alphai-haystack

Haystack components for AlphAI — AI-scored financial news and SEC Form 4 insider events, delivered as Haystack Document objects.

Every article on the AlphAI feed is enriched at ingest: per-ticker impact analysis, a category, and a 1-10 relevance score. SEC Form 4 filings become structured insider events about 6 minutes after they hit EDGAR. These components fetch that feed so your pipelines and agents can reason over pre-scored market news instead of raw headlines.

Components

• AlphaAINewsFetcher — the main news feed. Filter by ticker, category, and a relevance floor; optionally collapse same-story coverage into one item.
• AlphaAIInsiderNewsFetcher — SEC Form 4 insider events with a structured meta["insider"] block: side, shares, average price, total value, who traded, and whether it was a pre-planned 10b5-1 sale.

Installation

pip install alphai-haystack

API key

Get a free key at alphai.io/developers (free tier: 20 requests per minute, 100 per day, no card). The components read it from the ALPHAI_API_KEY environment variable by default:

export ALPHAI_API_KEY="ak_..."

Usage

Standalone

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"])

Insider events

from alphai_haystack import AlphaAIInsiderNewsFetcher

fetcher = AlphaAIInsiderNewsFetcher(min_relevance=7)  # higher floor = larger trades
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

A minimal market-brief pipeline: fetch scored news for a ticker, hand it to an LLM.

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 implement to_dict/from_dict, so pipelines serialize to YAML and back; 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:

Run parameters

run() accepts per-call overrides for the filters set in __init__: symbol, category (news fetcher only), min_relevance, and top_k.

Development

pip install -e ".[dev]"
ruff check . && ruff format --check .
mypy src/alphai_haystack
pytest

Tests run fully offline against a fake client.

Links

• AlphAI developer docs
• OpenAPI schema
• Python SDK (alphai-sdk) — this package is a thin Haystack layer over it
• MCP server — the same feed for MCP-speaking agents

License

MIT