ENA soared 13% in 24h after Ethena Labs added BNB as the first new collateral asset for its USDe stablecoin.]]>ENA soared 13% in 24h after Ethena Labs added BNB as the first new collateral asset for its USDe stablecoin.]]>
ENA Jumps Over 13% After Ethena Reveals BNB Backing for USDe
- Ethena (ENA) jumped over 13 in 24 hours after BNB was approved as the first new asset backing USDe.
- ENA extended its 30-day rally to 68.5%, outperforming the broader crypto market’s 4.01% gain.
The ENA token surged 13.28% in the last 24 hours, surpassing the crypto market average of around 4%.
Not only that, in the last 30 days, ENA’s rally has reached a staggering 68%. This surge wasn’t a surprise, as Ethena Labs recently announced something quite significant—BNB was officially accepted as collateral for the USDe stablecoin, following a selection process based on their new Eligible Asset Framework.
Market Cheers as Ethena Tightens Grip on USDe Stability
This framework was designed by Ethena Labs’ Risk Committee to screen crypto assets worthy of backing USDe’s value. The requirements? Quite stringent. Assets must have a two-week average open interest of $1 billion, plus daily spot and perpetual volumes of at least $100 million each.
Not to mention the market depth requirement, which is also not to be underestimated. But BNB appears to have passed without a hitch, and was immediately announced as the first asset to be listed.
BNB’s inclusion clearly expands the reach of the Ethena ecosystem. Interestingly, however, the market reaction to this news was more pronounced in the price of ENA.
This may be a signal that investors see Ethena as increasingly serious about maintaining USDe stability. Moreover, this stablecoin is now the third-largest in the world, with a supply approaching $12 billion. According to its founder, Guy Young, the next target could reach $20 billion. Ambitious? Possibly. But not impossible, given the continued market support.
XRP and HYPE Peek In, SUI and ADA Rejected
Meanwhile, XRP and HYPE have reportedly also entered the evaluation radar and meet all requirements. However, Ethena has not officially announced whether they will follow BNB.
Meanwhile, two other major tokens—SUI and ADA—were rejected because they were deemed to not meet sufficient volume and liquidity requirements.
Meanwhile, Ethena is also increasingly active in expanding its product offerings. In early August, CNF reported on Ethena’s partnership with Pendle, which allows users to lock in a fixed yield through a Principal Token (PT), then borrow against that PT on Aave to multiply their returns.
This strategy has already attracted $4.3 billion, which represents 60% of the total USDe supply. Pretty substantial, right?
Not only that, but last July, we also highlighted Ethena’s collaboration with Anchorage to issue the US-compliant USDtb stablecoin. This stablecoin is intended to be the first candidate to comply with domestic payment regulations under the GENIUS Act.
That same month, Strata joined forces with Ethena Network to design structured finance products based on sUSDe and real-world assets—a kind of investment package with exposure tailored to each risk profile.
]]>Market Opportunity
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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