Features Built for Careful, Long-Term Analysis
Alphandy combines structured data processing, model-based screening, and clear reporting so you can review information methodically before making any decision.
What Alphandy Does
Every feature is designed around one principle: give you organised, well-documented information rather than unexplained signals.
Structured Data Aggregation
Alphandy pulls together historical market data, filings, and public disclosures into a single organised view, reducing the time spent cross-referencing scattered sources.
Model-Based Screening
Our models apply consistent, rules-based criteria across large datasets, flagging patterns and outliers for further human review rather than issuing final verdicts.
Readable Reporting
Findings are presented in plain-language summaries with the underlying data attached, so you can trace any conclusion back to its source.
See the Reasoning, Not Just the Result
Rather than presenting a single score, Alphandy shows the components that feed into it — the data points considered, the weightings applied, and the assumptions made.
- Line-item breakdowns behind every summary output
- Version-tracked model logic so changes are documented
- Exportable reports for your own records and further review
Designed to Support, Not Replace, Judgment
Alphandy is built as a research and organisation tool. The features below are meant to sit alongside your own process, not substitute for it.
Configurable Watchlists
Group instruments or datasets into watchlists that update as new information arrives, so recurring review work stays organised over time.
Change Alerts
Receive a notification summary when tracked data points shift meaningfully, without being flooded by noise from minor fluctuations.
Historical Comparisons
Compare current outputs against past snapshots to understand how a model's view of an item has evolved and why.
Additional Feature Highlights
Data Export
Download underlying figures and summaries in common formats for offline review, archiving, or use in your own spreadsheets.
Audit-Friendly Logs
Every generated report retains a record of the inputs and model version used, supporting later review or comparison.
Custom Filters
Narrow large datasets by sector, size, or other criteria so you review only what is relevant to your current focus.
Scheduled Summaries
Set a cadence for receiving condensed updates on your tracked items, keeping periodic review manageable.
Multi-Source Cross-Checks
Where possible, figures are checked against more than one data source to help surface discrepancies before they reach your desk.
Plain-Language Notes
Technical outputs are accompanied by short explanatory notes describing what a given metric measures and its known limitations.
See These Features Applied to Real Data
Review a sample of how Alphandy organises and presents information before deciding whether it fits your process.
Explore the Data