq2c.

Data: Bitcoin spot ETFs saw net inflows of $1.67 billion last week

Data: Ethereum spot ETF had a net inflow of $515 million last week

Musk has more than 205 million followers on 𝕏

According to DogeDesigner on the X platform, Elon Musk has more than 205 million followers. He is the most followed account on 𝕏. Musk retweeted the post.

Earlier news, Musk released a theme video about TERMINUS, the first city on Mars .

A dormant address containing 400 BTC was activated after 12.5 years of dormancy

Whale Alert monitoring shows that at 5:30 Beijing time, a dormant address containing 400 BTC (US$35,843,512) was just activated after 12.5 years (worth US$2,149 in 2012).

SOL's market value hits a record high, currently about $116 billion

XRP open interest surpassed $2 billion on Saturday, setting a new all-time record

According to CoinDesk, due to regulatory clarification and technological updates, the XRP futures market has reached a new high in activity, with open interest (OI) breaking the record on Saturday, reaching over $2 billion. At the same time, the price of XRP has risen above $1.20 in recent days, hitting a three-year high and driving a weekly increase of more than 87%.

Market data shows that the current long-short ratio is slightly biased towards the short side, with 51% of traders betting on a price correction. However, the simultaneous rise in OI and price usually indicates the entry of new funds, showing that the market trend is bullish. The surge in XRP's price began on Thursday evening after 18 US states filed a lawsuit against the SEC, accusing it of overstepping its regulatory authority in the crypto industry. In addition, the market is optimistic that former US President Trump may promote policies that are more friendly to cryptocurrencies, especially tokens related to US companies such as XRP and UNI.

It is worth noting that Ripple Labs plans to launch the RLUSD stablecoin for cross-border payments and liquidity provision, further promoting the application of XRP in the field of decentralized finance (DeFi), which may provide support for its future price performance.

CoinShares: Digital asset investment products saw net inflows of $2.2 billion last week

According to the latest weekly data from CoinShares, inflows into digital asset investment products increased by a further $2.2 billion last week, bringing total inflows since the first rate cut in September to $11.7 billion, while inflows so far this year have reached a record $33.5 billion. Inflows in the first half of the week were $3 billion, despite the record high price of Bitcoin, which led to a total outflow of $866 million in the second half of the week. Recent market activity, especially Bitcoin's breakthrough of all-time highs, pushed total assets under management (AuM) to a new high of $138 billion earlier this week. The recent surge in activity appears to be driven by a combination of loose monetary policy and the Republican Party's sweeping victory in the recent US election. Regional sentiment was mixed, with the United States seeing a net inflow of $2.2 billion, followed by Hong Kong, Australia and Canada, with inflows of $27 million, $18 million and $13 million, respectively. Investors in Sweden and Germany took profits, with outflows of $58 million and $6.8 million, respectively. Bitcoin saw inflows of $1.48 billion, but recent record highs have spurred investors to pour $49 million into short Bitcoin investment products. Ethereum appears to have shaken off negative sentiment, with inflows of $646 million (5% of assets under management), likely due to Justin Drake's Beam Chain network upgrade proposal and the recent US election. Solana also saw inflows of $24 million.

Data: AVAX, ROSE, ADA and other tokens will be unlocked in large amounts this week, with AVAX unlocking approximately $61.3 million

Token Unlocks data shows that AVAX, ROSE, ADA and other tokens will usher in large-scale unlocking next week, including:

  • Avalanche (AVAX) will unlock approximately 1.67 million tokens at 8:00 a.m. Beijing time on November 18, accounting for 0.41% of the current circulation, with a value of approximately US$61.3 million;
  • Oasis (ROSE) will unlock approximately 176 million tokens at 0:00 on November 19th (Beijing time), accounting for 2.62% of the current circulation and worth approximately US$14.6 million.
  • Cardano (ADA) will unlock approximately 18.53 million tokens at 8:00 a.m. Beijing time on November 21, accounting for 0.05% of the current circulation, with a value of approximately US$13.4 million;
  • Pixels (PIXEL) will unlock approximately 54.37 million tokens at 6:00 pm Beijing time on November 19, accounting for 7.05% of the current circulation, with a value of approximately US$11.4 million;
  • SPACE ID (ID) will unlock approximately 18.49 million tokens at 8:00 a.m. Beijing time on November 22, accounting for 4.29% of the current circulation and worth approximately US$8.3 million;
  • Ethena (ENA) will unlock approximately 12.86 million tokens at 8:00 am Beijing time on November 21, accounting for 0.45% of the current circulation, with a value of approximately US$7.4 million;
  • Eigenlayer (EIGEN) will unlock approximately 1.29 million tokens at 3:00 a.m. Beijing time on November 20, accounting for 0.69% of the current circulation, with a value of approximately US$3.2 million;
  • SKALE (SKL) will unlock approximately 55 million tokens at 8:00 am Beijing time on November 20, accounting for 1.06% of the current circulation and worth approximately US$2.5 million;
  • Tribal Token (TRIBL) will unlock approximately 9.6 million tokens at 8:00 am Beijing time on November 21, accounting for 7.42% of the current circulation and worth approximately US$1.4 million;
  • Hooked Protocol (HOOK) will unlock approximately 4.17 million tokens at 8:00 am Beijing time on November 20, accounting for 2.05% of the current circulation, with a value of approximately US$1.8 million;
  • Hatom (HTM) will unlock approximately 1.07 million tokens at 8:00 a.m. Beijing time on November 19, accounting for 2.41% of the current circulation and worth approximately US$1.1 million.

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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
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Medium2025/09/18 14:40