De Bitcoin koers schoot vandaag omhoog richting 70.000 dollar. De digitale munt piekte boven 69.500 dollar. De stijging volgde op een herstel van Amerikaanse aandelenmarktenDe Bitcoin koers schoot vandaag omhoog richting 70.000 dollar. De digitale munt piekte boven 69.500 dollar. De stijging volgde op een herstel van Amerikaanse aandelenmarkten
Bitcoin koers richting 70.000 dollar na Amerikaans beursherstel
De Bitcoin koers schoot vandaag omhoog richting 70.000 dollar. De digitale munt piekte boven 69.500 dollar. De stijging volgde op een herstel van Amerikaanse aandelenmarkten en nieuwe instroom in spot Bitcoin ETF’s. Beleggers tonen opnieuw risicobereidheid. Bitcoin klom in minder dan 24 uur van ongeveer 62.400 dollar naar een weekhoogte van 69.500 dollar. Daarmee bedraagt de stijging bijna 8 procent sinds het recente dieptepunt. Op het moment van schrijven schommelt de koers rond 68.900 dollar. De opleving valt samen met: Positieve macro-economische signalen uit de Verenigde Staten Sterke kwartaalcijfers van grote bedrijven Een omslag naar netto instroom bij Amerikaanse spot Bitcoin ETF’s De combinatie zorgt voor hernieuwd vertrouwen in risicovolle activa, waaronder cryptovaluta. Amerikaanse beleidsduidelijkheid ondersteunt crypto markt Tijdens zijn State of the Union-toespraak benadrukte president Donald Trump dat de Amerikaanse economie een “historische ommekeer” doormaakt. Hij wees op dalende hypotheekrentes en een kerninflatie die in de laatste drie maanden van 2025 met 1,7 procent is gedaald. Financiële markten interpreteerden deze uitspraken als een teken van verminderde beleids-onzekerheid. Eerdere spanningen rond importtarieven en juridische procedures zorgden voor volatiliteit. De recente toon van stabiliteit bracht rust terug. Amerikaanse aandelen draaiden van rood naar groen. Dat herstel werkte direct door naar de cryptomarkt. Bitcoin fungeert steeds vaker als risicovolle technologie-asset die sterk reageert op macro-economische signalen. Voor Nederlandse beleggers is dit relevant. Institutionele partijen in Nederland volgen de Amerikaanse ETF-markt nauwgezet. Pensioenfondsen en vermogensbeheerders kijken steeds serieuzer naar digitale activa als alternatieve belegging. Spot Bitcoin ETF’s keren naar instroom Een belangrijke katalysator is de hernieuwde instroom in Amerikaanse spot Bitcoin ETF’s. Op 24 februari noteerden deze fondsen gezamenlijk 257,7 miljoen dollar aan netto instroom. Daarmee eindigde een periode van vijf weken waarin juist 3,8 miljard dollar uitstroom plaatsvond. Onder meer: Fidelity Investments trok circa 83 miljoen dollar aan BlackRock zag bijna 79 miljoen dollar instromen in zijn iShares Bitcoin Trust Deze instroom suggereert dat grote beleggers opnieuw positie opbouwen. Spot ETF’s kopen daadwerkelijk Bitcoin op de markt. Dat creëert directe koopdruk en ondersteunt de prijs. Voor de Nederlandse markt is dit een signaalfunctie. Wanneer grote Amerikaanse vermogensbeheerders hun blootstelling verhogen, volgen Europese partijen vaak later. Bitcoin futures data wijst op gezonde marktreset Opvallend is dat de rally niet wordt gedreven door excessieve hefboomposities. Data uit de futuresmarkt toont dat de totale open interest, het totaal aantal openstaande contracten, is gedaald van boven 240.000 BTC naar ongeveer 235.000 BTC. Een dalende open interest tijdens een koersstijging wijst erop dat eerdere speculatieve posities zijn afgebouwd. De markt heeft als het ware “lucht afgelaten”. Ook de funding rates, de periodieke betalingen tussen long- en shortposities, blijven licht negatief rond -0,0037 procent. Dat betekent dat shortposities nog steeds longs betalen. Handelaren jagen de koers dus niet agressief omhoog met geleend geld. Deze combinatie duidt op: Meer spotgedreven vraag Minder oververhitting Lagere kans op plotselinge liquidaties De stijging richting 69.000 dollar oogt daardoor technisch gezonder dan eerdere pieken. Wordt 70.000 dollar de volgende psychologische grens? De zone rond 60.000 tot 63.000 dollar fungeerde recent als sterke steun. In dat gebied absorbeerden kopers de verkoopdruk. Sindsdien is de koers met ongeveer 8 procent gestegen. De grens van 70.000 dollar geldt nu als psychologisch weerstandsniveau. Doorbraak boven dit niveau kan extra momentum aantrekken, mede door algoritmische handelssystemen die op ronde niveaus reageren. Tegelijkertijd waarschuwen marktanalisten voor mogelijke afkoeling. Optiedata wijst op zogenoemde positieve gamma bij market makers. Dat betekent dat zij bij stijgende koersen eerder verkopen om risico af te dekken. Dit gedrag kan sterke uitbraken juist afremmen. Voor Nederland is de ontwikkeling relevant in meerdere opzichten: Hogere koersen vergroten publieke interesse in crypto en blockchain Fintechbedrijven profiteren van stijgende handelsvolumes Beleidsmakers volgen marktvolatiliteit in het kader van toezicht Hoewel Bitcoin nog circa 45 procent onder zijn all time high van 126.160 dollar noteert, herstelt het vertrouwen zichtbaar. De komende dagen zullen uitwijzen of bulls voldoende kracht hebben om de symbolische 70.000 dollar te doorbreken.
Het bericht Bitcoin koers richting 70.000 dollar na Amerikaans beursherstel is geschreven door Robin Heester en verscheen als eerst op Bitcoinmagazine.nl.
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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