Look into converting sequences into bit strings

[ArtPoon]

Two options:

1. Encode nucleotides with two bits (four states = four nucleotides)
2. Encode with four bits for presence/absence of each nucleotide, which enables us to encode mixtures (preferred).

Computing number of differences should be attainable by a fast bitwise operator.

[kwade4]

The majority of the execution time is spent bootstrapping (eg: 151 seconds out of 164 seconds on Windows, using randint() ), so I am unsure whether using bit strings would provide much of a performance increase.

[ArtPoon]

Ok but won't bootstrapping the match/mismatch binary vector instead of sequences speed things up considerably?

[ArtPoon]

Also try replacing randint with random and round.

[kwade4]

Yes, that would.
Regarding random and randint, I realized I misread the numbers. I updated my previous comment with the correct numbers.

Using randint: 151 of 164 seconds are spent bootstrapping (on Windows)
Using random and round: 47 of 52 seconds are spent bootstrapping (on Windows)

I also found using random.choices to pre-compute a list of random numbers, and slicing the list at each window could increase performance.

Using random.random() and round

n = len(best_seq)
for rep in range(nrep):
    boot = [best_seq[round(random.random() * (n - 1))] for _ in range(n)]
    if sum(boot) / len(boot) < second_p:
        count += 1
quant = count / nrep

Total Time = 52 seconds (on Windows)
Bootstrapping Tme = 47 seconds (on Windows)

Using random.choices()

n = len(best_seq)
sample = random.choices(best_seq, k=n*nrep)
for rep in range(nrep):
    boot = sample[rep: rep + n]
    if sum(boot) / n < second_p:
        count += 1
    quant = count / nrep

Total Time: 27 seconds (on Windows)
Bootstrapping Time: 23 seconds (on Windows)

[kwade4]

I think using the match/mismatch binary vector and random.choices could speed things up (even on Windows).