The current Bitcoin market downturn created different opinions among market participants, including analysts and traders assessing the situation.The current Bitcoin market downturn created different opinions among market participants, including analysts and traders assessing the situation.

Is the Bitcoin Bull Run Really Over, or Just Catching Its Breath?

For feedback or concerns regarding this content, please contact us at crypto.news@mexc.com

SPONSORED POST*

A couple of gold coins photo – Free Candle Image on Unsplash

The current Bitcoin market downturn created different opinions among market participants, including analysts and traders assessing the situation. The market has reached its peak according to some analysts, but others predict it will experience a brief pause before continuing its upward trend. Billionaire investors have shared their opinions about market trends by analyzing global market performances. Even though the market value declines, long-term holders show no signs of panic, showing that underlying sentiment could be different from what appears on the surface.

Billionaire investors remain interested in Bitcoin despite the current market downturn. Analysts tracking market cycles note that pullbacks like this often appear near the midpoint of a long-term rally, not the end. And as new sectors adopt Bitcoin for payments and transactions, confidence in its use remains steady across industries.

Steady Adoption Since September 2025

Bitcoin has shown a slow but steady expansion since September 2025. Global payment processors now support crypto settlements, and multiple countries have established specific rules for their implementation. This removes uncertainty, allowing businesses to perform secure transactions through a streamlined process.

The institutional sector has maintained its increasing interest in the market. Businesses operating in travel, entertainment, and online services sectors now accept and support Bitcoin payments through their operational systems. For instance, betting sites that accept bitcoin in Australia, South America, Asia, and more have shown how crypto payments offer faster processing times, lower fees, and global accessibility. Players can deposit or withdraw funds instantly without waiting for banks or facing conversion costs.

The same convenience is showing up elsewhere. Airlines, hotels, and e-commerce platforms now support Bitcoin as an accepted payment option. Retail stores across Europe and Asia now provide Bitcoin cashback rewards to their customers instead of using traditional loyalty points for rewards. In the U.S., crypto usage grew by 50%. The currency now appears in actual market deals, showing its increasing status as a fundamental trading tool that goes much further than its previous status as a speculative asset.

Bitcoin exchange-traded products continue to attract investor money even when Bitcoin prices decline. Asset managers use this investment as their default portfolio diversification tool instead of considering it an outside bet. This acceptance helps cushion market drops by providing a base of long-term holders who are less reactive to short-term news.

Why Analysts Say the Odds of a Continued Run Are Still Strong

Market observers believe the current Bitcoin price decline follows the same patterns that happened during previous market cycles. According to a TradingView report, there’s a 55% chance that the bull run isn’t over yet. The analysts predict this price level based on historical market responses to halvings and liquidity adjustments, and whale accumulation events.

Bitcoin went through periods of stability before it started increasing to new price highs during past market cycles. The market allowed investors to sell their assets while it naturally decreased in value through these periods of market standstill. The retail market became less active, so institutional investors, together with long-term investors, made their purchases without drawing attention. The pattern seems to continue through this year because blockchain records indicate that big accounts have been buying assets at a consistent rate.

Some experts believe that Bitcoin’s present-day circumstances share similarities with the time periods before the two major protests took place in 2017 and 2021. The market used to experience 15%–20% corrections before it would experience a quick market rebound. The present market decline shows signs of developing into a beneficial situation instead of remaining as a negative occurrence.

Whale Market Activities Indicate That Investors Are Now Accumulating Assets

Research data indicates that major investors continue to support their current investments through their asset holdings. Blockchain. News reported that whale transactions have increased because wallets containing more than 1,000 BTC have started buying again after multiple months of reduced market activity.

These whales tend to move early. When they start accumulating, it often suggests that deep-pocketed investors expect higher prices ahead. The same data shows declining Bitcoin reserves on exchanges, meaning more coins are being moved into private storage. The market trend shows investors preparing for long-term investment rather than making short-term sales.

Whale market movements create subtle price changes that become visible only after price charts reflect these changes. The on-chain data shows market participants predict a market recovery based on analyst observations of these market trends. The market predictions show Bitcoin prices reaching $110,000, but most analysts expect ongoing market fluctuations.

High-Profile Investors Maintain Different Perspectives About the Market

Not everyone agrees on where Bitcoin is headed next. Several billionaire investors have recently voiced concern that the market may have overheated after its strong performance earlier in 2025. Analysts explain that worldwide economic instability, as well as stricter monetary rules, will create obstacles for short-term economic expansion.

Analysts who express caution about Bitcoin do not doubt its future importance. The market correction appears to investors as a natural market correction rather than a complete market breakdown. Their main concern focuses on when the market will move rather than the basic value of the assets. The number of institutional participants, including banks and funds, and corporate treasuries, continues to stay constant despite their issued warnings.

The market views of investors become evident through their cautious analysis methods and their confident portfolio construction approaches. Retail traders tend to withdraw from markets when negative news emerges, yet professional traders use such times to establish their positions through stealthy market entries.

The Pattern of Fear and Confidence

The market performance of Bitcoin has been influenced by investor emotions throughout its entire historical development. The market shows that price declines create fear, but investors start buying after the market stabilizes. Long-term holders boost their asset balances through market value drops because they can acquire assets at lower prices.

Social data supports this trend. The Bitcoin discussion continues to thrive online despite the recent price decline. The market discussion about crypto direction continues, but trading activity shows that crypto remains a popular investment asset. Market participants maintain their interest in future market directions through their ongoing discussion about market developments, which support financial liquidity.

Every major Bitcoin cycle has followed an alternating pattern of fear and confidence. The correction process eliminates market fundamental speculations before new investments can access the market. The market shows signs of stabilization instead of panic, according to current market behavior.

 *This article was paid for. Cryptonomist did not write the article or test the platform.

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 crypto.news@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

Tether Backs Ark Labs’ $5.2 Million Bet on Bitcoin’s Stablecoin Revival

Tether Backs Ark Labs’ $5.2 Million Bet on Bitcoin’s Stablecoin Revival

The post Tether Backs Ark Labs’ $5.2 Million Bet on Bitcoin’s Stablecoin Revival appeared on BitcoinEthereumNews.com. In brief Ark Labs secured backing from Tether
Share
BitcoinEthereumNews2026/03/12 21:44
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
PayPal USD Expands to TRON Network via LayerZero

PayPal USD Expands to TRON Network via LayerZero

The post PayPal USD Expands to TRON Network via LayerZero appeared on BitcoinEthereumNews.com. This content is provided by a sponsor. PRESS RELEASE. September 18, 2025 – Geneva, Switzerland – TRON DAO, the community-governed DAO dedicated to accelerating the decentralization of the internet through blockchain technology and decentralized applications (dApps), announced today that PayPal USD will be available on the TRON network through Stargate Hydra as a permissionless token, […] Source: https://news.bitcoin.com/paypal-usd-expands-to-tron-network-via-layerzero/
Share
BitcoinEthereumNews2025/09/18 23:12