tnl-intelligence
Typed synchronous and asynchronous Python clients for The Neural Ledger event and evidence intelligence API.
import os
from tnl_intelligence import TnlClient
with TnlClient(os.environ["TNL_API_KEY"]) as client:
for story in client.iterate_news(published_since="2026-07-01T00:00:00Z"):
print(story.title, story.impacted_assets)
import os
from tnl_intelligence import AsyncTnlClient
async with AsyncTnlClient(os.environ["TNL_API_KEY"]) as client:
page = await client.search_news("semiconductor export restrictions")
TNL supplies event, claim, source, contradiction, entity, and impact-path intelligence. The /v1/markets response is contextual display data, not an execution-grade quote feed.
Webhook Verification
Verify the exact raw request bytes before parsing JSON:
from tnl_intelligence import verify_webhook
verified = verify_webhook(raw_body, headers, {key_id: webhook_secret})
Persist delivery IDs for the replay window before performing consumer side effects. The helper accepts a mutable delivery-ID set for local tests; production consumers should use an atomic durable claim.
Point-in-Time Quant Research
Install optional engines without adding them to the base SDK:
python -m pip install 'tnl-intelligence[quant,notebooks]'
from tnl_intelligence.quant.lake import RevisionLake
from tnl_intelligence.quant.sample import sample_observations
from tnl_intelligence.quant.temporal import parse_utc
lake = RevisionLake("./data/tnl")
lake.ingest(sample_observations())
snapshot = lake.snapshot(as_of=parse_utc("2026-06-08T00:00:00Z"))
The toolkit preserves immutable revisions, UTC availability boundaries, deterministic manifests, leakage findings, optional Parquet/DuckDB/dataframe adapters, event studies with user-supplied outcomes, and executable synthetic notebooks. It does not execute or recommend trades. See the quantitative research toolkit guide.
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