{"id":97574,"date":"2025-09-18T06:40:08","date_gmt":"2025-09-18T06:40:08","guid":{"rendered":"https:\/\/mycryptomania.com\/?p=97574"},"modified":"2025-09-18T06:40:08","modified_gmt":"2025-09-18T06:40:08","slug":"summarize-any-stocks-earnings-call-in-seconds-using-fmp-api","status":"publish","type":"post","link":"https:\/\/mycryptomania.com\/?p=97574","title":{"rendered":"Summarize Any Stock\u2019s Earnings Call in Seconds Using FMP API"},"content":{"rendered":"

Turn lengthy earnings call transcripts into one-page insights using the <\/em>Financial Modeling Prep\u00a0API<\/em><\/a><\/h4>\n

Photo by Bich\u00a0Tran<\/a><\/p>\n

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\u00a0noise.<\/p>\n

With the right tools, you don\u2019t need to spend hours reading every line. By combining the Financial Modeling Prep (FMP) API<\/strong><\/a> with Groq\u2019s lightning-fast LLMs<\/strong>, 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.<\/p>\n

In this article, we\u2019ll build a Python workflow that brings these two together. You\u2019ll see how to fetch transcripts for any stock, prepare the text, and instantly generate a one-page summary. Whether you\u2019re tracking Apple, NVIDIA, or your favorite growth stock, the process works the same\u200a\u2014\u200afast, accurate, and ready whenever you\u00a0are.<\/p>\n

Fetching Earnings Transcripts with FMP\u00a0API<\/h3>\n

The first step is to pull the raw transcript data. FMP<\/a> 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\u00a0pattern:<\/p>\n

https:\/\/financialmodelingprep.com\/api\/v3\/earning_call_transcript\/{symbol}?quarter={q}&year={y}&apikey=YOUR_API_KEY<\/p>\n

here\u2019s how you can fetch NVIDIA\u2019s transcript for a given\u00a0quarter:<\/p>\n

import requests<\/p>\n

API_KEY = “your_api_key”
symbol = “NVDA”
quarter = 2
year = 2024<\/p>\n

url = f”https:\/\/financialmodelingprep.com\/api\/v3\/earning_call_transcript\/{symbol}?quarter={quarter}&year={year}&apikey={API_KEY}”
response = requests.get(url)
data = response.json()<\/p>\n

# Inspect the keys
print(data.keys())<\/p>\n

# Access transcript content
if “content” in data[0]:
transcript_text = data[0][“content”]
print(transcript_text[:500]) # preview first 500 characters<\/p>\n

The response typically includes details like the company symbol, quarter, year, and the full transcript text. If you aren\u2019t sure which quarter to query, the \u201clatest transcripts\u201d endpoint is the quickest way to always stay up to\u00a0date.<\/p>\n

Cleaning and Preparing Transcript Data<\/h3>\n

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\u00a0model.<\/p>\n

In Python, you can parse the transcript and strip out unnecessary characters. A simple way is to split by markers such as \u201cOperator\u201d or \u201cQuestion-and-Answer.\u201d Once separated, you can create two blocks\u200a\u2014\u200aPrepared Remarks and Q&A\u200a\u2014\u200athat will later be summarized independently. This ensures the model handles each section within context and avoids missing important details.<\/p>\n

Here\u2019s a small example of how you might start preparing the\u00a0data:<\/p>\n

import re<\/p>\n

# Example: using the transcript_text we fetched earlier
text = transcript_text<\/p>\n

# Remove extra spaces and line breaks
clean_text = re.sub(r’s+’, ‘ ‘, text).strip()<\/p>\n

# 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, “”<\/p>\n

print(“Prepared Remarks Preview:n”, prepared[:500])
print(“nQ&A Preview:n”, qna[:500])<\/p>\n

With the transcript cleaned and divided, you\u2019re ready to feed it into Groq\u2019s 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\u00a0pass.<\/p>\n

Summarizing with Groq\u00a0LLM<\/h3>\n

Now that the transcript is clean and split into Prepared Remarks<\/strong> and Q&A<\/strong>, we\u2019ll use Groq to generate a crisp one-pager. The idea is simple: summarize each section separately (for focus and accuracy), then synthesize a final\u00a0brief.<\/p>\n

Prompt design (concise and\u00a0factual)<\/h4>\n

Use a short, repeatable template that pushes for neutral, investor-ready language:<\/p>\n

You are an equity research analyst. Summarize the following earnings call section
for {symbol} ({quarter} {year}). Be factual and concise.<\/p>\n

Return:
1) TL;DR (3\u20135 bullets)
2) Results vs. guidance (what improved\/worsened)
3) Forward outlook (specific statements)
4) Risks \/ watch-outs
5) Q&A takeaways (if present)<\/p>\n

Text:
<<<
{section_text}
>>><\/p>\n

Python: calling Groq and getting a clean\u00a0summary<\/h4>\n

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\u2019ll write a helper to summarize any text block, then run it for both sections and\u00a0merge.<\/p>\n

import os
import textwrap
import requests<\/p>\n

GROQ_API_KEY = os.environ.get(“GROQ_API_KEY”) or “your_groq_api_key”
GROQ_BASE_URL = “https:\/\/api.groq.com\/openai\/v1” # OpenAI-compatible
MODEL = “llama-3.1-70b” # choose your preferred Groq model<\/p>\n

def 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()<\/p>\n

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.<\/p>\n

Return:
1) TL;DR (3\u20135 bullets)
2) Results vs. guidance (what improved\/worsened)
3) Forward outlook (specific statements)
4) Risks \/ watch-outs
5) Q&A takeaways (if present)<\/p>\n

Text:
<<<
{section_text}
>>>
“””
return textwrap.dedent(template).format(
symbol=symbol, quarter=quarter, year=year, section_text=section_text
)<\/p>\n

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)<\/p>\n

# Example usage with the cleaned splits from Section 3
prepared_summary = summarize_section(prepared, symbol=”NVDA”, quarter=”Q2″, year=”2024″)
qna_summary = summarize_section(qna, symbol=”NVDA”, quarter=”Q2″, year=”2024″)<\/p>\n

final_one_pager = f”””
# {symbol} Earnings One-Pager \u2014 {quarter} {year}<\/p>\n

## Prepared Remarks \u2014 Key Points
{prepared_summary}<\/p>\n

## Q&A Highlights
{qna_summary}
“””.strip()<\/p>\n

print(final_one_pager[:1200]) # preview<\/p>\n

Tips that keep quality\u00a0high:<\/strong><\/h4>\n

Keep temperature low (\u22480.2)<\/strong> for factual\u00a0tone.If a section is extremely long, chunk at ~5\u20138k tokens<\/strong>, summarize each chunk with the same prompt, then ask the model to merge chunk summaries<\/strong> 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.<\/p>\n

Building the End-to-End Pipeline<\/h3>\n

At this point, we have all the building blocks: the FMP API<\/strong><\/a> 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\u00a0summary.<\/p>\n

The flow looks like\u00a0this:<\/p>\n

Input a stock ticker (for example,\u00a0NVDA).Use FMP to fetch the latest transcript.Clean and split the text into Prepared Remarks and\u00a0Q&A.Send each section to Groq for summarization.Merge the outputs into a neatly formatted earnings one-pager.<\/p>\n

Here\u2019s how it comes together in\u00a0Python:<\/p>\n

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()<\/p>\n

if not data or “content” not in data[0]:
return f”No transcript found for {symbol} {quarter} {year}”<\/p>\n

text = data[0][“content”]<\/p>\n

# 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, “”<\/p>\n

# Step 3: Summarize with Groq
prepared_summary = summarize_section(prepared, symbol, quarter, year)
qna_summary = summarize_section(qna, symbol, quarter, year)<\/p>\n

# Step 4: Merge into final one-pager
return f”””
# {symbol} Earnings One-Pager \u2014 {quarter} {year}<\/p>\n

## Prepared Remarks
{prepared_summary}<\/p>\n

## Q&A Highlights
{qna_summary}
“””.strip()<\/p>\n

# Example run
print(summarize_earnings_call(“NVDA”, 2, 2024, API_KEY, GROQ_API_KEY))<\/p>\n

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\u00a0release.<\/p>\n

Free Stock Market API and Financial Statements API…<\/a><\/p>\n

Conclusion<\/h3>\n

Earnings calls no longer need to feel overwhelming. With the Financial Modeling Prep API<\/strong>, you can instantly access any company\u2019s transcript, and with Groq LLM<\/strong>, 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\u200a\u2014\u200afast, reliable, and powered by the flexibility of FMP\u2019s\u00a0data.<\/p>\n

Summarize Any Stock\u2019s Earnings Call in Seconds Using FMP API<\/a> was originally published in Coinmonks<\/a> on Medium, where people are continuing the conversation by highlighting and responding to this story.<\/p>","protected":false},"excerpt":{"rendered":"

Turn lengthy earnings call transcripts into one-page insights using the Financial Modeling Prep\u00a0API Photo by Bich\u00a0Tran 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 […]<\/p>\n","protected":false},"author":0,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-97574","post","type-post","status-publish","format-standard","hentry","category-interesting"],"_links":{"self":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts\/97574"}],"collection":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=97574"}],"version-history":[{"count":0,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts\/97574\/revisions"}],"wp:attachment":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=97574"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=97574"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=97574"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}