Fetch and Analyze Earning Call Transcripts
Fetch US company earnings call transcripts from discountingcashflows.com. Not for commercial use.
Why it matters
Access and process earning call transcripts for US companies to extract key information and insights for financial analysis.
Outcomes
What it gets done
Fetch earning call transcripts from discountingcashflows.com
Parse transcript data including speaker information and timestamps
Index transcripts for efficient querying and analysis
Answer questions based on the content of the transcripts
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/li-reader-readers-earnings-call-transcript | bash Capabilities
What this skill does
Pulls structured data fields from unstructured text.
Fetches and parses content from web pages.
Chunks, embeds, and indexes documents for semantic retrieval.
Condenses long documents or threads into key takeaways.
Overview
EARNING CALL TRANSCRIPTS LOADER
What it does
This loader fetches earnings call transcripts for US-based companies from discountingcashflows.com. It is not available for commercial purposes. The loader accepts year, ticker symbol, and quarter name as input. Metadata includes ticker, quarter, date_time, and speakers_list. It can be integrated with LlamaIndex and Langchain.
How it connects
Use this loader to retrieve earnings call transcripts for US companies from discountingcashflows.com. Do not use for commercial purposes, non-US companies, or other data sources.
Source README
EARNING CALL TRANSCRIPTS LOADER
pip install llama-index-readers-earnings-call-transcript
This loader fetches the earning call transcripts of US based companies from the website discountingcashflows.com. It is not available for commercial purposes
Install the required dependencies
pip install -r requirements.txt
The Earning call transcripts takes in three arguments
- Year
- Ticker symbol
- Quarter name from the list ["Q1","Q2","Q3","Q4"]
Usage
from llama_index.readers.earnings_call_transcript import EarningsCallTranscript
loader = EarningsCallTranscript(2023, "AAPL", "Q3")
docs = loader.load_data()
The metadata of the transcripts are the following
- ticker
- quarter
- date_time
- speakers_list
Examples
Llama Index
from llama_index.core import VectorStoreIndex, download_loader
from llama_index.readers.earnings_call_transcript import EarningsCallTranscript
loader = EarningsCallTranscript(2023, "AAPL", "Q3")
docs = loader.load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query(
"What was discussed about Generative AI?",
)
print(response)
Langchain
from langchain.agents import Tool
from langchain.agents import initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from llama_index.readers.earnings_call_transcript import EarningsCallTranscript
loader = EarningsCallTranscript(2023, "AAPL", "Q3")
docs = loader.load_data()
tools = [
Tool(
name="LlamaIndex",
func=lambda q: str(index.as_query_engine().query(q)),
description="useful for questions about investor transcripts calls for a company. The input to this tool should be a complete english sentence.",
return_direct=True,
),
]
llm = ChatOpenAI(temperature=0)
agent = initialize_agent(tools, llm, agent="conversational-react-description")
agent.run("What was discussed about Generative AI?")
Discussion
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