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Quantitative Finance · शब्दावली

Golden-file test क्या है?

Definition 27.3 Research, Data and Risk Platforms · अध्याय 27 — Testing and Continuous Delivery

A golden-file test compares a program’s outputs with reviewed values stored with the code — the golden file — each with a tolerance and the provenance of the value; any difference beyond the tolerance fails the test, and changing a golden value is itself a reviewed change.

The error unit behind each golden value, averaged by product: a relative 10-10 of prices near 12–15 for the closed forms, the 0.0019 gap between two PDE grids for American puts near 11, and the 0.053 Monte Carlo standard error of Asian calls near 8. The tolerance is k times this unit, so one k means eight orders of magnitude between products. Data: fig_goldtest.py. — Figure 27.1. The error unit behind each golden value, averaged by product: a relative $10^{-10}$ of prices near 12–15 for the closed forms, the 0.0019 gap between two PDE grids for American puts near 11, and the 0.053 Monte Carlo standard error of Asian calls near 8. The tolerance is $k$ times this unit, so one $k$ means eight orders of magnitude between products. Data: fig_goldtest.py.

The Monte Carlo tolerance trade-off. With the seed free to change, noise alone flags each Asian call with the probability that two estimates differ by more than k standard errors (theory in grey) and fails every run for k 2; the Asian-fixing bug, which only the Monte Carlo instruments see, is caught on all twenty at k 2 but on 9 of 20 at k=5, where noise still fails 2.5% of runs. Data: fig_goldtest.py. — Figure 27.2. The Monte Carlo tolerance trade-off. With the seed free to change, noise alone flags each Asian call with the probability that two estimates differ by more than $k$ standard errors (theory in grey) and fails every run for $k\le 2$ ; the Asian-fixing bug, which only the Monte Carlo instruments see, is caught on all twenty at $k\le2$ but on 9 of 20 at $k=5$ , where noise still fails 2.5% of runs. Data: fig_goldtest.py.

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