A leading crypto analyst states that Ethereum might not reach a new all time high in 2026. He cited the bearish state of the broader market and Bitcoin’s choppyA leading crypto analyst states that Ethereum might not reach a new all time high in 2026. He cited the bearish state of the broader market and Bitcoin’s choppy

Bittcoin News: Ethereum May Lag in 2026 as Pepeto Nears Exchange Listings

2026/03/20 08:57
4 min di lettura
Per feedback o dubbi su questo contenuto, contattateci all'indirizzo crypto.news@mexc.com.

A leading crypto analyst states that Ethereum might not reach a new all time high in 2026. He cited the bearish state of the broader market and Bitcoin’s choppy price movement as the main reasons. The bitcoin news cycle is full of caution right now, but the real opportunity is hiding in plain sight for those who act fast.

Pepeto features a full exchange ecosystem that traders will use every day, and with exchange listings approaching, the project could outshine every bitcoin news headline this year. People who wait even a few hours in this market miss the entries that create millionaires. Pepeto is that entry right now.

Bittcoin News: Ethereum May Lag in 2026 as Pepeto Nears Exchange Listings

Ethereum might not set a new all time high according to analysts

A prominent analyst suggests that Ethereum is unlikely to hit new highs in 2026, given the current conditions for Bitcoin. He cautioned that even if ETH reclaims previous levels, it could be a bull trap followed by a sharp reversal. While not impossible, the analyst believes this scenario will not trigger a broad rally in the coming months.

According to CoinDesk, Bitcoin dropped to $70,000 on March 19 as hot PPI data and Iran tensions rattled markets. The Fear and Greed Index plunged to 23 as whale wallets added 4,200 BTC.

Fortune reported that the bitcoin news on March 18 showed BTC at $72,483 before the decline. Ethereum fell 5.2% to $2,193 while total market cap contracted to $2.49 trillion.

Two coins showing massive potential in the bitcoin news cycle

  1. Pepeto: The exchange ecosystem that could outshine every bitcoin news headline

Pepeto is one of the top projects that has defied every bearish trend in the bitcoin news cycle, raising $8.1 million from thousands of wallets while the rest of the market bled.

It uses a full exchange ecosystem with PepetoSwap for cross chain swaps, Pepeto Bridge for moving assets between blockchains, and Pepeto Exchange for a complete trading platform to give traders real infrastructure they will use every single day.

All three products are close to ready for public launch, the smart contract is audited by SolidProof, and over 4 billion tokens have been burned from the supply.

With staking at 196% APY locking supply and the PEPE cofounder who built a $7 billion coin behind the project, Pepeto at $0.000000186 could be the bitcoin news story that turns today’s readers into tomorrow’s millionaires before exchange listings permanently close this window.

  1. Shiba Inu still searching for support

The Shiba Inu price has found fragile support around the $0.0000055 region on March 19. The meme coin is down sharply on the weekly and monthly charts as the correction continues. Ecosystem activity has dropped, signaling low interest from traders. CoinCodex forecasts that SHIB could reach $0.000009 by mid year if conditions improve. But in the current bitcoin news environment, SHIB’s multi billion dollar market cap means the kind of returns that build fortunes are simply not available here.

  1. Pippin continues upward movement

Pippin has been one of the stronger performers recently with double digit gains on the weekly timeframe. Technical analysis shows that bulls remain in control with RSI readings well above the midline. Some project the price could rally significantly by late 2026. Despite strong momentum, Pippin is still a meme coin without exchange infrastructure. For those reading the bitcoin news and looking for the entry that could change everything, Pepeto at $0.000000186 with real products offers a fundamentally different proposition.

Final verdict

The bitcoin news may be dominated by macro headlines, but the real story is Pepeto with $8.1 million raised, a PEPE cofounder, SolidProof audit, 196% APY staking, over 4 billion tokens burned, and three exchange products approaching launch at $0.000000186. The presale window is closing and exchange listings are approaching. Once listings arrive, this price disappears permanently and the countdown is already running.

Click To Visit Pepeto Website To Enter The Presale

FAQs

What is the biggest bitcoin news for investors right now? Pepeto raising $8.1 million with exchange listings approaching at $0.000000186 is the presale dominating attention.

Will Shiba Inu recover in 2026? SHIB needs the broader market to turn. Pepeto at presale pricing offers stronger upside without depending on macro conditions.

Is Pepeto a good investment? With a PEPE cofounder, SolidProof audit, and three exchange products close to launch, many see enormous return potential.

Comments
Opportunità di mercato
Logo Notcoin
Valore Notcoin (NOT)
$0.0003942
$0.0003942$0.0003942
+2.54%
USD
Grafico dei prezzi in tempo reale di Notcoin (NOT)
Disclaimer: gli articoli ripubblicati su questo sito provengono da piattaforme pubbliche e sono forniti esclusivamente a scopo informativo. Non riflettono necessariamente le opinioni di MEXC. Tutti i diritti rimangono agli autori originali. Se ritieni che un contenuto violi i diritti di terze parti, contatta crypto.news@mexc.com per la rimozione. MEXC non fornisce alcuna garanzia in merito all'accuratezza, completezza o tempestività del contenuto e non è responsabile per eventuali azioni intraprese sulla base delle informazioni fornite. Il contenuto non costituisce consulenza finanziaria, legale o professionale di altro tipo, né deve essere considerato una raccomandazione o un'approvazione da parte di MEXC.

Potrebbe anche piacerti

Trump-backed WLFI  launches AgentPay SDK open-source payment toolkit for AI agents

Trump-backed WLFI  launches AgentPay SDK open-source payment toolkit for AI agents

The Trump family has expanded its presence in the crypto community with a major development for artificial intelligence (AI) agents. According to reports, World
Condividi
Cryptopolitan2026/03/20 19:03
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
Condividi
Medium2025/09/18 14:40
Tom Lee Declares That Ethereum Has Bottomed Out

Tom Lee Declares That Ethereum Has Bottomed Out

Experienced analyst Tom Lee conducted an in-depth analysis of the Ethereum price. Here are some of the highlights from Lee's findings. Continue Reading: Tom Lee
Condividi
Bitcoinsistemi2026/03/20 19:05