# test

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## Test

The event of a price movement that approaches a support level or a resistance level established earlier by the market. A test is passed if prices do not go below the support or resistance level, and the test is failed if prices go on to new lows or highs.

## test

The attempt by a stock price or a stock market average to break through a support level or a resistance level. For example, a stock that has declined to \$20 on several occasions without moving lower may be expected to test this support level once again. Failing to fall below \$20 one more time would be considered a successful test of the support level and a bullish sign for the stock.
References in periodicals archive ?
A modified Perry conjugate gradient method and its global convergence.
Since neural network training in SAGRAD is based in part on Meller's scaled conjugate gradient algorithm which is a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neural networks, an outline of Moller's algorithm was presented that resembles its implementation in SAGRAD.
A nonlinear conjugate gradient method based on the MBFGS secant condition.
Wei, "Modified active set projected spectral gradient method for bound constrained optimization," Applied Mathematical Modelling, vol.
Based on the phase gradient method, we derive the exact expression of angle glint, which is applicable for the far-field angle glint.
It is solved through the conjugate gradient method minimizing the residue norm in (11).
The good point about Conjugate Gradient method is that it automatically generates direction vectors at the previous step.
Preconditioned biconjugate gradient method of large-scale complex linear equations, Computer, Engineering and Applications 43(36): 19-20 (in Chinese).
Algorithm of an iterative gradient method can also be used to search for a functional saddle point J(u, [psi]) with a limitation (u, [psi]) [member of] [PSI].
The NLP class of optimization problems can, in principle, be solved using several classical local search algorithms and their extensions such as the reduced gradient method (RG) by Wolfe (1963), the generalized reduced gradient method (GRG) by Abadie and Carpentier (1969), augmented Lagrangian (AL) by Powell (1969) and Hestenes (1969), sequential quadratic programming (SQP) by Powell (1978) and the interior point method (IP) by Karmarkar (1984).
A sampling of paper topics: skin segmentation based on double-models, a mixture conjugate gradient method for unconstrained optimization, property preservation of time Petri net reduction, integrated security framework for secure web services, the design and implementation of an electronic farm system based on Google maps, user downloading behavior in mobile internet using clickstream data, and ZigBee-based vehicle access control system, to name just a few.
For this reason, classical nonlinear diffusive approaches could be seen as a relaxation of the topological gradient method. By enlarging the set of admissible solutions, relaxation increases the instability of the restoration process and this could explain why the topological gradient method is so efficient.

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