jev_stock

# Hong Kong Stock Direction Forecasting with TypeSafe JEV

Language: **English** · [简体中文](README.zh-CN.md)

This repository is an experiment in forecasting the next Hong Kong stock-market direction with TypeSafe JEV. You enter a Hong Kong stock code, the program downloads market data, builds a past-only state, asks JEV for a structured `up` / `flat` / `down` forecast, validates the response, and can render a standalone HTML report.

The default reference market is deliberately small: the target stock, the Hang Seng Index (`HSI`), and the Hang Seng TECH Index (`HSTECH`). The project does not silently add a basket of unrelated stocks.

## Example output

The generated report presents the cutoff date, direction forecasts, model probabilities, and the main state summary in a compact layout.



## My view of JEV for stock forecasting

My current view is that JEV is more useful here as a disciplined reasoning layer than as a proven quantitative trading model.

The useful part is the separation of responsibilities. The Python program owns data retrieval, date alignment, feature calculation, leakage checks, label definitions, and scoring. JEV receives a readable state and makes a consistent classification choice. This can be helpful when several weak signals point in different directions and a human wants one repeatable interpretation of the same evidence.

The risky part is the word “probability”. A response such as `down: 0.68` is a model probability for the supplied question. It is not automatically a 68% historical win rate, and `confidence: 0.52` is not a calibrated 52% chance of being correct. Calibration has to be measured with many timestamped predictions and later observations. This repository therefore shows the raw output, but does not turn it into an investment reco