Finance and accounting decision
Financhle
A small-team replacement is realistic: core functionality (ingest options data, compute heuristic unusuality score, serve filtered realtime feed and alerts) can be built and maintained using existing open-source engines and libraries, but you will not reproduce Financhle's proprietary scoring or any licensed institutional data.
Visit website↗Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off93 h to build
$200/mo6 h/mo upkeep
No published price to break even against.
The code exists. It is not what you are paying for.
These 2 projects are real, published, and do the core job — and this page still says keep paying. What the subscription buys is proprietary models, and none of that ships in a repository. Fork one anyway if you want to. Go in knowing what it does not carry. What stays hard ↓ · All Financhle alternatives, with the arithmetic →
What a replacement has to do
- Ingest options trade / quote data, detect high-volume trades vs open interest, compute an unusuality score, store and index events, serve a filtered realtime/feed UI and alerts.
What it still won’t have
- The vendor's proprietary Unusuality algorithm and any tuned parameters
- Access to whatever raw aggregated institutional-level flows Financhle purchases
- Polish and latency of a commercial realtime feed and alerting infra
- Any included data partnerships or commercial licensing the product may have
What remains hard
- Proprietary models
Unusuality is a calculated using a proprietary algorithm that analyzes current and historical options trading patterns and various other factors, assigning a score between 0 (least unusual) and 10 (most unusual) to a singular large trade of
First-year cost
No published price
Financhle does not publish a price we could read, so there is nothing to compare against. What building costs is below.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal unusual-options feed in Python: use FastAPI + PostgreSQL + Redis + a frontend in Vue or React. In scope: (1) a scheduled worker that fetches options trades and quotes from a market-data API and normalizes them into a Postgres table, (2) an Unusuality scorer module implementing configurable heuristics (volume vs open interest, z-score vs historical windows), (3) REST endpoints and a WebSocket to serve recent unusual trades with filter parameters (ticker, expiry, trade value, sentiment), (4) a basic web UI to view/filter the feed and sign up for email alerts, and (5) an alerts worker that sends templated emails via an SMTP/SendGrid provider. Out of scope: proprietary machine-learned models, institutional data licensing, exchange-level low-latency ingestion. Include error handling, retries, schema migrations, and unit/integration tests for scoring, ingestion, and API endpoints.
How we checked
How the score was reached
- Pay verdict base20
- An open-source build was found+5
- 3 cited sources+3
- 3/3 assessment runs agreed+4
- Hard moats found in the evidence-3
- Evidence score29
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 3
Every page the run actually retrieved.
- official productUnusual Options Activity Feed | Real-Time Options Flow
- open sourceoptopsy
- open sourceLean
Integrity checks
What held up, and what did not.
