Predictive market radar
From scattered public records to market signals with visible uncertainty.
Anonymized case · internal access
- Role
- Strategy · Modeling · Builder
- Stack
- Python · Series temporales · Backtesting · Aplicación web
The problem
Public records contain useful signals, but they arrive fragmented and delayed. Without reconciling sources and measuring historical error, a forecast can look more certain than it is.
The system
I built a pipeline that creates historical series, checks consistency and compares forecasting methods across time-based backtests. Its output shows uncertainty ranges and signals for commercial review. The application remains closed because its processed results are internal.
A useful signal must be explainable and open to challenge.
This case shows the analysis architecture, not internal results. It starts with public records and ends with signals a person reviews before acting.
- 01
Prepare
The pipeline downloads public records, builds historical series and checks consistency against a reference source. It stops when the figures do not reconcile.
- 02
Validate
Forecast methods are compared using time-based backtests: each test uses only the history available at that point. Historical error informs the uncertainty range.
- 03
Prioritize
A closed-access application presents signals and ranges for prospecting review. The model does not predict sales or replace commercial judgment.
If you are building oneDesign lesson: in market intelligence, data provenance, source delays and out-of-sample error matter as much as the forecast itself.
What I built
An architecture you can examine.
- 01
Reproducible pipeline from public sources
- 02
Forecast selection through time-based backtesting
- 03
Visible uncertainty ranges for review
Do you have a similar operating challenge?