All they do is prove that to the human eye market returns, in the absence of any additional information, are indistinguishable from random processes.
Red P-values are below the threshold.005, and friday lottery jackpot green values are above.
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As can be seen below quite a few of the samples failed some of the tests and the conclusion for some of the tests was a fail because 90 of the samples passed each test.Logically this then implies that all random processes are non-computable functions because no algorithm which accurately replicates that function could exist.The test then works by testing the linear dependence between those matrices.Software Disclaimer, on another note I am thrilled to report that this Python implementation passes all of the unit tests specified in the nist C documentation and, as a bonus, includes tonnes of comments.Just under half the time (49.5) the dare ends before the first change.After that about 34 attempts within the first 9 diaper changes.All of the code can be found in my GitHub repository, r4nd0m.As such, the sequence is said to exhibit global randomness but only partial local randomness.A failure of this test may indicate the presence of either momentum or strong mean reversion.A failure of the monobit test may indicate something about the relative profitability of simple buy and hold strategies because, if the market is more likely to go up than down, then simply buying and holding the market is likely to be a profitable trading strategy.
the value, momentum, and reversion factors.
"Compression, tests for randomness and estimating the statistical model of an individual sequence." Sequences.
Unfortunately, the test requires a very significant amount of data to be statistically significant.
These tests look either for biases, runs, patterns, or some combination thereof.
This is because markets are, quite simply, not random.Well, because of the link between computability and randomness in order to prove or disprove the random walk hypothesis all one would need to do is use a Turing machine to determine whether or not an algorithm which replicates the market (our function) exists.Two additional conversion methods were implemented and tested which involved converting basis points and floating point returns into binary respectively.In the absence of information many systems may appear random, despite the fact that they are deterministic.In statistics this is known as a confounding variable.