Stock · Model detail
The behaviour of C · Change detection over time in this stock, and the models' hit rate on this stock alone.
Change detection activity
What it is: The daily activity probability of C · Change detection for this stock, and the 0.5 alarm threshold as a dashed line.
What data feeds it: signal_daily, model C, the selected horizon. Activity is combined from monitoring four series (volume z-score, log volatility, residual return, the 5-day sum of the residual) separately with BOCPD.
What it shows: Peaks above the threshold are alarm days. The width of the peaks gives an idea of how many days the change lasted.
What can be concluded from it: A stock that crosses the threshold often is either a genuinely eventful stock or one for which the detector is too sensitive; compare with the Events tab to tell the two apart. No alarm is raised in the first 90 observations, because the detector is warming up.
Model hit rates on this stock
What it is: The hit rate and count of each model's resolved signals for this stock at the selected horizon and in the selected date range.
What data feeds it: The signals and outcomes in the /api/stock/{code} response. All tiers in the range are counted together.
What it shows: For each model: a colour mark, the model's name, its hit percentage on this stock, and the n value showing how many resolved signals this percentage was computed from.
What can be concluded from it: This is only an observation, not a measurement: n is small for a single stock and the tiers are mixed. A model hitting 60% on this stock does not mean its overall performance is good; the Performance screen should be used to compare models.
The investment information, comments and recommendations given here are not within the scope of investment advisory services. Investment advisory services are provided under an investment advisory agreement to be signed between a client and brokerage houses, portfolio management companies, or banks that do not accept deposits. The signals here are produced from historical data with statistical models, are shown the same to everyone and are not personalised; they may not suit your financial situation or your risk and return preferences. Therefore, making investment decisions based solely on the information given here may not produce results that meet your expectations.