Integration: AlphaAI

AI-scored financial news and SEC Form 4 insider events as Haystack Documents

Authors

AlphaAI

Table of Contents

• Overview
• Installation
• Usage
  • AlphaAINewsFetcher
  • AlphaAIInsiderNewsFetcher
  • In a Pipeline
• Document shape
• License

Overview

AlphaAI is a financial-news platform built for AI agents: every article is enriched at ingest with per-ticker impact analysis, a category, and a 1-10 relevance score, and SEC Form 4 insider filings become structured events about 6 minutes after they hit EDGAR.

This integration provides:

• AlphaAINewsFetcher: fetches the scored news feed as Haystack Document objects, with ticker / category / relevance filters and optional same-story collapsing.
• AlphaAIInsiderNewsFetcher: fetches SEC Form 4 insider events with a structured meta["insider"] block (side, shares, average price, total value, who traded, 10b5-1 flag).

An API key is required — the free tier (20 requests/min, 100/day, no card) is enough to try it: alphai.io/developers. The components read the key from the ALPHAI_API_KEY environment variable.

Installation

pip install alphai-haystack

Usage

AlphaAINewsFetcher

from alphai_haystack import AlphaAINewsFetcher

fetcher = AlphaAINewsFetcher(symbol="NVDA", min_relevance=7)
documents = fetcher.run()["documents"]

for doc in documents:
    print(doc.meta["relevance_score"], doc.meta["title"])

Filters set in __init__ (symbol, category, min_relevance, top_k) can be overridden per run() call.

AlphaAIInsiderNewsFetcher

from alphai_haystack import AlphaAIInsiderNewsFetcher

fetcher = AlphaAIInsiderNewsFetcher(min_relevance=7)  # higher floor = larger trades only
for doc in fetcher.run()["documents"]:
    insider = doc.meta["insider"]
    print(insider["insider_name"], insider["side"], insider["total_value_usd"], doc.meta["tickers"])

In a Pipeline

from haystack import Pipeline
from haystack.components.builders import PromptBuilder
from haystack.components.generators import OpenAIGenerator

from alphai_haystack import AlphaAINewsFetcher

template = """Summarize what moved {{ symbol }} today, using only these articles:
{% for doc in documents %}
- {{ doc.content }} (relevance {{ doc.meta.relevance_score }}/10)
{% endfor %}
"""

pipeline = Pipeline()
pipeline.add_component("news", AlphaAINewsFetcher(min_relevance=6, collapse_stories=True))
pipeline.add_component("prompt", PromptBuilder(template=template))
pipeline.add_component("llm", OpenAIGenerator(model="gpt-4o-mini"))
pipeline.connect("news.documents", "prompt.documents")
pipeline.connect("prompt", "llm")

result = pipeline.run({"news": {"symbol": "NVDA"}, "prompt": {"symbol": "NVDA"}})
print(result["llm"]["replies"][0])

Both components serialize with the rest of the pipeline (to_dict / from_dict); the API key is stored as an environment-variable reference, never as the raw value.

Document shape

content is the article title plus summary. meta carries uid, url, title, source, source_domain, published_at (ISO 8601), tickers, category, relevance_score (1-10), and — on the insider feed — the structured insider block.

License

MIT — see the repository.