Only time stands between Bitcoin and gold’s price rally. While gold has surged 60% this year to over $4,350 per ounce, Bitcoin has been stuck in neutral. The top crypto is up just 15% in 2025, and has fallen 10% in recent weeks as trade tensions flared up between the US and China. The divergence is brutal. Gold keeps hitting fresh all-time highs while Bitcoin seemingly can’t hold on to $110,000 — despite a recent record of $126,000. For investors who bought into the “digital gold” narrative, watching the precious metal lap Bitcoin has been painful.But there’s a twist, say market watchers. “Don’t look at gold’s meteoric rise with envy. Look at it with anticipation,” Matt Hougan, chief investment officer at Bitwise, wrote in an October 21 note to investors. “It could end up showing us where Bitcoin is headed.”The pattern matters because gold’s 2025 surge didn’t happen overnight. Central banks started buying aggressively in 2022, but prices crept up slowly for three years before going parabolic. Bitcoin’s institutional buying spree, meanwhile, mostly kicked off in 2024. If the same delayed explosion pattern holds, Bitcoin may not be lagging behind gold for much longer. How high can Bitcoin go? Depends on who you ask. Arthur Hayes, Bernstein, and Standard Chartered have all called for a $200,000 Bitcoin price tag. Others, like 10xResearch, aren’t sold. They said it’s a coin toss that Bitcoin even tops $140,000 by the end of the year. Central banksThe key to understanding both assets lies in who’s buying them — and when.Central banks have been massive purchasers of gold since the US seized Russia’s holdings of US Treasuries following its invasion of Ukraine in 2022. According to Metals Focus, central bank purchases have roughly doubled since the war began, from about 467 metric tons per year to roughly 1,000 metric tons today.“That’s roughly twice the estimated buying by gold ETPs,” wrote Hougan. What about Bitcoin? Well, it just doesn’t have that institutional buying power yet. Sure, Bitcoin ETFs have devoured more than 6% of total supply, and corporate treasuries now control more than 1 million Bitcoin, but central Banks aren’t buying — at least not yet. Price sensitive investorsAlthough Central Banks have been on a multi-year shopping spree, gold’s price didn’t immediately rocket. It went to $1,800 in 2023 from $1,800 in 2022 — an 8% gain — and $2,386 in 2024. Moreover, prices didn’t really explode until this year, when they flew about 60% to nearly $4,400.Hougan’s theory is that price-sensitive investors sold into rising demand as central banks pushed prices up. Eventually that pool of sellers was exhausted, and prices took off.The same pattern may be playing out in Bitcoin. Since Bitcoin ETFs launched in January 2024, ETFs and corporations have bought 1.39 million Bitcoin while the network produced less than a quarter of that. Bitcoin prices have climbed 135% since then.During that run, price-sensitive holders have taken gains. But as gold’s example suggests, those sellers will eventually be exhausted.“As long as the combination of ETF and corporate purchases persists, Bitcoin will have its ‘Gold 2025’ moment,” Hougan wrote.‘Just plain scary’Not everyone is convinced. Mike McGlone, senior commodity strategist at Bloomberg Intelligence, sees gold’s velocity as a warning sign. “Gold is just plain scary,” he said on The Deep Dive podcast on October 21. “The velocity of this rally — up 60% in the year — last matched in 1979.”That 1979 rally marked the end of massive inflation before Fed Chair Paul Volcker aggressively raised rates. “When every major risk asset underperforms the rock, the ancient store of value, it’s not a good sign,” McGlone said.Pedro Solimano is DL News’ Buenos Aires-based markets correspondent. Got a tip? Email him at psolimano@dlnews.com.Only time stands between Bitcoin and gold’s price rally. While gold has surged 60% this year to over $4,350 per ounce, Bitcoin has been stuck in neutral. The top crypto is up just 15% in 2025, and has fallen 10% in recent weeks as trade tensions flared up between the US and China. The divergence is brutal. Gold keeps hitting fresh all-time highs while Bitcoin seemingly can’t hold on to $110,000 — despite a recent record of $126,000. For investors who bought into the “digital gold” narrative, watching the precious metal lap Bitcoin has been painful.But there’s a twist, say market watchers. “Don’t look at gold’s meteoric rise with envy. Look at it with anticipation,” Matt Hougan, chief investment officer at Bitwise, wrote in an October 21 note to investors. “It could end up showing us where Bitcoin is headed.”The pattern matters because gold’s 2025 surge didn’t happen overnight. Central banks started buying aggressively in 2022, but prices crept up slowly for three years before going parabolic. Bitcoin’s institutional buying spree, meanwhile, mostly kicked off in 2024. If the same delayed explosion pattern holds, Bitcoin may not be lagging behind gold for much longer. How high can Bitcoin go? Depends on who you ask. Arthur Hayes, Bernstein, and Standard Chartered have all called for a $200,000 Bitcoin price tag. Others, like 10xResearch, aren’t sold. They said it’s a coin toss that Bitcoin even tops $140,000 by the end of the year. Central banksThe key to understanding both assets lies in who’s buying them — and when.Central banks have been massive purchasers of gold since the US seized Russia’s holdings of US Treasuries following its invasion of Ukraine in 2022. According to Metals Focus, central bank purchases have roughly doubled since the war began, from about 467 metric tons per year to roughly 1,000 metric tons today.“That’s roughly twice the estimated buying by gold ETPs,” wrote Hougan. What about Bitcoin? Well, it just doesn’t have that institutional buying power yet. Sure, Bitcoin ETFs have devoured more than 6% of total supply, and corporate treasuries now control more than 1 million Bitcoin, but central Banks aren’t buying — at least not yet. Price sensitive investorsAlthough Central Banks have been on a multi-year shopping spree, gold’s price didn’t immediately rocket. It went to $1,800 in 2023 from $1,800 in 2022 — an 8% gain — and $2,386 in 2024. Moreover, prices didn’t really explode until this year, when they flew about 60% to nearly $4,400.Hougan’s theory is that price-sensitive investors sold into rising demand as central banks pushed prices up. Eventually that pool of sellers was exhausted, and prices took off.The same pattern may be playing out in Bitcoin. Since Bitcoin ETFs launched in January 2024, ETFs and corporations have bought 1.39 million Bitcoin while the network produced less than a quarter of that. Bitcoin prices have climbed 135% since then.During that run, price-sensitive holders have taken gains. But as gold’s example suggests, those sellers will eventually be exhausted.“As long as the combination of ETF and corporate purchases persists, Bitcoin will have its ‘Gold 2025’ moment,” Hougan wrote.‘Just plain scary’Not everyone is convinced. Mike McGlone, senior commodity strategist at Bloomberg Intelligence, sees gold’s velocity as a warning sign. “Gold is just plain scary,” he said on The Deep Dive podcast on October 21. “The velocity of this rally — up 60% in the year — last matched in 1979.”That 1979 rally marked the end of massive inflation before Fed Chair Paul Volcker aggressively raised rates. “When every major risk asset underperforms the rock, the ancient store of value, it’s not a good sign,” McGlone said.Pedro Solimano is DL News’ Buenos Aires-based markets correspondent. Got a tip? Email him at psolimano@dlnews.com.
How gold’s surge past $4,350 forecasts Bitcoin price
For feedback or concerns regarding this content, please contact us at crypto.news@mexc.com
Only time stands between Bitcoin and gold’s price rally.
While gold has surged 60% this year to over $4,350 per ounce, Bitcoin has been stuck in neutral. The top crypto is up just 15% in 2025, and has fallen 10% in recent weeks as trade tensions flared up between the US and China.
The divergence is brutal. Gold keeps hitting fresh all-time highs while Bitcoin seemingly can’t hold on to $110,000 — despite a recent record of $126,000.
For investors who bought into the “digital gold” narrative, watching the precious metal lap Bitcoin has been painful.
But there’s a twist, say market watchers.
“Don’t look at gold’s meteoric rise with envy. Look at it with anticipation,” Matt Hougan, chief investment officer at Bitwise, wrote in an October 21 note to investors. “It could end up showing us where Bitcoin is headed.”
The pattern matters because gold’s 2025 surge didn’t happen overnight.
Central banks started buying aggressively in 2022, but prices crept up slowly for three years before going parabolic. Bitcoin’s institutional buying spree, meanwhile, mostly kicked off in 2024.
If the same delayed explosion pattern holds, Bitcoin may not be lagging behind gold for much longer.
How high can Bitcoin go? Depends on who you ask. Arthur Hayes, Bernstein, and Standard Chartered have all called for a $200,000 Bitcoin price tag. Others, like 10xResearch, aren’t sold. They said it’s a coin toss that Bitcoin even tops $140,000 by the end of the year.
Central banks
The key to understanding both assets lies in who’s buying them — and when.
Central banks have been massive purchasers of gold since the US seized Russia’s holdings of US Treasuries following its invasion of Ukraine in 2022.
According to Metals Focus, central bank purchases have roughly doubled since the war began, from about 467 metric tons per year to roughly 1,000 metric tons today.
“That’s roughly twice the estimated buying by gold ETPs,” wrote Hougan.
What about Bitcoin? Well, it just doesn’t have that institutional buying power yet. Sure, Bitcoin ETFs have devoured more than 6% of total supply, and corporate treasuries now control more than 1 million Bitcoin, but central Banks aren’t buying — at least not yet.
Price sensitive investors
Although Central Banks have been on a multi-year shopping spree, gold’s price didn’t immediately rocket.
It went to $1,800 in 2023 from $1,800 in 2022 — an 8% gain — and $2,386 in 2024. Moreover, prices didn’t really explode until this year, when they flew about 60% to nearly $4,400.
Hougan’s theory is that price-sensitive investors sold into rising demand as central banks pushed prices up. Eventually that pool of sellers was exhausted, and prices took off.
The same pattern may be playing out in Bitcoin. Since Bitcoin ETFs launched in January 2024, ETFs and corporations have bought 1.39 million Bitcoin while the network produced less than a quarter of that.
Bitcoin prices have climbed 135% since then.
During that run, price-sensitive holders have taken gains. But as gold’s example suggests, those sellers will eventually be exhausted.
“As long as the combination of ETF and corporate purchases persists, Bitcoin will have its ‘Gold 2025’ moment,” Hougan wrote.
‘Just plain scary’
Not everyone is convinced.
Mike McGlone, senior commodity strategist at Bloomberg Intelligence, sees gold’s velocity as a warning sign.
“Gold is just plain scary,” he said on The Deep Dive podcast on October 21. “The velocity of this rally — up 60% in the year — last matched in 1979.”
That 1979 rally marked the end of massive inflation before Fed Chair Paul Volcker aggressively raised rates.
“When every major risk asset underperforms the rock, the ancient store of value, it’s not a good sign,” McGlone said.
Pedro Solimano is DL News’ Buenos Aires-based markets correspondent. Got a tip? Email him at psolimano@dlnews.com.
Market Opportunity
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$0.008016$0.008016
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4 (4) Live Price Chart
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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.
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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.
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